Mental Health Practice in A Digital World A Clinicians Guide (Naakesh A. Dewan, John S. Luo Etc.)

Health Informatics
Naakesh A. Dewan
John S. Luo
Nancy M. Lorenzi Editors
Mental Health
Practice in a
Digital World
A Clinicians Guide
Health Informatics
More information about this series at http://www.springer.com/series/1114
Naakesh A. Dewan • John S. Luo
Nancy M. Lorenzi
Editors
Mental Health Practice

in a Digital World
A Clinicians Guide
123
Editors
Naakesh A. Dewan John S. Luo
Behavioral Health Health Sciences, Professor of Clinical
BayCare Medical Group Psychiatry
BayCare Health System UCLA David Geffen School of Medicine
Clearwater, FL, USA Los Angeles, CA, USA
Nancy M. Lorenzi
Department of Biomedical Informatics
Vanderbilt University School of Medicine
Nashville, TN, USA
ISSN 1431-1917 ISSN 2197-3741 (electronic)

Health Informatics
ISBN 978-3-319-14108-4 ISBN 978-3-319-14109-1 (eBook)
DOI 10.1007/978-3-319-14109-1
Library of Congress Control Number: 2015932971
Springer Cham Heidelberg New York Dordrecht London

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I would like to dedicate this book to my wife
Devaki, and my sons Ashwin, and Shyam.
Naakesh A. Dewan
Foreword
We achieve understanding within a circular movement

from particular facts to the whole that includes them and
back again from the whole thus reached to the particular
significant facts.
Karl Jaspers, General Psychopathology1
In his classic treatise on the phenomenology of mental illness, Karl Jaspers

emphasizes the importance of viewing a problem from differing vantage points.
Also, in moving from the general to the specific and back again, we can identify the
gaps in our knowledge and address them gradually as new information accrues. In
some instances, filling those gaps will require research into the neurobiological and
psychological underpinnings of mental illness and its treatment. In other instances,
we will need to develop a deeper knowledge of the life experiences, hopes, and
goals of the patient who is sitting before us. With the explosive growth in available
technologies, as described in this volume, we add further dimensions to the process
of understanding that go well beyond anything that Jaspers could have imagined in
his era.
Although it is natural to be carried away by the excitement and potential of new
technologies, it is essential that we gain a detailed understanding of their impact
before rushing these technologies into everyday use. Unanticipated consequences
of new technologies are common and may be positive, negative or both. Even
with proven technologies, intended benefits are not invariably achieved. Here too, a
“circular” approach is worthwhile. Leadership is crucial in fostering appropriate
resources and culture for technological change but front-line staff must also be
engaged and participate in developing features and workflows. Patients and families
must also become partners in finding ways to make technology that meets their
1
Jaspers, Karl. General Psychopathology. Hoenig J and Hamilton MW, trans. Chicago: The
University of Chicago Press, 1963, p. 357.
vii
viii Foreword
needs. All too often health professionals implement innovations that we think will
meet patients’ needs without ever checking with them. In each of these realms, there
should be iterative reassessments, integration of feedback and further reassessments
so that technological advances can continue to be fine-tuned.
An additional dimension, which Jaspers could not have predicted, relates to the
ways in which health care financing and regulation can alter the use and the usability
of new technologies. No matter how amazing a new technology may be in improving
care, it is unlikely to be used widely or in an equitable fashion unless it is covered
by major health care payment models. Health care regulation can similarly foster
use of new technologies through financial or other incentives. On the other hand,
regulation can detract from effective use of emerging technologies when it mandates
elaborate changes to software, creates burdensome documentation, or interferes with
clinical workflows. Requirements for structured documentation to meet payment or
regulatory requirements can also have insidious negative effects by disrupting the
clinical thought process and fragmenting the patient’s “story.”
As you read the chapters in this volume, you will appreciate the enormous
potential of new technologies for enhancing care in mental health settings. You will
also learn about the complexities and possible pitfalls of those new technologies.
This book will serve as a launching point for your journey in adopting new
technological approaches to caring for patients. We can also foster continuing
refinements in these innovations through systematic analysis and astute observation
of the effects of these interventions – tasks that mental health professionals are
already skilled in doing. In this fashion, we can apply Jaspers’ advice about
achieving understanding by “circling” from the facts to the whole and then back
again. Above all, however, we cannot lose sight of the heart of the circle – the patient
and his or her family. When new technologies help us improve care and enhance our
understanding of patients as individuals, then we will be able to rejoice together in
their success.
Department of Psychiatry Laura J. Fochtmann

Department of Pharmacological Sciences
Department of Biomedical Informatics
School of Medicine, Stony Brook University
Stony Brook, NY, USA
Contents
1 Past, Present, and Future Policy and IT Landscape

in Public Mental Health Care . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 1
Ron Manderscheid
2 Electronic Health Records Technology: Policies and Realities . . . . . . . . 13
Lori Simon
3 Leading Health IT Optimization: A Next Frontier .. . . . . . . . . . . . . . . . . . . . 37
Greg Hindahl
4 Computer-Aided Psychotherapy Technologies . . . . . .. . . . . . . . . . . . . . . . . . . . 57
Marni L. Jacob and Eric A. Storch
5 Computerized Cognitive Training Based upon Neuroplasticity .. . . . . . 81
Charles Shinaver and Peter C. Entwistle
6 Clinical Communication Technologies for Addiction Treatment.. . . . . 123
Richard N. Rosenthal
7 Technology and Adolescent Behavioral Health Care . . . . . . . . . . . . . . . . . . . 141
Todd E. Peters, Theresa Herman, Neal R. Patel,
and Harsh K. Trivedi
8 An Overview of Practicing High Quality Telepsychiatry . . . . . . . . . . . . . . 159
Donna Vanderpool
9 Social Media .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 183
John S. Luo and Brian N. Smith
ix
x Contents
10 Technology Tools Supportive of DSM-5: An Overview.. . . . . . . . . . . . . . . . 199

Nathaniel Clark, Theresa Herman, Jerry Halverson,
and Harsh K. Trivedi
11 Summary and Look Forward . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 213
Nancy M. Lorenzi and Naakesh A. Dewan
Index . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 221
Contributors
Nathaniel Clark, M.D. Department of Psychiatry, Vanderbilt University School of

Medicine, Nashville, TN, USA
Naakesh A. Dewan, M.D. Behavioral Health, BayCare Health System, BayCare
Medical Group, Clearwater, FL, USA
Peter C. Entwistle, Ph.D. Pearson, Pembroke, MA, USA
Jerry Halverson, M.D. Rogers Memorial Hospital, University of Wisconsin
School of Medicine and Public Health, Madison, WA, USA
Theresa Herman, M.D., M.B.A. Vanderbilt Behavioral Health, Vanderbilt Uni-
versity Medical Center, Nashville, TN, USA
Office of Quality, Patient Safety, and Risk Prevention, Vanderbilt University
Medical Center, Nashville, TN, USA
Greg Hindahl, M.D. BayCare Health System, Clearwater, FL, USA
Marni L. Jacob, Ph.D. Department of Pediatrics, Rothman Center for Neuropsy-
chiatry, University of South Florida, St. Petersburg, FL, USA
Nancy M. Lorenzi, Ph.D. Department of Biomedical Informatics, School of
Medicine, Vanderbilt University, Nashville, TN, USA
John S. Luo, M.D. Health Sciences, Professor of Clinical Psychiatry, UCLA David
Geffen School of Medicine, Los Angeles, CA, USA
Ron Manderscheid, Ph.D. National Association of County Behavioral Health and
Developmental Disability Directors, Washington, DC, USA
Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins
University, Baltimore, MD, USA
xi
xii Contributors
Neal R. Patel, M.D., M.P.H. Health Informatics Technologies and Services,

Vanderbilt University Medical Center, Nashville, TN, USA
Department of Pediatrics, Vanderbilt University School of Medicine, Nashville, TN,
USA
Todd E. Peters, M.D. Department of Psychiatry, Vanderbilt University School of
Medicine, Nashville, TN, USA
Health Informatics Technologies and Services, Vanderbilt University Medical
Center, Nashville, TN, USA
Richard N. Rosenthal, M.D. Department of Psychiatry, Icahn School of Medicine
at Mount Sinai, New York, NY, USA
Charles Shinaver, Ph.D. Pearson, Carmel, IN, USA
Lori Simon, M.D. Department of Psychiatry, Payne Whitney Clinic, New York
Presbyterian Weill Cornell Medical Center, New York, NY, USA
Brian N. Smith, Ph.D. VA National Center for PTSD, Boston University School
of Medicine, Boston, MA, USA
Eric A. Storch, Ph.D. Department of Pediatrics, Rothman Center for Neuropsy-
chiatry, University of South Florida, St. Petersburg, FL, USA
Rogers Behavioral Health, Tampa Bay, FL, USA
All Children’s Hospital, Johns Hopkins Medicine, St. Petersburg, FL, USA
Harsh K. Trivedi, M.D., M.B.A. Department of Psychiatry, Vanderbilt University
School of Medicine, Nashville, TN, USA
Vanderbilt Behavioral Health, Vanderbilt University Medical Center, Nashville, TN,
USA
Donna Vanderpool, M.B.A., J.D. Vice President, Risk Management, Professional
Risk Management Services, Inc., Arlington, VA, USA
Chapter 1
Past, Present, and Future Policy and IT
Landscape in Public Mental Health Care
Ron Manderscheid
Abstract The purpose of this chapter is to trace the evolution of recent mental
health policy in the United States since the beginning of the fourth quarter of the
twentieth century, and to outline the relationship this evolution has had with mental
health service delivery and the use of IT in the field. The chapter begins with an
overview of mental health policy and the evolution of information technology. IT
use and policies from 1975 to 2014 are reviewed by time period. The author presents
each decade with Hallmarks, the Policy Context, and the IT Context, The early
years are characterized by angst in the mental health field. The later decades are
ones of alternating concern and hope about the future of mental health services and
technology. The author offers predictions for the future use of IT in mental health.
Keywords Deinstitutionalization • Electronic health records • Health insurance

exchanges • Hospitals • Psychiatric • Intellectual disability • Medical informat-
ics • Medicare • Mental health • Mobile applications • Patient Protection and
Affordable Care Act • Telemedicine
1.1 Introduction
Changes in national mental health policy exert dramatic effects upon the nature
and quality of mental health care delivered in the United States (see [1]). Although
intuitively a very closely related notion, the relationship between mental health
policy and the information technology (IT) employed by the mental health field
is much less widely known and understood.
The purpose of this chapter is to trace the evolution of recent mental health
policy in the United States since the beginning of the fourth quarter of the twentieth
century, and to outline the relationship this evolution has had with mental health
R. Manderscheid, Ph.D. ()

National Association of County Behavioral Health and Developmental Disability Directors,
Washington, DC, USA
Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University,
Baltimore, MD, USA
e-mail: rmanderscheid@nacbhd.org
© Springer International Publishing Switzerland 2015 1

N.A. Dewan et al. (eds.), Mental Health Practice in a Digital World,
Health Informatics, DOI 10.1007/978-3-319-14109-1_1
2 R. Manderscheid
service delivery and the use of IT in the field. As we recount the history of these
interactions, we will identify some key barriers that have prevented the translation
of policy into service practice and the translation of service practice into IT
applications. We also will discuss some potential future scenarios.
Before we can begin this work, however, two prior tasks must be undertaken.
First, we must circumscribe what is meant by mental health policy. Second, we
must describe the evolution of IT, its interaction with the evolution of programming
capacity, and the joint effects of these two factors upon the applications that actually
are possible at any given time.
1.2 Mental Health Policy
Mental health policy is a very imprecise concept. We must circumscribe it so that

we have a better understanding of its actual nature (see [1]).
First, we want to distinguish de facto from de jure mental health policy. De facto
policy is what actually governs day-to-day mental health care in the United States.
De jure policy is what has been codified into law. For example, the de facto policy
of community-based mental health care began in the late 1940s and the early 1950s,
and then evolved for more than a decade before in became de jure policy in 1963
with the passage of the Mental Retardation Facilities and Community Mental Health
Centers Construction Act of 1963.
A second important distinction is whether mental health policy actually is
national or not. National mental health policy always is codified in law, and it always
applies to the entire population. For almost the entire history of the United States,
either one or both of these conditions was not met. We had de facto policies that
did not become de jure policies, and we had de jure policies that did not apply to
the entire population. Only with the advent of the Patient Protection and Affordable
Care Act of 2010 (see [2]) do we actually have national mental health policy; it is
de jure policy that does apply to the entire population.
We will employ these distinctions as we discuss the evolution of mental health
policy in this paper.
1.3 Information Technology
Although modern IT was introduced in a very limited way even before the end of
World War II, it was not generally employed until the 1975–1980 period in the
mental health field. Hence, we begin our analysis with the year 1975.
Figure 1.1 presents a graphical representation of some nodal events in the
evolution of three key IT dimensions: hardware, software, and applications, and
their approximate relationship to each other.
In the past 40 years, hardware has evolved from large, bulky, mainframe comput-
ers, to small, fixed, personal desktop computers, to mobile personal computers, to
1 Past, Present, and Future Policy and IT Landscape in Public Mental Health Care 3
Fig. 1.1 Dynamic relationships among IT hardware, software, and applications
mobile handheld devices, such as I-pads and I-phones. At the same time, software
has evolved from small, fixed programs that produce fixed results, to large, complex,
smart programs that can learn and are cybernetic. This software evolution permitted
the introduction of the Internet approximately 20 years ago and cloud computing
approximately 5 years ago.
The interaction of these two dimensions, hardware and software, has given
rise to the IT applications that have been possible in the mental health field at
specific times. The mainframe computer and fixed programming lent themselves to
data storage applications, such as financial accounting, client record-keeping, etc.
Although telemedicine was introduced originally via a TV camera and monitor
system, and was hardwired between provider and client, this system was easily
adapted to fixed personal desktop computers and later to mobile personal computers.
Further software evolution permitted the introduction of online therapy in which a
client could interact with a smart program rather than a provider. Today, software
evolution has made possible the use of virtual reality in therapy, as well as a broad
array of micro applications (“apps”) for handheld devices.
The reader is encouraged to refer back to this figure to understand the evolution
of IT hardware, software, and applications, as we discuss their interaction with
evolving mental health policy and services.
1.4 The Early Years: 1975–1984
1.4.1 Period Hallmarks
This early period, 1974–1984, could be characterized as one of major transitions,

punctuated in the middle by the election of President Ronald Reagan. During the
4 R. Manderscheid
first half of this period, the National Institute of Mental Health (NIMH) reached
the midway point in construction of a national set of community mental health
centers, when more than 800 of these facilities were in operation (see [3], for
an informative description of major events in the history of NIMH). The states
continued deinstitutionalization during this period, and large numbers of persons
were released from the state mental hospitals. But warning signs were abundant.
Persons who were deinstitutionalized were not welcomed into community mental
health centers. The federal grants that governed the centers dictated that center
efforts should be directed toward paying clients with insurance coverage.
In the latter half of this period, homelessness grew to unprecedented large
numbers in the United States, and a significant portion of the homeless population
consisted of people with mental illnesses. Also in this period, an NIMH Community
Support Programs was begun by the federal government to provide case manage-
ment for adults with serious mental illnesses, and to link these people with needed
mental health, health, and social services.
1.4.2 The Policy Context
Throughout this period, the de jure mental health policy promoted the development
of community-based mental health services, and the de facto policy promoted
the reduction of inpatient psychiatric care (see [4], for an in-depth discussion of
mental health policy in modern America). The initial governing de jure policy
was the 1963 Act referenced above. In 1980, the Administration of President
Jimmy Carter was successful in achieving passage of the Mental Health Systems
Act designed to improve mental health service delivery, particularly for persons
with severe illness. Only a few months later, this legislation was repealed by the
Reagan Administration, which also defunded the original 1963 Act and replaced it
in 1981 with a Community Mental Health Services Block Grant to the states with
considerably reduced overall funding.
1.4.3 The IT Context
In the first half of this period, NIMH operated a program that promoted the use of
IT by community mental health centers to do automated financial accounting and
to collect rudimentary client and staff characteristics data, as well as program per-
formance indicators. In addition, through the Mental Health Statistics Improvement
Program (MHSIP), NIMH developed definitions and data standards for use in these
fledgling IT systems (see [5]).
All of the federal IT work by NIMH came to an end with the election of President
Reagan. However, the Institute continued its related work on data definitions and
standards, and these definitions and standards continued to be incorporated by states
and vendors into their evolving IT systems.
1.5 The Middle Years: 1985–1994
The middle years, 1985–1994, can be characterized as a period of struggle and angst
in the mental health field. Efforts continued at NIMH to develop the Community
Support Program to address the service needs of adults with serious mental illness,
albeit with minimal federal funding. A parallel program, the Child and Adolescent
Service System Program, was initiated on a small scale by the Institute to address the
service needs of children with serious emotional disturbance. Most federal services
work during this period was oriented toward the states because of the changes
introduced earlier by the Reagan Administration.
Community mental health centers continued to struggle financially with the
reduced funding that they now received from the states through the federal Com-
munity Mental Health Services Block Grant Program, and they increasingly turned
to Medicaid as a source of service funding. At the same time, efforts were made
by the Reagan Administration to limit the numbers of persons with mental illness
who could qualify as disabled under the Supplemental Security Income Program,
which would entitle them to receive Medicaid funding for health and mental health
services.
In an effort to conserve mental health financial resources, private sector managed
behavioral health care was introduced to control mental health service utilization
in private health insurance plans, and this innovation spread gradually into public
sector mental health services, principally at the state level. Clearly, this innovation
was quite controversial at the time, particularly among those in the mental health
provider community.
Partially as a reaction of these problems, the Substance Abuse and Mental Health
Services Administration (SAMHSA) was created in 1992 to separate the services
work of NIMH from its research mission, and to create a fuller national voice for
the development and implementation of mental health services in the community.
Throughout this period, the de jure policy of community-based mental health

services predominated, and growing attention was given to case management
services in order to coordinate the broad array of health and social services needed
by persons with serious mental conditions.
In 1993 and 1994, the Clinton Administration developed, but failed to achieve
passage of what came to be known as the Clinton Health Security Act. This Act
would have provided health insurance coverage to many uninsured Americans with
a defined benefit that included mental health and substance use care.
6 R. Manderscheid
Since neither NIMH nor SAMHSA had a defined IT program during this period, the
federal government did not play a significant role in the evolution of IT introduced
into the field during this decade. Hence, most of the innovation in IT applications
can be attributed to the way that private vendors responded to the needs expressed
by IT purchasers in the field who were delivering mental health services.
Clearly, a need existed to have detailed information on the characteristics of
clients served, their service utilization, and their service costs. Thus, in this period,
the rudiments of an electronic medical record began to emerge for behavioral
healthcare services. However, efforts were not made to link the behavioral health
record with parallel information on primary care services.
1.6 The Recent Years (1995–2004)
Again, this decade was one of alternating concern and hope about the future of
mental health services. Just before the beginning of this decade, the Clinton Health
Security Act had failed in the Congress in 1994, thus denying millions of citizens
access to needed health insurance benefits. Shortly before the end of his second
term, President Clinton hosted a While House Conference on Mental Health, which
resulted in an Executive Order extending parity to mental health benefits in the
Federal Employee Health Insurance Benefit Program, and the Surgeon General
issued the first-ever Report on Mental Health. Less than 3 years later, the new
President, George W. Bush, announced the President’s New Freedom Commission
on Mental Health, which resulted in a very clear philosophical direction for the field,
but very few actual resources to move the field. This Commission called for service
integration between mental health and primary care, improved quality of service
delivery, and better use of IT in care delivery. Throughout the decade, the needs for
care far outpaced the resources available. As a result, local and county jails began
to emerge as the new mental hospitals during this period.
Community-based mental health care remained the predominant de jure policy

motief throughout the decade. However, the Surgeon General had identified and the
President’s New Freedom Commission on Mental Health had endorsed the need to
transition to integrated care. Simultaneously, recovery became generally recognized
as a primary goal of care, due principally to the efforts of members of the national
consumer movement, which itself came of age during this decade.
The hope of recovery—the lifelong process of regaining one’s life in the

community—changed forever our approach to mental health care. The import and
effects of this change cannot be overemphasized. This change not only rekindled
hope in those with mental conditions, but it also promoted their increased self-
esteem, and even energized the national consumer movement.
Policy and service changes during this decade gave strong impetus to efforts
to design and implement an interoperable electronic medical record that would
encompass mental health, substance use, and primary care services. These efforts
were recognized formally when the Bush Administration created the Office of the
National Coordinator for Health Information Technology in the US Department
of Health and Human Services. In its early years, this Office spent considerable
time conceptualizing an electronic medical record and developing appropriate data
dictionaries to drive comparable content. However, little funding was made available
to implement these tools in the health delivery field.
At this time, it also became apparent that federal law and regulation (42
Combined Federal Regulations Part 2) governing substance use care would create
a major impediment to the sharing of information on substance use care in
electronic medical records. By contrast, the passage and implementation of the
Health Insurance Portability and Accountability Act (HIPAA) defined the limits of
privacy for personal health information and established penalties for inappropriate
disclosure of this information.
At SAMHSA, a decade-long project was undertaken to develop Decision Support
2000C, a next generation management information system that would permit
benchmarking across programs using real-time information from electronic medical
records, shared through the Internet, which itself came of age during this period.
This effort not only defined the next generation of data standards for mental health,
but also fostered a major partnership with the Software and Technology Vendors
Association (SATVA), which was formed to represent most IT vendors in the
behavioral health field. SATVA worked closely with the federal government during
this period.
1.7 The Modern Era (2005–2014)
In this most recent decade, major efforts have continued to develop integrated care
programs, goaded by the tragic finding that public mental health clients die 25 years
8 R. Manderscheid
prematurely [6]. This work has proceeded despite the fact that the Great Recession
dramatically reduced expenditures for public mental health services, perhaps by as
much as $4.5 billion. Legislative developments during the decade provided strong
support for the work on integrated care and provided financial incentives to promote
it. Throughout the decade, a growing recognition also emerged that a large and
ever expanding number of persons with mental health and substance use conditions
were becoming incarcerated inappropriately in local and county jails, and state penal
institutions.
This decade witnessed two major policy shifts. First was the passage of the
Wellstone-Domenici Mental Health Parity and Addiction Equity Act of 2008. Sec-
ond was the passage and implementation of the Patient Protection and Affordable
Care Act of 2010. Each is discussed below.
The Wellstone-Domenici Act extended parity for mental health and substance use
insurance benefits to all large private plans that offer these benefits and that insure 50
or more persons. Here parity means equality of behavioral health insurance benefits
and medical/surgical insurance benefits, as well as equality in how these benefits are
managed. Not enough can be said for how important this Act has been in changing
the dialogue in the health field around mental health and substance use care.
Even more monumental, the Patient Protection and Affordable Care Act of 2010
is on a par with the creation of Social Security in 1935 and the creation of Medicare
and Medicaid in 1965. This Act contains many reforms (see [2]), including dramatic
expansions in private and public health insurance coverage; needed adjustments
to long-standing insurance provisions, such as requiring guaranteed coverage,
elimination of annual and lifetime limits, mandatory benefit structure, and removal
of copays and deductibles for targeted preventive interventions; introduction of
federal financial incentives to promote integrated care and risk bearing capitation
payment systems; and introduction of federal incentives to promote use of electronic
medical records. For the mental health and substance use fields, the changes
introduced by this Act are major landmarks. In fact, this Act reflects the very first
national de jure policy on mental health every enacted since the founding of the
United States in 1776.
Importantly, the Patient Protection and Affordable Care Act of 2010 also
extended parity to all new enrollees under the State Health Insurance Marketplaces;
all new enrollees under the State Medicaid Expansions; and all new enrollees in
individual plans after July 1, 2014. As a result, more than 60 million citizens now
enjoy this protection.
Overall, the advances produced through these two pieces of legislation codified
the developments of the preceding quarter century: promotion of community-based
mental health and substance use care, promotion of integrated behavioral health and
primary care, and promotion of integrated electronic medical records.
Throughout the decade, efforts continued to implement electronic medical records

fueled by federal financial incentives in 2009. Despite the fact that behavioral
healthcare program were excluded from these financial incentives and despite the
fact that federal law and regulation create great disincentives to the inclusion of
substance use care data in these records, slow but steady progress continued in their
implementation.
Although this clearly was the decade of the Internet, with phenomenal growth
in use and a veritable explosion in mobile phone apps linked to the Internet, these
developments did not translate into a commensurate impact on the use of the Internet
for care delivery in the mental health or substance use fields. People can now
go to the Internet to find numerous support groups, to find detailed information
on disease and care, and to find a broad array of community providers, yet this
exciting world generally ends at the door of behavioral health providers. One can
speculate that many factors are in play to produce this dramatic discontinuity:
most behavioral health providers have no IT training; many state laws discourage
electronic communication between provider and consumer; and many behavioral
health entities have aging IT equipment and capacity. Whatever the specific reasons,
an ever growing digital divide exists between what is going on in the community and
what is transpiring in the offices of behavioral health providers, particularly those
working in the public sector.
Yet, even more very exciting digital developments are on the near horizon.
Perhaps most interesting is the development of virtual reality helmets now beginning
to be widely used in electronic games. These helmets have potential care changing
possibilities in behavioral healthcare that are just now beginning to be explored by
our research community. A second very exciting recent development is the growth
in the use of avatars for self and others, which also have great potential in behavioral
healthcare.
1.8 The Future (2015 and Beyond)
As we look to the future, it is advisable to seek a midground between what will be

potentially possible and what is actually likely to be achieved. With this balance in
mind, here are a few predictions for the next decade.
Our new de jure parity and health reform policies will continue to energize
behavioral healthcare. Additional people with behavioral health conditions will
continue to enroll in health insurance, the field will continue to grow, and integration
efforts will accelerate (see [7]). Each of these developments is very important, and
each is very positive for behavioral healthcare.
Predictions regarding the use of IT in behavioral healthcare need to be much more
guarded, with many caveats. Our work in implementing integrated, interoperable
electronic medical records will continue, but progress will be retarded by the
10 R. Manderscheid
workarounds necessary to accommodate federal law and regulation regarding the

confidentiality of electronic records for substance use care. Further, unless we are
able to extend federal financial incentives for implementing electronic medical
records to include behavioral healthcare entities, the mental health and substance
use fields are likely to progressively fall behind medical settings in implementing
and using these electronic tools. The development of integrated medical homes
and health homes may ameliorate this disparity somewhat, but will not completely
overcome it. Clearly, we need changes in federal law which will be very difficult to
achieve in the current environment.
Undoubtedly, a large disparity will continue to exist between what digital
applications are possible in care delivery and what actually occurs in the mental
health and substance use fields. The irony is that consumers and peers will be far
more advanced in digital application use than will care providers. Thus, without
specific planned interventions, care delivery is likely to continue to be provided
very much as it has been in the past.
If we are to reach the potential of IT in the delivery of mental health and
substance use care in the future, several national actions will be needed. Although
this topic goes far beyond the scope of the present chapter, here are a few possi-
bilities for consideration. First, the US Department of Health and Human Services
should create an Office of Digital Healthcare. This Office would be responsible
for identifying new digital care applications, assessing their effectiveness, and
disseminating them to the field. Second, SAMHSA should provide broad-scale,
ongoing technical assistance to current program managers and clinicians, so that
mental health and substance use care programs are aware of and are able to
implement digital tools for managerial, administrative, and clinical applications.
The initial goal of this technical assistance should be to help these mental health and
substance use care delivery programs catch up with the current mainstream of digital
care applications. Third, future clinical and managerial training for persons entering
the mental health and substance use fields should include fundamental courses in
digital technology and current mainstream digital applications, such as I-Phone apps
(see [8], for examples of emerging digital tools).
As we rapidly enter the era of the Affordable Care Act, we can expect the number
of mental health and substance use clients to almost double over the next decade.
Because no specific plans are in place to increase the mental health and substance
use workforce, the only way we will be able to cope with this dramatically increased
workload is through much more effective use of digital technology. Hence, this is
an urgent call to immediate action.
References
1. Manderscheid RW. Formulation of mental health policy in the United States, with comparative
case studies of South Africa and Thailand. In: Sorel ES, editor. 21st century global mental health.
Burlington: Jones and Bartlett Learning; 2012. p. 351–64, Chapter 15
2. Manderscheid RW. The Affordable Care Act: overview and implications for county and city
behavioral health and intellectual/developmental disability programs. J Soc Work Disabil
Rehabil. 2014. Can be accessed at: http://www.tandfonline.com/doi/full/10.1080/1536710X.
2013.870510#.UwePis7EUs
3. National Institutes of Health. National Institute of Mental Health: important events in NIMH
history. NIH 1999 Almanac. Bethesda: National Institutes of Health; 1999.
4. Grob GN. From asylum to community; mental health policy in modern America. Princeton:
Princeton University Press; 1991.
5. Mental Health Statistics Improvement Program. Guidelines for a minimum statistical and
accounting system for community mental health centers. Rockville: National Institute of Mental
Health; 1972.
6. Colton CW, Manderscheid RW. Congruencies in increased mortality rates, years of potential life
lost, and causes of death among public mental health clients in eight states. Prev Chronic Dis.
2006;3(2): published online
7. Manderscheid RW, Kathol R. Fostering sustainable, integrated medical and behavioral health
services in medical settings. Ann Intern Med. 2014;160:61–5.
8. Manderscheid RW, Wukitsch KA. Healthy people 2020: developing the potential of mobile
and digital communication tools to touch the life of every American. J Commun Health.
2014;7(1):8–16.
Chapter 2
Electronic Health Records Technology: Policies
and Realities
Lori Simon
Abstract This chapter begins with a succinct review of the history of electronic
health records (EHRs) in the U.S., including recent efforts by the federal gov-
ernment to encourage the use of them through their Meaningful Use program. It
then discusses the low participation in this program by mental health providers
and the reasons for the general lack of acceptance of EHRs by them. In fact, in
2012 only 7.1% of psychiatrists participated in the Meaningful Use program. The
chapter next proceeds to discuss various efforts to increase the use of EHRs in the
mental health field, including those by the American Psychiatric Association, the
Substance Abuse Mental Health Services Administration (SAMHSA), and the HL7
organization. The second part of the chapter provides in depth guidelines to selecting
and implementing an EHR, beginning with making the decision whether to actually
get one. Once the decision is made to do so, the chapter talks in great detail about
the preparation process prior to the selection, followed by the steps involved in the
actual selection and implementation processes. The chapter closes with a renewed
emphasis on the need to do a thorough job prior to the implementation so as to avoid
many problems after the EHR goes live, as well as the importance of including the
ultimate users of the EHR in the entire selection and implementation processes.
Keywords Centers for Medicare and Medicaid Services (U.S.) • Cost of illness •
Documentation • Expert systems • Health information management • Health
Insurance Portability and Accountability Act • Information systems • Meaningful
use • Medicaid • Medical informatics • Medical records • Mental health •
Motivation • Psychiatry
L. Simon, M.D. ()

Department of Psychiatry, Payne Whitney Clinic, New York Presbyterian
Weill Cornell Medical Center, New York, NY, USA
e-mail: LoriR.Simon@gmail.com

14 L. Simon
2.1 History
There are few industries where computer technology is needed as much as in

healthcare. Yet, for a long time now and for a variety of reasons, it has been
much more of a struggle to implement such technology than in other industries,
such as banking, insurance, media, transportation, and others. Back in the 1970s,
these industries began embracing computer technology quite successfully, because
for the most part, they were composed of private corporations with the financial
resources to devote to these efforts. As a result, they were able to build strong in-
house IT departments who developed software to suit the specific needs of their own
corporation by working very closely with the users of that software throughout the
development cycle. They were also able to purchase the hardware needed to run this
software, as well as build robust back-up systems.
With regard to healthcare, any efforts that were undertaken were primarily
initially done by two groups: (1) Academic institutions affiliated with major medical
centers. (2) Software vendors. With regard to academic institutions, as far back
as the 1960s–1970s, there were some major early successes, including the work
at Massachusetts General Hospital led by G. Octo Barnett, MD which led to
the development of the Computer Stored Ambulatory Record (COSTAR) using
the MUMPS programming language that was specifically developed for health
information technology. Robert Greenes, MD, PhD, a co-developer of MUMPS,
also made huge contributions to the field of medical informatics during this
period. Homer Warner, MD, PhD of the University of Utah and Latter Day Saints
Hospital was instrumental in developing the HELP system which first became
operational at LDS Hospital in 1967. It eventually expanded into a full-blown
integrated hospital information system with sophisticated clinical decision-support
capabilities. William Stead, MD and W. Ed Hammond, PhD also began developing
what became The Medical Record (TMR) at Duke University. Significant early work
was also done at Stanford University by Gio Wiederhold, PhD, a computer scientist
who did significant research on large scale databases and Edward Shortliffe, MD,
PhD, who developed MYCIN, a medical expert system, in the 1970s [1]. Lawrence
Fagan, MD, PhD and Terry Winograd, PhD began making important contributions
to the field of health related computer science beginning in the 1970s at Stanford
University, as well. During the 1970s, Clem McDonald, MD led the team at Indiana
University’s Regenstrief Institute which developed one of the first EHRs (RMRS)
in the US. However, these efforts represented only a small fraction of the healthcare
institutions in the U.S. With regard to mental health, Marvin Miller, MD, in his book
“Mental Health Computing”, describes a number of applications for specific mental
health functions that existed during the 1990s which were primarily developed by
small groups of academicians [3]. Since the 1980s, the National Library of Medicine
(NLM) has provided significant funding to academic institutions for informatics
related activities. One major program has been the Integrated Advanced Information
Management Systems (IAIMS) Awards.
Vendors also started developing software for the healthcare marketplace. Again,
as early as the 1960s, several companies, including two aerospace companies,
2 Electronic Health Records Technology: Policies and Realities 15
Lockheed and McDonnell Douglas, began developing software to fully automate

medical records for large hospitals. The aerospace company involvement was in
response to government inducements to develop software for the healthcare field.
Their healthcare software divisions subsequently became known as TDS and HBO,
respectively. During the 1970s, software to support patient billing and admissions,
discharge, and transfer functions for hospitals became prominent. During this
time, IBM developed the Shared Hospital Accounting System (SHAS) followed
by the Patient Care System (PCS) in conjunction with the work Dr. Stead and
his team were doing at Duke University. It evolved into an application generator
tool which allowed for additional clinical software applications to be incorporated.
Another early leader was Shared Medical Systems (SMS) which began in 1969 and
was eventually acquired by Siemens in 2000. Niche software for certain clinical
functions, primarily radiology and the laboratory, began appearing, as well in
the late 1970s-early 1980s [2]. During the early 1980s, Cerner developed a lab
system which was progressively expanded into a comprehensive EHR and other
supporting health information technology. Throughout the 1990s and early 2000s,
the percentage of hospitals implementing computer technology slowly increased,
but they still represented a minority of healthcare institutions. During this time,
vendors began developing software for office practices, especially primary care and
a small number of such practices began using it. Increasingly more robust EHRs
also began making their appearance.
However, at the same time, it started becoming apparent that there are significant
problems and complexities within healthcare which have been hindering the more
widespread adoption of computer technology. First, most hospitals have not had
sufficient IT staff to manage these projects and ensure that not only the software
truly satisfies their requirements, but that other needs, including training and
documentation are also being met. This problem has been compounded by the
software, itself. As vendors understandably try to market their software to the
largest number of customers, it becomes impossible to fully satisfy each customer’s
requirements.
Second, the healthcare environment is extraordinarily diverse with patient care
occurring in a variety of settings by many different providers and ancillary
staff, potentially involving the use of multiple software applications needing to
communicate with one another. Governmental reporting requirements also impose
the need for interoperability with these systems. In addition to the complexity of
having disparate software products from multiple vendors communicate with one
another is the added burden of privacy and security issues and the need to ensure
compliance with HIPAA and other governmental privacy/security laws.
Over the years, various efforts have arisen to help overcome some of these
difficult challenges. The Health Level 7 (HL7) organization was established in
1987 and as per its website, it is a “not-for-profit, ANSI-accredited standards
developing organization dedicated to providing a comprehensive framework and
related standards for the exchange, integration, sharing, and retrieval of electronic
health information that supports clinical practice and the management, delivery
and evaluation of health services. HL7’s 2,300C members include approximately
16 L. Simon
500 corporate members who represent more than 90% of the information systems
vendors serving healthcare”. In recognizing the importance of involving healthcare
professionals in their work, they have recently established a category of membership
for them.
Other organizations that have played a large role in supporting the use of
computer technology in healthcare include the Healthcare Information Management
Systems Society (HIMSS), the American Medical Informatics Association (AMIA),
and the American Health Information Management Association (AHIMA). HIMSS
was started in 1961. During its earlier years, it was focused more on IT and
healthcare management/administrators. In fact, its membership statistics for 1977
included Management Engineering (37.9%), Hospital Administration (23.1%),
Health Care Consultants (14.8%), Information Systems/Data Processing (11.5%),
Health Care Planning (4.7%), Financial Management (3.5%), University Professors
(1.9%), and Other (2.6%). Clinicians were not even mentioned. However, over time,
it has evolved into a multi-disciplinary organization representing all of the major
players involved in HIT, including clinicians. In 2005, they hosted a Physicians’
Symposium for close to 300 physicians and other healthcare professionals from both
the inpatient and outpatient community. It has also been working collaboratively
with other professional organizations.
According to their website, “AMIA is a professional scientific association that
was formed by the merger of three organizations in 1988: the American Association
for Medical Systems and Informatics (AAMSI); the American College of Medical
Informatics (ACMI); and the Symposium on Computer Applications in Medical
Care (SCAMC). AMIA’s program and services are centered around core purposes
to:
• advance the science of informatics
• promote the education of informatics
• assure that health information technology is used most effectively to promote
health and health care
• advance the profession of informatics
• provide services for our members such as networking and opportunities for
professional development.”
AMIA has traditionally been the home for more academically minded medical
informatics professionals, especially clinicians. However, as with other organiza-
tions involved in the use of technology in healthcare, they have recognized the need
to become more inclusive with other HIT professionals and collaborate with other
organizations, including HIMSS.
AHIMA traces its history back to 1928 when the American College of Surgeons
established the Association of Record Librarians of North America to deal with
issues involving clinical records. In subsequent years, it changed its name several
times as it expanded its focus to keep up with the changing landscape of health
information and the increasing use of computer technology. In 1991, it began using
its current name.
In 2004, the federal government stepped in with the establishment of the Office of
the National Coordinator (ONC) for Health Information Technology by Executive
Order. Five years later, it was legislatively mandated in the Health Information
Technology for Economic and Clinical Health (HITECH) Act. Among HITECH’s
many efforts to promote the use of computer technology in healthcare has been
the development of the Meaningful Use (MU) Program which provides monetary
incentives for Medicare and Medicaid providers to implement EHRs which have
been certified by ONC. ONC has worked with CMS to establish the criteria that must
be satisfied by Medicare providers using the certified EHRs, while the Medicaid
programs in each state have been overseeing the Medicaid incentive program.
Medicare providers who fully participate in the program will be able to receive
up to $44,000 while Medicaid providers can receive a maximum of $63,750. The
program began in 2011 and ends in 2016. 2014 was the last year that Medicare
providers could begin participation in the program and still receive incentive
payments, but Medicaid providers have until 2016 to begin participation. Starting
in 2015, there will actually be penalties to those Medicare providers who haven’t
yet implemented this technology unless they qualify for a hardship exemption, but
Medicaid providers will not be penalized.
2.2 Status/Ongoing Problems
So, how much progress has been made? As of the end of 2013, 58% of hospitals
had at least a basic EHR and some success with meeting the requirements of MU
Stage 1, but only 5.8% were able to meet all of the Stage 2 criteria. With regard to
individual physicians, the Fig. 2.1 shows the percentage of physicians by specialty
Specialty
Gastroenterology 54.3 45.7

Nephrology 52.3 47.7
Urology 51.0 49.0
Cardiology 50.6 49.4
Pulmonary Disease 50.6 49.4
Endocrinology 50.3 49.7
Oncology 46.6 53.4
Otolaryngology 44.5 55.5
Neurology 41.2 58.8
Surgery 37.1 62.9
Ophthalmology 31.7 68.3
Dermatology 31.0 69.0
Physical Medicine 28.6 71.4
Obstetrics/Gynecology 28.7 71.3
Radiology 12.0 88.0
Psychiatry 7.1 92.9
0 10 20 30 40 50 60 70 80 90 100
Percentage
Percentage of eligible professionals awarded an incentive payment for 2012

Percentage of eligible professionals not awarded an incentive payment for 2012
Source: GAO analysis of CMS data
Fig. 2.1 Percentage of specialty practice physicians who were awarded a Medicare EHR incentive
payment for 2012, by selected specialty
18 L. Simon
who received a meaningful use incentive payment in 2012. Although a number of

specialties reached the 50% mark, psychiatry ranked at the bottom with only 7.1%
participation.
Other statistics from 2012 further illustrate the low participation of psychiatrists
in the Meaningful Use program. Of the approximately 41,000 practicing psychia-
trists that year, 55% accepted Medicare and 43% Medicaid. Yet, only 375 Medicare
and 292 Medicaid psychiatrists received an incentive payment.
Why the low participation? First, 85% of psychiatrists are in solo private practice.
Most do not have the time, manpower, and financial resources to embark on the
selection and implementation of an EHR. Other psychiatrists work in mental health
clinics or psychiatric hospitals, both of which are not eligible to participate in the
Meaningful Use program. Second, as the Meaningful Use program’s focus is on
primary care, it is extremely rare to find any support for mental health providers
from the Regional Extension Centers (RECs) that were established throughout the
U.S. to assist providers with the selection and implementation of EHRs. Third,
the EHR software developers have been slow to incorporate functionality needed
by mental health providers, even for such basic requirements as support of DSM.
Fourth, more so than in many other specialties, as seen by the chart below, mental
health care involves many different types of providers. Therefore, EHRs must not
only satisfy the needs of this diverse group, but depending upon the care setting,
ensure that they are able to communicate amongst each other, as well.
Clinical Workforce Totals (2012)
• 41,000 Psychiatrists
• 96,000 Psychologists
• 193,000 Clinical Social Workers
• 14,000 Psychiatric nurses
• 48,000 Substance Abuse Counselors
• 145,000 Counselors
• 62,000 Marriage and Family Therapists
Source: Data assembled from various sources by SAMHSA and published in
Behavioral Health, United States, 2012.
In addition, as many studies have shown that a majority of visits to primary
care providers have some sort of mental health component, the need exists to not
only have EHRs for primary care include the ability to capture this information, but
also to communicate it with various mental health providers. Finally, an additional
burden placed upon vendors supporting mental health is the set of laws in place
to protect the confidentiality of patients with mental illness. Two in particular are
42CFR which addresses the confidentiality of substance abuse information and the
extra protection accorded to psychotherapy notes, over and above the confidentiality
requirements for all evaluation and progress notes. This is, of course, in addition to
the HIPAA confidentiality laws for all patients. As a result, many Health Information
Exchanges (HIEs) which were established throughout the U.S. to provide greater
accessibility to providers for their patients’ clinical information have restricted the
storage of mental health information, primarily because they don’t want to have to
deal with this additional confidentiality burden. In August 2011, ONC sponsored
the creation of the Behavioral Health Data Exchange Consortium amongst five
states, Alabama, Florida, Kentucky, Michigan, and New Mexico, to pilot the
secure interstate exchange of behavioral health records among treating health care
providers. In June 2014, the Consortium issued a report detailing their findings and
recommendations [4]. Not surprisingly, it highlighted the challenges of dealing with
the additional privacy concerns imposed by behavioral health data. The primary
lessons they learned include: “(1) Behavioral health data exchange is complex, but
possible (2) Provider education is key to success, and (3) Cooperation and flexibility
are invaluable when addressing complex problems.”
In 2004, the Certification Commission for Health Information Technology
(CCHIT) was established and 2 years later began certifying EHRs, including those
for Behavioral Health. As part of this work, they needed to develop extensive
requirements and test scripts for each health field they were certifying. However,
due primarily to the significant financial, manpower, and time resources needed by
the EHR vendors to achieve this certification, many chose not to participate. As a
result, only three Behavioral Health EHRs received the certification and in 2014,
CCHIT stopped their certification program altogether.
2.3 How to Proceed
In recognizing that the mental health community is not being adequately supported
by the Meaningful Use program, ONC has begun to investigate the possibility of
developing a voluntary certification program for EHRs that provide support for the
mental health community. As a result, early in 2014, they sought input from this
community, including both providers and patients, to better understand their needs.
They also asked vendors that have developed EHRs which provide some degree of
functionality for mental health for their opinion on the need for such a certification
program. They then asked for public comment and are now further deliberating on
whether to move forward with such a program, and if so, what should be the design
and content of such a program.
Mental Health professional organizations can play an important role in providing
assistance on computer technology to their members in several ways. First, they can
represent their members’ needs and serve as an intermediary with vendors. They can
also provide links to information sources on their websites and host vendors at their
conferences. For example, in recent years, the American Psychiatric Association
(APA) has steadily increased its activities in this area. During their annual meeting,
vendors have an opportunity to discuss and demo their software in the Exhibit
Hall and their EHR committee has presented workshops and symposia on relevant
computer technology topics. In 2013, the Committee changed its name to the Mental
Health Information Technology (MHIT) committee to reflect its widening scope
in dealing with HIT topics beyond EHRs. Its members have expertise and are
20 L. Simon
involved in such areas as privacy/security, health information exchanges (HIEs), and

telepsychiatry. It has been responsible for developing a detailed set of requirements
that EHRs need to satisfy to support psychiatry. Those requirements, along with
a variety of documents and links to other sources of information, are posted on
the APA’s website. In 2014, in recognizing the need to establish a more direct
relationship with vendors, the MHIT committee hosted a webinar for them during
which they acquainted the vendors with various HIT related activities going on
within the Mental Health field. Two MHIT Committee members are members
of the American Association of Child and Adolescent Psychiatrists (AACAP), as
well, which has also been working on various HIT related activities. The MHIT
Committee has recognized the importance of collaborating with other Mental Health
professional organizations, as well, and in the near future hopes to develop a more
formal structure for reaching out and working with them on HIT activities of mutual
interest.
The Substance Abuse Mental Health Services Administration (SAMHSA) is a
federal organization within the Department of Health and Human Services which
has been in existence since 1992. In recent years, it has had an increasingly strong
focus on HIT and its strategic goals for 2011–2014 have included:
Goal 6.1: Develop the infrastructure for interoperable EHRs, including privacy,
confidentiality, and data standards.
Goal 6.2: Provide incentives and create tools to facilitate the adoption of HIT and
EHRs with behavioral health functionality in general and specialty health care
settings.
Goal 6.3: Deliver technical assistance to State HIT leaders, behavioral health and
health providers, patients and consumers, and others to increase adoption of
EHRs and HIT with behavioral health functionality.
Goal 6.4: Enhance capacity for the exchange and analysis of EHR data to assess
quality of care and improve patient outcomes.
SAMHSA has been heavily involved in the HL7 organization, particularly with
one of their many workgroups, Community Based Collaborative Care (CBCC),
which has developed both a Behavioral Health Functional Profile based on HL7’s
EHR Functional Model and a Behavioral Health Domain Analysis model. The APA
is an organizational member of HL7 and several members of the APA’s MHIT
Committee have become involved, as well. The CBCC workgroup, with help from
their SAMHSA and APA members, will be working on integrating the APA’s
Function Requirements and CCHIT’s Behavioral Health Function Requirements
with their Behavioral Health Functional Profile with the intent on further expanding
it to other Behavioral Health settings. Eventually, the intent is for these requirements
to be used by software developers to build products that more closely satisfy the
needs of the Mental Health field and for prospective users of these products to be
able to determine which ones most satisfy their own needs.
The American Association for Technology in Psychiatry (AATP), a non-profit
organization of physician and mental health professionals, began in 1995 as a
meeting and in 2002 expanded its scope. Its mission is to:
• promote the use of information technology to improve the quality and availability
of psychiatry and mental health care
• promote the development and dissemination of knowledge in the use of technol-
ogy in psychiatry and mental health
• foster technology in psychiatry and mental health as a recognized body of
knowledge
• promote the development and dissemination of standards and best practices for
use of technology in psychiatry and mental health, including respect for, and
preservation of, confidentiality and privacy
• inform and influence public policy in the use of technology in psychiatry and
mental health
2.4 Decision to Use an EHR
The decision to incorporate an EHR into one’s practice involves a number of factors:
1. If you are a Medicare provider, you will need to determine to what extent
the penalties you will incur if you haven’t begun using an EHR in 2014 will
affect your income . With regard to having to satisfy the Meaningful Use criteria,
although as mentioned earlier they are oriented towards primary care providers,
there is sufficient flexibility so that it is possible to satisfy the criteria as a
psychiatrist, something I have been able to do in my solo private practice.
2. E-Prescribing, including controlled prescriptions, is starting to be required by
individual states. For New York physicians, this will be as of March 27, 2015.
Using a standalone product for this purpose, only, could be a good place to start
incorporating computer technology into your practice, especially if it is one that
is also integrated into an EHR which you are considering implementing at a later
date. I have found e-prescribing to be a huge time-saver, both in eliminating the
calls to pharmacies and recording what medications my patients are taking. The
software that you use should allow you to keep track of not only what you’re
specifically prescribing, but all of the medications your patients are taking, both
OTC and those prescribed by other providers. Most e-prescribing programs also
have drug interaction checking built into them.
3. Interoperability: If you practice in either an inpatient or large outpatient setting,
you probably have recognized the importance of being able to communicate with
other providers and staff within that setting through some form of automation.
Those of you who practice in solo or small group practices may feel that this
has not been an issue. However, the ability to easily communicate with other
providers, esp. those in primary care, with whom you likely share patients, can
decrease the time you normally take and staff resources you use to do this
manually. Of course, the computer is not intended to replace the sometimes
essential direct conversation with another provider, but often that conversation
can be all the more optimal if both parties have detailed clinical information
22 L. Simon
about the patient they are discussing right in front of them. In addition, for those
of you who practice in settings where governmental reporting requirements have
been mandated, computer software is likely going to be essential to providing the
data being requested.
4. New Practices: If you are just starting out in your own practice, other than
obtaining e-prescribing software, you would probably benefit from holding off
getting anything else for the first 6 months – 1 year to give yourself time to better
understand what your own needs are and how best an EHR can help satisfy those
needs.
5. Finances: As most EHRs are not free, you will want to determine whether you
have the financial resources to invest in one, either paying a one time fee for the
life of your contract or paying on an installment basis. Financial considerations
will be discussed in more detail later on in the chapter.
6. Time/Manpower Resources: In order to successfully implement any software
in your work setting, it is critically important that considerable time be spent
in preparation, which I describe in the following section. For solo/small group
practices, this can be made much more manageable if you give yourself plenty
of lead time prior to when you plan on beginning use of the software and
develop a plan which minimally impacts your practice. Realistically, some work
on weekends may be needed, but the more lead time you give yourself, the less
time you will need to spend during any one weekend. This is not the time to
procrastinate and then cram at the last minute – that may have worked for you
when taking tests, but it is not a good strategy to use here!
2.5 Selection Preparation
Once you make the decision to incorporate a clinical software product into your
practice setting, it is absolutely essential that you do a thorough analysis of that
setting to determine exactly what you are going to need. One of the primary
reasons there have been so many problems with the implementation of EHRs,
in general, is that the preparatory work is not done nearly as well as it should.
For hospitals and large outpatient settings, clinicians and administrators
representing every department who will possibly be affected by the EHR
should be involved in many of these preparatory steps. These steps include:
1. Assembling A Team: For large practices and hospitals, the very first step is
to assemble a team of clinicians, administrators, and staff representing the
involved departments who are willing to commit to working together with
the IT staff throughout the Preparation, Selection, and Implementation phases.
Typically, a Chief Medical Informatics Officer (CMIO) who is often, but not
always, a clinician, leads this team and works closely with the head of the
IT staff. As the work involved in this effort can be rather time consuming,
members of this team sometime need to be able to cut back on their normal
duties for a period of time. Their work is extremely important, as the knowledge
they acquire and provide from undertaking many of the following steps in the
Selection Preparation phase will be essential for selecting an EHR which best
fits the needs of their practice setting. They will then play a critical role in
ensuring that the Implementation phase goes smoothly and is successful.
2. Decide Which Functions and Data You Need: EHRs contain many functions
that providers may need, but it isn’t necessary to implement all of them at
one time or even at all. Therefore, you need to assess the settings in which
you will be providing care to determine which functions and data are most
important to you, both now and in the future. If you are a Medicare/Medicaid
provider and planning on participating in the Meaningful Use program, you
may need certain functions and data for that purpose. If you treat children
and adolescents, you will most likely need the EHR to maintain certain data
elements unique to those patients, ex. growth charts. As a solo practitioner, I
wanted to initially implement the billing, e-prescribing, and clinical charting
functions, but decided to hold off on the EHR’s appointment function while I
continued to use Microsoft Outlook.
Now 3 years later, I decided to switch over to their own appointment
function and implement both their patient portal and patient reminder functions.
I initially chose to do this to provide additional functionality to my patients, but
then discovered that I needed to use elements of these functions to satisfy the
Meaningful Use Stage 2 requirements.
The functions needed by hospitals will be somewhat different. For example,
computerized order entry (CPOE) for various tests, including labs, radiology,
etc. will be essential, but an appointment function likely will not be needed. I
do order lab tests for my patients and many EHRs for outpatient practices do
have functionality to allow providers to electronically send lab orders to specific
laboratories and receive the results electronically. However, the frequency that I
do so is relatively low, so for me, it isn’t important to use an automated function.
Instead, I continue to send lab requests the old-fashioned way, i.e. write the labs
I want on a prescription pad with a note to fax me the results and then either fax
it directly to the lab or give it to the patient.
Understanding a practice setting’s future needs, as well as their current ones
is extremely important, because you want to make sure that the EHR which is
selected will be able to support those future needs, both from a function and data
perspective. It may well be that some degree of customization may be needed to
support those needs, but if the underlying software and database structures are
not compatible with the customization that would be needed, you would want
to know that before the EHR is selected. The worst thing that could happen is
to have to replace the EHR in the future, because this upfront work was never
done.
Once it is determined what functions are needed, it is critically important
to understand how they are used in a particular care setting. This involves
a thorough analysis of the daily work flow, who is involved, what functions
they use, and what data gets accessed. This analysis can not only be helpful in
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preparing for the selection and implementation of an EHR, it can also be highly
useful in determining how work flows might be improved without automation.
Computers can’t and shouldn’t fix everything!
One hospital that did not do sufficient preparation in this area ran into
problems during their implementation which involved the need to transfer
patients from the medical/surgical inpatient units to inpatient psychiatry. The
first time this had to be done after the system went live, there was great difficulty
in doing so, because the EHR did not easily support the functionality that was
needed when this type of transfer occurred.
3. Who Needs Access to the EHR? You will need to decide which functions
other providers and staff in your office or department need to access and
whether those who access a particular function can both read and update
the corresponding data or only read it. For example, a staff member who
handles the patient billing would need both read and update access to all of
the billing related functions, but would likely only need to read any clinical
data. The bigger the practice or for an inpatient psychiatry department within a
hospital, both deciding what functions are needed and then who and to what
degree providers and staff have access to those functions becomes a much
more involved task, but again, is absolutely necessary to do. Within hospitals,
in particular, an additional complexity is that access to patient data can be
temporary, ex. for covering physicians, residents, or consultations.
Patient portals are becoming increasingly popular and are actually part of
Meaningful Use Stage 2 which requires more than 5% of your patients to actu-
ally access their clinical data online. These portals provide patients with such
functions as access to subsets of their own clinical data and links to diagnosis
related education, appointment scheduling, authorization to share parts of their
chart with other providers, and the ability to communicate electronically with
their own treatment providers, all within a secure environment.
4. User and Data Accessibility: Nowadays, EHRs can be accessed on more than
the computer sitting on your desk or at a nursing station. Once it is determined
what functions will be needed and by whom, it then needs to be determined
how and where these users will be accessing those functions. Even if you are
a sole proprietor, there are options. For example, do you want to be able to
readily access the EHR while you’re talking to patients to review medications
and other clinical data? Are you planning on writing progress notes during the
session? I know of one psychiatrist who actually projects the note he is writing
onto a large screen so the patient can view what he is writing and provide
input. How about using tablets or smart phones to access data? I personally
find it very helpful to be able to access my e-prescribing functions on my smart
phone so that if a patient calls me while I am not at home or in the office,
I can readily check their medication regimen and even send in a prescription
electronically. In addition, for many years, I have used one lightweight laptop
(approx. 3 lbs) that I take with me to both of my offices and also use at home.
Community mental health organizations may have outreach programs whereby
clinicians see patients where they reside. It could be quite advantageous for
these clinicians to have some form of portable device allowing them access to
a subset of the functions of the organization’s EHR.
In a hospital setting, there are many more possibilities for user access beyond
the nursing stations, including ORs, ancillary areas, satellite clinics, and even
patient rooms, as well as all of the administrative and support staff offices.
Within each area, is the hardware used for access stationary, i.e. on a desk or at a
nursing station, or does there need to be the option of having it be portable? For
example, should a clinician (nurse, physician, physical therapist, etc.) be able
to access their patients’ information from a tablet that they take with them into
the patients’ rooms or an admissions representative when they need to admit
a patient from the ER? If the hardware is stationary, ex. at a nursing station,
how many computers will be needed to ensure that every clinician who needs
one at any point in time will have the requisite access? If it’s portable, can they
be shared or should every clinician have their own? If shared, how many is
enough?
If you are in a solo or small group practice, the data and actual EHR software
typically physically reside in the “cloud”, which are actually remote servers
located anywhere in the US or abroad and accessible via the Internet. The larger
the practice setting, the more likely the data and software are housed on more
local servers, somewhere within the vicinity of that setting. If that is the case
and it is permitted, you may want access to the data and software remotely, ex.
from your home.
5. Volumes of Data: Again, if you are in a solo or small group practice, most
EHRs should be able to handle the amount of data associated with the patients
within your practice. However, larger outpatient settings and hospitals need
to be able to quantify the volumes of data they expect an EHR to handle,
both currently and in the future, optimally projecting out to the next 3–5
years. These numbers need to include both average and maximum amounts
during the course of a day, week, and month for each of the functions the
practice setting will be using. An important component of these calculations
are the number of concurrent users of the EHR, again both average and
maximum numbers, because this directly affects the overall volumes of data.
Interoperability requirements also place demands upon an EHR system and
need to be understood in detail, as well. All of this information needs to be
part of any discussion with vendors to determine whether their software will be
able to handle these volumes of data without any degradation in response time
and what accompanying hardware, ex. servers, will be needed.
6. What Data Needs to be Moved into the EHR? If you’ve been in practice
for a number of years, the thought of transferring every piece of data you
have for each patient into an EHR is enough to scare you away from ever
getting one! Not to worry – you don’t need to do that unless you want to.
First, you should focus on your current patients. The data that is particularly
important includes medications, both current and history, diagnoses, allergies,
other clinical information, demographics, insurance, and billing. If you have
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been seeing a patient for a long time and have lots of handwritten notes, don’t
feel you have to scan in every one of them. Rather, for each patient, decide how
far back and which notes you would like to have readily accessible. With regard
to billing information, a good strategy is to determine the current balance owed
by each patient as of a specific starting date. Then from that point, you can start
using the EHR to record each visit. Any payments you receive for visits prior
to that starting date should be able to be credited to the patient using one of the
EHR’s billing functions.
Clearly, implementing an EHR in a hospital setting can be much more
complex, because it is a very dynamic environment and great care needs to be
taken to ensure that every activity related to a patient is accurately captured. If
your practice setting is currently using software applications to capture data, it
may be necessary to have data conversion programs written to transfer the data
from the old system to the new one. I know of one hospital who rather than
doing that decided to manually enter every medication each patient was taking
up to a specific cut-off point. After that, they continued to use the old system,
but also entered new medication orders manually into the new EHR when it
went live the following day. However, what was not recorded in the new EHR
were medications that were stopped after the cut-off time, but prior to the new
EHR going live. Fortunately, this was discovered a short while later.
7. Hardware Platforms: If you are in a solo or small group setting, you will
have more decision making power regarding your preference for using specific
computers (PCs or MACs), smart phones and tablets (Apple, Android, Amazon,
Microsoft, etc.). You may need to have some flexibility if the EHR or other
software you like doesn’t support your choice in hardware platforms. If you
work in a hospital or large group setting, it is likely that these decisions will be
made for you.
8. System Availability: Hospitals, of course, need to ensure that their EHRs
are normally up and running 24 h/day, 7 days/week and, if not, alternative
procedures need to be established for planned and unplanned outages. This will
be further discussed in detail in the Implementation section of the chapter. As
the data and software are typically stored in local servers and hospitals have
backup generators to ensure that they have an ongoing supply of electricity,
they usually have more control over their system availability.
If you work in an outpatient setting, you or your facility needs to decide
when you need the EHR to be available and, as with hospitals, what to do when
you don’t have access to it. One concern I have always had about data and
software located in the cloud and accessible via the Internet is what happens
when your Internet service stops working, ex. during a storm. In that situation,
I can use the hotspot function on my smartphone as a backup as long as my
cell phone is still working, but if you don’t have unlimited data, that can very
quickly become costly. Usually, such outages are restored fairly quickly, but
after super storm Sandy hit the east coast several years ago, it took much longer.
One way to lessen the impact of an interruption in access to the cloud would
be for EHRs to provide a way for critical data to always be downloaded to
the computer and the software for important functions housed there, as well.
However, this would involve the need for the EHR to keep the data in both
places in sync and the software be kept up to date on the local computer.
9. Interfaces to other Applications/Systems: This is currently and typically
only of concern for most solo and small group practices in a limited way.
In these settings, interoperability can be relevant for interfacing with labs,
other providers, especially in primary care, and perhaps with a hospital with
whom you are affiliated. As health information exchanges (HIEs) become more
established and robust in the future, sending to and receiving patients’ clinical
information from them to achieve better coordination of care will become
increasingly important.
Hospitals have more complex interoperability needs, because in addition to
the EHR, they have a number of disparate systems, all of which need to be in
communication with each other in real time. Examples include lab, radiology,
OR scheduling, pharmacy, and billing. Nowadays, it is not uncommon for
multiple hospitals to be part of one overall healthcare system, increasing the
possibility that individual hospitals may need to communicate with each other,
as well.
One area of interoperability that can potentially affect both large and small
providers is the increasing reporting requirements, including clinical quality
measures (CQMs), being imposed by both federal and state governments. As
the states often have their own unique set of requirements, it can be difficult
for a vendor to support them for every state. Therefore, it is important to have
a good understanding of your or your institution’s own reporting requirements,
so that you can discuss this in detail with prospective EHR vendors.
10. Implementation Timeframes: There can be specific deadlines that impact the
need to implement an EHR or other software applications. These need to be
identified as soon as possible to ensure that sufficient time exists to complete
all of the steps needed for a successful implementation. One example is the
e-prescribing requirement mentioned earlier. This not only impacts outpatient
providers, but hospitals, as well, who routinely give patients prescriptions when
they are discharged.
2.6 Cost Considerations
When determining your budget for getting an EHR, there are a number of both initial
and ongoing costs that you need to consider. These include:
1. Does the vendor want you to purchase the EHR outright? If not, what is the
monthly cost and is any interest charged?
2. Are any discounts available for either paying the entire cost of the contract up
front or for purchasing a more long term contract?
3. Is there any charge for software updates?
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4. What is included in the costs? If they’re not, what are the additional charges for
each of the following:
(a) Additional Users.
(b) Data Conversion Programs.
(c) Hardware Needs Analysis and Purchase.
(d) Customization (pre and post-implementation) for templates, interfaces/
interoperability, additional functionality, etc.
(e) Documentation.
(f) Training (pre-implementation plus post-implementation for new users).
(g) Implementation Support.
(h) Technical Support (pre and post-implementation).
Many of these topics will be discussed in detail in the next section.
5. Additional staffing may likely be needed by larger healthcare entities to ade-
quately prepare and implement the EHR. However, even solo or small group
practices may find it beneficial to hire someone to help them with the additional
workload imposed by the preparation and implementation phases.
2.7 Selection
The more thorough the preparation, the easier will be the selection process. The first
step is to determine which EHRs satisfy the requirements you have identified
during the preparation. There may be some requirements that the EHR can’t
initially satisfy, but you may be able to receive assurances that they will be able to
do so within the timeframe that you need it. In that case, you would want to ensure
that the satisfaction of these future requirements are stipulated in your contract.
This is particularly relevant if you or your institution are planning on participating
in all stages of the Meaningful Use program. The clinicians and administrators
who worked on the Selection Preparation steps should also be involved in the
Selection process. They need to have the opportunity to actually use a demo
system to provide feedback on the functions they will be using, including how
user friendly they are.
A vendor’s reputation can be very helpful in the selection process. The best way
to determine that is to be able to speak directly to their current and former customers.
Vendors should be able to provide you with those contacts. However, in addition
and if possible, you should try to identify providers from alternative sources. For
example, the AmericanEHR organization works with professional organizations to
provide detailed information on EHRs and providers’ experiences with them via
member surveys and other means. This information is accessible on their website
(www.americanehr.com). You should also determine how many overall and mental
health customers the vendor has. Although a relatively new vendor with a limited
number of customers can have a robust product, you would want to make an extra
effort to ensure the excellence of their products and that they will continue to have
sufficient resources, including finances and manpower, to not only support your
practice setting, but remain in business for the foreseeable future.
Certification can be another way of determining how well an EHR performs.
Of course, a particular certification would only be important to you if the criteria
for that certification is relevant to your own needs. Currently, ONC is the primary
entity certifying EHRs which they do for their Meaningful Use program, but if you
have no intention of participating in that program, that certification may not be of
much value to you. Other considerations in choosing an EHR vendor include the
following:
1. Privacy/Security: In addition to HIPAA and other privacy regulations that
must be met for all healthcare specialties, you need to ensure that the EHR
vendor is satisfying the regulations that are unique to mental health, including
the two that I mentioned earlier in the chapter, 42CFR and those related to
psychotherapy notes. Care needs to be taken to prevent any patient’s data from
being transferred to any provider without the patient’s explicit authorization to
do so. Security is equally important, as it is essential that all data that travels
outside of the office or hospital be adequately encrypted and as safe as possible
from any form of hacking. Within an office or hospital, no one should be able to
gain access to any functions or data within the EHR unless they are specifically
authorized to do so. The ability should exist to have users automatically logged
off within a specific period of time of no activity to prevent unauthorized users
to gain access to the EHR during the previous user’s logon session.
2. Legal Ownership of Data: It is important to ensure in writing that the EHR
vendor does not have any intention of owning the data that is generated by
the EHR. The policies that are in place for data ownership, particularly patient
related, within a practice setting shouldn’t change merely because the data is
now being captured and maintained electronically instead of being stored in
paper charts.
3. Affordability: After discussing all of your requirements with the prospective
vendors and determining all of the costs discussed earlier, ultimately you will
need to make a decision whether you can afford to implement an EHR or other
software. If not, you may want to look into phasing in subsets of the EHR,
especially if you work in a solo or small group practice and anticipate your
practice growing.
4. Adequate Testing: It is extremely important that, as a prospective buyer, you
have access to an exact replica of the EHR to ensure that all functions work as
intended in a user-friendly manner which is acceptable to you. You should also
ask the vendor what volumes of data, concurrent users, and test scenarios they
used to determine if the functions could handle them without any degradation
in response time. This is an area where being able to discuss the experiences of
an existing or former customer of similar size and workload can be extremely
helpful.
5. Access to Test System: Having access to a test system throughout the imple-
mentation process is very important for additional testing of any customization
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that is done for your practice setting and then for subsequent training. Any
customization testing should be completed as much as possible prior to the
training so that the users do not see a system in constant flux as a result of
changes that have to be made to fix errors encountered during the testing.
6. Training: There are various ways that training can occur, including in-house
classes and on-line, either self-directed or with a vendor representative directing
the training by phone or through the computer. Many large practices and hos-
pitals use the “train the trainer” approach which involves initial comprehensive
training for a subset of users of the EHR who will then assist with the training
of their colleagues. The training needs to address the day-to-day work of each
user, not just how each function works. For example, if the EHR is providing
an inpatient order entry function, it is not sufficient to only provide training in
how to enter an order. A surgeon who needs to d/c orders when a patient goes
to the ER, enter new orders when the patient moves to the recovery room, and
then restart some of the original orders that were in effect prior to the surgery
would need to be shown how to accomplish that sequence of events.
The test system should ultimately contain the full functionality of the EHR
so that users can practice, even when they’re not in a specific training session.
Training needs to be available for not only current employees when the EHR is
first implemented, but ongoing for any new employees or existing employees
whose job responsibilities change.
7. Documentation: Comprehensive documentation for all functions provided by
the EHR is needed. It should be on-line and easily accessible from each
function. Optimally, it should also be available in hardcopy, especially during
training, so that users can enter their own notes and be able to use it as a personal
reference. As with training, it is important that the documentation reflect not
only the EHR’s basic functions, but how they are used in actual practice. The
documentation should also be updated whenever any changes/additions are
made to the EHR.
8. Ongoing Technical Support: It is absolutely essential that a robust set of
technical support services be provided. Key elements include:
(a) Availability: Coverage should be provided during the bulk of the time you
will be using the EHR, regardless of time zone differences and including
weekends. This is especially important for systems where the functions and
data are housed in the cloud where the customer has little ability to fix any
problems that may arise nor likely has the technical expertise readily at
hand.
(b) Responsiveness: Once you report a problem or need some kind of help,
how quickly can you expect that help or a resolution of the problem?
(c) Disaster support: In the event of a significant unplanned downtime, it is
crucial for the vendor to provide additional support in order to minimize
the impact on a customer’s daily functioning, particularly with regard to
direct patient care.
(d) Contact Modes: This is typically by phone and, optimally, includes the
ability of the vendor to take remote control of your EHR to fully investigate
a problem. For large customers, it is important to consider the need for

onsite vendor support if a significant upgrade/change is made to the EHR.
(e) Competence: It is also highly preferable to have that support be provided
by people who speak English well and truly understand the system they’re
supporting, not merely reading a script. Even better are support specialists
who actually know your practice, because when problems arise, it makes
it easier to diagnose and fix them. Large practice settings such as hospitals
should have sufficient in-house support to handle emergency problems as
those settings usually need 24/7 availability of most EHR functions.
9. Software Updates: These are updates the EHR vendor makes and are typically
to provide additional functionality. In rarer circumstances, they could also
be to fix a problem that has been detected. The vendor should be able to
provide a detailed plan of how they expect to implement these updates, both
on a scheduled and as needed basis, as well as the anticipated impact on the
availability of the EHR during this process.
10. Adequate Support/Manpower for Implementation, including Customiza-
tion and Date Conversion Needs: The vendor needs to commit to providing
sufficient manpower to support every step of the Implementation process. It is
important that they view the relationship with new customers as a partnership
with staff from both the vendor and the practice setting working together to
achieve a successful implementation.
2.8 Implementation
Once you have selected an EHR, now the work begins to prepare yourself and your
practice setting for eventually using it.
2.8.1 Develop a Work Plan/Staffing
By now, you should have decided exactly what functions you want to start using
in an EHR, what data you are going to need to move into the EHR, and made any
necessary changes to your practice setting’s workflow. At this point, you need to
start developing an explicit plan and schedule leading up to a start date for using
the EHR. You should decide whether you are going to be able to do all of the work
yourself, enlist a friend or family member to help, or hire a consultant. The schedule
needs to include time for data migration, testing, and training.
If this is a large outpatient setting or a hospital, it is extremely important
to maintain a team of IT staff and clinicians working together throughout the
implementation, usually the same people who worked on the earlier Prepara-
tion and Selection phases. The IT staff needs to clearly understand the needs
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of these clinicians and administrators, because they will be representing those

needs to the vendor. The clinicians and administrators need to be particularly
heavily involved in testing, developing a training plan, and deciding what
documentation they need. As with smaller practices, the development of testing,
training, conversion, and implementation timelines, along with plans for adequate
staffing/coverage, is extremely important in keeping the entire implementation
process manageable and under control.
2.8.2 Software Addition/Changes
An early step in the implementation process is to finalize any requirements for

additions/changes to the EHR, itself, as well as other programs that may be needed,
so that work can begin on them. This would typically include:
1. function modifications
2. required interfaces
3. data conversion programs
As previously mentioned, these needs should have been discussed with the
vendor during the Selection phase to determine whether this was feasible to do and
at what cost. For larger practices and hospitals with a robust IT staff, some of this
work can possibly be done in-house.
2.8.3 Hardware Needs
Any additional hardware that will be needed should be ordered early in the
implementation phase to ensure that they will arrive in time to install them prior to
their use in testing, training, and conversion to the new EHR. This would typically
include:
1. Laptop/desktop computers
2. Servers
3. Power/data transmission lines
4. Backup Generators
5. Mobile devices
2.8.4 Downtime Procedures/Disaster Recovery
1. Downtime procedures need to be developed to be used in the event of a system

failure. This is typically a set of manual procedures that utilize paper forms to
capture data (orders, demographics), etc. that can then be entered into the EHR
once the system becomes available, again. In addition, communications protocols
using phone, fax, etc. need to be established to ensure that daily work is not
compromised.
2. Disaster recovery consists of a set of protocols to address the sudden loss of the
use of the EHR and includes:
(a) Switching to downtime procedures.
(b) Assembling a support team to investigate the source of a problem and fix
it. For larger practices and hospitals, this would likely include in-house staff
working in conjunction with the vendor. For smaller practices, the vendor
would be the primary focal point for assistance.
(c) Entering any date that has been captured by the downtime procedures into the
EHR once it becomes available, again. In doing so, it is important to address
any synchronization issues. For example, during downtime a lab order may
have been recorded manually on paper. When the EHR becomes available,
again, the order would have to first be entered into the system before the lab
results could be recorded.
2.8.5 Testing
There are several levels of testing that need to be done to ensure a successful
implementation. First, the vendor/developer of the EHR needs to do their own
testing:
1. Unit: Each program within the EHR is tested to eliminate all errors.
2. Integrated: All programs are tested to ensure they work together without errors.
You should ask the vendor/developer to provide assurances that both unit and
integrated testing has been done.
3. Function: The next level of testing verifies that each function successfully works
with not only the vendor/developer’s own test data, but the customer’s data, as
well. This can be done even prior to actually purchasing an EHR by having access
to the vendor/developer’s demo system and testing out various real-life scenarios.
It can be a good way to determine if the EHR will fit the customer’s needs or
whether any customization, if possible, is going to be needed.
4. Systems: This testing is used to confirm that the EHR can handle expected
volumes of data and user utilization, both average and maximum, within response
time parameters. This is particularly important for customers who have large
volumes of data and many users. The vendor/developer should be able to give
you assurances that such testing has been done on their own even prior to your
purchasing their EHR. However, once the product is purchased and after any
customization is completed, the customer will need to repeat this level of testing
with their own data.
34 L. Simon
5. User: This is an opportunity for the actual users of the EHR to verify that it is
functioning exactly as expected. Test scripts should be developed with extensive
input from the users of the EHR which comprehensively reflect the daily work
being done by them. It is imperative that this be done prior to the EHR
actually being used in production.
Sometimes, particularly when a facility has an existing software product and is
converting to another, it may elect to run both systems in parallel for a short time
to ensure that the new system provides the same output as the previous one for
functions where this is expected to happen. This can be somewhat time consuming,
because it requires the same data to be inputted into two different systems and the
results then compared. For example, if a facility had an EHR which produced patient
billing statements a certain way and the facility needed the replacement EHR to
create a statement with the same information on it, it may want to use parallel testing
for this purpose.
2.8.6 Training
As described in the Selection section, the developer of the EHR which is selected
needs to provide comprehensive training and documentation. Once it has been
thoroughly tested and the test system is stable, a training schedule needs to be
established for everyone who will be using it. The test scripts that were used
for testing the EHR can be used as part of the training process. In addition, the
documentation requirements described in the Selection phase need to be customized
to exactly reflect your practice setting’s version of the EHR. It is important for all
users to know to what resources they have access if they run into difficulty after the
EHR goes live and they are using it in their daily work. For solo practices, this will
typically be the vendor’s technical support hotline. Larger practices and hospitals
usually benefit from having colleagues in their own departments serve as an initial
contact point with backup from the IT department and vendor, as needed.
2.8.7 Conversion/“Go Live”
After all of that hard work, you and your practice setting are finally at the point
where you can start using the EHR in your daily work. Final steps involve:
1. Migration of any data into the new EHR. For large practices and hospitals,
it is sometimes necessary for the vendor/developer to write computer programs
which automatically convert and move customer data directly into it. For smaller
practices, it will be up to the customer to manually enter any data that will be
needed using the functions provided by the EHR, but you should consult with
the vendor/developer to determine the optimal way to do this.
2. Developing a specific plan to switch from your current way of managing

your practice to using the new EHR. This plan needs to minimize the impact
on your daily work as you make the switch. One aspect of the plan is to determine
whether you want to implement all of the EHR’s functions immediately or phase
them in. Sometimes timing can be a critical issue, ex. in hospitals where a new
EHR may be replacing an older system. In such a dynamic place where the
system is constantly being used, consideration needs to be made to ensure that no
data is lost during the transition and sometimes downtime procedures may need
to be used for a short time as the switch is being made. Timing and coordination
are also critical with interfaces between any other software applications.
Sometimes outside events can influence the best time to go live with the EHR.
One hospital did not consider a yearly community event which typically leads to
increased activity in the ER and went live that weekend. As a result, the ER was
overwhelmed with caring for an increased patient load while trying to get used
to dealing with a new EHR.
3. Vendor Support: It is important to ensure that an adequate amount of ven-
dor/developer support will be available during the conversion process, both
in-house, if needed, as well as by phone.
2.9 Post-implementation/Ongoing
Congratulations, you did it!! Hopefully, you are now using the EHR or other
software that you have implemented in your daily work with patients. You need
to give yourself time to get used to it which can take weeks. Will everything go
perfectly right? Probably not, especially if your practice setting is a large one.
However, if you or your practice setting did a thorough preparation, those problems
should be minimal. One hospital reported receiving 6,500 calls to their “command
center” during the first day they went live and were actually proud of the fact that
those calls had decreased to “only” 1,000 on the fifth day. There should never have
been anywhere close to that many calls, either on the first day or the fifth.
For large practice settings, especially hospitals, it is important to do a post-
mortem within several weeks after the implementation to assess how well it went,
identify the problems that were encountered, determine the causes, and learn from
them so that future implementations can be improved.
2.10 Last Thoughts
A lot of information has been provided in this chapter, but the two most important
points to remember are:
1. Do as much planning as you can prior to starting to use an EHR or other software.
The more work you do upfront will undoubtedly save you countless hours and
36 L. Simon
headaches trying to fix problems that likely will develop after the implementation
if you don’t do sufficient planning in advance.
2. It is absolutely imperative that the users of the EHR be extensively included
in every step along the way to implementing it. In my opinion, the mismatch
between the EHRs that have been developed and what the users want and need
has been one of the biggest causes for the problems that have existed for years in
gaining greater acceptance of software in healthcare.
References
1. Shortliffe E, et al. Medical informatics, Computer applications in health care. New York:
Springer; 1990. p. 20–6
2. Sneider RM. Management guide to health information systems. Rockville: Aspen Publishers;
1987. p. 41–50, 55–8.
3. Miller M, et al. Mental health computing. New York: Springer; 1996.
4. Behavioral Health Data Exchange Consortium, ONC State Health Policy Consortium Project,
Final report. Triangle Park: RTI International Research; 2014. p. 5–3, 6–2.
Chapter 3
Leading Health IT Optimization: A Next
Frontier
Greg Hindahl
Abstract This chapter has four key points. (1) Physician IT Leadership: Depending
on the size of the organization this may be a part-time or full-time position. It should
be a physician or other clinician with a good general understanding of Healthcare
Information Technology capabilities but a great understanding of clinical workflows
and processes and how they will be impacted by the implementation of an EMR.
(2) EMR System Selection and Implementation: This process is very critical to an
organization’s success and can last from months to years depending on the size and
complexity of the organization. The five key phases are System Selection, Design,
Build, Testing and Go-live. (3) Governance: EMR governance is often an after-
thought and frequently undervalued. It is very important during the implementation
phase but equally important during optimization. The governance group(s) should
decide what will be done and often as important what will not be done. (4) EMR
Optimization: Finally getting an EMR live is not the end it is the beginning. Making
a system better and more efficient over time is critical so we can take the best
possible care of our patients. This is accomplished through constantly evaluating
and improving Clinical Decision Support (CDS) tools and trying to minimize alert
fatigue whenever possible. Optimizing an EMR is often as much and sometimes
more about improving the patient care processes and workflows than it is about
making changes to the EMR itself.
Keywords Computer systems • Consensus • Cooperative behavior • Delivery

of health care • Documentation • Electronic health records • Health information
systems • Knowledge bases • Meaningful use • Medical errors • Medical infor-
matics • Patient care • Patient rights • Physicians • Standard of care • Workflow
G. Hindahl, M.D. ()

BayCare Health System, Clearwater, FL, USA
e-mail: greg.hindahl@baycare.org

38 G. Hindahl
3.1 Organizational Issues and Oversight
3.1.1 CMIO Role Morphing: Crossing the Implementation

to Optimization Chasm
In the previous edition of this book the Chief Medical Information Officer (CMIO)
wasn’t listed as a part of the HealthCare IT Team. Over the last several years most
large organizations and many small organizations have created this role to help their
organizations negotiate all the challenges with not just getting an EMR implemented
but getting it optimized. It’s important for everyone to understand that getting any
health IT system live isn’t the end. It is just the beginning.
Depending on the size of your organization the CMIO may or may not be
an “official” role. If your organization or practice is not large enough to have a
physician dedicated to this work full-time, it still needs to have a physician(s) in
the organization or practice who is engaged with the team that’s getting the EMR
solution(s) live. This doesn’t need to be a physician who can build his or her
own computers or networks but it should be someone who understands the clinical
workflows very well. This physician also needs to be part of the decision making
team that decides what’s going to get built and how it will be used when it’s live.
Another key role for the physician IT leader is understanding and appreciating
how much change their organization will go through during an EMR implementa-
tion. This process is rarely smooth and it often changes the way everyone in the
organization does his or her job. The physician doesn’t have to be the one who leads
or drives the change management but they should be engaged and supportive of the
change even when times get tough because there WILL be some tough times. It is
also important to remember that getting from where you are to where you’re going
isn’t a straight line. There will be missteps, detours and course corrections along
the way. It is important to be flexible during this process and get input from other
members of your team. During this process it is critical to not let perfect get in the
way of good. Any health IT system has to be safe before you use it but it is much
easier to make a system better once it’s live and being used by your care team in
their day to day work.
Leveraging Your Systems’ Capabilities
Any organization that is involved with building or maintaining a Health IT System

must be familiar with that organization’s IT Roadmap. The size and complexity of
the roadmap can vary greatly based on the size and complexity of your healthcare
organization or practice. The roadmap needs to consider the organization’s existing
Health IT systems and capabilities. It needs to consider where the organization
needs to go from a patient care perspective. It needs to consider whether changes
are required to comply with new regulatory or billing requirements. It needs to
3 Leading Health IT Optimization: A Next Frontier 39
consider any existing or new competitive marketplace forces. Once all these items
are considered, gaps can be identified that exist between current systems and future
needs.
One pitfall for many healthcare organizations is moving to a new IT System
without maximizing the use of their current system or systems. This can happen
for many reasons. Some organizations don’t invest enough time or resources into
the evaluation of existing practice processes. It has been said (probably a thousand
times) that even the best Electronic Medical Record in the world can’t make a bad
process good.
Some organizations don’t keep up with system upgrades due to expense, practice
disruption, lack of office staff time or expertise, or various other hardships. Training
is another area that is very often under-valued and under-emphasized. This applies
to both the initial training of users when a system first goes live and then the
retraining of users as upgrades are performed and system capabilities change. Health
IT Systems are very expensive so make sure you have maximized the use of your
current system and have some really good reasons to change before you trade what
you know for what you don’t know.
Understanding Your Systems’ Limitations
Nothing has made more organizations face the realities and frustrations of health
IT system limitations than HITECH’s Meaningful Use Program (MU). Meaningful
Use has made all EMR vendors and many healthcare providers focus on Health IT.
Many healthcare entities had already started implementing EMRs before 2009. The
attraction of recovering some of the expenses connected with installing an EMR, and
the plan for eventual financial penalties for those not meaningfully using certified
Health Information Technology by 2015 has caused most health systems and
many physicians to focus on the capabilities of their Health Information Systems.
The money available for Eligible Providers who started Meaningful Use at the
beginning was $44,000 for Medicare or $63,750 for Medicaid providers if they met
all the criteria and requirements (http://www.cms.gov/Regulations-and-Guidance/
Legislation/EHRIncentivePrograms/index.html?redirect=/ehrincentiveprograms/).
Most EMR vendors have spent the last few years upgrading their systems so
they can meet the ever changing and increasingly more difficult requirements of
Meaningful Use. However just because a system has been upgraded to meet the
meaningful use requirements doesn’t necessarily mean it can do everything you
need it to do to manage your practice or your health system especially related to
Stage 1 requirements. Stage 1 MU was mainly about the basics for many EMRs and
many healthcare providers. It was visioned by the US Government as a way to speed
the adoption of Health IT especially for those who weren’t previously interested or
simply didn’t want to use an EMR to practice medicine. Stage 1 was about picking a
system. Designing the system to meet what you thought your needs were. Installing
the EMR and then using the EMR.
40 G. Hindahl
EMRs don’t come prebuilt with all the rules and alerts needed for every specialty
and every clinical situation. Rules and alerts can be lifesaving for patients but
they shouldn’t be overdone because they can cause alert fatigue. Alert fatigue
is when a system has so many alerts that most or all of them get ignored over
time (http://www.clinical-innovation.com/topics/ehr-emr/study-examines-negative-
consequences-ehr-alert-fatigue). Medication formularies aren’t always included in
the EMR and if they are they often don’t cover all the insurance plans your
patients may have. As new drugs come on the market they have to be added to
an EMR’s formularies either manually or through third part pharmacy databases
like Medispan™ or First Databank™. Also not all EMRs come with the ability to
ePrescribe (electronically send prescriptions from hospitals and medical offices to
pharmacies) or accept electronic refill requests from pharmacies. ePrescribing is a
menu requirement for meaningful use and often requires a contract with a vendor
like SureScripts™. SureScripts (http://surescripts.com) is used by a majority of the
pharmacies in the U.S. as a secure way to (ePrescribe). It also can manage electronic
refill requests coming from retail or mail order pharmacies back to physicians’ office
EMRs.
It is becoming more common that the information needed to care for your
patients is not all present in a single EMR or health IT system. This information
gap is sometimes remedied by exchanging health information with another EMR
or health IT system. A common name for this entity is a Health Information
Exchange (HIE). A critical component to Health Information Exchange is the
consent process. If you work for a health system they will likely manage this or
at least give you guidance. Developing a consent and managing the governance
for a Health Information Exchange can often take as long as or longer to develop
than to build the technical capabilities required to send the information from one
system to another. It is imperative that as you develop your consent you take into
account items related to HIPAA and Privacy Laws. It is also very important that
you understand the laws of your state because not all states have the same laws
related to the sharing of health information. Many states have an “Opt Out” model.
That means a patient’s health information is included in the HIE unless the patient
specifically states they want it excluded. Other states, like Florida, have an “Opt In”
model. This means that a patient’s information can’t be included in a HIE unless
the patient signs a consent that says they want their health information to be included
(http://www2.illinois.gov/gov/HIE/Documents/SNConsent%20Draft%207%2020%
2012%20(2).pdf).
Technology vs. Process: It Is Not Always the EMR’s Fault
Once an EMR is implemented it is not uncommon to attribute any and all problems
related to patient care to the EMR. No EMR EVER eliminates the need for clinical
reasoning and critical thinking by the clinician and other members of the healthcare
team. Health IT is a tool and often a very valuable tool but it doesn’t replace a
well-honed and experienced clinical brain.
An EMR goes through many interesting stages/phases in its “Organizational

Life-Cycle”. This life-cycle might take several weeks up to many months for a
small practice. The EMR life-cycle might take several years for a large healthcare
system. Each stage or phase has risks and challenges that have to be understood and
managed. The first stage is deciding that you need an EMR or if you already have
one that you might need a different one. Even with the EMR “dropout” caused by
MU there are still a few hundred EMRs on the market. Picking the right one for
your health system (System Selection Phase) or practice can be quite a daunting
task. There are lots of ways to approach this but the most common tactics are (1)
Hiring a consultant to help you decide (2) Talk to others who use the EMR you are
interested in (3) Consult entities, like KLAS, which rate EMRs and EMR vendors
(4) Let someone else decide for you (5) All of the above.
Once an EMR is selected you enter the Design Phase. Depending on the EMR
and the size of your organization the design phase may be as short as a few days to
as long as several months. Designing an EMR is as much about understanding the
processes and workflows that operate in your practice every single day as it is about
understanding how the EMR needs to be configured for your practice. I have heard
it said by many seasoned CMIOs and CIOs that a large benefit of implementing
an EMR is identifying your processes and workflows that aren’t optimal (aka bad,
broken) and improving or changing them before you go live with the EMR. It is
critical to involve Subject Matter Experts (SMEs) in the design of your system.
This may be as simple as using your office manager, billing person and nurse as
your SMEs. It can also be as massive as double digit teams including physicians,
nurses, pharmacists, therapists, case managers, etc, etc. meeting regularly for weeks
or months. The key is to involve enough of the right people who understand how you
deliver care to your patients. Dedicating enough time and resources to this stage is
critical for a successful EMR implementation.
Next is the Build Phase. This is the step where you take what you learned during
the Design Phase and you, your team, your EMR vendor or a combination thereof
“build” the EMR to match the design as closely as possible. Depending on the EMR
this can be fairly easy or very difficult. You and your team should have an idea of
how difficult the build will be if you and your team did a good job during the system
selection phase.
After Build comes the Testing Phase. Testing the EMR is usually divided into
one or more phases. Phase one of testing usually starts with “Unit Testing”. Unit
Testing is where you and your vendor try all the different sections of the EMR to
make sure everything works as designed and built. Testing is usually aided by the
use of “testing scripts”. Testing scripts are usually scenarios that try to simulate
or mimic what happens when you see a patient using the EMR. If issues are
identified during Unit Testing they need to be fixed before you progress to the
next phase, which is “Integrated Testing”. Integrated Testing involves testing the
EMR and the connections it will have with any other system like your billing,
scheduling and registration system. These functions are usually part of a Practice
Management System (PMS). The PMS may be part of the EMR or it may be
a totally different system from the same or different vendor. You also need to
42 G. Hindahl
test connections between the EMR and other components, if you have them, like
a scanning system, connection to a HIE, ePrescription and a Patient Portal, to
name some of the more common ones. The larger and more complex the Health
IT system the more “rounds” of integrated testing that are required. Any errors,
bugs, or glitches that are discovered during Integrated Testing need to be dealt with
before proceeding to the next round of Integrated Testing. Once Integrated Testing is
satisfactorily completed there should be at least some “Usability Testing”. Usability
Testing consists of physicians, nurses and other office personnel using the system
with “test” patients to make sure all the different components of the system work as
designed, built and tested.
Once your team is satisfied that the system will work when you start using it with
patients you are ready for the Go-Live Phase. Go-Live is usually anticipated and
planned out months in advance. Normal preparation for a Go-Live usually involves
at least the following: (1) Requiring everyone in the organization to go through
adequate training so they know how to use the system. (2) Arrange for go-live
support. Go live support are folks who know the EMR and PMS inside and out so
they can help your team continue to learn the system while you’re seeing patients.
(3) Reduce your scheduled patients if possible. Learning to use an EMR is stressful
enough with real live patients but it’s super stressful if you try to learn to use it
with a totally full schedule the first few days or weeks. Most practices reduce their
scheduled patients by 50 % and then increase the schedule based on how quickly the
care team becomes proficient in using the system. (4) Communicate with patients
ahead of time and during go-live that you and your office staff are using a new
system to better serve their needs. Ask them to be patient with you and your staff
while you learn the system. Don’t forget to take your signs down. I’ve seen offices
“go-live” for 2 years (at least that’s what their signs said).
Once your EMR has been live for many weeks to many months and your users
are fairly comfortable with using the system, you likely will enter an Optimization
Phase. Once EMR optimization starts it never stops so I’d like to dedicate a whole
section to it.
3.1.2 Governing Optimization: Balancing Control

with Throughput
Anytime you Select, Design, Build, Test, and Go-Live with any Health IT System
you need to remind all of your clinical and non-clinical users that going live isn’t
the end it is the beginning. Some users might think going live with an EMR is the
end of the world as they know it and it sort of is. The good news is it usually keeps
getting better and it is fairly uncommon for users to say they would really want to
go back to paper.
EMR Optimization is adjusting an EMR along with your care processes so
it works better for the clinicians, non-clinicians and your patients. In small
organizations and individual physician offices EMR optimization can be as simple

as the physician saying I wish the EMR would work this way. Someone on your
EMR team or the vendor’s team makes the change(s) (you TEST the change). Then
you continue to use the EMR to see if the change was helpful.
However, for healthcare organizations with any significant size or degree of
complexity (200–1,000 physicians or more), Optimization is “not just a job it’s an
adventure”. The reason it’s an adventure is because every single change you make
to the EMR has the potential to impact 5 or 20 or 200 other systems depending
on how many systems the EMR is connected to. Sometimes these impacts aren’t
good and can even be disastrous with great impact to patient care and patient safety.
Another major challenge with EMR Optimization is that continuous changes to the
system, especially if based on the desires of a few users, can lead your organization
away from One Standard of Care and Evidence Based Care, if that is something
your organization cares about.
Next let’s talk about what you might want to optimize. Most EMRs go-live with
a pre-built set of note templates and order sets for the clinical users. This is what
most new EMR users want to adjust or add to first. Depending on the EMR and size
of the organization these changes can often be made by the users themselves. Many
EMRs are more “locked down” by design so they require the EMR vendor or an
analyst on your “build team” to make the changes to the system.
Another EMR capability that is never perfect out of the box is Clinical Decision
Support (CDS). CDS are important tools built into every good EMR. CDS tools
are any rules, alerts, or third party content, like drug formularies and “clinical
knowledge bases” that help the clinicians make better clinical decisions as they
use the EMR to care for their patients. Most EMRs go-live with too many alerts
turned on. Well-designed alerts are critically important to leverage the “life-saving”
capabilities of an EMR. Unfortunately all alerts aren’t created equally. Even if an
EMR’s alerts aren’t too numerous many are often what are referred to as “nuisance”
alerts. A nuisance alert is an alert that pops up for the user and makes them stop
to consider the information presented before they proceed. The issue the nuisance
alert warns of might be inaccurate or in many cases just not clinically relevant for
that particular patient in that particular situation. Too many alerts and especially
too many nuisance alerts and your users are well on their way to alert fatigue. As
mentioned above alert fatigue is common and is where the EMR users ignore all of
the alerts, even the important ones. Optimization of an EMR’s CDS tools over time
is extremely important and is rarely successful without involvement of the clinical
users of the system.
EMR vendors are spending more time designing their systems with users in mind
but clinical users are rarely satisfied with a system’s look, feel, colors, buttons,
number of clicks, etc. General EMR look and usability is important to improve
if possible (because sometimes it’s not possible or is possible only at a great cost
of time, resources and or money), but shouldn’t take priority over optimizing an
EMR’s clinical content and CDS tools.
Any discussion about EMR Optimization without a laser focus by you and
your team on your organization’s workflows and clinical process is likely to fail
44 G. Hindahl
miserably. Remember the EMR is a very valuable tool that can greatly support your
clinical processes and workflows but it is not a replacement for them.
An often undervalued aspect of Optimization is Governance especially for larger
organizations. Governance of EMR Optimization is critically important and needs
to happen before any changes are made to any Health IT system EVER. This
governance will look very different depending on the size and complexity of your
practice. In single physician practices it can be as simple as physicians and staff
talking through what changes need to be made and why. In larger organizations
Governance is a multi-step and multi-layered process involving Physicians, Nurses,
Pharmacists, IT SMEs and the vendor if necessary.
Despite its critical importance, EMR Optimization Governance has the potential
to significantly slow down the pace of optimization and not always for the better. No
organization ever has enough resources to do all the optimization that is requested
nor should it. Part of EMR Optimization Governance is taking all the incoming
requests and deciding which changes will be made to the EMR and the clinical
processes. As important as deciding what you’re going to optimize is deciding
what changes you’re NOT going to make. Another critical step in Optimization
Governance is prioritizing the items to be optimized. After these decisions are
made and agreed to by all the committed parties, the decision and “progress”
should be communicated to the person or group who made the optimization request.
This communication is very important even if the Governance decision is that the
system/process change will not occur.
3.1.3 Major Challenges for the CMIO/Clinical Health

IT Leader
One of the roles of the Health IT Physician Champion, whether they have the title of
CMIO or not, is to identify the many issues and challenges inherent in implementing
and managing a Clinical Health IT System. As I mentioned above this doesn’t have
to be a physician with an extremely technical knowledge of computers and computer
systems. It should be a physician who understands clinical workflows extremely
well however. This should also be a physician who is good at collaboration and
working with matrixed teams because EMR implementation is never something that
one physician can or should even want to manage without lots of help.
3.1.4 Clinical Standardization: How Can There Be So Many

Experts and Why Do They Never Agree?
One thing EMRs can be very good at, if that functionality is embraced, is helping an
organization drive clinical standardization. Unfortunately some physicians will see
this as trying to force them to practice “cookbook medicine”. The truth of the matter
is a lot of medical errors happen because of the inconsistencies, omissions and a

general lack of practicing to one standard of care (http://www.ahrq.gov/research/
findings/factsheets/errors-safety/improving-quality/index.html). This One Standard
of Care, when such a standard exists, should be based on medical evidence, best
practice or both. A coexistent challenge suggested by the bullet above is trying to
manage the expectations of medical specialists or “experts” who all have different
opinions about what the One Standard should be for a particular clinical care item,
issue or situation.
The goal should be to establish a group of the right clinical experts who are
adept at evaluating clinical best practices and the medical literature. They don’t
necessarily have to do all the work themselves (review all the literature or best
practices), but they should have the authority to review collated data and put forward
a consensus statement for the organization. It is important to establish, before this
process even begins, that One Clinical Standard proposed by the group will have
weight and will be adopted and followed by other members of the organization’s
clinical care team. This is a major challenge and one that very few healthcare
organizations in the U.S. have completely figured out.
Copy/Paste/Cloning: Doing the Wrong Thing Faster
Anyone who has anything to do with EMR implementation or usage has probably
heard the term copy/paste and cloning. There has been tremendous focus on this
practice recently because CMS feels that many providers are using this “efficiency”
tool inappropriately and that it is leading to fraudulent billings to CMS for Medi-
care and Medicaid patients (http://oig.hhs.gov/oei/reports/oei-01-11-00571.pdf).
The CMS claim, and it certainly can be valid, is that some providers electronically
copy large sections of their, or even other providers’, clinical notes and then
paste them into newly created clinical notes. Most clinicians go in and edit the
pasted text so it accurately reflects what is going on with that patient right then.
Most importantly the documentation should reflect what has changed with that
patient since the original copied note was generated. When it is used appropriately
copy/paste saves a clinician time and creates an accurate clinical note that helps
everyone take better care of their patients.
Unfortunately many clinicians do not appropriately edit their newest note so
that it has the most updated information in it. This is a horrible practice that often
creates a very unsafe environment for the patient because other members of the care
team are acting on information that is not up to date and thus may be inaccurate. If
a physician or other clinician is billing CMS for this “unedited work” it is viewed
by CMS that the physician is billing for work that they didn’t actually perform
(http://journal.ahima.org/2012/10/17/hhs-warns-hospital-groups-on-ehr-fraudulent-
billing/). That, of course, is illegal. Copy/paste can be easy or less easy depending
on the changeable settings in the EMR you’re using. Because almost all EMRs
function in a Windows environment, it is technically impossible to completely shut
off copy/paste so that it can’t be used in an EMR. Most organizations have addressed
46 G. Hindahl
copy/paste/cloning through EMR Appropriate Use policies. If your organization

hasn’t yet developed a policy addressing copy/paste practices I would strongly
recommend that you do so.
Managing Note Bloat: More Is Not Always Better
One of the unfortunate byproducts of most EMRs is the relatively effortless

creation of massive and sometimes relatively meaningless notes (http://www.
healthcareitnews.com/news/note-bloat-putting-patients-risk). In my experience this
is typically the daily progress or rounding note but can also be seen with electron-
ically documented History and Physicals and Discharge Summaries as well. In the
olden days clinicians were writing their daily progress notes. This usually led to
briefer notes that, if you could read them, usually gave a concise but accurate “story”
of what was going on with the patient. In the current EMR era with copy/paste,
smarttext, smarttemplates, macros, smartforms, smartphrases, quick this, quick that
etc. it is very easy to generate a very large note in a very short period of time.
Very few organizations understand this issue before they go live with their EMR.
It is common to teach physicians all the different EMR tools and “tricks” they
can use to pull everything under the sun into their notes. Just because all of this
information is in the EMR doesn’t mean you need to include every bit of it in every
note every single day. It is important to review the clinical information and document
that you reviewed it. It is also appropriate to include pertinent normal and abnormal
results from time to time. The detailed information and normal values and reports
are usually contained somewhere in the EMR and can be viewed anytime you want
to see them. Clinicians justify these multipage progress notes with arguments that
all of the information needs to be included for billing purposes or to decrease their
risk of being sued. Large notes are like white noise and most frequently a distraction
and a nuisance to the busy physician. The large mass of text might have some useful
information included but good luck finding it. Some organizations have tried to
combat this practice with different tactics like the APSO note. An APSO note puts
the Assessment and Plan at the top of the note so the clinician can easily find what’s
being done for the patient. The Subjective and Objective part of the note can be
quickly scanned by an interested clinician after they’ve reviewed the assessment
and plan.
Voice Recognition: Roaming Profiles and Other Major Challenges
Though it is becoming less common, using an EMR has been a major challenge
for the physician who can’t type well. Most EMRs require at least some typing
by the clinicians to get what is in their head into the patient’s electronic medical
record. Many EMRs have templates that physicians can point and click with a
mouse or keyboard but this type of information entry is usually very tedious and
time consuming.
Voice recognition software is usually well suited for physicians who don’t type
well. The technology has improved greatly over the years and has gotten to be fairly
accurate and fairly fast and efficient, especially over the last couple years. That being
said, voice recognition is not without its challenges. Voice recognition is like most
other electronic tools used by physicians. It requires training and practice to become
proficient in its use. Many physicians don’t like to spend the time training their voice
profile and this leads to greater inaccuracy in the recognition of their spoken words.
Voice recognition inaccuracy leads to dissatisfied users. Even worse this practice
can lead to inaccurate information in a patient’s chart if the voice recognized text
contains errors and isn’t proofread and corrected by the dictating physician, or
someone else with the knowledge and privileges to make the corrections for the
physician.
In a small organization the deployment of voice recognition is usually simplified
by installing the software on the physician’s local or personal device. This creates
what is called a “local profile”. This is a file that has learned how the physician
speaks and also is what recognizes spoken words anytime a physician uses the
speech recognition software. In larger organizations, and especially where physi-
cians are seeing patients in multiple locations and facilities, it is usually preferable to
set the physician or other clinicians up with a “roaming profile”. A roaming profile
is unique to each physician and sits on a server. This allows the physician to access
their voice profile no matter where they are seeing patients and entering spoken text
into the EMR.
Whether the profile is local or roaming it is important that the software is
configured correctly so the user’s profile is regularly optimized. Individual voice
profiles can get quite large. A large profile can slow down performance and is a
dissatisfier for the user. Optimization also takes words that a physician has “trained”
the system to recognize correctly and incorporates those words into the users profile
so they won’t be misrecognized in the future. Training the system is particularly
helpful for unusual names which are being used frequently by the physician in their
notes or correspondence to other physicians.
Regulatory Compliance: HIPAA and Privacy and Why They Make Health
Information Exchange Harder
One of the major recognized benefits of electronic medical information (compared

to paper) is that the information is in a format that allows that information to be more
easily shared with another clinician(s) who is taking care of that patient in the same
location or several thousand miles away. “Global” access to patient information
is seen as a way to eliminate the performance of unnecessary duplicated and
expensive medical tests (http://www.healthit.gov/providers-professionals/medical-
practice-efficiencies-cost-savings). Sometimes reperforming an expensive test is
necessary. However, many times an expensive test is reperformed because the
physician doesn’t have access to the original result and the patient may not even
remember they had a particular test done let alone what the results were.
48 G. Hindahl
A significant component for Meaningful Use (and rumored to be a much bigger

part of MU Stage 3) is the sharing of a patient’s health information with providers
outside of your health system and particularly those who may use a different EMR
than you do. Many states are making progress in the Health Information Exchange
(HIE) space but in most areas of the country there is still not the ability to easily
share a patient’s information over a large geographic area. Besides the technological
challenges of sharing Protected Health Information (PHI) between providers in your
own organization or with another organization come the challenges of making sure
the information is secure and that it is only viewed by someone who has a legal and
clinical right to view that information.
Inappropriate Record Access: It’s Not Just for Celebrities
Most healthcare organizations of any significant size have a person or persons

dedicated to ensuring that stored PHI is secure and only being accessed by those
with a clinical or business need and right to see the information. There are many
different ways an organization can secure their information. It is critical that every
device that contains PHI is encrypted. Theft of PHI is becoming a big money
opportunity for those who are unscrupulous and have a basic understanding of
health information systems. We have seen many stories in the news over the last
several years of hospitalized celebrities having their information inappropriately
viewed by large numbers of hospital workers with no justification to view the
information (http://journal.ahima.org/2010/04/29/californian-sentenced-to-prison-
for-hipaa-violation/). Most of these cases have resulted in the firings of all of
the hospital employees who viewed the celebrity’s PHI. More recently the news
has informed us of cases where hundreds and even millions of patients have had
their PHI stolen by various means (http://www.phiprivacy.net/community-health-
systems-says-4-5-million-patients-data-stolen-in-cyber-attack/). This information
is often sold to those who then use it to perform identity theft or use it to receive
health services as the person whose PHI was stolen.
Besides encrypting all of your devices (especially anything portable) it is critical
to enforce a strong password policy and require your users to change their strong
passwords at least every 90–180 days. A strong password is considered to be
one of at least eight characters with three out of the following four choices:
Capital letters; lower case letters; numbers; special characters. Strong passwords
should not contain names of you or your family members, birthdates, addresses,
or any other information that could easily be learned or guessed by someone
who is snooping around either at your work or online (like the public facing
part of your Facebook page). User names and passwords should NEVER be
shared with anyone and anytime you think someone has acquired knowledge of
your username or password they should be changed immediately. Besides the
generalized trauma of having your patients’ identities stolen, not protecting PHI
can result in massive fines to healthcare organizations which allowed a PHI breach
to occur (http://www.healthdatamanagement.com/news/breach-notification-hipaa-
privacy-security-wellpoint-ocr-46377-1.html).
3.1.5 ICD 10: I Don’t Want to Be a Coder I Just Want

to Practice Medicine
ICD 10 getting delayed until at least October 1, 2015 received “mixed” reviews
from those trying to get it live. Many organizations put a hold on their ICD 10
go-lives once the announcement was made. Other organizations moved forward
to finish the work to be ready if and when ICD 10 ever goes live. One of the
many complaints physicians have with EMRs is they often feel that a lot of
the work that used to be done by a “clerical” person has been shifted to them.
Physicians also frequently argue, and sometimes with good reason, that they are
being asked to perform coding tasks that should be performed by coding experts
and not physicians. Physicians’ modern mantra that they don’t want to be coders
they “just want to practice medicine!” has the potential to enter a whole new level
of “realism” when ICD 10 goes live. Going from ICD 9 to ICD 10 will expand the
number of diagnosis codes used by physicians from somewhere in the 13,000 range
to something more like 68,000 with ICD 10 (http://www.medicaid.gov/Medicaid-
CHIP-Program-Information/By-Topics/Data-and-Systems/ICD-Coding/ICD-10-
Changes-from-ICD-9.html).
Many feel the only way for physicians to survive ICD 10, 11, or 20 (besides
retiring) is to have tools embedded in EMRs that help physicians select the correct
codes and warn them when a code is not specific enough for the documentation that
is included in the medical record. Most EMR vendors understand this and have been
working to embed these tools in their EMRs for quite some time. One of the tools
that is being looked at to help physicians with ICD 10 coding is Clinical Language
Understanding (CLU), often an add-on (requires a separate license and additional
fee) to speech recognition software. CLU works with typed or dictated text and can
identify “discrete” clinical terms that exist in a large document of “non-discrete”
text. Discrete terms can be mapped to coding software or can work with an EMR to
make it easier for a physician to do things like add pertinent problems to a patient’s
problem list or even respond to a “coding query-like” question in near real-time.
3.2 Work Flow Problems and Solutions
3.2.1 How Do EMRs Improve Care and Safety: What Is

the Evidence and Why Is It Important?
The act of implementing an EMR doesn’t guarantee you or your organization will
practice safer better medicine. There are tools which have been shown to improve
patient safety if they are used consistently. CPOE (Computerized Physician Order
Entry (http://psnet.ahrq.gov/primer.aspx?primerIDD6)) and BCMA (BarCode
Medication Administration (http://www.ncbi.nlm.nih.gov/pubmed/19592882)) are
50 G. Hindahl
two activities with evidence suggesting they improve patient safety. CPOE is felt to
be most valuable when it is tied to well built CDS tools.
3.2.2 Improving Clinicians’ Efficiency: WIIFM (What’s In It

For Me)
Up until the last couple years it was fairly easy for a physician who didn’t want to
use an EMR to find a healthcare organization that was still using paper processes. It
was also fairly easy to find organizations that were not yet mandating that all their
physicians use CPOE. Those opportunities are rapidly fading because it is getting
harder to find organizations who haven’t implemented at least some type of EMR.
Most of these organizations are also live with CPOE. So for the (“I’m not going to
use an EMR”) physicians, retirement is not just an option it’s quickly becoming the
only option. Despite the reality that most physicians have to use an EMR it is still
necessary that EMR vendors and those implementing EMRs do everything in their
power to make physician usability one of their top priorities. EMRs are intended to
make patient care better and safer and to make clinicians more efficient. These things
don’t just happen. They require effort by everyone on the information technology
and care teams. Making physicians more efficient is something that all EMRs should
do to help answer their question of “What’s In It For Me”. Now let’s look at some
of the ways EMRs can make physicians more efficient if they are designed and used
correctly.
3.2.3 Physician Portals
Unfortunately many of the Health Systems requiring physician interaction do

not have a common platform. This causes many physicians to log into multiple
systems (often with different user names and passwords) in order to get access
to all the information they need to care for their patients and access to other
activities related to their work as physicians. Sometimes the next best thing to
having everything in the same system is to at least have everything in a common
online location like a physician portal. Well built physician portals can be “one-
stop shopping for physicians”. It can have “connections” to all the different types of
professional information a physician needs. Examples of useful connections through
a physician portal might be: access to an inpatient or ambulatory EMR; access to
medical staff calendars, documents, policies, procedures, meeting minutes; access
to library resources and various knowledge databases; access to online radiology
and cardiology images; personalizable links to their favorite websites (like specialty
societies); and secure messaging and collaboration with other physicians. Most
physician portals are designed to be accessed from a PC or laptop but more and
more they are being mobile enabled for use with tablets and smartphones.
3.2.4 Mobility: Citrix vs. Native Apps
Seems like everything in the world is going mobile and that certainly pertains to
information physicians and other clinicians need to take care of patients. We used
to be satisfied with receiving past medical records in the mail from another practice
within 2–4 weeks. Now we’re not happy if retrieving patient records takes longer
than brewing a cup of coffee with whatever instant coffee machine you prefer.
HealthCare still generates forest loads of paper but more and more clinicians are
happiest if they can see their patient’s information on some sort of portable device.
There are lots of challenges with retrieving PHI on a portable device. Probably
the most critical is maintaining the privacy and security of the information on
the device (http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_
049463.hcsp?dDocName=bok1_049463).
Another challenge with delivering medical information to a portable device is
getting it in a format that can be easily viewed by the clinician. There are two general
ways physicians access this information. One is through a tool called Citrix ™.
Citrix is a secure way to remotely access a system and it is similar to being logged
into the device that the system resides on (like a desktop PC). With Citrix all of the
information “lives” on the remote server so if the portable device is lost or stolen
there is no risk that PHI will be recoverable from the device. One of the challenges
with Citrix is that the EMR or other application typically behaves as if you are using
it on a PC with a big monitor. This usually causes lots of scrolling to view all of the
available information.
One technique used to present medical information in a more “optimized” way
for portable devices, like tablets and smartphones, is through what is known as a
native app. A native app is similar to any other free or purchased app from any App
Store. The app is downloaded to the portable device. Once the user is provisioned
(given appropriate security clearance based on the user’s credentials) the app can be
opened and used to view health information in a way that is better suited for the size
of the screen of the device being used.
3.3 Technology Problems and Finally
3.3.1 Care Transitions and Patient Engagement: Critical

Elements for Population Health
As the U.S. tries to rein in the rising cost of health care and improve the care we
provide to our population it has become fairly widely accepted that we need to do
a better job managing patients’ conditions over long periods of time (http://www.
forahealthieramerica.com/ds/impact-of-chronic-disease.html). It is also critical we
do a better job interacting with the population before they become patients so we can
52 G. Hindahl
help them maintain their health (http://www.healthit.gov/providers-professionals/

patient-participation). There are lots of terms being used currently to describe
this model but Population Health may be at the top of the list. One important
component of Population Health is CDM (Chronic Disease Management). CDM
can be challenging due to a lack of medical or financial resources, patient non-
compliance related to medication or medical follow up, and failure of patients to stay
in the same location so they can always go to the same hospitals, the same physician
offices, and see the same physicians. Many health systems and healthcare providers
are trying to leverage existing and new technologies to manage populations and
engage patients. Patient engagement is loosely defined as getting patients and their
families interested in actively participating in their own health care.
3.3.2 Customer Relationship Management Tools (CRM)
One of the challenges and often frustrations for both patients and healthcare
providers is the ability or inability to keep track of their patients and their patients’
needs in a useful way. For decades non-healthcare industries have been excellent
at knowing who their customers are and often everything there is to know about
that customer. This has been fairly difficult in healthcare because of the siloed way
care is delivered in many parts of the country. Many large healthcare providers are
starting to develop CRM tools (also called Care Management tools). These tools
are used by many different members of the healthcare team to identify who their
patients are. Once patients are identified the system documents the interactions the
healthcare providers have with the patient. Many patients get very frustrated when
they are being called by large numbers of healthcare workers trying to follow up
on a procedure or hospitalization the patient just had. Often the sixth caller doesn’t
know what questions were asked by the first five callers and they frequently ask the
same questions over and over. This lack of coordinated followup can be perceived
by the patient and their family as the healthcare system or provider not knowing
who the patient really is and what their needs are.
3.3.3 Patient Portals
Another patient engagement tool that has recently gained wider acceptance and
is becoming more available for patients is the Patient Portal. Patient portals are
websites or apps patients can use to interact with their healthcare system and their
healthcare providers. Not all patient portals are created equally but the majority
give patients access to similar information like lab results, hospital and office
discharge instructions and patient education, medication lists, lists of allergies and
immunizations, and a list of their ongoing medical problems. Many patient portals
give patients the ability to message their physicians or their physicians’ office
staff. Some allow patients to request an appointment or even schedule their own
appointment at the desired date and time. Patient portals should never be used to
provide care for emergencies or other serious medical conditions. These should still
be handled by the nearest emergency room or other acute intake center.
3.3.4 Home Monitoring and Data Integration
One challenge when providing care for patients with chronic conditions is making
sure they get regular meaningful followup with the different members of their
care team. Technology is making this easier for patients with certain conditions
through the use of home monitoring devices. There are currently many devices
that can be used by patients in the home with physician supervision. These
include scales to monitor the weight of heart failure patients. Blood pressure
monitoring devices are used with patients with a history of high blood pressure
or conditions where blood pressure control is critical like previous history of
a stroke. Many devices that measure a patient’s blood glucose now have the
ability to transmit those results to their physician office and sometimes directly
into the patient’s medical record in the physician’s office EMR. More recently
various devices have emerged to monitor medication compliance by patients
(http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3264437/). This may involve a
“smart” medication box or dispenser that tracks which pills are removed and records
the date and time. Some researchers are working with medication dispensing devices
with video capabilities. These are being developed so a healthcare provider’s staff
or behavioral health staff can observe the patient taking their medication after it
is removed from the dispenser. Devices like this are becoming a reality due to the
widespread availability of high speed internet.
Finally let’s discuss Emerging technologies and Standards meant to optimize
Health IT for patients and clinicians.
3.4 Emerging Technologies
Seems like every few months brings a new tool or technology to Healthcare IT. This
is likely to continue as digital memory continues to get cheaper and high speed wire-
less connectivity improves. Most new technologies are aimed at making it easier,
better, faster for clinicians to care for their patients. They are also aimed at making
it easier for patients to be involved with the management of their own health. One
of the challenges with dealing with the massive amount of data present in today’s
EMRs is being able to use that data for many different purposes besides just caring
for the individual patient like (billing, quality reporting, decreasing readmissions,
chronic disease management, value based purchasing to name just a few). In order
for EMR data to be useful for the above purposes it almost always needs to be
54 G. Hindahl
in a discrete data format (http://library.ahima.org/xpedio/groups/public/documents/

ahima/bok1_050085.hcsp?dDocName=bok1_050085). Discrete data means having
a data element in a format that is consistent and understood by users to have
roughly the same meaning every time that data element is present. Historically
getting discrete data into EMRs meant the user had to click a selection or pick
from a list of discrete choices for things like medications, allergies, medical
problems, procedures, etc. This type of data entry often slows down busy physicians.
Historically this made it difficult if not impossible to use typed or dictated text for
anything that required discrete data unless it was manually abstracted by a person
skilled in abstracting that type of information.
Technology has been developed to help convert typed or dictated text into
discrete data elements which can be used for tasks, reports, or activities that
require discrete data. This technology is often described as NLP and CLU
(http://www.nuance.com/for-healthcare/resources/clinical-language-understanding/
index.htm). NLP stands for natural language processing. NLP is computer software
that can take typed or dictated text and analyze it related to words that are present
and the order of the words. It works similar to the way we process speech when
we hear it related to context certain words may be used in. CLU stands for Clinical
Language Understanding. In a greatly simplified description, CLU is NLP but from
a clinical perspective. It is software that is able to identify clinical words and terms
in “blobs” of text. Once identified these clinical terms can be “discretized” so they
can be use for some of the tasks and activities listed above. NLP/CLU has been
around for a while primarily in the hospital coding world but will likely soon find
more widespread use by clinicians in the clinical sections of an EMR.
I would like to close by summarizing what I feel are the four key points from this
chapter. (1) Physician IT Leadership: Depending on the size of the organization
this may be a part-time or full-time position. It should be a physician with a
good general understanding of Healthcare Information Technology capabilities but
a great understanding of clinical workflows and processes and how they will be
impacted by the implementation of an EMR. It should also be a physician who
works collaboratively with other members of the healthcare and clinical IT teams.
(2) EMR System Selection and Implementation: This process is very critical to an
organization’s success and can last from months to years depending on the size and
complexity of the organization. The five key phases are System Selection, Design,
Build, Testing and Go-live. (3) Governance: EMR governance is often an after
thought and frequently undervalued. It is very important during the implementation
phase but equally important during optimization. The governance group(s) should
decide what will be done and often as important what will not be done. Once
decisions are made as to what will be done, the governance group(s) help prioritize
what will be done in what order to maximize the use of an organization’s valuable
IT and clinical resources. The governance group should also have a good process
for communicating their decisions to the users who will be impacted by those
decisions. (4) EMR Optimization: Finally getting an EMR live is not the end it
is the beginning. Making a system better and more efficient over time is critical so
we can take the best possible care of our patients. This is accomplished through
constantly evaluating and improving CDS tools and trying to minimize alert fatigue
whenever possible. Always remember, optimizing an EMR is often as much and
sometimes more about improving the patient care processes and workflows than it
is about making changes to the EMR itself.
Chapter 4
Computer-Aided Psychotherapy Technologies
Marni L. Jacob and Eric A. Storch
Abstract Computer aided psychotherapy addresses the many barriers of access

to evidence-based psychotherapy. Programs are either completely computer-based
delivered via the Internet or a stand-alone program, or they may be combined with
intervention by a therapist. Telepsychiatry is another popular method for delivery of
psychotherapy. Self-guided computerized treatment programs have the advantage
of translation into several languages, eliminate the barriers of appointment time and
location, and offer more anonymity to patients. The majority of self-guided pro-
grams utilize cognitive-behavioral therapy (CBT). Studies have demonstrated that
these programs result in significant reduction in anxiety and mood symptoms. Self-
guided substance use treatment uses motivational interventions to decrease quantity
and frequency of drinking. Computer-assisted treatment programs incorporate ther-
apist interventions to clarify skills or tailor treatment approaches. These programs
have been effective in treating anxiety disorders, obsessive-compulsive disorder, and
mood disorders. Virtual reality therapy has been used in exposure therapy to treat
post-traumatic stress disorder, phobias, and anxiety disorders. Telepsychiatry is an
effective delivery mechanism for CBT, and has demonstrated effective treatment of
anxiety disorders, with the advantage of facilitating clinician access.
Keywords Agoraphobia • Anxiety • Cognitive therapy • Computer literacy •

Depression • Drinking behavior • Eating disorders • Electronic mail •
Headache • Mental health • Mental health services • Minors • Mood disor-
ders • Motor activity • Obsessive-compulsive disorder • Outcome assessment
(health care) • Parental consent • Personal satisfaction • Phobic disorders •
Psychopathology • Stress disorders post-traumatic • Suicidal ideation
M.L. Jacob, Ph.D. ()

Department of Pediatrics, Rothman Center for Neuropsychiatry, University of South Florida,
880 6th Street South, Suite 460, Box 7523, St. Petersburg 33701, FL, USA
e-mail: mjacob1@health.usf.edu
E.A. Storch, Ph.D.
Department of Pediatrics, Rothman Center for Neuropsychiatry, University of South Florida,
880 6th Street South, Suite 460, Box 7523, St. Petersburg 33701, FL, USA
Rogers Behavioral Health, Tampa Bay, FL, USA
All Children’s Hospital, Johns Hopkins Medicine, St. Petersburg, FL, USA

58 M.L. Jacob and E.A. Storch
Mental health problems are a significant public health concern, with numerous
children and adults worldwide endorsing various symptoms of psychopathology,
as well as associated distress and impairment. Mental health problems are common,
with reports estimating the prevalence of mental illness to be approximately 20 %
in individuals in the U.S. [1, 2]. Left untreated, mental health problems cause
a significant burden both to the individual and society at large due to reduced
productivity and cost of disability [3–5].
Effective psychological and pharmacological treatments exist for mental health
problems. Psychotherapy seeks to help individuals identify and manage their
symptoms to improve functioning and quality of life. However, dissemination and
implementation of evidenced-based psychotherapy approaches can be challenging
given difficulty finding effectively trained clinicians or obtaining access to effective
treatments given their geographic location, financial constraints, taking time off
from work or having to arrange childcare to participate consistently in therapy,
concerns about stigmatization, or avoidance of treatment if the nature of their
difficulties (e.g., agoraphobia) limits their ability to leave the home, or if symptoms
are primarily manifested at home (e.g., hoarding).
Given these barriers, along with the upsurge of technology, computer-aided psy-
chotherapy programs have been developed. Programs may be completely computer-
based, which involve receipt of the intervention exclusively through a self-guided
delivery format. Self-guided treatment programs are often highly interactive and
may involve several forms of media (e.g., video, audio) delivered either through
the internet or as stand-alone computer programs [6]. Computer-assisted treatments
provide intervention strategies that are combined in conjunction with some degree of
interaction (e.g., face-to-face, via email) with a therapist. Another form of computer-
aided intervention is the provision of therapy via videoconferencing, also known as
telepsychiatry. The demand for increased access to treatment, advances in relevant
technology, and the increasing publication of outcome research supporting the use
of computer-aided treatments make this feasible.
4.1 Descriptions of Computer-Aided Psychotherapy

Technologies
4.1.1 Self-Guided Computerized Treatment Programs
One manner of improving access to treatments is to utilize self-guided computerized

approaches. These programs can be used at home or in health-care settings. Self-
guided computerized treatment programs may address some of the significant
barriers to receipt of treatment, such as geographic location and having little or no
access to a therapist, scheduling issues, transportation difficulties, or if the patient is
reluctant to attend therapy for some reason. Patients can also work at their own
pace and review material as often as desired [7]. Another benefit of computer-
4 Computer-Aided Psychotherapy Technologies 59
administered treatments is that they can be translated into different languages

(e.g., [8, 9]). Individuals may even be more willing to reveal sensitive personal
information to computers rather than human interviewers if there is anonymity.
However, self-guided treatment programs require a sufficient reading level and
degree of computer literacy, and internet based programs require internet access.
Cartreine et al. [6] emphasize several empirical questions that are necessary to
better understand the utility of self-guided treatment. These include: (1) to what
extent is tailoring, the use of an individual’s personal characteristics to customize a
computer program, necessary, and for what types of people, clinical problems, and
interventions?; (2) What level of health care provider involvement, if any, is needed
for the intervention to be effective?; (3) How can self-guided treatment programs be
improved to maximize their effectiveness?; and (4) What are the active components
of a given self-guided treatment program?
Many current self-guided treatment programs utilize cognitive-behavioral ther-
apy (CBT), which is a psychosocial treatment that involves several core treatment
components (e.g., psychoeducation, identification of maladaptive thinking patterns,
use of exposures to feared stimuli, modification of maladaptive behaviors). The
structured nature of CBT along with the learning of particular skills is conducive
to a computerized approach [10]. Spek et al. [7] conducted a meta-analysis of the
effects of 12 randomized controlled trials of internet-based CBT across anxiety
and mood disorders, with minimal or no therapist assistance. Treatments resulted
in significant reductions in anxiety and mood symptoms, with interventions for
depression demonstrating a small mean effect size (d D .27), and interventions for
anxiety demonstrating a large effect size (d D .96). However, this meta-analysis
suggested that effects of treatment are more substantial if computer-delivered
treatment is accompanied by regular contact with a clinician [7], as treatments
with therapist support had a large mean effect size whereas interventions without
therapist support had a small mean effect size. Self-guided treatments have also been
utilized for different types of psychological disorders, which are reviewed next.

for Anxiety Disorders
Several self-guided computerized treatments use CBT for anxiety disorders. Reger
and Gahm [11] completed a meta-analysis of 19 internet and computer based
cognitive-behavioral treatments of anxiety disorders in adults, which used software
on a standard PC to automate the delivery of CBT. Internet and computer based
programs included a variety of skills, such as encouragement of goal setting,
modules which facilitate use of cognitive-behavioral therapy strategies to modify
cognitive distortions, and use of exposure strategies. Interventions were deliv-
ered almost exclusively via the computer, though some studies included minimal
clinician contact, such as some degree of individual feedback via email for
homework, for questions associated with different modules, or to provide more

tailored recommendations at different stages in treatment. Other studies included
the therapist working with the patient for the first 5 min of each session (either
to demonstrate the computer program or set the stage for the session), the ability
for patients to post messages on an online discussion forum, prompts to guide
participants as they move through the program, or just technical support. Of note,
in one of the studies in the meta-analysis (i.e., [12]), the computer-based treatment
augmented therapist-delivered CBT, and another of the studies incorporated 20 min
of coaching during six sessions (i.e., [13]). Results of this meta-analysis showed
a moderate-to-large mean effect size of Cohen’s d D .76 between the internet and
computer based treatments compared to a waitlist control group. The benefits were
also statistically equivalent or superior to treatment as usual. Other self-guided
computerized treatment programs have been used to successfully treat individuals
with phobias or panic disorder (Fearfighter; [14, 15]).
Some work has also looked at use of self-guided computerized treatment
programs for youth with anxiety disorders. March et al. [16] examined the efficacy
of CBT for child anxiety disorders when delivered entirely over the internet,
supplemented by minimal therapist contact by phone or email. The intervention
(BRAVE for Children-ONLINE) was adapted from a clinic-based CBT program
(BRAVE program) for youth aged 7–14 years old with anxiety disorders [17]. The
BRAVE program is comprised of 10 weekly, 60 min child sessions and 6 weekly,
60 min parent sessions. Two booster sessions are also conducted, at 1 month and
3 months post-treatment. In the March et al. [16] study, 73 children with anxiety
disorders and their parents were randomly assigned to either the internet-based CBT
(BRAVE for Children – ONLINE) or a wait-list condition. Sessions in BRAVE
for Children-ONLINE are comprised of reading material, question and answer
exercises, games, quizzes, and homework, all in an appealing and eye-catching
online format. Anxiety management strategies include recognizing physiological
symptoms of anxiety, relaxation, use of cognitive restructuring, exposure, problem-
solving, and self-reinforcement of “brave” behavior. At post-treatment, youth
in the internet-based treatment program showed small, but significantly greater
reductions in anxiety symptoms than youth in the waitlist control group, and these
improvements were enhanced at 6-month follow-up [16].

for Obsessive-Compulsive Disorder
Self-guided programs have been used to successfully treat individuals with

obsessive-compulsive disorder (OCD; BT steps, [18, 19]). For example, BT
Steps, available via Waypoint Health Innovations, is a web-based CBT program
which teaches skills to facilitate self-management of OCD symptoms. Lack
and Storch [20] completed a review of eight studies which utilized self-guided
computer-administered treatment for OCD in adults. The programs were either

completely self-administered, partially self-administered, or used as an adjunctive
treatment method, and studies generally showed moderate to large effect sizes in
demonstrating a significant reduction of OCD symptoms as well as improvements
in overall functioning.

for Mood Disorders
Studies have provided support for computer-guided treatment for depression [21–
23]. Thrive, available via Waypoint Health Innovations, is a web-based self-help
program for depression, which teaches individuals to identify depressive symptoms,
modify maladaptive thinking and behaviors, and manage mood. Modules specifi-
cally teach cognitive restructuring, social skills training, and behavioral activation.
A program developed by Carter et al. [24] for the National Aeronautics and Space
Administration (NASA) uses problem-solving therapy for managing psychosocial
problems, such as interpersonal conflict and depression, on long-duration space
flights. Kaltenhaler et al. [25] reviewed four randomized clinical trials of self-
guided CBT treatments for mild to moderate depression and found three of the four
treatments to be efficacious.

for Substance Use
Self-guided programs have been used to successfully treat individuals with sub-
stance use [26, 27]. One example, is the Drinker’s Check-Up (DCU, [28, 29]),
which is a brief, computer-based motivational intervention designed for individuals
who are ambivalent about changing their drinking. It provides a comprehensive
assessment of drinking behavior and seeks to motivate behavioral change. The
program consists of integrated assessment, feedback, and decision-making modules
that are sensitive to the person’s level of readiness for change. This program
has been evaluated in a randomized trial of 61 participants who either received
immediate treatment or were in a wait-list control group, which received treatment
after a 4 week delay [30]. Analyses demonstrated that the improvement of the
immediate group between 0 and 4 weeks was significantly greater than in the
delayed condition; however, the improvement of the delayed group between 4 and
8 weeks was not significantly greater than in the immediate group. The mean effect
size for the immediate group was .93 as compared to .21 in the delayed group for the
baseline to 4-week period, whereas the mean effect size for the delayed group was
.32 as compared to .04 for the immediate group in the 4-week to 8-week period. Both
groups exhibited 50 % reductions in the quantity and frequency of drinking from
baseline to 12-month follow-up. Research has also examined web-based programs
for smoking cessation ([31]; see [32] for a review).

for Other Mental Health Difficulties
Self-guided programs have been used to successfully treat individuals with eating
disorders [33] and for insomnia [34]. Doyle et al. [33] completed a multisite
randomized controlled trial evaluating a 16-week internet-delivered cognitive-
behavioral program targeting weight loss and eating disorder attitudes and behaviors
in adolescents. Results showed significant reductions in body mass index scores
from baseline to post-intervention as well as greater use of healthy eating-related
and physical activity-related skills in the adolescents who completed the internet-
delivered CBT program compared to adolescents receiving usual care. In regards
to insomnia, Strom et al. [34] investigated a 5-week cognitive-behavioral self-help
intervention which primarily consisted of sleep restriction, stimulus control, and
cognitive restructuring. Some statistically significant improvements were found in
the treatment group on many outcome measures, but some improvements were also
noted in the control group, suggesting that further research is necessary to examine
the potential utility of such programs. Programs have also shown benefits of applied
relaxation and problem solving, administered via the internet and email, in treating
headaches [35].
4.1.7 Summary
Based on the research thus far, self-guided computerized treatments seem to be a

promising avenue by which patients can receive mental health services. Despite the
potential benefits, there are challenges involved in the administration of self-guided
treatments [36]. It may be challenging to ensure that patients attend to and process
the information in the self-guided treatment, as this would likely be easier to monitor
in a face-to-face intervention. To increase the likelihood of this with youth, Spence
suggests using eye-catching graphics and interactive tasks to facilitate attention and
comprehension [36]. Given that one of the aims of developing an online program is
to reduce concerns about stigma, BRAVE-online avoids using the term “therapist”
and uses the term “trainer” instead. Spence et al. [36] describes that development
of an exposure hierarchy in self-guided computer-based anxiety based treatment is
challenging, and it may be harder to monitor other things like treatment resistance,
non-compliance, and completion of sessions in a reasonable time frame. The content
of the self-guided program should also take in to account developmental factors and
fit with the age of the patient. Illness severity is also very important to consider when
determining the fit of such programs.
Further, Cartreine et al. [6] discusses that liability is a question for self-guided
computer treatment programs. It is indicated that whereas a clinician might be
deemed liable if a patient receives improper care, authors of self-help books are not
likely to be held liable if a reader commits suicide or experiences some other delete-
rious outcome while reading the book. This begs the question of whether developers
of self-guided computerized treatment programs should be liable. Cartreine et al. [6]
also asks about the ethical and legal obligations that may be relevant; if a computer is
able to notify someone of a user’s suicidal ideation, must it in fact do so? And if so,
who should be notified? There is also an issue of parental consent for minors. Would
minors have access to self-guided interventions, or would access depend on some
method of obtaining parental consent to the treatment program. In the March et al.
[16] study, parents and children visited an online information page explaining the
procedure of the study, which was followed by provision of online informed consent.
What is ethical in terms of obtaining informed consent for treatment? Would a
clinician be accessible to answer questions? A potential answer to this dilemma
would be that self-guided computerized interventions should still be overseen by
a healthcare professional or only used in health care settings where clinicians are
accessible. For quality control, there should also be some way for consumers and
professionals to identify self-guided programs that have met the standards to be
considered empirically-based treatments. Thus, data is necessary to demonstrate that
such treatments are in fact evidenced-based treatments.
Overall, continued research is necessary to better understand the efficacy of these
interventions, as it seems that some treatments work better than others [6]. Ran-
domized controlled trials using these methods need to be conducted to obtain more
information about their efficacy. Interestingly, one benefit of internet and computer-
based approaches is that treatments may be more likely to provide standardized,
equivalent doses of treatment which is helpful in randomized controlled trials to
ensure that all participants received the same dose of treatment [11].
4.1.8 Computer-Assisted Treatment Approaches
The majority of the studies mentioned above use the computer as the primary
source of training. However, computer-assisted treatment programs have also shown
promise for a variety of mental health problems. These programs combine the
structure of a computerized program with real life participation in clinician-
administered therapy. Benefits of computer-assisted treatment programs include
several of the potential advantages mentioned above for self-guided treatment, such
as the maintenance of structure and the flexibility associated with using a computer
program. However, although treatment modules can be completed independently,
clinicians can be available to clarify any therapy skills that the patient finds
confusing, facilitate problem-solving, or attempt to tailor treatment approaches
more to the individual patient if the computer program does not do so adequately.
4.1.9 Computer-Assisted Treatment Approaches for Anxiety

Disorders
Computer-aided treatments have been utilized for a variety of mental health

difficulties in adults. Research has shown that computer-aided vicarious exposure
for spider phobia shows comparable results to therapist-delivered live exposure [37],
with both treatments being more effective than a relaxation placebo treatment. One
program, CALM Tools for Living, is used in session by clinicians to guide patients
with anxiety disorders through CBT [38], and another program guides posttraumatic
stress disorder (PTSD) treatment in session and facilitates therapy homework [39].
Other studies have found support for the use of internet-based CBT (I-CBT) for
social anxiety [40], with even greater gains attained when compared to group CBT
for social anxiety.
Computer-aided treatments have also been used with youth with anxiety disor-
ders. Spence et al. [17] compared youth, aged 7–14, with anxiety disorders who
were randomly allocated to either a group CBT clinic-based treatment, the same
CBT delivered partially over the internet, or a wait-list control group. The group
CBT treatment consisted of 10 child sessions and 6 parent sessions, plus booster
sessions at 1 and 3 months post-treatment. A similar format was used for the
CBT C internet condition, as half of the child and parent sessions were delivered via
internet. Results showed that children in the clinic and clinic C internet conditions
showed significant reductions in anxiety from pre to post-treatment and were more
likely to no longer meet diagnostic criteria for their anxiety diagnoses, compared to
the waitlist group. There were also no significant differences between the clinic and
the clinic C internet treatment conditions at post-treatment [17]. Another example
of a computer-assisted program is Camp Cope-A-Lot [41, 42], which is a 12-
session computer-assisted interactive cognitive-behavioral treatment program for
anxiety in 7–13-year-old youth, based on the Coping Cat treatment [43]. Participants
complete the first six sessions independently, during which they follow along with
a camper at Camp Cope-A-Lot to learn strategies to cope with anxiety using an
interactive and engaging format, with each session focusing on learning a different
skill (e.g., identifying physiological symptoms of anxiety, problem solving skills
training). The remaining six sessions are completed with the therapist and involve
use of graduated exposures. In those sessions, the child initially watches a short
video at the beginning of the exposure sessions in which the camper demonstrates
exposure completion, and this is then followed by the child completing exposures.
Two parent sessions are also included to convey the treatment plan. The program
also incorporates optional video game rewards. In a randomized controlled trial of
Camp Cope-A-Lot [44], 49 children with primary anxiety disorder diagnoses were
randomized to the Camp Cope-A-Lot condition (CCAL), individual CBT (ICBT),
or computer-linked education, support, and attention (CESA). Results demonstrated
that the CCAL and ICBT groups showed significantly higher remission rates of
primary anxiety disorder diagnoses compared to CESA, with 81 %, 70 %, and
19 %, respectfully, no longer meeting criteria for their principal anxiety disorder
diagnosis. No significant differences were found between youth who participated in
the CCAL group compared to ICBT. Additionally, parents and children participating
in CCAL and ICBT reported higher satisfaction than CESA children, supporting the
acceptability of those treatment approaches. Programs such as CCAL are thought
to ensure effective, standardized education in cognitive-behavioral strategies while
also maintaining the benefits of face-to-face treatment [44] with no negative effect
on treatment alliance. Computer-assisted programs have also demonstrated positive
outcomes in treating spider phobia [45] in youth.
4.1.10 Computer-Assisted Treatment Approaches

for Obsessive-Compulsive Disorder
Andersson et al. [46] completed a pilot study of internet-based CBT (ICBT) for
adults with OCD. In I-CBT, the patient accesses a website and works with written
self-help materials and homework assignments, and this work is supported by
regular contact with an online therapist. In this study, all participants read the
same material regarding psychoeducation and treatment rationale for OCD, yet
specific examples of obsessions and compulsions were provided based on the
participant’s individual symptoms. The self-help program consisted of 15 modules,
and a homework assignment had to be submitted after each module. A psychologist
provided feedback and support on all homework assignments. Significant reductions
in OCD symptoms, as well as positive changes across several other measures
of functioning (e.g., increases in global assessment of functioning, decreases in
depressive symptoms) were also shown.
4.1.11 Computer-Assisted Treatment Approaches for Mood

Disorders
Several computer-assisted programs have also been used for depression (see [47] for
a review). Commonly used programs include Good Days Ahead: The Multimedia
Program for Cognitive Therapy [48], Beating the Blues [49–51], and MoodGYM
[52, 53]. Cavanagh and Shapiro [54] conducted a meta-analysis of five depression
computerized CBT self-treatment studies and found pre-post improvements for
individuals who used these programs versus wait-list controls or people receiving
treatment as usual, though computerized CBT treatment was not as effective as face-
to-face clinician-administered CBT treatment.
4.1.12 Computer-Assisted Treatment Approaches for Other

Mental Health Difficulties
Other studies have found support for the use of internet-based CBT (I-CBT)
compared to an attention control condition for hypochondriasis [55]. Computer-
assisted programs have also been used with patients with irritable bowel syndrome
[56] and in patients with schizophrenia [57, 58]. Several programs have also been
used to treat eating disorders (e.g., Student Bodies Internet program, [59, 60]).
Another study found eight sessions of participation in a private online chat room
overseen by a moderator, to improve eating habits and body image concerns in
college-age women at risk for an eating disorder, when compared to a control group
[61]. Self-help based on CBT in conjunction with internet support was also shown
to be effective in treating bulimia nervosa and binge eating disorder in adults [62].
Internet support consisted of contact with a graduate student via email to provide
feedback on homework and guidance on using the self-help program. Patients
also had access to an online private discussion forum. Computer-assisted programs
have also demonstrated positive outcomes in treating encopresis [63] and selective
mutism [64] in youth.
4.1.13 Summary
As indicated, a variety of computer-assisted approaches have been effectively used

to treat a variety of mental health problems. Certain aspects of treatment, such
as psychoeducation, can be conveyed via this means without requiring face-to-
face contact with a clinician. Computer-assisted treatments may also help provide
structure for less experienced therapists. However, there are also limits to computer-
assisted programs. Computer-assisted treatments may be focused on addressing
particular symptoms or disorders, and may not be able to tailor treatments as
specifically as individualized treatment. For instance, whereas the randomized con-
trolled trial of Camp Cope-A-Lot showed significant reductions in anxiety disorder
diagnoses, many children continued to meet diagnostic criteria for a comorbid
diagnosis for which more treatment for non-anxiety issues might be warranted
[44]. Individuals may present with complex psychopathology or comorbidities that
may impact or limit the effectiveness of the computer-assisted intervention. Thus,
it is important to investigate whom computer-assisted treatment programs will most
likely benefit, in terms of type and severity of disorder. Large scale effectiveness
research is necessary to determine the cost-effectiveness of the programs, as well as

how they can be best implemented and sustained [44]. Further, now that research has
demonstrated support for computer-aided psychotherapy programs, future research
that includes appropriate control groups (e.g., clinic-based treatment group) is
also needed to determine the ideal level of clinician contact required to show
treatment efficacy [16]. See Newman et al. [65] for a review of the literature on how
much therapist contact is necessary for a positive treatment outcome, ranging from
completely self-administered to predominantly therapist administered, for anxiety
and depression. Overall, continued research will likely identify ways to improve
treatment compliance and improve the impact of treatment.
4.1.14 Virtual Reality
Virtual reality technology has been used as a treatment tool to immerse the
patient in a setting where the technology can be used to control the patient’s
visual, auditory, tactile, and olfactory experiences. Virtual reality programs can also
facilitate assessment by examining behavioral and physiological responses when in
virtual environments, such as in the case of individuals with phobias of tunnels [66],
or anxiety responses in individuals with test anxiety [67] or OCD [68]. Additionally,
since it is not always feasible or realistic to expose the individual to a feared
scenario, they provide an environment in which the individual can practice using
coping skills without being in the actual situation. The individual can first learn
the skills (e.g., diaphragmatic breathing, cognitive restructuring, exposure) and then
practice them in the safe and controlled virtual environments at their own pace [69].
This may facilitate treatment for individuals who may be too anxious to undergo
exposure in vivo.
4.1.15 Virtual Reality for the Treatment of Anxiety Disorders,

Phobias, and Post-traumatic Stress Disorder
Virtual reality therapy has been used in exposure therapy to treat PTSD symptoms
resulting from warfare [70, 71], the September 11, 2001 terrorist attacks [72, 73],
criminal violence [74], or motor vehicle accidents [75–77]. It has also been used
to treat phobias such as fear of public speaking [78, 79], social anxiety [80, 81],
panic and/or agoraphobia [82–84], claustrophobia [85], fear of heights/acrophobia
[86, 87] and fear of flying [88–90]. A meta-analysis by Powers and Emmelkamp
[91] examined 13 studies comparing virtual reality exposure therapy (VRET),
administered as a stand-alone treatment, with in vivo exposure and with control
conditions for anxiety disorders and specific phobias. Results showed a large mean
effect size for VRET compared to the control conditions (Cohen’s d D 1.11), and
VRET was shown to be equally effective and in fact have a slight advantage over
in vivo exposure (Cohen’s d D 0.35). However, the Powers and Emmelkamp [91]
meta-analysis investigated VRET as a stand-alone treatment, therefore excluding
studies which combined VRET with CBT in treatment. However, more recently, a
meta-analysis of 23 studies was conducted by Opris et al. [92], which compared
use of virtual reality in conjunction with classical evidenced-based interventions
(e.g., CBT or behavioral therapy) to evidence-based interventions in which no
virtual reality component was utilized. Results showed a large and statistically
significant effect size when comparing VRET (consisting of behavioral therapy
or cognitive-behavioral therapy augmented by virtual reality exposure) to waitlist
control at post-treatment (weighted D D 1.12). When comparing VRET to classical
evidenced-based treatments, results revealed no overall effect for VRET compared
to the classical evidenced-based treatments. Thus, results showed similar efficacy
between the cognitive-behavioral and behavioral interventions incorporating a
virtual reality component to the classical evidenced-based interventions with no
virtual reality component [92]. Results also showed no significant differences in
dropout between the virtual reality and in vivo exposure conditions.
Other studies have examined the use of online virtual environments in psycho-
logical treatment. Second Life is an online virtual environment, created by Linden
Research, Inc., in which users create their own avatar which they use to interact
with other avatars within the virtual environment. Conversations occur via typed
messages or through voice-over IP headsets. Yuen et al. [93] conducted a study
of use of Second Life to treat social anxiety. Treatment used acceptance-based
behavioral therapy and cognitive-behavioral therapy strategies, and therapists and
patients met in a private, secure virtual room and communicated vocally and visually
using avatars. In session exposure exercises were conducted within the virtual
world, such as conducting a presentation inside of a virtual conference room. After
12 treatment sessions, participants showed significant pretreatment to follow-up
improvements in social anxiety symptoms, depression, disability, and quality of life.
Another study developed the T2 Virtual PTSD Experience for use on the Second
Life platform, which was created to educate individuals about combat-related PTSD
[94]. Sarver et al. [95] examined the utility of an interactive virtual environment
for adolescents with social anxiety. Children received Social Effectiveness Therapy
for Children [96] in conjunction with the opportunity to practice skills in a virtual
environment, and both clinicians and participants were satisfied with the program
and indicated that they would recommend the program to others.
4.1.16 Virtual Reality for the Treatment of Other Mental

Health Difficulties
Although studies have shown the utility of virtual reality in treating anxiety and
phobias, virtual reality has been used for other disorders as well. For instance, virtual
reality programs have been used in the treatment of attentional difficulties [97], to
elicit craving and cue reactivity with alcohol [98] and cocaine [99], when conducting
pediatric rehabilitation (see [100] for a review), to improve social skills in patients
with schizophrenia [101], and to address body image concerns in individuals with
eating disorders ([102]; see [103] for a review).
4.1.17 Telepsychiatry
Telepsychiatry, in which therapy occurs using videoconferencing technology, is

being used more often by clinicians. Telepsychiatry has a variety of potential
benefits. First, it can connect individuals who do not have geographic access to a
specialist to an expert clinician so that the individual is able to receive empirically-
based treatment that he or she may otherwise not have had access to. Second,
telepsychiatry can also be used to facilitate exposure therapy in the patient’s home
setting, which provides a more realistic setting than a therapist’s office, which
maximizes ecological validity. Third, telepsychiatry can also help people who are
housebound due to physical abilities or mental health difficulties (e.g., agoraphobia,
social anxiety). Fourth, telepsychiatry provides patients with privacy due to being
able to connect to clinicians in their home setting, rather than having to visit a
clinician’s office. This may be particularly helpful for patients who have concerns
about stigmatization. It also provides convenience in that practitioners may be able
to offer more flexible scheduling options.
Studies have shown CBT delivered via telepsychiatry to be effective for a variety
of mental health difficulties. Kessler et al. [104] conducted a randomized trial (RCT)
that demonstrated the effectiveness of therapist-delivered online CBT for adults
with depression, with gains maintained after 8 months. In another RCT, Stubbings
et al. [105] compared 12 sessions of in-person CBT to CBT via telepsychiatry
in adults with anxiety or mood disorders, and results showed the two conditions
to be comparable. Cognitive-behavioral therapy via telepsychiatry has also been
successfully used to treat OCD [106, 107], panic disorder with agoraphobia [108,
109], PTSD [110, 111], bulimia [112], and to provide psychological treatment for
patients with cancer in rural settings [113].
Administration of CBT via telepsychiatry also has shown promise when working
with children and adolescents. Storch et al. [107] conducted a randomized controlled
trial of family-based CBT delivered via web-camera compared to a waitlist control
group, in children and adolescents with OCD. Results showed that web-camera
delivered CBT was superior to the waitlist control group at post-treatment on all
primary outcome measures, which demonstrated large effect sizes of d 1.36.
Comer et al. [114] conducted a case series of five youth (ages 4–8) with early-
onset OCD who participated in family-based CBT (based on Family-Based CBT
for Early Childhood OCD; [115]) delivered via telepsychiatry. All youth in the study
showed OCD symptom improvements and global severity improvements from pre to
post-treatment, and 60 % no longer met criteria for OCD at post-treatment. Nelson

et al. [116] conducted a randomized control trial of 28 children which compared
8-weeks of cognitive-behavioral therapy face-to-face to the same treatment over
telepsychiatry. The CBT treatment across both conditions was effective, and an
interaction effect reflected a faster rate of decline in child reported depressive
symptoms for the telepsychiatry group.
Aside from CBT, other treatment methods have shown promise when admin-
istered via telepsychiatry. Acceptance-based behavioral therapy administered via
telepsychiatry has been successful in treating adults with social anxiety [117].
Behavioral activation treatment delivered via videoconferencing was shown to
be effective in reducing depression in older adults [118]. One study compared
internet-based group therapy (e-Getgoing, CRC Health Group, Inc) to on-site group
therapy for substance abuse [119]. E-Getgoing is a telepsychiatry platform that was
developed to deliver verbal- and visual-based therapy to individuals with substance
abuse problems. Participants have their own log-on identification and password
to ensure confidentiality. The group leader is able to verify the identity of all
participants in the group, yet participants could not view other members of the group
and were just provided with a real-time video picture of the group leader. Patients
in both conditions responded positively to treatment, with treatment satisfaction
being comparable across conditions. Overall, increasing research is demonstrating
the numerous benefits provided by psychotherapy via telepsychiatry.
Though there seem to be significant benefits to use of telepsychiatry in therapy,
several concerns are present, such as issues of privacy, confidentiality, as well
as liability and risk management in situations where the patient’s safety may
be at risk (e.g., suicidality) and inaccessible to the clinician. For instance, it is
not well-established how emergency situations such as suicidality be managed in
this format. Another question is how therapy strategies must be modified to be
delivered this way. Accordingly, the ethical and legal questions related to use of
telepsychiatry are not clearly established. However, some practice guidelines have
been established by the American Psychiatric Association (see APA, [120]) and the
American Telemedicine Association (see ATA, [121]). The use of telepsychiatry
treatment is also limited due to the fact that treatment may not be reimbursed by
insurance companies, due to the absence of various trials demonstrating its efficacy
as well as some of the ethical and legal concerns mentioned. Studies to determine
clinical efficacy and cost-effectiveness of services are necessary to determine if this
treatment modality should be supported by insurance companies. Clinicians using
telepsychiatry may face the barrier of not being licensed to practice across state
lines. Further, patients may feel that they are in need of treatment when they happen
to be in a different state, and should they be denied treatment simply because they
are traveling? Is it in the interests of the patient’s welfare if the clinician is not
allowed to interact with the patient from different locations? Continued discussion
of these issues will likely result in clear standards by which telepsychiatry can be
used in treatment.
4.1.18 Future Innovations
Interest is also growing in the use of other computerized technologies for therapy
goals, such as using of smartphones, tablets, and personal digital assistants. Several
applications have been developed for use with smartphones or computers and are
available to assist individuals or clinicians in symptom monitoring, ecological
momentary assessment, treatment, and tracking progress [122–124]. For instance,
interventions have used multimedia mobile phone programs for symptoms of
depression, anxiety, and stress [125], smoking cessation [126, 127], recovery
from alcoholism [128, 129], and weight loss [130]. An application called the
Dialetical Behavior Therapy (DBT) Coach demonstrated some support in a pilot
study in which the application was used for individuals with borderline personality
disorder and substance use disorder [131]. Mobilyze is an application that has been
successfully used to decrease symptoms of depression [132]. Another smartphone
application, “PE Coach,” has been used to support the provision of prolonged
exposure therapy [133]. Jones et al. [134] conducted a pilot study of a technology-
enhanced version of the evidence-based behavioral parent training program, Helping
the Noncompliant Child (HNC), for youth, ages 3–8, with clinically significant
disruptive behavior from low-income families. Families were randomized to receive
either standard HNC or technology-enhanced HNC (TE-HNC), which included
several smartphone enhancements: skills video series, brief daily surveys, text
message reminders, video recording home practice, and midweek video calls. Both
groups exhibited clinically significant improvements in disruptive behavior, but
between-groups analysis suggests TE-HNC may enhance child treatment outcome,
likely due to the increase engagement in that condition. Some support has also
been demonstrated through the use of a smartphone application, Anxiety Coach, to
enhance the treatment of pediatric OCD [135]. Text messaging has also been used
for the aftercare treatment of individuals with bulimia [136], in college students
for smoking cessation [137], and as an adjunct to CBT in adults with depression
[138]. Despite the potential for these programs to facilitate treatment given the
widespread use of smartphones and similar technology, the majority of mental
health applications that are available to date lack scientific evidence about their
efficacy [122]. It is also important to consider the issues of confidentiality and
patient safety when using these programs. Accordingly, continued research will be
important to further assess the utility of such technology.
4.2 Conclusion
This chapter has reviewed the utility of several different computer-aided psychother-
apy options. Self-guided programs, computer-assisted programs, use of virtual
reality technology, and engagement in therapy via telepsychiatry have all shown
promise in addressing a variety of psychological difficulties. Accordingly, such

programs offer a variety of potential advantages. Together, they offer increased
accessibility to treatment, allow clinicians to help more people, and address other
barriers such as the logistics of traveling to treatment or dealing with stigmatization.
Another motivator for the development of computer-assisted treatment programs
is the potential savings in cost. Several studies detail the overall savings in cost
when using computer-assisted treatments [139, 140]. Additionally, use of computer-
assisted treatments will also likely save on clinician time [13], and studies indicate
that use of computer therapy programs also allow clinicians to reach a greater
number of patients [141, 142]. Use of computer-assisted treatment may also save
on patient time if it minimizes the need to travel to a therapy session, potentially
missing work. McCrone et al. [139] showed that patients who participated in the
computerized therapy program, Beating the Blues, had fewer doctor-certified days
absent from work in the 8 months following randomization when compared to the
treatment as usual group. Cavanagh and Shapiro [54] discuss that the main cost
implications of computer-assisted treatment involve three components: (1) the costs
of the computer treatment software, (2) overhead of housing and maintaining such
treatment systems, and (3) the cost of oversight by a facilitator, administrator, or
clinician. They discuss that the potential cost savings include more efficient use of
therapist resources, short- and longer term health service costs offsets, and sickness
absence cost offsets. These costs must be considered in regard to treatment outcomes
for each program.
Despite the potential advantages noted, these programs may also be associated
with several disadvantages. As indicated, the structured nature of some computer-
aided programs may focus on particular symptoms or disorders, and thus programs
may not tailor treatment to consider the individual factors and potential comorbid
symptoms or disorders that an individual exhibits. It may also be more challenging
to monitor treatment engagement and resistance in the absence of face-to-face
interaction with a clinician. Additionally, there are ethical and legal issues such
as confidentiality, liability, and risk management which are not clearly established
with these technologies. In sum, this chapter emphasizes the importance of more
research examining the efficacy of computer-aided psychotherapy programs. It will
be important to have a clear understanding of the utility of such programs, as well
as for whom they are appropriate. It is hoped that continued research on the use
of computer-aided psychotherapy will likely facilitate more people getting the help
that they need.
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Chapter 5
Computerized Cognitive Training Based upon
Neuroplasticity
Charles Shinaver and Peter C. Entwistle
Abstract Computer assisted cognitive training is used in the treatment of traumatic

brain injury (TBI), schizophrenia, and attention deficit hyperactivity disorder
(ADHD). Pilot studies and case studies have now progressed to randomized
controlled trials and meta-analyses that demonstrate efficacy of computer assisted
cognitive training. In patients with schizophrenia, computer assisted cognitive
remediation has demonstrated improvement in general and social cognition, as
well as verbal and working memory, attention/vigilance, and speed of processing.
Cognitive rehabilitation in TBI has shown evidence for effectiveness of attention
training and language and visual spatial training for aphasia and neglect. Cogmed,
a computerized massed practice approach to working memory training, has an
adaptive process, to adjust to the person’s performance. Cogmed has frequently
been used with patients diagnosed with ADHD. Compliance with treatment is a
key to achieving benefit. Working memory training is based on neural plasticity,
the concept where the brain is stimulated and reacts by changing. These changes
are in neural pathways and synapses. Computerized cognitive training does result
in changes in the brain, and these changes are sustained over time. Cogmed has
resulted in increased verbal and visual spatial working memory and improvements
in attention with ADHD clients. Also gains in reading comprehension and mathe-
matics have been found after completing Cogmed.
Keywords Attention deficit disorder with hyperactivity • Brain injuries • Cog-

nition • Control groups • Early intervention (education) • Memory disorders •
Memory • Short-term • Psychotic disorders • Research design • Schizophrenia
C. Shinaver, Ph.D. ()

Pearson, Carmel, IN, USA
e-mail: charles.shinaver@gmail.com
P.C. Entwistle, Ph.D.
Pearson, Pembroke, MA, USA

82 C. Shinaver and P.C. Entwistle
5.1 Computerized Cognitive Training Holds Exciting

Potential
Computer assisted cognitive training is an exciting innovation with seemingly

surprising potential that has been studied fairly extensively and with rigor within
some specific clinical populations (e.g. schizophrenia, traumatic brain injury (TBI)
and Attention Deficit Hyperactivity Disorder (ADHD)/working memory deficits)
and even with some frequency in normal or typically developing samples. There are
numerous additional clinical populations for which computerized cognitive training
has been researched tepidly and for which there is optimism but as yet limited data.
With a few exceptions, most of those clinical areas have studies that have been
limited by research design with case studies, pilot studies, small sample sizes and
single sample test-retest designs lacking control groups. However, computerized
cognitive training has been applied in more substantive and systemic ways in
randomized controlled trials (RCT’s) and meta-analyses in three key clinical areas:
schizophrenia, TBI and ADHD/working memory deficits. Yet, even in these areas
this approach is not without controversy. Computerized cognitive training appears
to have the most research and least controversy in treating schizophrenics, then TBI
and lastly with ADHD/working memory deficits. Given the more contentious area of
working memory training with ADHD & those with working memory deficits some
studies of Cogmed with typically developing children will also be considered to
shed light on some conceptual issues and the likelihood or possible obstacles of near
and far transfer. Cogmed working memory training will be explored in detail partly
because of the present authors have worked extensively with Cogmed extensively
and partly because it has an substantial body of research. However, the development
of science is, by nature, a contentious process such that claims within each of these
subfields merit review. Research in these three clinical areas has enough substance
to allow for defensible, though not totally unassailable, empirical conclusions. In
fact, consideration of this data can inform whether and how the utilization of such
interventions might be efficacious in some of the more recently broached clinical
areas as well.
5.2 Computerized Cognitive Training with TBI, ADHD &

Schizophrenia
To start at the ending, one finds that all three of these areas, schizophrenia, TBI
& ADHD come to somewhat similar conclusions in meta-analyses regarding the
usefulness of computerized cognitive training with these clinical populations. It
helps. It is “efficacious”. It is worth doing and in some respects may be a key
element among intervention components one might use to help such clients. Yet,
it is not typically considered to be optimized when it is delivered in isolation as a
solo intervention. The degree to which computerized cognitive training helps and
5 Computerized Cognitive Training Based upon Neuroplasticity 83
in what specific ways one might benefit will be discussed in more detail below.
We will first examine the seemingly less contentious areas of cognitive training for
schizophrenia and then TBI which has had some mild controversy to determine how
they might inform the more contested area of computerized cognitive training as it is
applied to ADHD/working memory deficits and in some cases typically developing
clients.
5.3 Computerized Cognitive Training with Schizophrenia
In considering recent meta-analyses of cognitive training with schizophrenics

investigators have found the “usefulness of cognitive remediation when applied in
the early course of schizophrenia and even in subjects at risk of the disease” [1].
Keep in mind that several investigators do not separate out computerized cognitive
training from cognitive training. This is partly because often today cognitive training
has at least components that are computerized. Barlati and colleagues further assert
an even stronger claim regarding cognitive training: “Cognitive remediation is a
promising approach to improve real-world functioning in schizophrenia and should
be considered a key strategy for early intervention in the psychoses” [1]. The
empirical basis for such strong narrative statements is worth consideration.
The actual effect sizes, the basic metric of meta-analyses, which are the basis
of the narrative conclusions stated above [1] are intriguing to consider. In terms
of clinical outcomes (essentially symptom reduction) for cognitive training for
schizophrenics the effects sizes ranged from .26 to .68 [1]. This range would be
typically described as small to moderate effect sizes in Cohen’s terms. However,
utilizing the binomial effect size display method (BESD) suggested by Rosenthal [2]
as a more meaningful way to interpret clinical intervention data somewhere between
26 and 68 % more patients would be positively affected by this intervention than
those not receiving it. Functional outcome effect sizes ranged from .35 to .78 [1]
which would also be described as small to moderate effect sizes. Again, utilizing
BESD [2] somewhere between 35 and 78 % more patients would be positively
affected in terms of functional outcomes than those who received no treatment.
While neurocognitive effect sizes, the largest effect sizes reported, ranged from .32
to 1.01 [1]. Using BESD somewhere between 32 % to all of the subjects would be
affected. This would typically be described as small to large effect sizes. Across
all of these domains cognitive training would be expected to affect anywhere from
26 % to all of the subjects positively. Given the rather strong narrative conclusions
these investigators make it is attention-grabbing that the range of effect sizes is
so broad from .26 (small) to 1.01 (large). The range of effect sizes specifically in
the neurocognitive domain in particular is also quite wide from .32 to 1.01. These
interpretative narrative comments and their relationship to actual effect sizes are
instructive when considering other bodies of scientific literature and their effect
sizes. In other words effect sizes from .32 to 1.01 were considered “efficacious”
in the neurocognitive domain for schizophrenics while effect sizes which ranged
from .26 to .68 for symptom reduction were also categorized as efficacious.
With regard to computerized cognitive training specifically, Grynszpan et al.
[3] conducted a meta-analysis of computer-assisted cognitive remediation (CACR)
with schizophrenics. In the previous meta-analysis, Barlati et al. [1] did note that
eight studies included computer-assisted programs and five that were not computer
assisted [1] yet there was no comparison of the two in their analysis. Grynszpan
et al. [3] in contrast, focused specifically on CACR and found an improvement in
general cognition with an effect size of .38. They also found that this approach
resulted in an improvement in social cognition with an effect size of .64 [3].
Additionally, significant improvements in several areas including verbal memory,
working memory, attention/vigilance and speed of processing but each of those with
small effect sizes [3] were noted. Interestingly, the narrative comments of Grynszpan
et al. [3] are positive but certainly more restrained than Barlati et al. [1]: “The
results lend support to the efficacy of CACR with particular emphasis on Social
Cognition: The difficulty in targeting specific domains suggests a ‘non-specific’
effect of CACR.” [3] This restraint makes some sense given the comparatively
smaller effect sizes found. It is important to note here that an effect size of .38
was descriptively categorized as “efficacious”. This is important to consider in other
applications of computerized cognitive training.
Earlier in the literature there were hints that evidence was building in support
of cognitive training for schizophrenics, but not without some mixed findings. As
early as 2003 a review of cognitive training in schizophrenia by Twamley et al.
considered some meta-analyses and concluded “the different types of approaches,
whether computer assisted or not, all have effective components that hold promise
for improving cognitive performance, symptoms and everyday functioning” [4] The
basis for the meta-analysis by Twamley et al. [4] were 17 studies with 14 of them
reporting positive findings for at least one variable. In their analysis they found effect
sizes for neuropsychological performance (d D .32), reductions in symptom severity
(d D .26) and improvements in everyday functioning (d D .51) [4]. Certainly, these
effect sizes are smaller than the ranges reported by Barlati et al. [1] and again the
narrative comments were more cautious, but clearly positive. These effect sizes
ranged from .26 to .51 and were still considered, at minimal, promising. Earlier
Kurtz and Moberg in 2001 [5] reported in a meta-analysis that there was mixed
evidence on the training of attention with schizophrenics, but that “practice based
attention drills can improve performance on measures of sustained attention in
schizophrenia” [5]. They also reported that with “semantic and affective elaborate
encoding strategies” that verbal memory could be improved with schizophrenic
patients [5].
Interestingly, in contrast to the supportive findings for cognitive training for
schizophrenics, Pilling et al. concluded that “Cognitive remediation had no benefit
on attention, verbal memory, visual memory, planning, cognitive flexibility or
mental state” [6] for schizophrenics. Yet, Pilling et al. only cited five studies for
their analysis, whereas Twamley et al. [3] cite 17. Also, Pilling et al. [6] were based
in England while Twamley et al. [3] were based in the United States. Although
Twamley et al. [3] used most of the studies cited by Pilling et al. [6] they also used
several more studies. It is possible that Pilling et al. [6] did not have access to the
same published data. Certainly within the research literature on cognitive training
of schizophrenics there were some differences of opinion. Yet the accumulation of
data appears to have outweighed the findings of Pilling et al. from 2002 [6]. One
might interpret this shift as indicative of the impact of an accumulation of data over
time that becomes more convincing.
A meta-analysis in 2011 by Wykes et al. [7] added a deeper understanding of
how cognitive remediation can be more powerfully delivered. Interestingly, these
investigators state that “No treatment element (remediation approach, duration,
computer use, etc.) was associated with cognitive outcome” [7]. In this respect
computerized cognitive training and non computer-assisted cognitive training had
positive effects that were statistically indistinguishable from one another. Impor-
tantly, Wykes et al. [7] found that cognitive training “yielded durable effects on
global cognition and functioning.” However, Wykes et al. [7] added that cognitive
remediation was more effectively delivered when patients were ‘clinically stable.’
Presumably some intervention must be delivered to get schizophrenic patients to
the point where they are stable whether it is pharmacological or psychotherapeutic
or both. Wykes et al. [7] also reported that they found “significantly stronger
effects on functioning” [7] when cognitive remediation was not provided alone but
along “with other psychiatric rehabilitation, and a much larger effect was present
when a strategic approach was adopted together with adjunctive rehabilitation”
[7]. Wykes et al. [7] cite McGurk et al. [8] who conducted additional exploratory
analysis on psychosocial functioning effects of remediation and noted that studies
which included psychiatric rehabilitation along with remediation cited effect sizes
of .59 where as those using cognitive remediation alone reported effect sizes of
.28 [7]. Similarly, when drill plus strategy was used the effect size reported was
.47 whereas when drill and practice was used the effect size was .34, but this
difference was not significant [8]. The term strategy includes the explicit focus upon
learning and applying strategies [8]. Whereas the concept of the drill and practice
approach is considered to be implemented when whether or not the subject engages
in a particular strategy is left to the subject and chance [7]. Additionally, when
evaluating only studies that did include the adjunct psychiatric rehabilitation and
drills plus strategy significantly larger effect sizes were produced with an effect
size of .8 for those studies compared to those only using drill and practice without
psychiatric rehabilitation which showed an effect size of .3 [7]. These results are
quite informative and suggest the possibility that cognitive remediation along with
psychiatric rehabilitation are at least additive to one another. One wonders whether
psychiatric rehabilitation along with cognitive remediation may be additive in other
clinical areas as well.
There was also a rather heuristic clinical finding reported by Twamley et al. [4]
of the predictive role that a number of investigators have found that neurocognitive
impairment has in relation to the functional outcome of schizophrenia. Green et al.
in 2000 asserted that 20–60 % of the variance in functional outcomes was predicted
by neurocognitive impairment [9]. Conceptually this seems plausible. That is,
with greater neurocognitive impairment one would expect more functional deficits.
Additionally these impairments are considered to play an important role in social
deficits and struggles with daily living for schizophrenics [4]. These findings support
the notion of at least early intervention when these symptoms emerge in higher
risk individuals and what might be even conceptualized as prevention depending
upon how early such an intervention was delivered. This is intriguing in light of
ADHD in that often social deficits are reported in this population as well. Similarly,
neurocognitive deficits are often found to relate to functional outcomes for TBI
patients.
5.4 Computerized Cognitive Training with TBI
In the case of TBI, in 2005, Cicerone et al. [10] concluded based on their review
of literature from 1998 through 2002 that there “is substantial evidence to support
cognitive rehabilitation with TBI, including strategy training for mild memory
impairment, strategy training for post acute attention deficits, and interventions for
functional communication deficits.”[10] Yet, as is noted in this conclusion there
is a focus upon strategy training for both mild memory impairment and attention
deficits. Their analysis included 118 studies initially and excluded 31 leaving 87
studies for their consideration. Obviously this is a sizeable number of studies.
However, this was not a meta-analysis as they noted that the literature at that
point lacked sufficient studies that reported effect sizes [10]. Yet, intriguingly, the
article cites some professional organizations and their involvement in reviewing
the literature which may be one reason as to why this database appears less
contentious than that of ADHD. Cicerone et al. report that their review contributes
to the recommendations of this organization: Brain Injury Interdisciplinary Special
Interest Group of the American Congress of Rehabilitation Medicine for cognitive
rehabilitation of people with traumatic brain injury (TBI) and stroke [10]. They
also cite the European Federation of Neurological Societies and state that that
organization also came to the conclusion that there was:
Substantial evidence to support attention training in the post acute phase after TBI (but not
during the period of acute recovery) and compensatory memory training for subjects with
mild memory impairments (10, p. 1681)
So, although Cicerone et al. focus upon strategy training this addition lends some
support to attention training which is more along the interest of this chapter.
However, there is no specific mention of computerized attention training. Yet, the
conclusion by this European organization, according to Cicerone et al. [10] was
based upon a review of similar studies using similar methods. So, interestingly, as
of 2005, the subfield of cognitive training with TBI appears to make more forceful
assertions than those treating schizophrenia despite not having cited a meta-analysis
although they do not clearly specify computerized cognitive training.
Not long thereafter in 2009, Rohling et al. [11] did conduct a meta analysis
of data on cognitive treatment of TBI and concluded more specifically “the meta
analysis revealed sufficient evidence for the effectiveness of attention training after
traumatic brain injury and of language and visual spatial training for aphasia
and neglect syndromes after stroke” (11, p. 20). Importantly, the basis for this
conclusion was not an effect size as large as one might expect. They found a
small but significant effect size of .30 for studies which included active control
groups [11]. It is critical to note here that an effect size of .3 is a smaller effect
size of .3 was considered sufficient evidence for effectiveness, or ’efficaciousness’
with this TBI meta-analysis. Using BESD one can argue that 30 % of the subjects
that received this treatment benefitted more than the non-treatment control groups.
However, Rohling et al. [11] make a critical methodological point related to studies
that do and do not have control groups. What they actually found was an effect
size improvement of .71 that was attributed to treatments, but no treatment control
groups showed some improvement as well at an effect size of .41. They interpreted
this improvement in non-treatment control groups to a combination of spontaneous
recovery – which does occur in the case of TBI and practice effects. So, the effect
size of .41 due to practice effects and spontaneous recovery was subtracted from the
treatment effect of .71 to get the .30 effect size. This finding gives an interpretive
context to effects sizes reported in studies with no control group, namely that studies
without control groups would be expected to report notably larger effect sizes than
those with control groups. An even higher standard would be to expect to find a
significant effect size in relation to active treatment groups – possibly this might
even be considered an unreasonable standard. This methodological point made by
Rohling et al. [11] that practice effects (of taking and re-taking assessments) account
for some of the effect size when one does not use a control group in a research design
is a critical consideration in other databases.
Like cognitive training with schizophrenics cognitive training with TBI has met
with some resistance and some negative findings. In terms of timing, the meta-
analysis by Pilling et al. in 2002 [6] which reported no effects of cognitive training
for schizophrenics was within 1 year of a similar finding by Park and Ingles in
2001 of attention training for TBI [12]. One might interpret this as suggesting that
the synergy between technological developments and research may be affecting the
growth of adoption of such approaches in a similar way across these two fields.
Park and Ingles did a meta-analysis of a more delineated category of attention
rehabilitation with TBI and reviewed 30 studies. Their results were even more
extreme than those of Rohling et al. [11]. Park and Ingles found that studies
which employed only one group with a test, re-test design reported significant
improvement but either no significant gains or almost no significant gains were
found when a control group was used in the research design. For example, with
the pre-post only design attention improved with an effect size of .68 whereas
studies that used a pre-post and a control group the effect size was only .15. They
found similar issues with different aspects or components of attention like working
memory with an effect size of .78 with a pre-post test design and .12 effect size
when using a pre-post test with control design [12].
The Park and Ingles meta-analysis of attention rehabilitation is more clearly

what here we are considering to be cognitive training [12]. In contrast, Park and
Ingles describe an alternate approach used by a few studies called specific-skills
training [12]. In other words training which has functional significance like learning
how to drive may also result in improved attention. The rationale here is that the
practice of a specific skill in a carefully designed sequence allows victims of TBI
to relearn skills. They can compensate to develop that skill and in the process of
doing so improve attention or other cognitive functions. This is a different rationale
and tactic than the direct retraining of cognitive skills considered throughout this
chapter. This is in the category of compensating for a deficit or developing a way
around a loss of capacity as opposed to directly re-training a cognitive skill. Larger
effect sizes were reported for that approach, using a specific-skills training approach
with an effect size for attention behavior of 1.01 when a pre-post included a control
group [12]. However, they did have a somewhat limited number of studies which
applied this approach. Although this approach is not the focus of the present chapter
it is noteworthy. If one is able to improve or directly re-train a cognitive skill like
attention and follow that with skill specific training like driving it is likely to be
an optimal way to complement that cognitive improvement and solidify the skill
acquisition.
Lynch in 2002 did a review of the history of computer-assisted cognitive
retraining with TBI reported that in the early 1980s personal computers and software
for cognitive retraining was available [13]. He notes that there were critics at the
origination of the field and some of that controversy has continued [13]. Yet, even in
2002, Lynch concluded “computer-assisted cognitive retraining can be an effective
adjunct to a comprehensive program of cognitive rehabilitation” [13]. Lynch added
that generalizability of skills remained a key issue [13].
5.5 Computerized Cognitive Training with Working Memory

Deficits & ADHD
Computerized cognitive training of working memory began to achieve critical

mass when it was developed in Sweden from the Karolinska Institute. In fact one
can find a citation by Ryan from 1986 in the literature that describes a program
called “Memory for Goblins”: A computer game for assessing and training working
memory skill [14]. However, this program received rather a limited following as
Ryan again published a second article in 1994 [15] reporting that when the game
was used by older adults that they found it interesting. Otherwise this approach
never gained sufficient following from other investigators in the scientific literature.
(It is important to disclose that the present authors of this chapter work for Pearson,
the company that acquired Cogmed Working Memory Training or Cogmed in 2010.)
Klingberg’s 2002 rather small (seven children in each of the treatment and control
groups) original randomized placebo controlled study [16] (RCT) was followed in
2005 by a larger multi-site RCT investigation [17] that included follow up measures
at 3 months which the original study lacked in 2002. Strong results generated by
both of those studies stimulated an onslaught of research which presently includes
50 peer-reviewed published studies in just over a decade with approximately
80 ongoing studies (www.cogmed.com/research). This rather generative rate of
research of a specific intervention is unusual. As such, this program is worth detailed
consideration to give a more in depth sense of the status of computerized cognitive
training in possibly its most publicized, scrutinized and utilized present format.
Cogmed is a computerized massed practice approach to working memory
training. In 2011 the program was characterized as a “core training” approach in
contrast to “strategy training” by Morrison and Chein [18]. An essential component
of Cogmed is that it is adaptive which means that it adjusts live to a person’s
performance getting slightly more difficult if one succeeds with a trial and slightly
easier if a person fails a trial. Keeping the challenge level high has been found to be
a critical factor in successful training. The most common research design includes
students who get adaptive Cogmed versus those who get non-adaptive Cogmed with
a low ceiling of two or three items. This research design has resulted in showing the
efficaciousness of the program and it has emphasized the critical role of adaptive
training while being able to rule out other aspects of training delivered in the non-
adaptive version (e.g. computer time, individual attention, etc).
Cogmed is an integration of neuroscience, video game programming and psy-
chology. The basis for the emphasis of visual spatial working memory within the
program is a study by Westerberg et al. conducted in 2004 in which a group of
boys between ages 7–15 who did not have ADHD was compared with a group who
had ADHD on visual spatial working memory (VSWM) [19]. It was found that
over childhood without any intervention that typically developing boys increased
substantially in terms of VSWM capacity. See Fig. 5.1. This was not the case with
boys with ADHD.
Westerberg et al. [19] are not the only investigators to note increases in the
working memory capacity of typically developing children. In 2004 Gathercole et al.
reported increases occurred over childhood from ages 4–15 that each component
of working memory – phonological, visual spatial and central executive all show
expansion during that time [20]. Others have made similar assertions about the
growth of working memory capacity of typically developing children [21–24].
5.6 Psychology & Cogmed Coaching: The Role

of Compliance
Once the original “RoboMemo” was developed by the collaboration of the neu-
roscientists and the game programmers Dr. Klingberg has explained that there
were some families in Sweden who had been interested in help for their children
with ADHD. So, the team sent the program home with 10 families to do with
Fig. 5.1 Graph illustrating the research of Westerberg in 2004 [19]
their children. None of them actually completed the program. This was when
psychologists were engaged to develop a coaching program. Once the coaching
program was developed families began completing the program. This is important
as compliance is a critical consideration in any form of treatment. Consider the fact
that Oldham et al. as recently as 2012 in a meta-analysis of methods to increase
attendance at psychotherapy noted that previous investigators had estimated that
about 40 % of referrals refuse treatment [25]. Similarly, according to a meta-
analysis earlier in 1993 by Wierzbicki and Pekarik [26] premature termination was
found to be rather high in a meta-analysis of 123 studies at 46.8 %. Given these
substantial challenges to compliance with traditional approaches to psychotherapy,
compliance might be considered an even more challenging issue to address with
computer-assisted approaches to treatment. In fact one wonders about whether such
interventions can effectively be delivered without support. That is, is it likely that
a person diagnosed with a mental health disorder would simply get online, find
and complete a treatment program without any support from another person or a
professional?
Newman, Szkodny, Llera and Preworski, in 2011 reviewed computer-based
cognitive and behavioral interventions for anxiety and depression and concluded
that these approaches were efficacious with what they categorized as ‘sub-threshold’
mood disorders – those which did not meet the full criteria for diagnosis, but
did show some symptoms of the disorder [27]. However, with depression at a
clinical level computer based self-help treatments were ineffective. In the case
of diagnosed mood disorders the traditional therapist treatment approaches were

most optimal [27]. Similarly, deGraaf, Huibers, Riper, Gerhards, and Arntz in 2009
found that with depressed patients that computerized cognitive behavioral treatment
was not confirmed in an RCT they conducted [28]. So they took a closer look at
compliance. What they found was that when 200 subjects were given the codes
for unsupported online treatment that a good number of them started the treatment
but that dropout was high [28]. Their conclusion was similar to that of Newman
Szkodny, Llera, Preworski in 2011, they found that although this computerized
approach for depression was feasible there was a need to increase adherence for
moderately to severely depressed subjects [28]. They also noted a significant number
of users of internet interventions often use them only very briefly or a significant
number of people may simply not want to engage in this approach. They point
to the therapeutic relationship as potentially critical variable to consider as noted
by Cavanagh et al. in 2010 [29]. More specifically, Cavanagh et al. describe the
triad between the patient, internet intervention and the practitioner as potentially
an important issue and if it were effectively addressed may improve this lack of
compliance [29]. Cavanagh et al. characterize these practitioners as ‘low intensity
practitioners’ who support clients who use internet interventions. In the context of
Cogmed the ‘low intensity practitioner’ would be considered the Cogmed coach
[29]. Certainly many clinical skills such as rapidly developing rapport, creating
a supportive alliance, helping to select appropriate target behaviors to track as
indicators of improved working memory capacity and identifying and ensuring the
delivery of appropriate reinforcement would all be such skills. All of these skills
are utilized by Cogmed coaches. The notion of low intensity practitioner might be
considered in contrast to the high intensity of individual therapy or counseling.
5.7 The Cogmed Coaching Program
Consistent with the concept of a low intensity practitioner, the elements of the
Cogmed coaching program are manageable, but are arguably more fluently imple-
mented by a skillful practitioner. Cogmed coaching includes: an initial interview
session, start-up session, weekly coaching calls of about 20 min in length and a wrap
up session after the last training session. Through the course of the initial interview a
Cogmed coach makes the decision of whether the potential trainee is appropriate for
the program. The Cogmed coach will acquire salient information about the potential
trainee to determine goodness of fit. It is important to note that Cogmed includes
exclusion criteria for specific diagnoses such as ODD, CD, mental handicap, photo
sensitive epilepsy. As these diagnoses are among those that are expected to interfere
with completing the program. As such, studies which include these diagnoses are
considered to be testing the limitations of the application of Cogmed. This typically
includes rating scale data, direct assessment data, the interview session itself which
includes several questions about symptoms and the potential candidates’ personal
background and presenting difficulties.
Actual data from the Cogmed Coaching Center, the online repository of Cogmed
data, and statistics derived from that data provide some of the fodder for coaching
sessions. Coaching sessions or calls play a critically supportive role in helping
trainees to optimize training by helping the trainee to stay within a high challenge
range and still persist until the completion of the program. We surmise that without
such support it some trainees would give up to the inherent frustration of coping with
such consistent cognitive challenge. The review of the individualized data points of
a particular trainee is a critical component of Cogmed coaching. That is, each day
of training is reviewed before a coaching call or session. Then 2 or 3 data points are
chosen to discuss with the trainee. This can evoke what was occurring for the trainee
during training. This can be processed. This helps the trainee to better learn to
manage himself or herself in the midst of potentially frustrating cognitive challenge.
For example, taking breaks after missing a couple items consecutively would be one
method to manage frustration. This coaching method is specific and individualized
to that particular trainee. It is driven by that user’s data and does appear to help
trainees to manage frustration and persist with difficult training. This data can be
accessed by the coach, the training aide and the trainee. The data can be accessed
through an PC, laptop or tablet computer. This approach keeps the coaching method
data driven with the focus on completing the program. An additional element of
support within the program is the training aide. In the context of private practice
which typically uses a home training format the training is usually a parent. At
schools the training aide is usually a teacher or teacher’s assistant or other such
staff member. The training aide is a person who is in the room when the trainee is
actually doing Cogmed. In the school setting the same staff member may be both
the training aide and the coach. Whereas in a private practice setting the coach is
typically the professional offering Cogmed and the training aide is one or the other
parent. Ultimately this support structure results in high compliance.
5.8 The Premise of Cogmed: Neuroplasticity
Our discussion of Working Memory (WM) training continues with a review of the
concept of neuroplasticity. As the term may be unfamiliar to the reader, the term will
be described, and examples from recent research will illustrate the importance of this
concept. Neural plasticity is what happens when the brain responds to stimulation
according to Pascual Leone et al. [30]. Brain structures do not remain impervious to
demands placed upon them but rather the brain is sensitive to those stimuli and reacts
by changing. Plasticity suggests the brain can be shaped by training. The brain is not
rigid but instead it may alter its functioning. Neuroplasticity is the brain’s reaction
to intensive intervention. When parts of the brain are stimulated there may be an
increase in activation in those areas [31].
The brain is affected by the environment, whether good or bad. For exam-
ple, dopamine affects working memory, and WM training affects the number
of dopamine receptors. The hippocampus affects memory consolidation, or the
formation of long term memories, and a stimulating environment and opportunities

for exploration increase the functioning of the hippocampus [32].
Plasticity is an umbrella term that encompasses both synaptic plasticity and non-
synaptic plasticity – it refers to changes in neural pathways and synapses which are
due to changes in behavior, environment and neural processes, as well as changes
resulting from bodily injury [31, 33]. Research by Maguire and Woolett, among
others, have demonstrated that the brain continues to create new neural pathways
and to alter existing ones in order to adapt to new experiences, learn new information
and create new memories, even taxi drivers in training in London, create new neural
pathways [34–36].
There are three essential questions about brain neuroplasticity that come to
the fore with regard to computerized cognitive training [37, 38]. First, does
computerized cognitive training change the brain? Secondly, do changes in brain
activity suggest the possibility that behavior change might be sustained for a longer
period of time? Third, do some groups show greater or less plasticity in their brains
and as a result respond differently to computerized cognitive training?
With regard to the first question investigators have found that computerized
cognitive training given its intensity and rigor does result in changes in the brain [39,
40]. However, even more pointedly, is the brain changed in the location one would
expect given a particular deficit or disability? Furthermore, is the brain changed in
a way that is normalizing? Pinpointing those activities that stimulate the brain in
the location of deficit and engaging in such activities with correct ‘dosing’ or with
sufficient intensity and duration that it is normalizing seems more likely to hold
the possibility of sustaining more enduring change. In other words to engage in
activities with sufficient intensity and rigor that results in changes in the brain is a
rather hopeful proposition.
Some of the training-related changes that have been found in brain functioning
will be elaborated below. However even those findings leave one with more
questions than answers. Accepting that cognitive training actually changes the brain
one then might put forth the idea that activities that result in brain changes may
survive longer than if there were no changes in the brain. Also, the rate of change
within the brain may vary depending upon the plasticity of different brains. Different
diagnostic groups may vary in their level of plasticity. All of this evokes more
questions many of which, as yet, are unanswered. One would expect that different
groups would show different levels of plasticity in their brains [38]. One may
presume that healthy brains of children or young adults might be among the more
responsive to change [33].
Conversely, critics could argue that anything a person does changes the brain.
This may be the case, but what is distinct here is computerized cognitive training
is requiring the trainee to engage with persistence in intensive activities for which
the challenge level must remain high to attain the desired effects. This is not the
same thing as doing any activity with any level of intensity for a random time of
duration. So, it is the combination of the specific activities with which one engages,
the challenge level of them and persistence over time doing those activities that
result in changes in the brain. Feedback from the brain in the form of changes of
activity levels and/or normalization can confirm that, yes, those areas representing
the functions that were less active have now become more activated through rigorous
work by the subject. Importantly, it is not something that was done to them or
given to them. The engagement of the trainee is critical because challenge level
must remain elevated over time for the desired effects. Studies of brain activity
can confirm or disconfirm this in a way that is fairly unusual among psychological
interventions. In short, it holds the possibility of suggesting that computerized
cognitive training can change how your brain functions.
5.9 Brain Imaging & Cogmed: Does It Answer or Evoke

Questions?
Is brain imaging valuable for understanding the effects of computerized cognitive

training? Does it help us to better understand the areas of the brain that may be
affected in children and adolescents with ADHD? Frith [41] and a number of other
fMRI researchers would argue in the affirmative. Is there any evidence that brain
development is different in children with ADHD? Again, Frith, Fassbender, [42]
Cortese [43], McCarthy [44] Valera [45] and other researchers discuss this issue.
These studies include the use of structural equation modeling to estimate genetic
and environmental components relating to regional brain volumes measured by MRI
in normal children and those with the diagnosis of ADHD. From this collection
of studies, it is clear that brain imaging is helping to identify differences in brain
morphology and functioning among those with ADHD.
In a meta-analysis of atypical brain morphology in children with ADHD, Valera
and colleagues found a difference in the corpus callosum [45]. This is a brain
structure that connects the two hemispheres of the brain, permitting communication
between them. In a study done by Hutchinson et al. [46] in 2008 of ADHD
children with co-morbid disabilities, they also explored gender differences and
found the splenium of the corpus callosum was smaller in children diagnosed with
ADHD compared to healthy controls, especially in girls with ADHD. The splenium
connects the posterior parts of the brain, predominantly the parietal and temporal
lobes of the brain; it also links the visual cortex of both occipital lobes. Boys also
had a smaller rostral body than normal children. One might consider the brains
of those with ADHD as having less well developed connections between the two
hemispheres of the brain.
Ellison-Wright et al. [47] found what may be a marker for fronto-striatal circuits
mediating cognitive control. As is well known cognitive control is a primary deficit
in ADHD. Also, improvements in WM may be linked to the maturation of white and
grey matter in the fronto-parietal network according to Darki and Klingberg [48].
In a comprehensive meta-analysis of 55 task-based functional MRI studies
of attention deficit hyperactivity disorder relative to a comparison sample, by
Cortese et al. [43], it was discovered that in children, hypoactivation in ADHD
was observed mostly in systems involved in executive function (fronto-parietal

network) and attention (ventral attentional network). Significant hyperactivation in
ADHD relative to comparison subjects was observed predominantly in the default,
ventral attention, and somato-motor networks. Somatosensory and motor networks
are correlated with planned activity or motor output in children. In adults, it was
different. ADHD-related hypoactivation was predominant in the fronto-parietal
system, while ADHD-related hyperactivation was present in the visual, dorsal
attention, and default networks. Implications of these changes are a combination
of less hyperactivity, but also less efficient problem solving among adults with
ADHD. Subjects focused on different features and as a result different brain
structures were recruited to problem solve. Significant ADHD-related dysfunction
was detected even in the absence of co-morbid mental disorders or a history of
stimulant treatment [43].
One of the most consistent findings exploring the neurobiological underpinnings
of ADHD is the lack of activity in the left middle frontal gyrus, (McCarthy, 44).
One would expect that working memory training likely would result in an increase
of activity in the left middle frontal gyrus. There is also evidence of differences in
cortical thinning, blood flow, and electrical activity [48]. Differences in IQ could be
related to right caudate activity [44]. The caudate may be more involved in implicit
learning [48]. A lack of specialization is noted in ADHD patients compared to
controls, in a number of studies, as the left middle frontal area is more active in
normals than patients with a diagnosis of ADHD, [42, 49–52].
The most consistent deficits in ADHD patients relative to controls in the study by
Hart, Radua, Mataix-Cols, and Rubia [53] were reduced activation in typical areas
of timing such as left inferior prefrontal cortex (IFC)/insula, cerebellum, and left
inferior parietal lobe. The findings of left fronto-parieto-cerebellar deficits during
timing functions contrast with well documented right fronto-striatal dysfunctions
for inhibitory and attention functions, suggesting cognitive domain-specific neuro-
functional deficits in ADHD [53]. “The striatum may play a role in predicting
improvement later” (48, p. 7).
In studies of WM capacity it is argued that WM activity is localized to the
intraparietal sulcus, the dorsolateral prefrontal cortex, and the superior frontal
sulcus [32]. These are the areas identified on some fMRI studies with ADHD
children as less mature. With working memory training one would expect an
increase in activation in these areas, and potentially increased activation of the
superior longitudinal fasciculus, the fiber tract or pathway connecting the parietal
and pre-frontal lobes. This can be found in Diffusion Tensor Imaging [48].
5.10 Healthy Young Adults, Cogmed & Neuroplasticity
Research of the effects of Cogmed has included studies of functional resonance

imaging (fMRI) that can begin to respond to some of the core questions about the
effects of this training. Olesen et al. [40] measured the amount of brain activity
of healthy adults with functional resonance imaging (fMRI) before, during and
following Cogmed training. Intriguingly post training activity in the middle frontal
gyrus and superior and inferior parietal cortices increased. This was interpreted
to be an indication that training resulted in changes in the neural system that
undergird working memory. In fact, these changes were phrased as “training-
induced plasticity” as if to say the training resulted in this plasticity [40]. This study
was conducted on three adults in their early 20s.
Similar to Olesen et al. [40] study Westerberg and Klingberg [39] studied the
effects of training in three young healthy adults. Also, like Olesen et al. [40]
Westerberg and Klingberg found a change in the middle frontal gyrus, but also in
the inferior frontal gyrus indicating WM-related brain activity [39]. Interestingly,
the training for these young adults generalized to both a non-trained WM task,
but also to a reasoning task [39]. What is intriguing to consider is whether that
generalization may be evidence of their existing neuroplasticity given their age and
health or whether one can attribute it all to the cognitive training or possibly an
interaction of the two? In fact, Westerberg and Klingberg [39] assert that the changes
in brain activity were due to Cogmed training and that they were “best described
by small increases in the extent of the area of activated cortex”. Furthermore, they
explain that this is similar to what they call the “changes in the functional map
observed in primate studies of skill learning, although the physiological effect in
WM training is located in the prefrontal association cortex.”
McNab et al. [54] in another study of healthy adults considered the biological
foundation of working memory the density of cortical dopamine D1 receptors. What
they found in 13 healthy adult males ages 20–28 was that after doing Cogmed
that they showed a significant improvement in visual spatial working memory and
that there was a change of density of dopamine D1 receptors in their brains. This
change occurred in both the prefrontal and parietal areas of the brain. This shows
a captivating interaction between the rigorous activity of working memory training
and brain chemistry. More specifically change in WM capacity were found to be
related to changes in the binding potential of D1 receptors: “larger decreases in
D1 BP (binding potential) being associated with larger improvements in WM.” [54]
This is particularly potent since on a biological level changes in behavioral capacity
are confirmed or undergirded. Again, the tacit assumption is that this would seem to
increase the likelihood of sustained change.
5.11 Older Healthy Brains, Cogmed & Neuroplasticity
It does appear that younger brains show greater plasticity however, older more
mature brains can still show signs of plasticity according to Brehmer, Westerberg,
and Bellander, [55]. In studies of an older patient population brain oxygen levels
(BOLD) changed as a result of activation in specific areas due to cognitive training.
Strikingly, the magnitude of cortical change in this case was also directly related
to the amount of gains in scores within the training program [56]. This suggests
further validation of the notion that the more you improve in the training within the
program the more your brain changes. One further wonders whether this finding
may have relevance for the degree of generalization of improved function related
to the amount of improvement within the training itself. Brehmer et al. [56] found
that there was a decrease in brain activity that was larger in the group with adaptive
Cogmed than controls. They interpreted this finding to mean that the intervention
resulted in “increases in neural efficiency” [56].
5.12 Cogmed Improves Working Memory?
In the domain of Cogmed working memory training the amount of peer-reviewed

published research has substantial momentum with 50 peer-reviewed studies pub-
lished from 2002 to 2014. Presently there are over 80 ongoing studies of Cogmed
underway throughout the world. Yet, at the core of reviews of this research is the
simple question: Does Cogmed improve working memory? For the purposes of this
chapter we will divide research based upon age groups broadly. In particular, studies
of brain plasticity have been primarily done with populations of typically developing
adults. We will start with Preschool children. Keep in mind that an actual meta-
analysis of the research literature on Cogmed is beyond the scope of the present
chapter however there are thought-provoking observations which can be made and
trends that can be highlighted in this body of work. We invite the reader to return to
some of the source documents mentioned herein to pursue the present discussion in
the literature on Cogmed.
In the domain of Cogmed working memory training research it is not a
particularly controversial idea that one might improve upon tasks that are similar to
the originally trained tasks. The controversy lies in two issues. The first is whether
in fact it is working memory capacity that has improved or simply the more effective
deployment of strategic approaches to the present tasks. The second more pragmatic
question is whether such gains might be generalized. There appears to be less debate
about whether subjects improve upon visual spatial working memory tasks after
doing Cogmed. For example, as noted in Shinaver, Entwistle, and Soderqvist in 2014
[57] even some of the most enthusiastic critics of Cogmed have acknowledged that
working memory training has resulted in reliable changes in what is termed working
memory skills by Melby-Lervåg and Hulme [58]. Note the careful choice of words
‘working memory skills’ – not working memory capacity. Similarly, another group
of Cogmed critics Shipstead et al. [59] admit that this approach to training does
show evidence of improving what they call ‘attentional stamina’. Others might call
this construct ‘sustained attention’. However, our focus in this work is particularly
on the clinicians, the patients, their families and those who care for them. Certainly
for a clinicians, parents, teachers, and children the ability to increase the amount
of time on task is salient to school functioning and academic achievement as well
as having implications socially and in the workforce. This is not a small gain on
a practical level. The question of whether a child, after doing Cogmed is simply
better at deploying WM strategies or has increased his WM capacity doesn’t look

much different from the point of view of the patient. Either way the patient will
function more effectively. Furthermore, can the change in brain activity indicate
whether it was actually increased capacity or more effective strategy use? It doesn’t
appear so. In fact, if strategy deployment can be facilitated through training and it
indeed generalizes does it matter for the client? How this debate moves from the
theoretical to the practical is unclear. For our purposes, when measures show after
Cogmed training a gain in how much a person can hold in their working memory,
we will consider it a gain in capacity.
For the distinction between improved strategy usage versus increased working
memory capacity to achieve substantially greater clarity will require more refined
research and likely a large amount of it. On a practical level this debate is of
more limited value although it may suggest the explicit addition of strategy focused
training in addition to this massed practice method.
Although this source is not a meta-analysis of gains in working memory
following Cogmed, it is a table of recorded effect sizes of peer-reviewed published
studies of Cogmed of improvements in VSWM that can be found within the Cogmed
website www.cogmed.com/research on a document called “Cogmed Claims and
Evidence” [60] published online by Ralph, on page 18–19, Table 2 [60]. We suggest
the reader scrutinize that document. The individual effect sizes are all the results
of peer-reviewed published studies and that document provides an easily accessible
way to review them. This data gives one reference points to consider in light of
the aforementioned discussion of computerized cognitive training. As can be seen
in that document, the effect sizes for visual spatial working memory (VSWM)
primarily range from moderate to large for both children and adults ranging from
a post-test effect range from .61 to 2.29 with the exception of the first study by
Klingberg et al. in 2002 which found an effect size of .13 [16]. This includes 13
studies with 9 of children and 5 of adults [60]. Furthermore, two studies of children
did have follow-up re-assessments. One such study reported an effect size of .81
[17] and the other was .65 by Dahlin [61]. There were also two follow-up studies of
adults and those effect sizes were 1.36 for 20–30 year olds and 1.65 for 60–70 year
olds [55].
Similar data on verbal working memory (VWM) can be reviewed at the Cogmed
website www.cogmed.com/research in the same document in the same table of the
“Cogmed Claims and Evidence” published online by Ralph, on page 18–19, Table 2
[60]. As is expected the effect sizes for verbal working memory (VWM) are smaller
than those for VSWM given the emphasis of VSWM with the Cogmed training
program. Yet, these effect sizes are also moderate to large. The range was from
.02 by Bergman Nutley et al. [62] to 1.07. An average effect size (uncorrected for
sample size) was .69 (seven studies) for verbal working memory at the conclusion of
the program and at follow up one study found it was .62 reported by Klingberg et al.
[17] and .50 by Dahlin [61]. For adults, the range was from .33 to 2.21 (five studies).
For adults the gain at follow-up was recorded at 1.04 for typically developing 20–30
year olds and .08 for 60–70 year olds [55]. So, while these effect sizes for VWM
are less than the effect sizes for VSWM they are still significant and at a moderate
effect size that stands up well to other domains of computerized cognitive training
stated in the literature on schizophrenia and TBI reviewed here previously. Again,
one is invited to do one’s own analysis of this data at length as is desired.
Similarly, Ralph notes in the Cogmed claims and evidence [60] that studies
that do involve a follow up assessment that gains in working memory have been
sustained from 2 months to 1 year. Presently 10 studies have had 2–5 month follow
ups, while nine studies have had 6–8 month follow ups and three studies have had
a follow up of 12 months [33]. One might conceptualize this in vernacular terms in
this way, if a student does Cogmed the benefits are likely to continue for a whole
school year. Similarly, an adult who completes Cogmed might expect gains to persist
for a full year of employment. Hence the claim in Cogmed Claims and Evidence
states: “Gains in WM and behavioral outcomes are sustained over the long term.”
Given evidence of gains in WM this claim appears substantiated.
5.13 Cogmed, Preschool Children & Generalization
Consider Table 5.1, of Cogmed & Preschool Children, which captures data from
five studies.
One point to note with Cogmed JM for preschoolers is that all of the tasks
are visual spatial working memory (VSWM), whereas in the school-aged version
of Cogmed (RM) and the adolescent/adult version of Cogmed (QM) there is a
mix of VSWM tasks with verbal working memory tasks (VWM). In the study by
Table 5.1 Cogmed & preschool children

Study Study design Sample Measures
Bergman Nutley et al. RCT-double-blinded, Typical VSWM-complex
(2010) [62] Pseudo-randomized, pre-schoolers memory span task
(stratified for sex) (odd one out)
Thorell et al. [63] RCT- double-blinded Typical Attention – Auditory
pre-schoolers CPT
Grunewaldt et al. [64] Stepped wedge Very Low Birth Auditory attention,
design (waitlist Weight (VLBW) phonological
control) pre-schoolers awareness, visual and
verbal memory &
sentence repetition.
Foy and Mann [67] Randomized, waitlist Economically VWM, executive
control disadvantaged control- Heads-Toes-
typical preschoolers Knees-Shoulders
(HTKS)
Söderqvist et al. [68] Pseudo-randomized, Typical WM, fluid
(stratified for sex) pre-schoolers intelligence, variation
in DAT1 influenced
improvement.
Here we refer the reader to a resource on the Cogmed website.
Bergman Nutley et al. [62] improvement was found on non-trained tasks of VSWM
and thereby generalization of Cogmed. This gain was interpreted by the authors
as not simply a reflection of the use of better strategy but by an “enhancement of
underlying ability” [62]. However, Thorell et al. in 2009 [63] did find generalization
on an auditory CPT measure of attention in this RCT double blinded study [35].
Similarly, in the study by Grunewaldt et al. in 2013 [64] auditory attention was
also found to have been improved on a wait list control designed study of very
low birth weight preschoolers [64]. Yet, Grunewaldt et al. also found that children
improved upon phonological awareness, visual memory (memory for faces), and
verbal memory – described as narrative memory and sentence repetition [64].
Obviously these findings are quite important as they relate to the acquisition of
language and reading comprehension. This data is important preliminary results in
that sentence repetition has been found to be associated with language skills while
verbal complex memory has been associated with reading skill for children with
learning difficulties by Alloway et al. in 2005 [65]. Similarly, phonological loop
has been found to be correlated to vocabulary knowledge [20, 66]. Furthermore,
these findings appear to be extended by Foy and Mann in 2014 who described
improvements in VWM as a far transfer which is plausible because Cogmed for
preschoolers is strictly comprised of VSWM tasks [67]. Foy and Mann also reported
a significant improvement of executive control [67]. In total the impact across these
studies for preschoolers suggests exciting potential.
Söderqvist et al. [68] reports rather interesting data regarding preschoolers
variation in the dopamine transporter gene (DAT1) and improvements in WM and
fluid intelligence following Cogmed in preschool children [68]. Söderqvist et al.
found that genetic polymorphism of the DAT1 gene were related to the effects of
training [68]. They noted that the single nucleotide polymorphism (SNP) rs27072
which they note has been indicated in genetic studies of ADHD as related to a higher
risk for ADHD. They also highlight that the same allele which showed association
with training gains has also been reported as having protective effect against ADHD
[68]. This is rather intriguing in that it affords an explanation on a genetic level for
why some may show either more or less benefit from training with Cogmed than
others. It allows for a distinct interpretation for why transfer effects may be harder
to achieve with some subjects.
5.14 Cogmed & Typically Developing Adults
We will only consider a couple of studies of typically developing adults with regard
to the generalization of effects of Cogmed. A finding of interest by Bellander et al.
in 2011 was of another single nucleotide polymorphism (SNP)(rs4657412) found
to be associated with greater gains in verbal working memory after Cogmed [69].
Similar to the finding of Söderqvist et al. 2011, [9] this result suggests a potential
limiting factor or beneficial factor affecting the generalization of the training effects
of Cogmed [60]. The subject sample in this study was 29 healthy subjects between
ages 20–31 years of age. However, these were the same subjects who participated
in the Brehmer et al. 2009 study [70].
In a study of healthy older adults with an average of 63.7 years Brehmer et al. [56]
found that after Cogmed these adults also had made significant gains on attention
and episodic memory compared to controls [56]. Unusually these improvements
in performance were associated with fMRI evidence of a decrease of neocortical
brain activity interpreted as an increase of neuronal efficiency that were interpreted
as intervention-related [56]. Also, neocortical decreases and subcortical increases in
activity were associated with the maximum gain score achieved during training. This
was interpreted as being functionally related to one another. In this case gains within
the training program were salient and related to these changes of activity suggesting
the possibility that effort extended in training can make a difference in terms of
outcome. The importance of gains within the program correlating to generalization
will be discussed in other studies below.
In a study of younger (20–30 years old) and older (60–70 years old) adults who
completed Cogmed, gains in sustained attention and a reduction of self-report of
cognitive failure was reported [55]. The subjects in this study were subdivided into
two segments of each age group and randomized to either adaptive Cogmed training
or non-adaptive training. This study found that both gains acquired through training
and transfer effects were a bit greater for younger than for older adults [55]. This
might be considered a limit of plasticity based upon age whereas previously we have
noted limits based upon genetic factors. Given the greater rigor of the methodology
of this study it also provides data related to the generalization of training effects to
increased sustained attention and reduced self-reported cognitive failure [55].
To summarize the data on healthy adults who have trained with Cogmed provides
an additional possible limit of neuro-plasticity and delineation of transfer effects.
The additional limit of neuroplasticity in this case is age, but what is interesting is
that two different groups of older adults in their 60s do show neuro-plasticity with
accompanying changes in brain activation that were associated with transfer effects.
Two different studies that were controlled and randomized found improvements in
sustained attention (PASAT – auditory attention) [55] and are submitted as transfer
effects. Additionally, one study found an improvement in episodic memory [56]
while another found a reduction of self-reported cognitive failure [56].
When the data on predominantly healthy preschoolers (including one study of
very low birth weight children) and healthy adults are considered in conjunction
there are a number of studies that are well designed and that show generaliza-
tion. Interestingly, auditory attention was a transferred effect across both healthy
preschoolers and among healthy adults. This area was found to have improved in an
RCT-double blinded study by Thorell et al. [63] and in a stepped wedge designed
(waitlist control) study by Grunewaldt et al. [64]. Whereas among randomized and
controlled studies of healthy adults two different groups of adults in their 60s and
a group of adults between ages 20 and 30 showed improved auditory attention
[56]. On the basis of this data one would expect other patient samples to show
improvements in auditory attention, but certain limits may apply. In the case of
healthy subjects genetic factors for both preschoolers [68] and adults were found
[39]. In the case of adults age was a factor which suggested more limited neuro-
plasticity. With sample populations of subjects of diagnosable disorders one would
expect to also find limiting factors, one of which may be the severity of the disorder
while another could be the number of co-morbid disorders among subjects. In
short, one expects there to be factors which limit transfer effects. However, as is
seen among predominantly healthy preschoolers and adults Cogmed has shown to
have resulted in transferred effects in well-designed studies. While in the case of
preschoolers there have not been a sufficient number of studies that have used the
same outcome measures to establish a stronger empirical trend. The same could
be argued with healthy adults in these studies. Yet, the fact that two studies of
preschoolers and two studies of healthy adults all found a transferred effect of
improved auditory attention suggests this as a generalization of Cogmed.
5.15 Cogmed & Working Memory Deficit Children
It is important to acknowledge that studies that do not include control groups are still
rather important in the context of an innovation as data is accumulated to determine
its utility. However, in the present chapter there is not ample space to consider
all of those studies. Yet, one such study will be considered due to it particular
salience. With the noted exception of the Holmes et al. 2010 study [71] which
was a single group with ADHD we will leave out a study by Mezzacappa et al.
2010 [72] which was a pilot of a single group of low income minority children
with ADHD due to concerns about inflated effect sizes with single group designs
[72]. A study conducted by Holmes et al. in 2010 with ADHD children [71] will
be reviewed briefly. The key point of this study was the finding that stimulant
medication combined with Cogmed resulted in an additive increase of VSWM.
More specifically while VWM and verbal short term memory (VST) and visual
spatial short term memory (VSST) were all unchanged on the automated working
memory assessment (Holmes et al. 2010) when stimulant medication was given
to ADHD children VSWM increased. Additionally, at the conclusion of doing
Cogmed training these ADHD children were significantly improved in all four of
those areas, VWM, VSWM, VST and VSST. That is, they made an additional
significant gain in VSWM combined with the original significant gain after taking
stimulant medication. This is important because it poses the possibility that there
could be additive effects when one combines stimulant medication with Cogmed
with ADHD children and possibly other populations. This is not unlike the finding
that with schizophrenic patients that cognitive remediation along with psychiatric
rehabilitation resulted in larger effect sizes. One might argue that this is consistent
with the notion of clinically “stabilizing” a patient before doing Cogmed. This data
provides preliminary support for the hypothesis that medication may provide an
additive effect to Cogmed.
A working hypothesis in this chapter is the notion that there are limiting factors
to neuroplasticity. As we have discussed previously there is some data that supports
both genetic factors and age may play such a role in the treatment gains seen in
Cogmed. We propose that severity of disorder and the amount of co-morbidity
among subjects are possibly other such ‘limiting’ factors. In the case of ADHD
severity of disorder may in fact overlap with the amount of co-morbidity.
Consider data from a meta-analysis by Willcutt et al. [73] that differentiates the
frequency of co-morbid disorders from both ADHD – Combined type (ADHD-
C) and ADHD-Inattentive type (ADHD-I). This data supports the notion that
ADHD-C is a more severe disorder in several areas of co-morbidity in which
there was significantly greater frequency of the following disorders: Oppositional
Defiant Disorder (ODD), conduct disorder (CD), Seasonal Affective Disorder
(SAD), Bipolar disorder, and Tic Disorders. Interestingly the ratio of Oppositional
Defiant Disorder (ODD) from the Willcutt et al. (2012) [73] meta-analysis data was
essentially (2:1) for ADHD-C to ADHD-I, while the ratio for CD was about (3:1)
between those two groups [74]. With ODD the estimated percentage of ADHD-C
cases was 51.8 % while with ADHD-I it was only 24.9 %. Similarly with CD it
was 21.6 % with ADHD-C while it was only 7.1 % with ADHD-I. With SAD it
was 13.5 % with ADHD-C and 8.7 % with ADHD-I. With Bipolar disorder it was
6.9 % for ADHD-C and 3.2 % with ADHD-I. And finally, with tic disorders the
frequency of those with ADHD-C and tic disorders was 15.8 % and 12.1 % with
ADHD-I. As can be seen by these different frequency rates internalizing disorders
were less skewed but they were still higher among ADHD-C as were the more severe
externalizing disorders to an even greater extent.
There was an exception in which learning disabilities were more frequently
comorbid with ADHD-I than with ADHD-C. This is particularly noteworthy in that
a computerized cognitive training program that focuses upon improving working
memory and through near transfer is expected to improve sustained attention would
appear to have a more targeted impact upon these problems. This suggests such an
intervention might be a more efficient strategy with this group. Learning disabilities
were found among 29.1 % of those with ADHD-I versus 24.2 % of those with
ADHD-C. Additionally, speech and language problems were found more often
among 17.8 % of those with ADHD-I than those with ADHD-C 14.8 %, but the
difference was not significant.
The data are illuminating, suggesting increased risk overall for those with
ADHD-C than ADHD-I for serious behavioral and social problems whereas
although ADHD-C children are at risk for learning disabilities the frequency of
learning disabilities is even greater among those with ADHD-I. While in the areas
of generalized anxiety disorder and major depressive disorder the groups were not
significantly different. Additionally, given the role of excessive activity or the lack
of inhibition of action plays in externalizing disorders one wonders whether distinct
interventions are merited for these different aspects of functioning. That is, one type
of intervention for inattention and learning disabilities and a distinct intervention for
overactivity may be needed.
However, it may not only be the case that severity of disorder is greater based
solely upon the amount of co-morbidity for ADHD-C compared to ADHD-I, but
there is some evidence that this is the case across neuro-cognitive domains. Nikolas
and Nigg [74] found that ADHD-C children were worse on all neurocognitive
domains measured in their study of 498 youth ages 6–17. The study included
213 in the control group, 107 ADHD-I and 137 ADHD-C. The cognitive domains
measured included: “cognitive control (executive functions, working memory, and
memory span), arousal, and response variability” [74]. Nikolas and Nigg (2013)
[74] noted all of those areas provided uniquely incremental prediction of symptom
dimensions and subtype presentation. In contrast, temporal information processing
and processing speed did not do so [74]. Certainly this is only put forth as a
hypothesis and the notion that ADHD-C is a more severe disorder than ADHD-I
appears to be a fairly recent proposition. Certainly, one would require further data
to confirm it, but we believe it is a useful framework from which to consider the
transferred effects of Cogmed.
With reference to Cogmed research, consider Table 5.2 which includes the
treatment sample co-morbidity of various studies and whether the children are
working memory deficit, ADHD-I, ADHD-C or ADHD predominantly hyperactive
(ADHD-HI). One way to think about the structure of this table is that it moves from
the lowest level of disorder/disability to the highest from top to bottom. Yet, this is
an inexact table due to the inconsistent reporting of data on co-morbidity in these
published studies. However, although this table is inexact bear in mind our interest
in delving this literature is clinical and applied in nature. In many respects analyzing
this data conceptually from this vantage point provides a useful heuristic with which
to inform both clinical applications and future research.
What is rather complicated about the Cogmed studies in Table 5.2 is that many
of these studies do not report co-morbidity and often in the published papers they do
not delineate whether a subject was diagnosed with ADHD-C, ADHD-I or ADHD-
Hyperactive/impulsive (ADHD-HI). As such, one has to make some inferences
about the ways in which samples were captured. As a result these two hypotheses
about severity of disorder and amount of co-morbidity can only be posed rather
tentatively. The data reviewed herein will neither confirm nor disconfirm them. Only
further research can do that. However, we can point to the trends in the data which
can inform both clinical practice and additional research.
5.16 Cogmed with Children with Working Memory Deficits
Neither Holmes et al. [75] nor Dunning et al. [76] note any additional co-morbidity
in their published studies. Neither did they make note that any of these children
were being medicated. They were selected based upon scoring low on a working
memory test administered routinely in England in the case of Holmes et al. 2009.
In the study by Dunning et al. [76] children were selected between ages of 7–9 on
the basis of having a working memory deficit as determined by how they scored on
two tests of the Automated Working Memory Assessment. As such, unlike ADHD
which typically requires the endorsing of several symptoms and evidence of the
Table 5.2 Co-Morbidity with Cogmed training with children with working memory deficits,
ADHD-I, ADHD-C, learning problems & learning disabilities
ADHD-I
WM attention
Study deficit problems ADHD-C ADHD-HI Rx% LD ODD/CD
Holmes et al. [75] 100 % NRa NR NR NR NR NR
Dunning et al. [76] 100 % NR NR NR NR NR NR
Dahlin [61] NR 33 % diag. NR NR NR 9.5 %c 0%
60 % rated
inatt.b
Dahlin (2013) [77] – 100 % 22 % NR NR 22 % 0%
Klingberg et al. [16] – NR 100 %? NR 43 % NR NR
Klingberg et al. [17] – 25 % 75 % 0% 0% NR 0%
Hovik et al. [80]d – 0% 100 % 0% 69.6 % NR NA/0 %
Green et al. [79] – 42 % 42 % 17 % 67 % 0% NR
van – 7.7 % 80.8 % 11.5 % 0% NR 3.8 %/0 %
Dongen-Boomsma
et al. [82]
Beck et al. [83] NA 71 % 29 % NR 61 % NR 46 %
Chacko et al. – 34 % 66 % 0% 27 % NR 50 %/9 %
(2014) [84]
Gropper et al. [87] – 51 %e NR NR 26 % 57 % NR
Gray et al. [85] – NR 100 % NR 98 % 100 % 100 %/0 %
Severe
a
NR Not reported
b
Dahlin [61] note 33 % were diagnosed with ADHD while more than 60 % were rated as inattentive
by teachers. They only focus on the issue of inattention in this paper so they are presumed ADHD-I
c
Dahlin (2013) [77] dissertation notes the number of subjects with Dyslexia that was in the study
published in 2010 was actually a small number, but all the children were considered to have
‘general learning problems’
d
Hovik et al. [80] and Egeland et al. [81] were composed of the same subjects
e
Gropper et al. [87] did not report whether the subjects in their study were ADHD-C or ADHD-I.
They simply reported that 42 % were diagnosed with ADHD and an additional 9 % were both
ADHD/LD
disorder in two settings, this appears to be a lower level of dysfunction. Screening

for a working memory deficit does not require this level of scrutiny or dysfunction.
As such, one presumes that more co-morbidity is not noted because it is likely not
there. It can persuasively be argued that the level of disability and/or disorder among
these groups is relatively mild – particularly compared to the population of ADHD-
C as noted by Willcutt et al. [73]. These two studies reported transferred effects or
generalization. Improvement in mathematical reasoning with an effect size of .49
was reported by Holmes et al. [75] after 6 months. Holmes et al. 2009 also reported
an improvement in following instructions with an effect size at the conclusion of
the program which was .83 but was maintained at .52 after 6 months had passed
since the completion of treatment. Furthermore, the gains in mathematics reasoning

were not seen at the conclusion of treatment but 6 months later [75]. The gain in
following instructions suggests a possible mechanism by which the mathematics
reasoning was improved.
Dunning et al. [76] reported significant training effects for VSWM, VWM and
visuo-spatial short term memory along with significant improvements in sentence
counting (a processing task) and written expression at the conclusion of treatment.
Then 12 months after the conclusion of treatment VWM was still significantly
greater for the treatment group compared to the control group along with the
processing component of the sentence counting task.
This second study by Dunning et al. 2013 might be considered a confirmatory
RCT in relation to the original study by Holmes et al. 2009 and was conducted by
the same group of researchers which are independent of Cogmed. Additionally, this
transferred effect of VWM maintained at 12 months is important given the verbally
dominated environment of school classrooms [76].
Dunning et al. [76] argued for the need of “scaffolding” or systematically
layering in additional training or teaching following Cogmed to ensure more
generalization of a new skill set [76]. This is a concept with which the present
authors wholeheartedly agree. In fact, capturing and utilizing the gains in VSWM,
VWM and/or attention made initially with a more systematic complement to
training with scaffolding of skills building seems to be both more realistic and more
likely to optimize the effects of training. However, there were still transferred effects
reported in both of these studies while enhancing them is certainly the next step.
5.17 Cogmed with Children with Inattention and Learning

Difficulties
Two studies by Dahlin focused upon children with inattention and learning diffi-
culties found important transferred effects. In the 2010 Dahlin study [61] children
showed significant improvement in reading comprehension with an effect size of .91
at 6 months post intervention. While in the Dahlin et al. [77] study the boys showed
improvements on the Basic Number Screening test (BNST) and in addition. With
addition the effect size initially was .59 while at 6–7 months post intervention it was
.33 Dahlin et al. (2013) [77]. With BNST the initial effect size was .74 and at 6–7
months it was .90. The BNST is a screening test with a focus on number concept
and number operation items. No time limit is involved in this test.
The far transfer of these studies by Dahlin is engaging, but sorting out the
samples of subjects is rather complicated. Dahlin [61] included a mix of subjects
with “special needs” all of whom were described as having “attention issues”, but
are not all diagnosed with ADHD. Among the subjects 33 % were diagnosed with
ADHD while an additional ‘more than 60 %’ were described as rated as inattentive
by teachers. As noted before, this a less severe standard than the diagnosis of
ADHD. As such, this group is considered less severe than a group in which all
or the majority have been diagnosed of ADHD.
Also, these subjects were reported as having been diagnosed with ‘general learn-
ing problems’, but none in this document were specifically listed as having dyslexia.
In fact, the Dahlin [78] dissertation notes the number of subjects with Dyslexia
that was in the study published in 2010 was actually a small number (9.5 %),
but all the children were considered to have ‘general learning problems.’ Again,
arguably this is a less stringent standard than those diagnosed with specific learning
disabilities. This Dahlin [61] study used a pretest-program-posttest-retention test
design. However, they did use a control group in this study that received ‘ordinary
special education’ within small groups. Yet, the subjects were not randomized which
limits the generalizability of this study. However, gains in reading comprehension
were maintained at 36 months [78], gains in addition and BNST are all noteworthy
generalizations of Cogmed. These effect sizes range from moderate to large and all
meet the established level of “efficaciousness” noted previously.
5.18 Cogmed with ADHD-C Without Co-morbidity
In the 2005 Klingberg et al. [17] study in the treatment group that was used for
analysis there were 20 children and of those 15 were ADHD-C and 5 were ADHD-I.
However, this was an unusual sample of ADHD-C children due to the comparative
lack of co-morbidity and that the children were not being treated with medication.
This suggests the possibility that this was a less severe group of ADHD-C children.
One might infer this partly due to the fact that they were not medicated although
there could be other factors at play like the beliefs of the parents. Secondly, ADHD-
C has been found to have higher co-morbidity than ADHD-I, yet this group did not
have comorbidity. Having ODD was one of the exclusion criteria. Note Cogmed is
not recommended for those with ODD or CD. In the original Klingberg et al. 2002
study three subjects in the treatment group were on medication [16].
In the case of the 2002 Klingberg et al. [16] study it is not clearly reported within
the text of the published paper that all of those subjects were ADHD-C. However,
since in the discussion of the paper it was noted that the training on WM tasks
significantly reduced the number of head movements and that improvements in WM
was shown to correlate with a reduction in movement this revealed a clue as to how
cognitive deficits might be related to impulsivity. In our table we are assuming that
all the children in that study were ADHD-C.
From the Klingberg et al. 2002 study transferred effects of Cogmed were
improvements on VSWM, Stroop test (an inhibition task), Ravens progressive
matrices, a fluid reasoning task and a reduction of head movements indicative
of a reduction in hyperactivity. The 2005 Klingberg et al. study replicated the
finding of improved VSWM and added a measure of VWM which was improved and
maintained at a follow-up of 3 months. However, the finding of improved inhibition
(Stroop) was not replicated, nor was the reduction in movement captured by an
infrared camera. The finding of improvement on the Raven’s was replicated.
Keep in mind that this was, a comparatively mild sample of ADHD-C with no
Comorbidity. Additionally, in the 2002 study, 43 % of the students were medicated.
In hindsight one wonders whether medication may have played an ‘additive’ role in
conjunction with Cogmed in the finding of reduced head movements and improved
inhibition.
5.19 Cogmed with ADHD-C School-Age Children

with a Majority Medicated
First both the study by Green et al. [79] and Hovik et al. [80] are RCT’s. One
can draw more definitive conclusions from their results. Green et al. 2012, in their
description of their study explains that their sample did not have a lot of comorbidity
or oppositional behavior. As is seen in Table 5.2 previously the sample in Green et al.
2012 included 42 % ADHD-I, 42 % ADHD-C and 17 % ADHD-HI. Also, 67 % of
the Green et al. sample was medicated. The Hovik et al. 2013 study included 100 %
ADHD-C with 69 % medicated. The averages of both studies were similar with
those in Green’s treatment group at an average age of 9.9 years old and those in
Hovik et al. 2013 study with an average of the treatment group was 10.5 years old.
In the Hovik et al. [80] CD was one of the exclusion criteria. Comorbidity was not
reported in these studies. So, like the Klingberg, studies this appears to be a milder
ADHD-C group at least in terms of comorbidity.
Green et al. [79] found a reduction of off task behavior on the restricted academic
situations task (RAST). This is a structured task in which a child engages in
academic work and is videotaped through a one-way mirror to determine whether
she stays on task. The reduction in off task behavior coincided with improvements in
VWM. Similarly, Hovik et al. 2013 with this mild ADHD-C group with the majority
medicated also reported transfer effects of gains on all outcome measures (VWM,
VSWM & manipulation WM) while gains in the visual domains were greater than
the auditory. Six measures of these three areas of WM were made. At 8 months
these gains were maintained. So, again, gains between these two studies of VWM,
VSWM, manipulation WM and a reduction in off task behavior.
In addition to the near transfer effects noted by Hovik et al. 2013 a separate
publication by Egeland et al. [81] using the same subjects details the far transfer
effects. Egeland et al. 2013 did find far transfer effects in improvements in LOGOs
which was reading fluency, % correct, and Word decoding quality (% correct).
Yet, they did not find improvements on ADHD rating scales, the BRIEF (Behavior
Rating Inventory of Executive Function), or Strengths & Difficulties Questionnaire.
However, Egeland et al. 2013 did find a significant improvement on psychomotor
speed after Cogmed. The increases in reading scores remained 8 months after the
intervention. Interestingly, in contrast to what is posed here Egeland et al. 2013 in
their discussion considered the possibility that medication may have “exhausted the
possibility for further improvement” (81, p. 7). They did not find improvements on
the neuropsychological tests, but on the Conners’ continuous performance test they
did find the improvement of psychomotor speed. They did pose the explanation that
the lack of change on the Conners’ may have been because the majority of subjects
were in the normal range at pretest due to medication. However, these investigators
make a point which we think is critical in Cogmed studies moving forward, which
is that medication should be controlled in investigations.
5.20 Cogmed with a Majority of ADHD-C Preschoolers –

Un-medicated
In 2014 van-Dongen-Boomsma et al. [82], published a study of ADHD-C preschool-

ers who completed Cogmed and were not medicated. These were not a group with
high comorbidity yet there were 3.8 % in the treatment group who were diagnosed
with ODD. 80.8 % of the treatment group was ADHD-C, 7.7 % was ADHD-I and
11.5 % were ADHD-HI. Given the majority of ADHD-C on a pragmatic level one
wonders whether hyperactivity and impulsivity would interfere with training. The
average age of the children in the treatment group was 6.5 years old.
Importantly van-Dongen-Boomsma et al. did find for the active training group
that they made significant gains in VWM [82]. However, according to van-Dongen-
Boomsa et al. this gain did not survive their statistical correction for multiple
testing. This finding is still interesting, partly because these 6 year old children
were training with JM which is all VSWM. So, although the statistical correction
removed the significance of this finding it was a transfer of VSWM training to
VWM. This brings up another important pragmatic issue. In the applied context of
Cogmed the age ranges describing programs are not strictly applied. For example,
for some 6 year old children the JM version of Cogmed may be less rigorous than
it could or possibly should be. That is, there are fewer exercises and no VWM
tasks. This amounts to less time spent training also or a lower ‘dose’ of training.
So, in the applied setting of individual trainees doing Cogmed a child who is 6
years old may do JM, but it may be more appropriate for her to do RM instead.
This reality emphasizes the importance of a clinician or educator-mediated model
of delivery of this program. Clinicians and educators can more effectively make
this distinction. Deciding whether to use RM instead of JM is a rather important
decision because it could increase the intensity of the training and quite likely the
impact. Additionally, van-Dongen-Boomsma et al. study investigators found that
their index improvement significantly contributed to the ADHD-RS and Behavior
Rating Inventor of Executive Function scores both rated by the teacher, but revealed
no significant group differences. This further supports two issues. One is that
coaching that was not blinded (see discussion below) would have been more likely
to result in a greater index improvement and could have revealed significant group
differences. Secondly, greater time on task which would have been afforded by
Cogmed RM could have also tipped the scales in this area to greater transfer of
effects.
There was an unusual twist to the van-Dongen-Boomsma et al. study from 2014
which was it was ‘triple blind’. What this meant was that it was a RCT designed
study, but that children, parents, teachers, training coaches, and investigators were
blind to treatment assignment. Having ‘blinded’ coaches meant that coaching was
not based upon actual performance on the tasks of individual trainees. In our view
this is significantly different than how Cogmed is normally delivered. As noted
before training data is critical grist for the coaching process. Investigating actual
training sessions of individual trainees is a critical way in which a coach can
motivate a trainee. How the child or adult manages errors, what he was thinking, did
he have a strategy for managing himself if frustrations arose, etc. The actual experi-
ences of frustration in training which can be pinpointed in the data and discussed are
too necessary for the coaching process to be discarded. All of these issues are part
of the coaching process. With blinded coaches this meant that the support was more
general and generic and quite likely less effective. This is a substantial departure
from typical training. As was noted in our previous discussion of compliance the
motivational role of the coach in training is very important, not just for compliance,
but for keeping the trainee motivated to maintain a high challenge level in the
training through his or her training sessions. van-Dongen-Boomsma et al. highlight
other possible explanations for the lack of transfer in this study such as time effects
which were not controlled for due to a lack of a passive control group. From our
perspective of the pragmatic application of this program coaching with blinded
coaches is simply a different program. Furthermore, although the compliance in
this study was not sub-par, the fact that there was a significant difference between
completers versus non-completers independent of group assignment supports the
possibility that the level of hyperactivity and impulsivity may have interfered with
the treatment effectiveness such that comparing a group that was on medication
to one that was not would have allowed researchers to test whether there was an
additive effect of combining medication with Cogmed.
5.21 Cogmed with High Co-morbidity
This set of studies moves into groups with high co-morbidity, with Beck et al. [83]
and Chacko et al. [84]. Yet, if one accepts the notion that ADHD-C is a more
severe disorder than ADHD-I then the Chacko et al. 2014 study is composed of
more severely disordered children with 66 % of them being ADHD-C and 34 %
ADHD-I. In contrast, the Beck et al. 2010 study has a majority of ADHD-I trainees
at 71 % and only 29 % ADHD-C. See Table 5.2 previously. However, the Chacko
group is more severe also on the level of comorbidity with 50 % with Oppositional
Defiant Disorder (ODD) and 9 % with CD. Beck et al. 2010 had 0 % with CD
and 46 % with ODD. So, at each level the Chacko et al. 2014 group was more
severely disordered, but additionally only 27 % of the Chacko, et al, 2014 students
were medicated while 61 % of the students in the Beck study were medicated. One
would not be surprised if these differences affected the transfer effects of these two
studies and it appears that they did. However, other issues could be argued to be
contributing as well. Beck et al. 2010 used a wait list controlled design in which
raters were not blinded whereas Chacko et al. used an RCT design. However, as seen
in this comparison to simply dismiss significant findings by Beck in favor of those
by Chacko based solely on the designs of these studies appears disingenuous. Also,
importantly Beck’s study provided a 4 month follow up. Chacko did not provide any
follow up. This is an important oversight because other studies have found effects
to emerge post training.
Beck et al. 2012 found several transfer effects for Cogmed. On ADHD symp-
toms parents’ ratings of ADHD index, cognitive problems/inattention, DSMIV-TR
Inattentive Scale were all significantly reduced. At post-treatment Beck et al. 2012
found significant improvement on the BRIEF as rated by parents on Metacognition
Index, Working Memory, and Initiate all had effect sizes over .90 and Plan/Organize
with an effect size of .42. At post-treatment among teachers there was a trend
toward those in the treatment group as significantly worse on oppositional behavior.
One reason this is interesting is given the challenging nature of the program one
could imagine that those with a tendency to be oppositional and defiant could be
evoked. This is one plausible reason on a practical level that students with high
co-morbidity – especially those with ODD may have more difficulty finishing
this program or working hard in the face of challenge that is required to make
greater gains on index scores. Additionally, as noted previously Cogmed is not
recommended for those with ODD or CD for this very reason.
Similarly, Beck et al. 2012 found that at a 4 month follow up that these gains were
maintained with the addition of some other improvements: metacognition Index
(ES D .83), WM (ES D .94), Initiate (ES D .76), Monitor (ES D .42), Organization
of Materials (ES D .43) and Plan/organize (ES D .72). Another change emerged
at 4 months which was an improvement on the teacher rating of the BRIEF on
Initiate (ES D .25). At follow up parents rating of the ADHD Index was significantly
reduced, cognitive problems/inattention, hyperactivity, oppositional behavior and
the DSM-IV inattention scale all made significant improvements. Teacher ratings
did not.
In contrast to the findings by Beck et al. 2012, Chacko found that the treatment
group did show significant improvements in verbal and nonverbal WM storage, but
not in WM storage plus manipulation or processing. Chacko et al. 2014 conclusion
is particularly critical and appears to base the forcefulness of the conclusion upon
the fact that this was an RCT. Yet, as the aforementioned analysis shows there is
much more going on here than simply a rigorous comparison. As has been noted
before other Cogmed studies have used an RCT design and found significant transfer
effects [79, 81, 17] with ADHD samples. It might be more apt to say that no other
study has attempted to do Cogmed with a population that had 50 % ODD and 9 %
CD students with 66 % who were ADHD-C and 34 % ADHD-I with only 27 %
of the sample taking medication. In fact with this severe of a population and only
27 % medicated it would be interesting to see what behavioral interventions are

effective. Finally, the fact that there were no follow up assessments further limits
the generalization of this study. As noted it is often the case that Cogmed effects
emerge over time. With no follow up evaluation this possibility was not explored.
5.22 Cogmed with ADHD-C and Learning Disabilities
Among all the Cogmed studies with children with ADHD to date Gray et al. [85]
captures the most severely disabled children. Not only were they severe LD and
ADHD, but also ODD. In that study Gray et al. 2012 used the Iowa Conners Rating
scale for teachers and parents to assess oppositional defiant disorder (OD) and
inattention/overactivity. On the OD scale based upon the recommended cutoffs as
suggested by Waschbusch and Willoughby in 2008 [86] for ODD for both parents
and teachers of the children were rated above the 90th percentile in both treatment
and control groups. Additionally, these are children that, to be eligible for the school
they attended, had to be diagnosed with both ADHD/LD along with severe problems
in behavior and learning AND they had to have already had a poor response to both
medication treatment and special education treatment. The children in the treatment
sample were an average of 14.4 years old which means that oppositional behavior is
further complicated by peer interactions with what is often maladaptive peer groups.
This also means that they have gone for several years in the school system without
successfully acquiring academic skills.
Similarly, the Iowa Conners does not differentiate between inattention/overactivity
with its IO scale which is somewhat confusing. However, based upon the cutoffs for
that scale as suggested by Waschbusch and Willoughby [86] all the children were
likely ADHD-C. Their level of elevation is only likely if both questions addressing
inattention and hyperactivity were significantly elevated. Not surprisingly, given the
elevated comorbidity, this was a group of severe ADHD-C children.
The level of learning disability was also severe. These students were full
time students in this residential facility. Not only severely impaired in working
memory, but these subjects as stated by Gray et al. 2012, were severely struggling
academically “Notably, all academic scores were more than two standard deviations
below the mean (WRAT-4) at baseline.” [85]. Given the averages of these students
of 14.2 years old for the control group and 14.4 years old for the treatment group
these were students that for the majority of their academic life had had very severe
behavioral and academic problems and were at risk for poor social as well as
academic outcomes. The implication of this is that they have had several years of
missed opportunity to develop social and academic skills which has led to their
placement in this school.
With all this severe disability, what was intriguing about Gray et al. 2013 results
was that they did find that there was a subset of WM criterion measures upon which
this group improved significantly compared to the control which was a math-training
group. Additionally, they found that “those who showed the most improvement on
the WM training tasks at school were rated as less inattentive/hyperactive at home

by parents.” This theme has arisen in other studies that there is a trend toward greater
increases on the training index or the training tasks result in greater improvement.
A trend like this was seen with the preschoolers in the van-Dongen-Boomsma et al.
study. Where the index significantly correlated to an ADHD rating scale and the
BRIEF by the teacher, but there were not significant group differences. One wonders
whether more severely disordered subjects needed more training time to accomplish
greater gains?
Gray et al. 2013 notes that further development of Cogmed would be needed to
result in transfer effects to other domains of function, but other explanations are
also plausible. For example, a complicating factor in this study was the reality that
both treatment and control groups were in a setting with intense remedial school
along with psychopharmacological treatment that resulted in gains for both groups
of children. This included attention, reading, math and behavior. Not only that, in
this case the control group was also receiving a math intervention. So, it would
seem illogical to expect that the Cogmed treatment group would make gains in
math while not receiving that math treatment. One issue was that the difference
provided by adding Cogmed did not result in additional significant benefit, which
arguably is a very high standard in this setting. Possibly most captivating was the
discussion point that they made which was that “A possible explanation for these
findings is that longer and more intensive training may be required to ameliorate
severe difficulties in WM” [85]. This is a matter of dosing which in other areas of
computerized cognitive training has been explored in more depth. This notion of
dosing and adjusting the ‘dose’ of Cogmed by increasing the number of training
days for more severe populations is an as yet unexplored undeveloped method of
inquiry which the present authors believe is rather important to consider. A longer
period of Cogmed training or ‘dosing’ with more severe groups seems a highly
plausible place to consider modification with such populations.
The subjects in the study by Gropper et al. [87] while having both issues of
ADHD and learning difficulties are still quite distinct from those in the Gray et al.
2012 study in terms of severity. Gropper studied ADHD/LD college students who
were registered with disability services at a Canadian college and who did Cogmed.
The fact that these students made it to college shows a comparatively high level of
adaptation in contrast to their counterparts in the Gray et al. 2012 study who had
to have failed in the community before being placed into that residential facility. To
be eligible for inclusion in the study, Gropper trainees had to have a previously
confirmed diagnosis of ADHD, or a learning disability or both [87]. However,
Gropper et al. note that those that were not diagnosed with one disorder still had sub-
threshold levels of that disorder. As such Table 5.2 in this document is somewhat
misleading for this group due to this sub-threshold overlap. Gropper’s 2013 study
included 26 (42 %) students that were diagnosed with ADHD, 30 (48 %) diagnosed
as LD and 6 (9 %) diagnosed as both of a total of 62 students. To make comparison
easier in the Table 5.2 noted above 9 % was added to both the ADHD category
giving 51 % with ADHD and to the LD category to give 57 % in that category.
Gropper et al. 2014 did not explain whether their ADHD group was ADHD-C or
ADHD-I. However, the original dissertation by Gropper discussed the reduction in

hyperactivity and impulsivity in adults with ADHD, but also notes the discussion
in the meta-analysis by Willcutt et al. [73] that since ADHD changes presentations
over time categorizing it as simply ADHD is more appropriate. Also, given the
small number on medication here we will infer that these subjects were more likely
ADHD-I although that is a judgment call.
Still given the amount of dysfunction in the sample of Gropper et al. 2014
study they found several transfer effects including WM, self-reported fewer ADHD
symptoms, and a reduction in cognitive failures which might be characterized as an
indicator of attention in daily life activities. At a 2 month follow up the students had
maintained gain in WM and the reduced cognitive failures. This was a randomized
wait list control group study. Additionally student comments from the study are
important to consider. The students reported that they were better able to recall
verbal information including phone numbers, names, appointments, etc. They were
better able to recall information from lectures, reading material without rereading.
The students said that they could stay alert for longer periods of time [87].
5.23 Summary/Conclusion
After having reviewed all this data comprehensively we conclude that one finds that
in all three of these areas, schizophrenia, TBI & ADHD regarding the usefulness
of computerized cognitive training with these clinical populations one comes to a
similar conclusion. It is effective. Computerized cognitive training does result in
improvement in the targeted areas. Certainly a meta-analysis of transfer effects
especially in the area of attention would further bolster the empirical case for
Cogmed working memory training with ADHD. That is beyond our present scope.
Yet given that much of the debate in the working memory literature is conceptual
in nature we do not expect this would wholly quiet the critics – even with
sizeable effect sizes. However, based upon the existing data and the larger scope of
computerized cognitive training reviewed here the effect sizes for Cogmed training
in peer reviewed published research in the areas of visual spatial working memory
and verbal working memory certainly fit the minimal standard discussed previously
in this document of exceeding an effect size of .3. In fact in the majority of studies
that are not testing the limits of the Coaching method or that are applying Cogmed
to non-recommended diagnostic groups the effect sizes far exceed this minimal
standard. In this way the argument for its efficaciousness is well-founded. The
exceptions are in the minority and are essentially consistent with more severe cases
of ADHD or an atypical way of coaching Cogmed.
Numerous transfer effects are associated with Cogmed training. The most
consistently found transfer is an improvement of attention. There are other areas that
are also gaining support like reading comprehension and mathematics. One should
keep in mind the primary target here is working memory. Cogmed has been found to
improve visual spatial working memory and verbal working memory on untrained
tasks. Transfer would be to increased attention. An additional “far transfer” would

include areas like reading comprehension or mathematics. Yet as noted by Dunning
et al. 2013 it is more reasonable to employ scaffolding with more systematic
layering in of training in these areas of desired far transfer. In fact we consider this a
way to optimize Cogmed training. That is, after a reasonable break at the conclusion
of Cogmed training we suggest new learning opportunities which will continue
to challenge working memory of the trainee. Without scaffolding to facilitate far
transfer to desired areas one is expecting working memory training to magically
make up for deficient skill development over a portion of a school year, an entire
year of school or in some cases several years or possibly even a decade. How could
an increase in working memory that is transferred to improved attention make up
for 1 year of academic training in math or reading comprehension let alone several
years? This appears to be an unreasonable standard. However, we reiterate that
computerized cognitive training is not typically considered to be optimized when it
is delivered in isolation as a solo intervention with TBI or schizophrenia. Why would
one expect this to be different with Cogmed working memory training with ADHD
or any other population? In the context of severity of disorder it is important to keep
in mind that computerized cognitive training has been found to be effective with
schizophrenics. Obviously by anyone’s standards schizophrenia is a more severe
disorder than any representation of ADHD. However, children with ODD and CD
certainly have disorders that are considered severe, but more importantly the nature
of those disorders themselves directly interfere with compliance in a computerized
cognitive training program in which not only compliance is critical but persistence
under cognitive challenge is necessary. The very nature of these disorders include
defiance and rule breaking.
Throughout this chapter we have proposed various possible limiting factors
of neuroplasticity that may affect the transfer effects of Cogmed. Both genetics
and age are among such factors consistent with existing research. However, we
have posited additional possible factors for consideration such as the notion of
severity of disorder and the amount of comorbidity. Although our review and
analysis of data appears to support these hypotheses, at this time they are only
hypotheses. Research that compares ADHD-C to ADHD-I directly in otherwise
matched samples would begin to answer this question. Additionally, studies with
groups of ADHD-C that receive medication and one that does not would address
other questions. Well-designed, well-controlled studies could start to address the
issue of the role of medication in conjunction with Cogmed in the case of ADHD-
C as well. Also, the role of medication should be controlled in research whenever
possible. In more severely debilitated populations medication may be helpful to
facilitate transfer effects and also to increase both compliance within training and to
maximize benefit. For example, in the case of schizophrenia ‘stabilizing’ the patient
is considered a prerequisite for computerized cognitive training. Most typically this
includes a pharmacological intervention or psychiatric rehabilitation [7]. In fact,
larger effect sizes were noted when this was done [7]. One would expect the same
may be the case with working memory training with ADHD-C. Could it make sense
that ‘stabilizing’ ADHD-C patients requires having hyperactivity/impulsivity under
better control? Studies considering comorbidity versus no comorbidity could be

conducted. Finally studies which include ODD specifically could help to resolve
what seem to be particularly muddy issues with this category of comorbidity with
ADHD-C.
The more recent studies of Cogmed represent the more extreme end of the sever-
ity continuum provide an opportunity to reflect upon “use case” scenarios that can
inform effective implementation of this program. We will briefly consider practical
implications here. In the cases of both van-Dongen-Boomsma et al. [82] and Gray
et al. [85] these investigators themselves suggested that dosing issues could facilitate
expanded transfer of effects. Also, in the case of van-Dongen-Boomsma etc., the
use of ‘blinded’ coaches is not advised. The Chacko et al. [84] study brings together
other concerns. We believe both dosing issues as well as medication issues may be
complicating factors to consider here. The fact that a high rate of ADHD-C children
with rather elevated rate of comorbidity and only small minority of these children
were medicated in the Chacko et al. study [84] is a concern. In other words there
are questions about whether better ‘stabilization’ might have occurred with more
children being medicated. These are some of the important considerations when
attempting to understand the effects of that study, not simply that it was a more
rigorously designed study. In fact, the Chacko et al. [84] study was of a more severe
sample with 66 % ADHD-C, 50 % of whom were ODD while another 9 % were
CD. ODD may provide a unique challenge for a computerized cognitive training
program as one would presume may be the case with CD as well. However, even
though the conclusion of Chacko et al. [84] was critical they did find found that
the children who completed Cogmed showed significantly greater improvements in
verbal and nonverbal working memory storage, but not in storage plus processing or
manipulation. Similarly, van-Dongen-Boomsma et al. found that the active training
group that they made significant gains in VWM that was only lost with statistical
correction for multiple testing [82]. This suggests that if larger sample sizes were
used which would increase statistical power then these differences may have been
captured, but instead they were lost.
In the case of the study by Gray et al. 2013 certainly the severity of disorder and
level of comorbidity were at the extreme, but the fact that all the children had to have
failed their community placement which already combined psychopharmacology
with special education gives one pause [85]. Furthermore, all the children were
getting active treatment in addition to the control group getting a math intervention.
In this case one wonders about the level or neuroplasticity of these children. Clearly
the most plausible adjustment that could be made with this population would be to
increase the dosing of Cogmed so they did the program longer. Yet, also interesting
was that Gray et al. 2012 found that working memory training resulted in greater
improvements of a subset of criterion measures [85]. Additionally, those children
who showed the greatest improvement on the WM training tasks at school were
rated as less inattentive/hyperactive at home by parents. Like the critical studies of
Chacko et al. [84] and van-Dongen-Boomsa et al. [82] this finding suggests that
the possibility that a longer term of training may have increased the possibility of
transfer effects.
There is an interesting conceptual matter to entertain. There continues to be

work to understand the mechanism of generalization for Cogmed effects. Increased
activity in the brain is one way to think about this. Also, the construct of improved
attention partly satisfies this issue, but the matter still seems somewhat unresolved.
Here we will submit what we will call the “executive control hypothesis”. For
example, in a study by Foy et al. 2014, of economically disadvantaged preschoolers
they found that after Cogmed that the preschoolers had improved on an executive
control task, the Head-Shoulders-Knees-Toes task [67]. This was considered to be
a far transfer measure. Importantly this task has been found to be predictive of
academic achievement in kindergarteners. Children significantly improved on this
task after doing Cogmed. Keep in mind that other than economic disadvantage
these children were otherwise typically developing. Consider this in light of the
fact that the Holmes et al. 2009 study of working memory deficit children found
improvement in following instructions both at the end of training and at follow up
6 months later. They also observed improvements in mathematics at 6 months. One
wonders if this finding might relate to the “executive control hypothesis”. Similarly,
other studies of ADHD children hint at what might be something with conceptual
overlap with the notion of executive control. For example, in the Klingberg (43 %
were medicated), et al. 2002, study of ADHD investigators found a reduction
in impulsive movement. The Green et al. 2012 RCT (67 % medicated) found
a reduction in off task behavior. Possibly most interesting is that in the Hovik
et al. [81] study of ADHD-C children ages 10–12 with a majority of the children
medicated (69 %) found that the children significantly improved upon processing
speed and reading in terms of fluency, and word decoding quality [81]. One wonders
whether utilizing a variety of measures which might assess executive control could
contribute to understanding the conditions for generalizing gains from Cogmed.
Finally, there are a few other hypotheses to consider in the context of transfer
effects of Cogmed. First there is the notion of “mind set” that involves the extent
to which a person believes skills like working memory can be changed. According
to Dwek [88], the subject’s mind set, or approach to the Cogmed program may
be influenced by their belief about how their effort can impact the outcome. If
subjects believe that actually working with vigor on a task may result in improved
working memory capacity, attention, and improved ability to remember, then that
may influence how hard they work and thereby their outcomes. If a child or an
adult does not hold this mindset they may think their effort will be useless and
of no benefit. Consequently, one would expect limited transfer to result. Mindset
may influence outcomes. Similarly, it should also be noted that students actually
do vary in ability and this lack of balance among individuals that varies by chance
can result in samples which vary in their ability to learn. This in turn can affect
the ability of a study to find differences in transfer effects among these samples
as noted by Moreau [89]. As such, there may be a learning curve and students
with a disability may actually encounter a greater challenge that other subjects in
doing Cogmed. This may result in smaller gains within the program and thereby
less transfer of effects. This variation itself also noted by Moreau [90] that may
result in heterogeneous outcomes, and that is to be expected as Cogmed clients do
not start at the same place, even if they are of similar ages. Finally, as noted by
Rast [90] in the context of verbal learning of older adults that three factors predicted
such learning: verbal knowledge, working memory, and processing speed. As such,
there is some complexity to the notion of transfer effects. Different subjects arrive
at training with varyingly levels of development in these areas. This is expected
to affect the level of transfer. As is seen here, these various factors of individual
differences be considered when evaluating transfer effects of Cogmed.
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Chapter 6
Clinical Communication Technologies
for Addiction Treatment
Richard N. Rosenthal
Abstract This chapter provides a review and framework for the technological
and clinical approaches to helping those who suffer from substance use disor-
ders. Clinical Communication Technologies, Self-Management Technologies, and
Device based support for treatment and recovery are discussed. International Efforts
at reducing the disease burden of substance use disorders are reviewed and a peak
at future innovations is summarized.
Keywords Adaptation • Addiction • Adolescent • Alcohol drinking

• Analgesics • Anxiety • Behavior • Cannabis • Cocaine • Cognition •
Cognitive therapy • Clinical assessment • Depression • Digital health •
Heroin • Methadone • Motivational enhancement • Motivational interviewing •
Opioid • Psychological • Recovery support • Self-management • Sensors •
Social support • Street drugs • Substance use disorders • Telemedicine • Text
messaging
The development of digital communications is continuing to have a profound

effect on the development of the health care delivery system. As the technology
matures, it is becoming increasingly clear that in addition to providing a platform
for the efficient and rapid exchange of health data through EHRs, microprocessor-
based technology is providing a platform for the evolution of behavioral health
care, including treatment of substance use disorders (SUD). These advances are
being designed both in the academic research community and in the private sector.
They take the form of applications and devices for clinical assessment, monitoring
and process innovation as well as treatment interventions such as computer- or
Web-based psychosocial interventions, recovery management and self-management
support. Computer-based applications are typically run at clinical sites and serve as
adjuncts to the clinical treatment program delivered on-site. Other programs may
be run on patients’ home computers, again as an adjunct to the care received in
the clinic. Web-based interventions are typically available on various platforms
R.N. Rosenthal, M.D. ()

Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
e-mail: rrosenthal@chpnet.org

124 R.N. Rosenthal
in addition to desktop or laptop computers, including tablets and smartphones,

allowing for greater in situ use of the technology when patients are in their natural
environments and at greatest risk for relapse.
Researchers have described the domain at hand as “computer-based” or
“technology-based” [1, 2], but these labels are either too narrow or too expansive.
Computers are involved as servers or hosts along the electronic communications
chain, thus describing the range of functionality as computer-based is technically
accurate, but as the end user may be on a smartphone or tablet it is not descriptive
in a way that generally matches user experience. Conversely, describing the target
as “technology-based” may be overinclusive and lacking in specificity, since other
technologies such as transcranial magnetic stimulation or deep brain stimulation
are being explored in the treatment of SUD as well as other diagnostic groups of
mental disorders, but are clearly not yet in the same domain as automated delivery
of psychosocial treatment and recovery support for SUD. What the technologies
targeted by this chapter have in common are that, regardless of the particular
platform, device or software, they support the transfer of clinical information, often
bidirectionally, between patients (through active interaction or through passive
sensors) and host applications, servers, and/or clinicians, and thus are best described
as clinical communication technologies or CCT (see also Johnson) [3].
What Are the Benefits Over Traditional Treatment of Using CCT to

Support Clinical Assessments and Interventions?
CCT may offer certain critical advantages over traditional face-to-face clinical
exchange, that when taken together make a strong argument for broader
adoption:
1. Patients’ access to care is improved through 24 h/7 day availability, and
availability in a range of physical settings [4].
2. Patients may access specific services on their own time and acquire
rehabilitative training at their own pace.
3. Those who otherwise would not seek face-to-face treatment may access
it in this modality [5].
4. CCT-based platforms may support more accurate self-disclosure of
sensitive clinical information than face-to-face [6, 7].
5. Care continuity can be extended beyond the traditional acute treatment
cycle [3].
6. Empirically based treatments can be readily converted to digital for-
mats [8].
7. Standardized treatment interventions can be delivered with high-fidelity
across treatment settings, with less vulnerability to program or clini-
cian bias.
8. More precise control of treatment exposure can be delivered [4].
(continued)
6 Clinical Communication Technologies for Addiction Treatment 125
9. Patients may review repetitive but necessary skills training or educational

tasks without clinician involvement [9].
10. Application modularity may allow patients to more rapidly and precisely
access and acquire pertinent information or instruction [10].
11. Treatment may be tuned more precisely and individually to patient
feedback.
12. Clinicians’ capacity to treat increases by decreasing time spent per
patient [11].
13. Given the expense and time to train staff to reliably and competently
deliver evidence-based psychosocial interventions, and then to supervise
them, CCT-based interventions are generally robust to staff turnover.
14. Interventions delivered through CCT are likely to be cost-effective [12].
6.1 Computer-Based Psychosocial Interventions
A major barrier to patients’ access for evidence-based individual or group psychoso-

cial interventions for SUD is that these effective interventions are costly, requiring
trained staff, and as such a majority of community-based SUD treatment programs
operating on narrow margins are unable to offer them [13]. Similarly, in primary
care settings, evidence-based screening and brief interventions for alcohol and other
substance use problems are infrequently applied due to clinicians’ lack of time or
enthusiasm, or due to downright skepticism [14, 15]. There are a host of evidence-
based psychosocial interventions for SUD that have been demonstrated to retain
their efficacy after translation to the electronic platform with process automation
to enable patient interaction. A recent meta-analysis of studies to 2006 of Internet-
based psychotherapeutic interventions for a broad array of mental disorders covered
9,764 clients in 92 studies and demonstrated a medium effect size of 0.53, not
dissimilar to that of face-to-face therapy [16]. The Community Reinforcement
Approach (CRA) [11] and Contingency Management (CM) [17, 18], Cognitive
Behavior Therapy (CBT) [19, 20], and brief motivational therapies such as Motiva-
tional Interviewing (MI) and others [21–23], are psychosocial interventions for SUD
with a substantial evidence base that have been tested with demonstrated efficacy in
the CCT format on various platforms and with different subpopulations.
6.1.1 Self-Management and Predominantly Administered

Technologies
For people with drinking problems of less severity and duration than those with
DSM-IV alcohol dependence, motivation-based interventions have a significant
126 R.N. Rosenthal
evidence base, such as behavioral self-control training (BSCT) that teaches

participants how to change their drinking behaviors towards a goal of non-
problematic, moderate drinking [24]. BSCT teaches establishing one’s goals,
self-monitoring (logging the date, time, and quantity before each drink), controlling
the drinking rate and drink refusal, setting rewards for goal achievement, functional
analysis of drinking situations, assessing overdrinking triggers, learning alternative
methods of coping, and relapse prevention [25]. In a randomized controlled trial
among 40 participants with mild to moderate drinking problem severity and follow-
up to 12 months, Hester & Delaney [24] tested 8 weekly sessions of a computerized
version of the behavioral self-control training program for Windows (BSCPWIN),
which incorporated the participant’s drinking levels and assessment scores and was
begun immediately after assessment, compared to a lagged control condition that
began the intervention only after the 10-week follow-up to the initial randomization.
The study was not conducted in the context of typical face-to-face clinical treatment,
although the computers were at the research site offices. At the 10-week follow-up,
the BSCPWIN group had a significant reduction in total drinking from baseline with
a moderate-to-large effect size, and significantly greater reduction in total drinking
compared to the control group that had not yet been exposed to the intervention.
The significant reductions in alcohol intake that were achieved by 10 and 20 weeks
respectively in the immediate and delayed groups were sustained at 12 months [24].
This is an early and striking example of the robust efficacy of a self-help oriented
CCT-based application after only a maximum of 6 h exposure.
Hester et al. [26] then developed a brief motivational interview, the Drinker’s
Check-up (DCU) both as a Windows program for clinical use in the treatment
context and a web application that the general public could use independently of
clinical contact (www.drinkerscheckup.com). The DCU application assesses the
quantity and frequency of alcohol use, drinking patterns and consequences, severity
of dependence, and motivation for change, and then it offers the participant clinical
feedback about consequences of drinking based on the individual information that
the participant has entered. Once the feedback is completed, the program begins
the decision-making module, which uses Motivational Interviewing techniques to
support tipping the patient’s decisional balance toward deciding to change his/her
drinking behavior, after which the application negotiates the patient’s goals for
change. The investigators tested the DCU intervention on 61 non-treatment seeking
adults with risky drinking using a randomized design of immediate intervention
after assessment versus a delay of 4-weeks when the DCU assessment and feedback
intervention was applied [26]. As expected, the immediate intervention group had
significant reductions in the main alcohol use measures at 4 weeks (with a large
effect size) and both groups sustained their reductions at 1-year follow-up for a
net 50 % reduction in quantity and frequency of alcohol use. The DCU seems
to be effective in increasing problem drinkers’ motivation for change, and can be
considered highly likely to be cost-effective given the minimal amount of time
(35 min) and support necessary to deliver the intervention. A subsequent study
optimized the DCU assessment and feedback modules for college age drinkers and
their typical drinking situations, the College Drinker’s Check-up (CDCU), for both
a Windows and web-based platform [21]. The authors conducted a randomized trial
in 82 college students between the ages of 18–24 who were episodic heavy drinkers,
comparing the effects of the CDCU in one 35 min session to a control group that
had an assessment visit that was delayed to the 1 month follow-up visit to control for
previously demonstrated effects of assessment alone on reducing drinking behavior.
The CDCU showed significant impact on all tested alcohol indices compared to the
control group at the 1 month follow-up assessment [21].
In a strategy to reach a wider, more diverse, non-clinically engaged population,
Hester et al. [27] used the behavioral self-control training concept behind the
DCU as the foundation for a new, more structured and individualized web-based
application, ModerateDrinking.com (Hester, Delaney & Campbell, 2011). Eighty
non-dependent heavy/problem drinkers were recruited into a randomized study
of comparing Moderatedrinking.com (MD) along with the online resources of
Moderation Management (MM; moderation.org), both web applications, to a control
group of using MM alone, with follow-up assessments at 3, 6, and 12 months.
MM is a web-enabled mutual help group and listserv for learning how to moderate
drinking behavior, and all subjects were asked to read the listserv and/or post to it
at least twice a week for at least the first 12 weeks of the study. While both groups
demonstrated significant reductions compared to baseline in alcohol consumption
and alcohol-related problems over the 12-month study interval, the MM C MD
group experienced a greater increase than the MM group in Percent Days Abstinent
at follow-up [27].
Clearly, self-administered interventions should have the greatest reach and
acceptability of any CCT, and make sense especially for those with lower severity
of substance-related problems. However, for those with sufficient severity or
impairment to warrant a diagnosis, Newman et al. (2011) reviewed the literature up
to 2010 on computerized treatments for SUD by disorder and by degree of therapist
contact, concluding that self-administered and mostly self-administered computer-
based cognitive and behavioral interventions are efficacious [28]. However, they
also found that having some therapist contact supported greater reductions in
substance use for a longer interval, suggesting that, at this stage of development,
self-administered recovery support may be best used as an augmentation strategy
for traditional clinical treatment, rather than as a replacement.
6.1.2 Clinician/Program-Supported Technologies
Several CCT-based interventions have demonstrated efficacy when delivered as an

adjunct to standard treatment of SUD. Carroll and colleagues [19] adapted the
evidence-based and manualized CBT intervention for SUD [29] to a six-module
computer-based training in CBT (CBT4CBT) and conducted an 8-week randomized
clinical trial in outpatients with SUD (n D 77) comparing treatment as usual plus
biweekly access to CBT4CBT at the clinic with standard treatment alone [19].
Based on simple computer learning games, the application modules were presented
128 R.N. Rosenthal
in multimedia format with graphics, video examples of the concepts, narration,

minimal text, interactive assessments, verbal instructions and practice exercises that
addressed basic CBT topics such as understanding one’s substance use routines
and how to change them, how to cope with craving, to learn and practice drug
refusal skills, to exercise problem-solving skills, to recognize and derail thinking
regarding drugs and alcohol, and to improve decision making skills. Compared to
the control group, the group receiving CBT4CBT had significantly more negative
urine specimens and proportion of specimens that were negative over the study,
a moderate effect size, suggesting that adding an automated 45 min CBT4CBT
session to weekly individual and group sessions of general drug counseling can
have important clinical impact on SUD without much added clinical burden [19].
Marsch and colleagues [9] developed a self-directed and interactive Web-
delivered HIV and sexually transmitted infectious disease prevention program for
high-risk adolescents and tested it in a randomized trial among adolescents (n D 56)
in two outpatient SUD treatment programs, where the standard group received
a traditional 1 h small-group prevention intervention and the enhanced group
received the traditional 1 h prevention intervention plus access to the 25 Web-based
program modules. The Web-based prevention program is fluency-based in that it
has interactive exercises with graphics and animations, and quizzes participants at
feedback-adjusted levels of required response speed and accuracy as they increase
their mastery and retention of the skills being taught [30, 31]. At study follow-up
assessment, the Web-based intervention group had significantly greater prevention-
related knowledge and greater intentions to choose partners carefully and they also
perceived the intervention to be significantly more useful [9]. Because this Web-
based application is browser-based, it may be able to be more broadly disseminated
if optimized for mobile devices.
Although much of the relevant research has been on computer-based psychoso-
cial interventions adjunctive to clinic-based treatment, more recent trials have
included designs in which the CCT-based intervention is tested directly against
treatment as usual or time matched controls. For example, a recent randomized
12-week trial among opioid treatment program patients who had Internet-capable
computers received either Web-based videoconferencing intervention “eGetgoing”
at home or traditional face-to-face individual weekly counseling in the clinic
[32]. Although a formal non-inferiority analysis was not performed, results of the
two interventions were comparable regarding counseling attendance and positive
drug screens, treatment acceptability and therapeutic alliance, suggesting overall
feasibility of at-home video counseling, and all else being equal, the potential for
increased access to care [32].
Screening and brief intervention is a well documented MI-based procedure for
patients with mild to moderate problems with alcohol and other substances that
places them at risk for medical and other sequelae, even without meeting formal
SUD diagnostic criteria. Schwartz and colleagues [22] tested illicit drug-using
adults (N D 360) in a primary care setting with a tablet-based brief interven-
tion delivered on-site, compared to a traditional brief intervention delivered by
a masters-level behavioral health counselor. Both interventions had comparable
content based on Motivational Interviewing, including personalized feedback and

empathic reflection. The tablet-based application was self-directed and delivered
synchronous feedback via headphones that was tailored to the patient’s motivation
to change (measured interactively) and presented by an animated narrator who asked
questions based on the patient’s responses, just as would be done in the in-person
intervention. While overall follow-up at 3 months did not demonstrate differences in
global drug use using the Alcohol, Smoking and Substance Involvement Screening
Test (ASSIST) [33] ratings, there was a significant difference in drug problem
scores for marijuana and cocaine at 3 months favoring the computerized intervention
[22]. These findings suggest that automated delivery of brief interventions for
problem substance use may a feasible alternative to in-person interventions in busy
primary care settings. Similarly, Ondersma and colleagues [23], who developed
the Motivational Enhancement System (MES) used in the ASSIST study [22],
conducted among 143 women who self-reported illicit drug use in the month before
becoming pregnant, a randomized trial in the immediate post-partum period of a
single 20-min tablet–delivered screening and brief intervention to facilitate self-
change, against a time-matched control condition. The intervention content and
process was as described above in Schwartz et al. [22], –subjects were not required
to use the keyboard and provided all answers by touching a visual analogue scale
or by selecting from a list, and again, the animated narrator behaved in much the
way as a clinician trained in brief intervention would. Intention to treat analysis
of follow-up data demonstrated a significant impact of the intervention on past-
week abstinence for illicit drugs at 3 months (p D 0.01) after childbirth compared
to the control group, but not at the 6 month point, although the intervention group
maintained a higher rate of abstinence [23]. Taken together, these studies suggest
the efficacy of motivation-based screening and brief intervention delivered by the
MES application on a tablet computer on patients with risky substance use.
Several psychosocial interventions for treatment of diagnosed SUD have
been conducted using Web-based applications. The Community Reinforcement
Approach (CRA) to treatment of SUD entails teaching skills to patients and
supporting pro-social behaviors that lead to reinforcement that is not related to
drug use [34]. When paired with a system of vouchers of increasing value to reward
continuing provision of drug-free urine samples, a version of contingency manage-
ment (CM), CRA plus vouchers became the foundation of a CCT-based intervention
called the Therapeutic Education System (TES) that has been tested against both
the face-to-face version and treatment as usual [34]. Bickel and colleagues [11]
conducted a three-arm randomized trial of CRA treatment with vouchers delivered
by therapists, mostly computer-based CRA treatment (provided at the clinic)
with vouchers, or treatment as usual among N D 135 outpatients being treated
with buprenorphine for opioid dependence. Both CRA with vouchers groups had
significantly greater weeks of abstinence from opioids and cocaine than the standard
treatment group (p < 0.05) demonstrating their efficacy, yet those the computer-
based TES intervention achieved these results with about one sixth of the contact
time spent with their counselor [11]. This suggests that the computer-assisted
intervention may be cost-effective. Similarly, in a 12-week non-randomized trial,
130 R.N. Rosenthal
Budney et al. [35] compared a therapist-delivered 9-session treatment combining

elements of motivational enhancement therapy (MET), cognitive-behavioral
therapy (CBT), and abstinence-based contingency-management (CM) including
vouchers, against an on-site computer-assisted version of the same combination
treatment in 38 cannabis dependent adults seeking treatment and found comparable
results for retention in treatment and cannabis use outcomes between the two
groups.
In a sample of depressed adults (n D 97) with risky alcohol and or at least weekly
cannabis use in New South Wales who were given one brief MI, advice and rapport-
building intervention, Kay-Lambkin et al. [36] conducted a randomized controlled
12-month outcome study comparing no further treatment (control), a manualized
9-session MI/CBT therapy focused on mood and substance disorder, and an on-site
computerized version of the MI/CBT therapy, Self-Help for Alcohol and other drug
use and Depression (SHADE) delivered on-site in the clinic with a brief (10 min)
therapist check-in after sessions. Both of the MI/CBT interventions demonstrated
significant reductions in hazardous alcohol and other drug use over the 12-months
compared to the control group (p < 0.01), and the computer-delivered treatment
group had a significant (P < 0.01), threefold reduction in cannabis use over the
12 months compared to the control group [36]. A multisite replication study of
similar design with 274 subjects demonstrated that both the therapist-delivered and
computerized versions of the MI/CBT intervention were superior to a control group
given nine sessions of manualized supportive counseling in reducing depression
and alcohol and cannabis use at 3 months, and the computer-delivered therapy was
associated with significantly greater reduction in alcohol use than the therapist-
delivered treatment [37, 38]. The computer-delivered SHADE intervention required
an average of 16 min of clinician time per session compared to 57 min per session
for the therapist-delivered CBT/MI treatment, suggesting again the probable cost
effectiveness of this approach.
Marsch et al. [39] conducted a 12-month randomized trial to test the effective-
ness of the Web-based Therapeutic Education System (TES) without contingency
management (CRA without vouchers), substituted for 30 min of the 1 h regularly
allotted weekly or biweekly for standard counseling, compared to 1 h standard coun-
seling only among 160 subjects newly admitted to a community-based methadone
maintenance treatment program. The TES group demonstrated significantly greater
rates of opioid abstinence across all study weeks (p < 0.05), and participants were
twice as likely to be abstinent than those receiving standard counseling [39].
Interestingly, poorer cognitive function predicted poorer opioid abstinence in the
standard counseling group, but not in the TES/reduced counseling group, suggesting
that the Web-based TES intervention, which is based on a cognitive, skills-based
approach, mitigated the effect of poor cognitive function on abstinence outcomes
[40]. This is further evidence that properly configured and delivered CCT-based
interventions may have differential efficacy compared to treatment as usual. Overall,
these studies suggest that CCT-based psychosocial interventions have demonstrated
efficacy as good as or better than evidence-based behavioral treatments provided by
trained clinicians.
6.2 Smart Phones
In 2014 it is estimated that there are 6.9 billion cell phone subscriptions in the
world, which is equivalent to 95.5 subscriptions per 100 inhabitants and 2.3 billion
mobile broadband subscriptions, an 800 % increase over 7 years [41]. Thus, mobile
phones are nearly ubiquitous and as such may offer distinct and novel advantages
over traditional bricks and mortar requirements for interventions for reducing
addictive behavior. Smart phone platforms enable several different CCT modes
that are of potential clinical utility, such as traditional live voice conversations, text
messaging, access to Web-based information and applications, native applications,
GPS functionality, and a host of intrinsic (e.g., accelerometer) and plug-in (e.g.,
Bluetooth-enabled sensor) devices. The potential advantages for treatment of SUD
include: the ease of use anywhere at anytime; cost effective delivery and scalability
to large populations regardless of location; the ability to tailor messages to key
user characteristics (such as age, sex, ethnicity); the ability to send time-sensitive
messages with an ‘always on’ device; the provision of content that can distract the
user from cravings; and the ability to link the user with others for social support. The
efficacy of mobile phone-based interventions is demonstrated in a recent Cochrane
meta-analysis of five studies with >9,000 participants, which showed that text
messaging smoking cessation programs increased 6-month quit rates by 71 % when
compared to controls, a strong effect [42].
6.2.1 Interactive Voice Response in SUD Assessment

and Treatment
Once the province of home phones, interactive voice response (IVR) technology is
accessible now through any device, including mobile ones, which can at least carry
audio information to and keypad information from the end user. As questions of end
users can be formatted in simple yes/no, multiple choice, or numeric values from
a keypad, IVR technology lends itself readily to data gathering, assessment and
monitoring for patients in clinical treatment for problems and disorders related to
substances. The platform allows for easy coupling of data collection and population
of those data into a database for statistical analysis. In SUD research, daily process
methods have been developed, as getting more assessment time points increases
one’s clinical knowledge, as well as power to capture trends for research purposes.
Other than seeing patients in traditional weekly clinic visits or more frequently
in intensive or opioid treatment programs, monitoring daily intake of substances
such as alcohol for treatment or research purposes used to rely on paper and
pencil diaries or retrospective assessments, which were not easily verified [43].
By the mid 1990’s, it was demonstrated that IVR-based daily assessment was a
valid method for measuring instances of daily alcohol and tobacco use, especially
in frequent, heavy drinkers who tended to underreport using traditional methods
[44, 45]. Such IVR systems can be call-out, or call-in (if intrinsically motivated or
132 R.N. Rosenthal
externally incentivized), or both. The data captured at a higher frequency by this

method more easily supports analyses of longitudinal health-related data that are
more problematic to obtain by other means, such a conducting time-series analysis
to reveal behavioral patterning and rhythmicity that might not otherwise be apparent
[46]. In addition, the use of IVR can explore behavioral correlates of SUD that might
not easily be captured through traditional methodologies. For example, Cranford
et al. [47] used IVR to assess daily drinking behavior, marital interactions and
depressed, anxious, angry, or good moods or fatigue in patients with alcohol use
disorders and their spouses (N D 54 couples). In addition to anxious mood and
marital conflict predicting daily non-compliance with calling in to the IVR system
(which triggered an automated reminder call), and intoxication predicting next-day
noncompliance in the patients, the strongest predictor of non-compliance was the
spouse’s compliance [47]. Thus, the data that can be gathered through IVR sampling
may be used in new ways that can potentially augment treatment outcome. Although
in this study, anxiety predicted noncompliance and generated an automated call,
an IVR system could just as easily record urges and cravings, and after further
exploration using branch-chain logic, initiate a support call in response, etc.
Although IVR has been historically used for assessment and reminder purposes,
more recent studies have attempted to add more complex treatment components to
the applications, such as self-monitoring, setting goals and rehearsing coping skills,
in order to create a therapeutic IVR. In a randomized, controlled 4-week trial in
methadone maintenance patients who continued to use illicit opioids and cocaine
(n D 46), Moore et al. [48] tested treatment as usual (TAU; one mandatory individual
counseling session/month with optional daily groups), against TAU plus “The
Recovery Line,” an automated, IVR-based 24 h-accessible telephonic treatment
based on CBT theory and principles. The IVR treatment modules (<15 min each)
covered typical CBT topics such as self-monitoring, recognizing triggers, coping
with urges and cravings, avoiding high-risk situations, and dealing with stress
and negative moods, offered direct guidance and role-playing for exercises that
accompanied the didactic portions, and also contained a section that provided
encouragement. Although experimental group subjects patients used fewer days on
average (<10) of that recommended (daily), they were significantly likely to abstain
from illicit drugs on the days they did (p D 0.01) [48]. One benefit of The Recovery
Line IVR system is that, compared to the time patients are involved in recovery
activities in a clinical environment, patients may access the system while they are in
their regular environment, thus up to and during a time of and in a location of high
risk for use of illicit drugs. Given that patients may not be compliant with calling in
to an IVR system, in an add-on substudy to a cocaine abstinence trial, Lindsay et al.
[49] tested what type of incentives might improve subjects’ adherence to calling
in daily to answer questions about craving and use of cocaine, and compared a
consistent $1 reward against a contingency management fishbowl drawing for prizes
of varying values. The odds of the variable prize group calling in were 4.7 times
greater than that of the fixed payment group, demonstrating that evidence-based
behavioral interventions (i.e., CM with intermittent reinforcement) can also be used
to shape behavior regarding CCT [49]. Across groups, the percentage of IVR calls
was associated with achievement of cocaine abstinence.
What Are the Benefits of IVR?

1. Increases the population with SUD that can be reached through landlines
or mobile phones.
2. Automated calls can remind patients to adhere to treatment, such as taking
medications.
3. Potential to gain clinically relevant information from patients not con-
strained by being present in a traditional treatment setting
4. Increases accuracy through reduction in patients’ recall time and bias [50]
5. Allows for verification of the date and time of daily reports [47]
6. Has the flexibility to capture data at specific times of day for particular
analyses.
6.3 Monitoring: Asynchronous and Real-Time

(Synchronous)
Other than when a clinician directly observes or elicits symptoms from a patient
during a face-to-face examination, the bulk of clinical assessments monitor clinical
events in an asynchronous fashion, that is, at a time other than when the event
occurred. This time lag between the event and the documenting of it increases
the likelihood of failures of recall, biased interpretation by the patient or clinician,
loss of information, and so on. The availability of portable CCT on the platforms
of palmtop computers, smartphones and tablets allows for ecological momentary
assessment (EMA)—a record of a patient’s in vivo response, that is, while in
their natural environment and going about their normal activities. Data gathered
through EMA methods should, by definition, have greater ecological validity than
data gathered in a treatment clinic or research setting, and thus revealed behavioral
patterns more generalizable to both specific individuals and to target populations.
EMA on handheld devices moves beyond traditional daily process methods in that
it can sample patient responses at multiple times during the day on a fixed or random
schedule, inquiring about events and experiences since the last assessment, and can
also be responsive to pertinent events in real time [51]. For example, SUD patients
can report exposure to conditions likely to induce substance craving or otherwise
putting them at high risk for use, and can be followed up at intervals to record
whether use occurred or not. Adherence to the assessments are also tracked and
recorded as clinical information [52], since non-adherence with the program is
a likely indicator for relapse in SUD patients. EMA procedures have facilitated
a more precise determination of antecedents to relapse to substance use [53],
elucidated characteristics of drug withdrawal with higher resolution and accuracy
[54], and are clearly more accurate than patients’ recall of events [55]. As with IVR,
EMA multiple daily sampling allows for longitudinal data acquisition that lends
134 R.N. Rosenthal
itself to analyses of temporal patterning and sequencing such as cyclic or cascade

phenomena [56, 57], determinations that would not otherwise be available with
cross-sectional or typical clinical information. For example Schiffman & Waters
[58] determined using EMA that negative affect on preceding days did not predict
lapse to smoking during quit attempts, but that negative affect did increase in the
5–6 h before a lapse, shortening the window for potential clinical interventions.
Similarly, in using EMA to study the precedents to cocaine or heroin use in
methadone-maintained patients, Epstein et al. [59] demonstrated that at 5 h prior to
the onset of cocaine use, there were significant linear increases in 12 of the putative
relapse triggers, such as having a good mood, seeing the drug, being tempted out
of the blue, and wanting to see what happened with using a small amount. While
initial electronic versions of EMA assessment initially relied on devices such as
handheld computers that needed to be physically downloaded at intervals, newer
versions of the technology are available on smartphone platforms adding significant
functionality and reducing the barriers to access [60]. One of the evolving strengths
of the CCT platform is the ability to capture and examine clinical data that otherwise
would remain unanalyzed and unused in the treatment of patients. For example, the
presence of GPS data on smartphones allows that those data can be paired with EMA
data for novel analyses and determination of correlates of addictive behavior [61].
Epstein et al. [62] collected random EMA mood, stress, and drug craving responses
on PalmPilot PDAs along with concurrent GPS data on no-display GPS loggers
(geographical momentary assessment, GMA) over 16 weeks in a cohort study
of 27 methadone maintenance patients who were abusing other illicit drugs, and
demonstrated that, when compared to measures of physical and social disorder and
drug activity by neighborhood blocks in Baltimore City, that contrary to hypothesis,
more drug activity in the neighborhood was associated with less cocaine craving,
heroin craving, and stress. In addition, more social disorder was associated with
lower cocaine craving and more neighborhood physical disorder was associated with
lower cocaine craving, heroin craving, negative mood, and stress [62]. Regardless of
the ultimate explanation for these counter-intuitive results, without EMA and GMA
technology, generating these linked clinical and geospatial data for analysis would
be improbable.
What Are the Benefits of EMA on Smartphones?

1. Given the ubiquity of smartphones, patients are typically carrying them
most of the time.
2. Programmable operating systems allow development of applications that
tap intrinsic resources.
3. Obviates the need for separate, often expensive other devices with high
replacement costs.
4. Because it is “the phone,” participants are more likely to respond.
(continued)
5. Real-world monitoring in the patient’s own environment increases the

ecological validity of potential clinical interventions derived from those
data.
6. Random and event-driven sampling diaries allow for patient-specific
determination analysis of behavioral patterning [63].
7. Prevents back- or forward-filled responses and batching of responses as
seen in paper diaries [51].
8. The time and location information can be captured when the EMA is
enacted and when it is responded to [60].
9. Qualitative data may also be captured, such as verbal descriptions of the
context of a relapse event [64].
10. Information not otherwise captured clinically can be assessed by the
EMA application, such as response time, and in parallel such as data
from the phone’s built-in sensors.
6.4 Sensors
The pairing of location data using geospatial technology, with human experiential or
behavioral data, is but one of the real-time opportunities available using increasingly
available devices included in smartphones (e.g., motion sensor/accelerometer,
gyroscope, digital compass, magnetic field detector, proximity sensor, touch sensor,
barometer, ambient light sensor) as well as wearable wireless peripheral sensors
that either feed to smartphones or other platforms. For example, the combination of
geospatial information coupled with an increase in sampled heart rate could trigger
an intervention based in the patient’s identified pattern antecedent to relapse to
substance use. Either way, the trend is towards the use of sensors with frequent
or continuous sampling, which, in addition to being unobtrusive, could bring the
highest level of ecological validity to the integration with more traditional self-
reported behavioral health event data.
Sensors can passively gather data when the subject is unwilling or unable to
respond. Transdermal electrochemical sensors have been in use for several years,
which allow for relatively accurate non-invasive monitoring of alcohol consumption
through sampling at intervals from 30 s to 10 min, but some versions may have
variable performance and they also are not inexpensive [57]. As part of a system
designed for use in CBT for patients with SUD and PTSD, Fletcher and colleagues
[65] used wearable analog sensor wrist/ankle bands that contained circuits to
measure electrodermal activity, temperature and 3-axis motion and sent the data
to a mobile phone via Bluetooth radio, or cached it on a 2 GB MicroSD card.
The 4 Hz sampling rate (adjustable) data fed into an application resident on a
mobile phone running Android 2.1 or 2.2 that could deliver just-in-time CBT-based
136 R.N. Rosenthal
messages to a patient that related to the sensor data that was processed locally
on the phone. eHealth is quickly adopting the use of sensors in logging health
activities for general consumers. Given the traditional lack of attention to treatment
of SUD among primary care clinicians, this presents a novel opportunity to capture
important clinical data in people with SUD and make it available for the electronic
health record.
6.5 Future Research and or Trends Regarding Future

Innovations
Much in the way that smart phones and tablets are platforms for digital com-
munication that are increasingly convergent, the future will bring an integration
of the various components described above into systems of CCT assessment and
treatment. As the modalities integrate, clinical intervention will be linked more
closely in time to key patient events. The opportunity for ultra short-loop feedback
of patient information and tightly linked clinical response has rarely existed in most
of healthcare save for patients in obviously high-risk situations: for surgical patients
during anesthesia, during “codes,” and for those in intensive care, postoperative
recovery, and cardiac monitoring units. The technological advances presented here
offer the promise of extending this clinical responsivity outside of traditional acute
care environment, through the domain of outpatient care and into the rest of patients’
environmental context. For example, in response to an episode of craving identified
by EMA or by algorithms of sensor data in a patient who is walking towards an
area where he used to buy drugs (an individual “hot-zone” identified by GPS), a
responsive text reminder about avoiding high risk locations or situations, or specific
video support from a skills module on coping with urges could automatically be sent
to the patient’s smartphone. The patient’s successful management of the craving
episode and/or change in route can be given immediate reinforcement, or in the case
of symptom escalation or increased risk as predicted by the patient’s own history
and geospatial data, the system cues a movement to a higher level of intervention.
The boundaries between gathering data for research purposes and the gathering
of data to be used for treatment of individual patients are narrowing as the CCT-
based procedures that used to be solely in the realm of research are being used to
improve the quality of treatment.
Currently, most of the published research applies technology to augment assess-
ment and treatment interventions for SUD that have originated in the bricks-and-
mortar clinical realm, and have first established an evidence base in that realm.
However, the potential exists for the development of novel assessment protocols
and clinical interventions that are fully native to the microprocessor-based realm. A
bridging strategy should be to use current CCT to explore the relapse and recovery
process in a more defined and complete way, which, in addition to capturing patient
behavior in a potentially more rigorous fashion, might be elucidated from the more
precise contextual (time, interval, antecedent stimuli, location) information derived

from CCT-based evaluation, intervention and feedback.
Finally, the movement towards data integration using the output of continuous
devices and wearable sensors that capture location, activity and physiological
responses, in addition to registration of subjective states, should transcend even
EMA to create powerful new contextual arrays whose analyses will guide ever more
individualized and comprehensive clinical interventions.
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Chapter 7
Technology and Adolescent Behavioral
Health Care
Todd E. Peters, Theresa Herman, Neal R. Patel, and Harsh K. Trivedi
Abstract The interface between adolescence and technology offers both the
greatest need for increased clinical caution as well as the greatest opportunity for
future exploration. Where else can the seasoned clinician feel like a mere novice
compared to the tech-savvy adolescent patient? Where else can the tech savvy
adolescent, who in the prime of invincibility and curiosity, find easy access to
situations and dangers once only the subject of science fiction? With this, consider
the limitless potential of technology-based solutions powered by the ever-connected
adolescent. Imagine a world where electronic gadgets can track clinical data points,
such as a smart phone that monitors sleep patterns, or can provide real time clinical
guidance, such as a guided relaxation module when the patient’s heart rate starts
to climb. In this chapter, we review the interface of technology and adolescent
behavioral health care with an emphasis on how technology also impacts access
to information, parenting, and maintaining patient safety.
Keywords Adolescent psychiatry • Bullying • Depression • Divorce • Legal

guardians • Obesity • Shame • Social media • Social networking • Students •
Substance-related disorders • Suicide • Text messaging
T.E. Peters, M.D. ()

Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, TN, USA
Health Informatics Technologies and Services, Vanderbilt University Medical Center,
Nashville, TN, USA
e-mail: todd.peters@vanderbilt.edu
T. Herman, M.D., M.B.A.
Vanderbilt Behavioral Health, Vanderbilt University Medical Center, Nashville, TN, USA
Office of Quality, Patient Safety, and Risk Prevention, Vanderbilt University Medical Center,
Nashville, TN, USA
N.R. Patel, M.D., M.P.H.
Health Informatics Technologies and Services, Vanderbilt University Medical Center,
Nashville, TN, USA
Department of Pediatrics, Vanderbilt University School of Medicine, Nashville, TN, USA
H.K. Trivedi, M.D., M.B.A.

142 T.E. Peters et al.
The interface between adolescence and technology offers both the greatest need for
increased clinical caution as well as the greatest opportunity for future exploration.
Where else can the seasoned clinician feel like a mere novice compared to the tech-
savvy adolescent patient? Where else can the tech savvy adolescent, who in the
prime of invincibility and curiosity, find easy access to situations and dangers once
only the subject of science fiction? With this, consider the limitless potential of
technology-based solutions powered by the ever-connected adolescent. Imagine a
world where electronic gadgets can track clinical data points, such as a smart phone
that monitors sleep patterns, or can provide real time clinical guidance, such as
a guided relaxation module when the patient’s heart rate starts to climb. In this
chapter, we review the interface of technology and adolescent behavioral health care
with an emphasis on how technology also impacts access to information, parenting,
and maintaining patient safety.
7.1 Background/History
The area of clinical documentation is one which clinicians struggles with

issues regarding how much document, how to maintain confidentiality, which
parent/guardian can access the record, How much to share with other treatment
providers, and how to vary the response based upon the specifics of the case and the
developmental age of the youth. Documentation of clinical work has always served
many purposes: to serve as an internal record of interactions with patients, to provide
the details of these interactions and diagnostic assessments for other providers, to
monitor progress and treatment plans, to provide a record of care externally to
others in times of litigation, and to allow for justification of billing. Historically, the
medical record has been considered to be the property of the provider or care system
and was shielded from patients. Over time, this view has transitioned to the present
day concept that an adult patient has the right to freely access their records when
requested and are the true “owners” of the medical record. How does one consider
the rights of a minor in regards to access their medical record? Who ‘owns’ the
record of a minor child in cases where parents/guardians are embroiled in litigation
regarding custody and medical decision-making?
7.1.1 Federal Policy Implications
Electronic health records (EHR) have allowed for more rapid dissemination of
information, but have also brought concerns regarding safety and confidentiality.
While concerns exists regarding all electronic records, both medical and psychiatric,
they are often magnified in the views of mental health providers and patients
seeking mental health care. Recognizing these confidentiality concerns, the Health
Insurance Portability and Accountability Act (HIPAA) of 1996 sought to create
regulations for “covered entities,” defined as any practices and/or institutions that
7 Technology and Adolescent Behavioral Health Care 143
transfer information electronically including via the use of facsimiles. This ruling
has emphasized the importance of signed disclosure policies detailing who has
access to one’s personal information and treatment record, including what elements
may be shared with other providers and insurance companies [1].
Further complicating issues have been technological advances that make it
possible for more providers to utilize electronic data entry and secure electronic
communication over recent decades. Through the Health Information Technology
for Economic and Clinical Health (HITECH) Act, under the American Recovery
and Reinvestment Act, over $20 billion of incentive bonuses have been made
available to Medicare/Medicaid providers who adopt EHR-based systems that
meet meaningful use criteria. With increasing use of EHRs, there has also been
a greater development of health information exchanges (HIE) that seek to migrate
clinical data across EHR programs. HIE increases the risk for security breaches or
inadvertent disclosure of protected/private information, particularly through release
by other non-mental health providers. Despite these efforts, recent studies indicate
barriers to information continue and prevent optimal information sharing both
within and across organizations [2].
For example, many mental health providers were initially excluded from the
incentives due to Social Security Act definitions of the term “physician” that
excluded some mental health providers and clinics. Some mental health providers
have side stepped this issue by incorporating their practice into the “medical home”
model as part of the Accountable Care Act (ACA) of 2010. Within this model,
rapid collaboration and information sharing will be essential to coordinate the most
efficient, cost-saving care model. Many advocates of EHRs for integrated mental
health care report improvements in care coordination, patient engagement within the
treatment system, fewer medication errors and improved medication compliance [3].
7.1.2 Clinical Concerns Urge Caution
Many mental health providers are confronted with the current challenge of incor-
porating EHR systems into their practices. A major struggle is concerns about
private, at times intimate, information being easily accessed by others either within
or outside their practice. It has become imperative for health record companies,
individual providers, and treatment centers to balance appropriate access to the EHR
while maintaining privacy of patient information [4]. The balance of access versus
confidentiality is tenuous surrounding documentation of mental health visits. It is
further amplified when working with children and adolescents.
In the medical setting, parents have access to their child’s medical record until the
child turns 18 years old or becomes emancipated. In the psychiatric setting, however,
access to the child’s protected health information becomes much more complicated.
For some children and adolescents, allowing the parent(s) to have access to protected
health information could expose the minor to potential psychiatric or physical
trauma. The question becomes, “When do parents/legal guardians not have the right
to view protected health information?”
First, if state law allows a minor to seek mental health treatment without parental
consent (even if parental consent is given), the minor also has the right to refuse
parental access to protected health information for that specific treatment. Next,
access to protected health information by parents/legal guardians can be restricted
or refused if the licensed provider, in his/her professional judgment, feels that
sharing the information would not be in the minor’s best interest. Finally, any
reasonable belief that the parent(s)/legal guardian is abusing or neglecting the minor
or that access to protected health information would endanger the minor allows the
provider, in their professional opinion, to refuse access to the medical record [5].
In addition, psychotherapy process notes that are not utilized for care planning,
medication management, or billing may be kept separate from the main medical
record and remain exempt from disclosure in the legal medical record, based on a
US Supreme Court ruling (Jaffee v. Redmond). It is imperative to learn the specific
state laws in which you practice that pertain to protected information and access to
the EHR.
Given these concerns, psychiatrists and mental health providers have been
delayed adopters of EHRs [6–8] and have some of the lowest overall rates of EMR
use [9]. Some previously identified barriers to global EHR use, as reviewed by
Stewart [10] include: limited time with patients, diminished eye contact, disruption
to workflow, missed non-verbal cues, and current issues with interoperability
between most EHR systems [11–15]. Several peer-reviewed research studies have
sought to examine health care provider’s concerns for EHR use. The first known
article, published in 1998, studied beliefs of providers after the implementation
of an EHR system. Many providers in this study felt that the quality and content
of patient interactions were improved with an EHR system. However, the article
did not formally address any other concerns with this study [16]. Another post-
implementation survey study explored psychiatric providers’ views and beliefs
after transitioning from paper records to a sequestered psychiatry database as part
of a hospital-wide EHR at Vanderbilt University. One year after this transition,
outcome metrics including confidentiality/stigma of mental illness, quality of the
EHR, release of information, reporting behaviors, and providers’ views of patients’
responses in light of this change were measured [17]. The results demonstrated
perceived maintenance of therapeutic communication with patients. Providers also
noted that their records were more complete and legible. However, a majority
(63 %) of providers remained wary to include highly confidential information in
the record and most providers (83 %) hoped their own psychiatric records would
not be included in the more accessible EHR system.
7.1.3 Dangers of Technology Use in Youth
Outside the discussion of documentation, there are also very real dangers with
technology and adolescents. Not only are impressionable youth exposed to cyber-
bullying and sexting, they have the world available at their fingertips with access to
a host of seedy and unscrupulous individuals.
Sexting
The “sending, receiving, or forwarding of sexually explicit messages, photos, or

images via cell phone, computer, or other digital device” constitutes sexting [18]. In
a study conducted by the Pew Research Center, 4 % of teens have sent a sexually
suggestive nude or nearly nude image of themselves to someone via text messaging
and 15 % of teens have received a sexually suggestive nude or nearly nude image of
someone they know by text [19].
Sexting usually occurs in three situations: between romantic partners; with
images forwarded to friends or classmates; or with images sent between two people
in which at least one is hoping to become romantically involved [19].
Depression has been associated with sexting. Adolescents who feel depressed
or alone may be more willing to sext due to peer pressure and wanting to fit in.
Likewise, feelings may surface after sending a sext message of shame or guilt
which can lead to a depressive episode. In a study conducted by The Educational
Development Center at Boston University, 36 % of students who had “sexted”
reported feeling depressive symptoms in the past year. More importantly, those
who had “sexted” were significantly more likely to have a suicide attempt in the
previous year. Of the 23,000 high school students interviewed, 13 % of those who
have “sexted” reported a suicide attempt in the past year as compared to only 3 %
of students who had not “sexted” [20].
Cyberbullying
Cyberbullying is defined as “an aggressive, intentional act carried out by a group

or individual, using electronic forms of contact, repeatedly and over time against
a victim who cannot easily defend him or herself” [21]. Cyberbullying reaches a
peak in high school. In children aged 12–14, victims of cyberbullying are three
times more likely than non-victims to report depressive symptoms, self-injury, and
serious consideration of suicide. Adolescents, aged 14–17, who are bullying victims
are four times more likely to report attempting suicide in the past year [22].
Social Media
Social media use is extensively covered in Chap. 10 of this publication. As stated

by the American Academy of Child and Adolescent Psychiatry Facts for Families,
“over 60 % of 13–17 year olds have at least one profile on a social networking site,
many spending more than 2 h per day on social networking sites.” Social media
can help children stay connected to friends and family around the world. It also can
help youth express their interests and form their identity, connecting with other like-
minded peers. However, terms such as “Facebook depression” [23] and “iDisorders”
[24] have become a part of the nomenclature of mental health treatment, recognizing
the correlation between excessive technology use and mental health issues.
Growing research has demonstrated the ability to associate certain trends seen in
underlying mental health disorders [25]. Social media provides a concrete, objective
way for a child to compare oneself to peers – having less friends or followers than
other friends can cause some teens to become demoralized and ridiculed. Social
media also serves as a platform for bullying and taunting throughout the day and
night without relief.
Chat Rooms
Chat rooms expose children to unknown people – potential bullies, predators, and
mentors. The ability for people in chat rooms to remain anonymous allows untold
possibilities to arise. Parents would never allow their children to open the front door
for a stranger, yet may be completely unaware of the multiple hours their child is
chatting with a sexual perpetrator on the Internet. Chat rooms also expose children
to experiences that they may not otherwise be exposed to: drug parties, new ways to
hide cutting, graphic sexual discussions, and other risky behaviors.
7.2 The Path Forward: How to Effectively Document,

Treat, and Innovate
7.2.1 Balancing Confidentiality and Transparency
The fear that many child and adolescent providers have is that safety must be
maintained for both the pediatric patient and their caretakers/family members [26].
Valuable historical information, such as family history of mental health issues and
details regarding family dynamics, outcomes from separation/divorce, and family
legal issues, can have damaging implications if accessed inappropriately or in
accordance with the law, such as a shared custody situation. Secondary to these
fears, many providers have altered their documentation styles in light of EHRs.
Recent publications have sought to discuss strategies for navigating these ethical
issues in the digital age. Nielsen et al. [27] detailed several preventative measures
for providers and organizations to best safeguard personal health information (PHI)
and avoid privacy concerns when working with pediatric patients. It is imperative to
release the least amount of data possible in an effort to coordinate care [28]. It is also
essential to avoid practicing outside of one’s scope of practice, avoiding discussion
of topics/disorders that you are not directly treating and avoiding release of other
providers’ notes [27]. Documentation should occur with the expectation that the
child or family member will eventually see the record [27, 29, 30].
Some providers and institutions have taken the push for transparency further,
giving patients full access to their medical record, including notes regarding mental
health care. One of the largest studies to date, entitled OpenNotes, allowed the
patients of over 100 primary care providers (PCPs) from three separate clinical
settings to elect to have access to their entire record. The PCPs directly invited more
than 20,000 patients to enter the study [31, 32]. During the 1 year study period,
13,564 patients accessed at least one note through a web-based patient portal [33].
At the end of the study period, patients could decide to continue with open access or
to terminate access. In post-intervention studies completed by 41 % of patients in the
study, a majority of patients felt that they had more control of their care and reported
improvement in medication compliance/clarity of regimen. Between 20 and 42 %
of patients shared their notes with others. However, 26–32 % (dependent on clinical
site) of patient responders had concerns about their privacy and 1–8 % reported
that this access heightened their worry or confusion regarding their care. From a
provider standpoint, there was no discernable change to the number/frequency of
electronic messages sent from patients who were in this study. Most providers felt
that this change had minimal effect on their practice or documentation methods.
A minority of providers noted an increased time duration of documenting notes (up
to 21 % based on site) and changing the content of their documents (3–36 %). The
four major topics that providers noted change in documentation style were: mental
health issues, substance abuse issues, obesity, and cancer [34]. At the end of the
study, almost all patients included wished to continue their access (99 %), whereas
no providers withdrew their access [33, 34].
These positive findings have led a group of providers from Harvard Medical
School to recommend that behavioral health notes have the same open access,
given that these notes are often excluded from patient portals or kept in a separate
database in the electronic record [35]. They speculate that the hesitancy to include
these records from a clinician/institution standpoint often stems from concerns that
patients will find the information “devastating” and/or may feel unable to question
or challenge information listed in the note, such as aspects of the examination or
diagnosis. They argue that giving patients’ access allows for the chance to review
their clinician assessments in an unpressured, home environment, providing time to
digest the presented material and potentially dissipate defense mechanisms seen in
subsequent appointments. However, they support the idea that some notes should
be able to be excluded by providers if deemed potentially harmful for that patient.
They state: “By writing notes useful to both patients and ourselves and then inviting
them to read what we write, we may help patients address their mental health issues
more actively and reduce the stigma they experience.” [35].
7.2.2 Maintaining the Doctor Patient Relationship
Does the use of technology during the patient visit negatively affect the patient-
provider or caretaker-provider relationship from a patient perspective? Studies over
the last 30 years have demonstrated that computer use within the examination room
has not been seen as a barrier to patient care or satisfaction in the medical arena
[36–42]. However, several researchers and psychiatric providers have speculated
that the changes in room design and interaction style when actively using EHR
systems during the appointment may have a negative impact when working with
patients with mental health issues [11]. Distancing of the therapeutic interaction by
a computer or electronic device has been likened to having an extra person in the
room, which may disrupt the quality of the therapeutic connection and overall care.
A recent research study sought to explore this topic further, utilizing a pre- and post-
test survey study of psychiatric patients after implementation of an EHR system. The
non-validated screening tool utilized in adult psychiatric clinics did not demonstrate
a statistically-significant difference in pre- and post-data when examining eight
facets of patient care, including communication, interpersonal interactions, and
confidentiality [10]. Despite the study being conducted on adults, data conferred
may also be applicable to child and adolescent patients who often demonstrate a
preference for multitasking with electronic devices and for utilizing these devices
as a major form of communication.
The potential power, both positive and negative, of the electronic medical record
does not end with the patient/provider interface when working with patients with
mental health issues. Studies demonstrate that the transparency of EHRs is essential
to targeting psychiatric issues that are often underreported, such as substance abuse
issues. Studies have shown that patients with substance use issues often delay care
for more than 10 years after initial substance use [43], which often first occurs
in adolescence. Utilization of an EHR system may allow for better tracking of
substance use in youth across multiple providers [44]. It may apply that there is
benefit in tracking other internalizing mental health disorders, such as anxiety/OCD,
eating disorders and depression as they may have a similar pattern of delay with
reporting.
7.2.3 Effective Documentation for Child/Adolescent Patients
Given the push for broader EHR use, patients’ wish for more transparency, and
governmental requirements for improvements in interoperability between record
systems, mental health providers are faced with the challenge of potentially altering
their practice models and documentation techniques to meet these needs [45]. Some
practitioners in the field have lobbied for replacing highly medical jargon with more
common, everyday language in a push for a more patient-centered care [46]. As
more patients have access to their notes, this change may help to avoid extended
discussions regarding diagnostic terms during appointments.
In parallel with current media trends, working to move toward summarizing
care in a “medical tweet,” in which care/formulations are synthesized into a more
easily digestible summary for both adolescent patients, their caretakers, and other
providers alike [46]. Clinicians providing a nonjudgmental, descriptive summary of
care can demonstrate a clear understanding of one’s underlying issues and struggles
[35]. This may help to reduce rogue cutting and pasting seen in many EHR system
notes that unnecessarily lengthens notes and opens channels for confusion about
care provided [46].
Studies have demonstrated that mental health professionals also include uncer-
tainty terms, called “hedge phrases,” in clinical documentation [47]. Hedging can
lead to greater ambiguity and misinterpretations by patients and their caretakers,
which may lead to more negative patient-provider encounters or impressions [48,
49]. This may partly be due to difficulties in the construction of DSM diagnoses
as clinician attempt to re-apply adult definitions of disorders to youth patients. In
addition, providers are also less likely to formally diagnose a potentially chronic and
debilitating diagnosis in childhood (i.e. schizophrenia) without having some level
of certainty. Being more transparent in our notes surrounding diagnostic uncertainty
may improve communication and involvement with diagnostic discussions [50] and
improve patient satisfaction [51].
A commonly excluded portion of medical and mental health care documenta-
tion is notation of patient strengths. Detailing these strengths, such as resiliency
despite multiple adversities, support systems, attitudes, participation in care, aca-
demic performance, awards, and faith/spirituality, can be an effective message
of validation for our patients [35]. This is especially important when working
with children/adolescents, who often feel “trapped” in situations where control is
marginalized or are forcibly brought to treatment by others when they “feel normal.”
Balancing the message of pathology and health to our patients gives them a clearer
roadmap for overall health and lets them know that we are viewing them as an
individual, not just a “patient with issues.”
7.2.4 Tracking Longitudinal Safety and Risk
An often missed opportunity in many clinical encounters is the structured risk

assessment. Technology can aid in documenting the discussion of safety with each
clinical encounter by detailing evaluation of suicidality and self-harm. Suicide
continues to be one of leading cause of death for the pediatric population, ranking
second or third (based on the study/timeline) behind only unintentional injury.
Suicide accounted for 5,104 deaths in the United States among persons 10–24 years
of age in 2011 [52]. However, Milton et al. [53] demonstrated that a physician
completed and documented a risk assessment 38 % of the time in patients who later
committed suicide. Use of EHRs can better help to systematize safety assessments
into each clinical encounter. Alerts can be built into the medical record to prompt
providers to screen for safety issues with each visit, especially for patients who are at
highest risk. Many EHR systems allow for screening to occur in the waiting room on
electronic tablets or personal computing devices and be uploaded into the medical
record. These screening forms can be very helpful with the pediatric population,
as patients may be uncomfortable discussing issues of safety with their caretakers
present. Transparency of safety issues in the record are important to decrease risk for
legal action in an unfavorable outcome and to best collaborate with other providers,
who share these inherent risks. Since elicited safety issues require breaking confi-
dentiality between a pediatric provider and their caretakers, clear documentation of
risks should not be problematic when reviewed by a patient or their family.
7.2.5 Role of Increased Functionality and Improved

User Interface
As seen in these studies, harnessing the power of the EHR has been an exciting
yet daunting task for mental health providers, especially in the field of child
and adolescent mental health care. This fear has likely contributed to the delay
or avoidance of use by many providers in the community. Giving providers a
resource to efficiently track clinically-relevant information, such as vital signs,
growth charts, labs, medication dosing, and information from self-reports, will
undoubtedly augment the care of our patients. Some providers also note concern
for “data overload,” which has prompted some clinicians to advocate for tailored
dashboards for psychiatric providers [54].
7.2.6 Discussing Technology Use with Youth and Their Parents

or Guardians
As providers working with children and families, we are often asked questions by
families regarding technology and media use by youth. Questions include how much
cell phone or Internet use is appropriate, how to limit usage if excessive, how to
manage tantrum or defiant behaviors when limits are set, and how to keep up with
the latest technology or app that the youth are using? The Pew Research Center
found in 2013 that 78 % of teens have a cell phone and almost half own smartphones.
Approximately one in four youth utilize his or her smartphone as the primary access
to the internet. 93 % of children have a computer or access to one in the home. In
turn, 95 % of youth in the US have access to the Internet on a regular basis [55].
Screening for technology use has become vital in working with children and
adolescents. The American Academy of Pediatrics recommends asking as least two
questions regarding media use with each visit/intake: (1) How much recreational
screen time does your child or teenager consume daily? and (2) Is there a television
set or Internet-connected device in the child’s bedroom? [56]. The findings from
these questions can stimulate discussion regarding technology use and determine
potential maladaptive use patterns that may interfere with sleep hygiene, school per-
formance, socializing with peers, physical activity, and other spheres of functioning.
It is important to assess a child’s ability to limit use of technological devices in the
home and caretakers’ awareness of the amount/type of media use.
Providers are encouraged to take a developmental perspective on media and
technology use based on the age of the patient [57]. For preschool-aged children,
limiting screen time (television, mobile/portable devices, etc.) to less than 1 h per
day in 15–30 min increments is recommended. For school-aged children, discussion
of internet and media safety is imperative. Counseling children and parents on the
fact that technology use is a “privilege, not a right” is important when balancing
other demands (school, exercise, time with peers/family). Development of a “Family
Media Agreement” (found at: www.commonsensemedia.org) can be helpful to teach

children not to disclose personal information online without parental support along
with not sharing passwords or account information with others. Development of a
family home plan can be vital, establishing clear rules regarding technology use,
storing of devices, acceptable websites or forums, and rules and regulations with
certain sites [56]. As children attempt to assert their independence in preadolescence
and early adolescence, encouraging parents to take interest in their children’s online
activities and having children teach them about their favorite sites and devices can
be helpful. Parents can also help with troubleshooting and problem-solving should
unsafe situations arise. Establishing regular family meetings to discuss online topics
and reviewing online activities, accounts and profile settings can be very helpful
during this age [23]. Development of open discussion regarding media use can pay
significant dividends in future years. In mid-adolescence to early adulthood, it is
important for caretakers to monitor for warning signs of misuse and problematic,
high-risk behaviors with technology/media, such as sexting [57].
Many youth have become dependent on technology as the primary form of
communication. Limiting the use of mobile devices can cause heightened levels
of anxiety and distress for high users. This is seen clinically in patients that we
treat, but has also been examined by researchers who found statistically significant
elevation in State/Trait Anxiety Inventory (STAI) scores after 1 h of limited cell
phone access in high utilizing young adults [58]. Multitasking has become more
of the norm in children and adolescents [59]. It is often perceived as a strength of
the current generation and is felt by many youth to help them be more efficient.
However, growing data suggests that youth who prefer to multitask or task-switch
spend minimal time on a task prior to switching (6 min in one study), which may
directly impact GPA/academic performance and impair study strategies [60].
7.3 Utilizing Technology with Behavioral

Healthcare in Youth
Most pediatric patients will not know of a time without the availability of mobile
devices. They have always had the internet at their fingertips. This availability has
affected all aspects of life, from data searching, to research, to schooling, and
to communication with others. Given the extensive use of online resources and
mobile computing devices in the pediatric population, it is important for child
and adolescent mental health providers to be aware of emerging technology and
trends. As stated above, families will often look to providers for assistance with
identifying maladaptive technology trends and overuse, which may potentially
exacerbate or cause mental health issues. Youth often feel estranged from their
caretakers, demonstrating an inability to effective communicate needs given the
wide chasm in technological awareness that exists between their generation and
their parents’ generation. Helping patients and their caretakers’ bridge this gap is an
essential aim in effective care.
It is important to model techniques described in the previous section when

working with youth. Asking questions about their favorite applications and sites
can open doorways of discussion for even the most internalized and withdrawn
patient. It is helpful to review each patient’s preferred means of communication and
interaction with peers and family. Reviewing potential dangerous online activity can
be helpful as part of a thorough safety assessment for impulsivity. It is also helpful
to review online activities by close peers, which can positively or negatively impact
safety.
Some mental health providers have become more comfortable with the idea of
employing patient-centered applications (“apps”) or other technological advances
into their practices [61]. There are thousands of medical applications, most of which
are not systematically reviewed or approved by medical societies or the Food and
Drug Administration (FDA). It is recommended that a provider fully review and
explore a medical app prior to clinical use to determine issues of confidentiality,
data sharing, and access to data. If apps are utilized in patient care, the risks and
benefits should be fully reviewed with both the patient and parents/guardians prior
to implementation, much like medication initiation [62]. Apps focusing on charting
mood may be valuable in determining factors that contribute to worsening mood or
mood lability (sleep, stressors, etc.). Some apps provide information on medications
and treatment, allowing for reminders to take medication, chart mood, or perform
therapeutic homework. Other apps target internet-based therapy, which will be
covered in a separate chapter in this book. Due to the lack of research evidence
to date coupled with active discussions on directions for clinical oversight [62, 63],
these applications should not be viewed as a standard of care but may be helpful for
augmentation of care in the right clinical situation [62].
Practical Scenario
An 10 year old male and his mother present to your outpatient child and adolescent
clinic due to concerns about his recent behaviors over the last several weeks.
Mother states that he is more irritable and short-tempered. His grades have recently
declined as well. He has been more isolative in his room and asking to avoid
interacting with the family, including not wishing to come to family meals or events
on the weekend. He has become more preoccupied with using the computer and
becomes very upset when his parents attempt to engage with him on his activities.
In meeting with him individually, he reports opening a social media and social
messaging account recently to connect with his friends. He reports that several older
children at school have found out about this and began teasing him on these sites.
Since this time, others have joined, which has caused him to be more upset and
overwhelmed. He reports that his family does not know of his accounts, stating that
his parents feel he is just working on schoolwork or watching videos while on the
computer.
The first recommended goal of treatment would be to open up a dialogue about
the child’s recent online activity, since it has likely contributed to his worsening
mood and behaviors. Given his age, he must have misrepresented his age to sign-
up for these accounts, which may pose a risk if accessing adult-like content.
Additionally, he may not have fully understood recommended security features or

the inherent risks of social media. Given the potential safety concerns, it would be
important to work on making his family aware of these activities to gain support and
guidance.
Despite the age restrictions on social media sites, some families are open to
children misrepresenting their age to create accounts. This is strongly not recom-
mended, since younger children may not have the emotional and social maturity to
handle topics routinely discussed on these sites. However, if patients/families chose
to do so, it is imperative that the parents monitor these accounts and have the log-in
names/passwords to routinely check the accounts – being “friends”/contacts may not
provide enough monitoring based on safety settings. It is important for the provider
to work with the family on establishing rules around electronic devices in the home.
We would recommend that a child of this age not have full access to the Internet
and media sites in his room for extended periods of time, particularly overnight.
We would recommend having the child use the computer or mobile devices in a
more public setting and that family work to set-up parental controls to the internet.
It would be helpful for the child to understand risks involved with these sites,
including cyberbullying and inappropriate content that may be easily available, and
for the family to closely monitor for changes in behaviors. Helping the child to
disengage with these online peers will be important. Prioritizing schoolwork and
other activities before allowing access to multimedia accounts will also be important
in an effort to better balance functioning in all settings.
Discussion of appropriate technology use and safeguards is just as important
as discussing sex and drug/alcohol use with children during this digital age.
Opening dialogue early with children will help to avoid pitfalls in later adolescence.
Additionally, parental modeling of appropriate technology use will be important.
Avoiding use of mobile devices during family engagements (dinner, family func-
tions, etc.) will help to model appropriate use for children. Joining with children’s
interest in multimedia sites will also stimulate dialogue and discussions with the
child, opening doorways of communication in even the most reserved child.
7.4 Summary and Future Research/Trends
We are firmly entrenched in the electronic age. The use of electronic devices
has altered the way we do business, communicate, travel, and interact. For years,
medicine has lagged behind other areas of business with adoption of technology,
especially in the area of computerized health records. Due to governmental stan-
dards and regulations, there is strong push for standardized use of EHRs across
all providers – this train has left the station. Despite this, many mental health
providers are still waiting to decide whether to jump aboard and run the risk of
being left behind. It is important that all providers educate themselves on EHRs and
the policies surrounding implementation. Both the American Academy of Child
and Adolescent Psychiatry (AACAP) and American Psychiatric Association (APA)
have information on their websites for choosing the correct EHR system for your
practice. We will need efficient and secure systems to connect with other providers
in this multidisciplinary environment or risk being excluded from the discussion.
Psychiatrists have already been asked to change the way we treat patients over the
years in all settings of care. Altering the way we document and communicate in this
digital age is another necessary change. Historically, mental health providers have
been wary to allow access of our treatment notes to our patients or other providers
within the medical field, as seen by studies on providers after transitioning to an
EHR system or before enrolling in a health information exchange. Transparency will
be an essential part of care moving forward. Providers should review information
listed in progress notes, including diagnoses and rationale for treatment. Based on
multiple studies, our patients will welcome this transparency and the dialogue it
brings. As mental health providers, we work to de-stigmatize mental health illness
and treatment – sequestering all aspects of this treatment from the rest of medical
record will not help to break down these barriers.
Future areas of research should include follow-up studies on provider perceptions
of EHR use and need for sequestration of all psychiatric records, especially in
the field of child and adolescent psychiatry. While policies are forming regarding
regulation of patient portals and health information exchanges, especially as it
pertains to access of records by minors, child and adolescent mental health providers
need to continue to advocate for inclusion in these discussions. Interoperability of
medical records for minors will be difficult to manage between different facilities
due to the independent policies in each organization and laws regarding access
to care in each state. Further research on the effects of additional transparency
of child/adolescent psychiatric notes with patients and their caretakers would be
ideal. Development of a study in psychiatry similar to the OpenNotes study would
undoubtedly help our field move closer to our partners in medicine and help to
identify issues to effect necessary change.
Child and adolescent providers also need to remain salient in the field of
technology. We are seen as experts in childhood development – recognizing the
intricate role that technological advances play in this development is vital. The
families and caretakers of minors are often relying on us to assist them in this arena.
Continued education in the area of technology should remain a focus in professional
conferences and online learning modules for psychiatric providers. Discussion of
technology in session can open doorways of communication with our patients and
give us access to their social and emotional development. It can also help to identify
early risk behaviors that may manifest in the future if left unattended. There is
a growing field of research surrounding computer-aided psychotherapy tools and
techniques, which will be an invaluable tool to augment the care of the child and
adolescent patients we treat.
Overall, child and adolescent psychiatry must embrace technology as a field.
During this time of healthcare transition, we must align ourselves with other
technological advances in the field or risk endangering our seat at the table with
other medical specialties. This will help us stay relevant, not only in the healthcare
field but with our patients as well.
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Chapter 8
An Overview of Practicing High Quality
Telepsychiatry
Donna Vanderpool
Abstract Providing psychiatric services remotely via telepsychiatry can be an

effective care delivery model. Given the increasing need for psychiatric services,
utilization of telepsychiatry is expected to increase for both consultation and
treatment purposes. There are currently regulatory constraints, such as licensure,
in-person examination, and prescribing requirements that pose significant barriers
to the widespread adoption of telepsychiatry. However, these regulatory barriers are
being evaluated by the states and are slowly being resolved. The steps to practicing
quality telepsychiatry are: determine exactly what type of telepsychiatry you want
to practice; determine how you want to practice and what technology will be used;
address licensure requirements in the patient’s state; address in-person examination
and prescribing requirements in your state and the patient’s state; address other
relevant legal issues such as fraud and abuse, credentialing, and reimbursement
requirements; determine the standard of care and how to meet or exceed it when
practicing telepsychiatry; and evaluate the quality and effectiveness of the services
rendered via telepsychiatry.
Keywords Adolescent psychiatry • Continuity of patient care • Controlled sub-

stances • Electronic mail • Liability legal • Malpractice • Medical informat-
ics • Mental health services • Patient satisfaction • Social media • Suicide •
Telefacsimile • Telemedicine • Teleradiology • Videoconferencing • Wireless
technology
8.1 Introduction
The use of telepsychiatry has grown remarkably in recent years. Telepsychiatry

refers to the delivery of psychiatric services via telemedicine. Telemedicine very
broadly defined means the use of technology to enable the practice of medicine
delivered remotely, with the physician and the patient in different locations, or the
physician consulting remotely with another provider.
D. Vanderpool, M.B.A., J.D. ()

Vice President, Risk Management, Professional Risk Management Services, Inc.,
Arlington, VA, USA
e-mail: vanderpool@prms.com

160 D. Vanderpool
8.2 History of Telepsychiatry
The earliest form of telemedicine utilized the telephone. The military, space
programs, and various governmental organizations are credited with the develop-
ment of telemedicine applications. Massachusetts General Hospital in Boston was
instrumental in the early use of telemedicine by establishing a microwave link to a
medical clinic at Logan airport in 1968, and then expanding services in the 1970s
to schools, courts, and a prison. The first psychiatric application of telemedicine
was employed in 1959 and involved the use of a two-way, closed circuit microwave
television. This linked the Nebraska Psychiatric Institute of Omaha with the state
mental hospital 200 miles away to provide consultations, education, training, and
research [1].
In the 1990s, telemedicine expanded with greater adoption in healthcare systems
and networks and within specialties such as teleradiology and teladermatology.
Technology continued to expand, allowing the public to access more broadband
and wireless technologies. So the use of telemedicine grew from large settings such
as hospital systems to private practices.
The use of telemedicine, and specifically telepsychiatry, is expected to continue
to expand given:
• Advances in technology that allow for improved patient assessment
• Increased technology options at decreased prices
• The value of consultation from remote experts
• The increase in patients with insurance seeking care under the Patient Protection
and Affordable Care Act
• The shortage of psychiatrists, including sub-specialists such as child and adoles-
cent psychiatrists
• Convenience, for both the patient and psychiatrist
8.3 Current Status of Telepsychiatry
8.3.1 Evidence Supporting Telepsychiatry
Evidence supports the efficacy of telepsychiatry, including research finding that

telepsychiatry may be better than in-person treatment for some patient populations,
particularly children and adolescents. After completing a review of outcomes across
patient populations and diagnoses, Hilty et al. [2] concluded that telepsychiatry
services “are unquestionably effective in most regards, although more analysis is
needed. They are effective for diagnosis and assessment, across many populations
(adult, child, geriatric, and ethnic), and in disorders in many settings (emergency,
home health), are comparable to in-person care, and complement other services in
primary care.”
8 An Overview of Practicing High Quality Telepsychiatry 161
8.3.2 General Delivery Models
While there are many different telepsychiatry delivery models for providing
treatment, they fall into three general models:
1. The patient is seen remotely at a facility or other formal telemedicine program.
This can be with or without another clinician present for the session. This option
typically presents the least professional liability risk as there are other clinicians
available for emergencies, facilities tend to have policies and procedures, etc.
2. Direct to consumer, but via a third party company selling telepsychiatry services.
Typically in this model, the company schedules the appointments, provides the
equipment, may have control over the clinical record, etc. With this model, it
is important to be alert for corporate practice of medicine concerns (see section
“Corporate Practice of Medicine”).
3. Direct to consumer, outside of a third-party selling telemedicine services. With
this model, care can be delivered with or without a commercial telemedicine
platform. Regardless, the psychiatrist needs to ensure the choice of technology is
appropriate and does not violate any regulations under state or federal law, such
as the Health Insurance Portability and Accountability Act (HIPAA).
8.3.3 Regulation of Telemedicine
Governmental regulation of telemedicine activities, while sometimes unrecognized,

is extensive, but often inconsistent. This lack of a coordinated regulatory infrastruc-
ture is currently a significant obstacle to the implementation of telepsychiatry.
Practice of Medicine
The practice of medicine is regulated by the states, specifically by the legislatures

(via statutes), as well as by state agencies, such as medical licensing boards, (via
regulations, policies, guidelines, and position statements). This has resulted in a
patchwork of regulations, with individual states varying widely in telemedicine
requirements. There basically are only two points about which all states that have
addressed telemedicine agree: (1) services are rendered where the patient is located,
and (2) the standard of care for telemedicine services is the same as for in-person
visits. Beyond that, there is no consistency in what is telemedicine. Most, but not
all, states exclude telephone, e-mail and fax communication with patients, defining
telemedicine as secure videoconferencing or store-and-forward technology.
162 D. Vanderpool
Licensure
States’ early regulation stemmed from online prescribing, whereby individuals went
to a website, filled out an online questionnaire, and requested medications. The
history was reviewed by a physician and the prescription was usually filled by the
online pharmacy.
Case Example
In 2006, a Colorado psychiatrist was charged with a single felony count of
practicing medicine without a valid California license. He had prescribed
an antidepressant over the internet to a 19-year old college student in
California. The student committed suicide, and the medical examiner found
the prescribed medications in the patient’s system. The psychiatrist was
criminally prosecuted, but argued that California had no jurisdiction. After
the California appellate court ruled that California did have jurisdiction, the
psychiatrist pled no contest and was sentenced to 9 months in jail. The
patient’s parents also filed a civil lawsuit against the psychiatrist, the online
company, and the pharmacy that shipped the medication. The parents settled
their claims against the online company and the pharmacy, and dropped their
suit against the psychiatrist [3].
As illustrated by this case, it is the patient’s state where services are rendered, and
states require physicians to be licensed in the patient’s state; however, the specific
license required varies by state. According to the Federation of State Medical Boards
(FSMB) in its Telemedicine Overview, [4] the majority of medical and osteopathic
licensing boards require a full license in the state where the patient is located; a
few of these states have an exception for physicians licensed in bordering states.
Some state boards require a special purpose or telemedicine license rather than a
full license. And one state merely requires out of state telemedicine physicians to
register with the licensing board to treat patients in its state.
Prescribing
While the issue of where telemedicine occurs is clear – where the patient is
located – there are other regulatory issues that are much less clear. In reaction to
the early internet pharmacies that were providing medication based solely on online
questionnaires, states enacted laws and regulations requiring an in-person physical
examination prior to prescribing. Congress also reacted to internet prescribing
based on a questionnaire by passing the Ryan Haight Online Pharmacy Protection
Act (Act) in 2008, which amends the federal Controlled Substances Act (CSA).
Ryan Haight was a 17-year old male who easily acquired narcotics from an online
website simply by filling out a questionnaire. The physician, without ever seeing
him, wrote the prescription and the drugs were mailed directly to Ryan’s house.
He overdosed on the narcotic and died. The Act bans the selling or dispensing
of prescription drugs via the internet when the online pharmacy has referred the
customer to a physician who then writes the prescription without ever seeing the
patient. The Act also amends the CSA’s requirement of an in-person evaluation
(with the patient in the physical presence of the prescriber) to allow an exception for
telemedicine, as defined by the CSA. Under the CSA’s definition of telemedicine,
the remote treatment must occur with the patient in a hospital or other facility
registered with the DEA and by a prescriber with a DEA license in the patient’s
state. There are exceptions to the licensure requirement, such as providers in the
Veterans Health Administration, and other situations where the government does
not require licensure in the patients’ state.
Not all states have moved on from online questionnaires to embrace appropriate
telemedicine models that allow for a remote evaluation that is the equivalent of an
in-person evaluation. State requirements for in-person visits are not always explicit,
much less consistent. Some states’ requirements can be found or may be implicit in
provisions relating to the physical examination or establishment of the physician-
patient relationship, even when they have not been explicitly stated in relation to the
practice of medicine.
Licensing Board Discipline Example

The Idaho Board of Medicine disciplined an Idaho-licensed physician for
an antibiotic prescription after a telephone consult. This was done through
an online company “Consult-A-Doc” which was subsequently acquired by
Teladoc. Teladoc had to stop servicing its more than 20,000 members in
Idaho because of this Board decision. In its opinion, the Board stated that
a telephone patient encounter is telemedicine. The physician was disciplined
for, among other things, failure to do an appropriate physical examination of
a patient complaining of a respiratory tract infection, and for being part of
the company’s ads in Idaho (she was found to be assisting the company with
the corporate practice of medicine, discussed further in section “Corporate
Practice of Medicine”) [5].
Documentation
Physicians are required under state law to create and maintain appropriate treatment
records. However, specific documentation requirements can vary by state. When
treating patients out of state via telepsychiatry, psychiatrists must be familiar with
and comply with both states’ documentation requirements.
Ownership and control of the clinical record from a telepsychiatry session must
be clarified to ensure the record’s availability in the future, whether for subsequent
164 D. Vanderpool
treatment purposes, or in the event of litigation related to the treatment. Use of a

delivery model involving a facility should not be problematic from a documentation
retention perspective, as the facility will likely maintain the records as with any
encounter in the facility. Similarly, use of a direct delivery model, without the
involvement of any third party vendor, should pose no documentation retention
issue, as the treating psychiatrist will maintain the record as is done with patients
seen in the office. However, if there is a third party vendor involved, either a
company through which telepsychiatry is provided or an internet platform to allow
direct to patient communication, the issue of documentation needs to be addressed.
If the third party retains the documentation, how can you be assured it will be
available if needed in the future? If so, will it be in a format that you can use, or
in the third party’s propriety format? What if the third party goes out of business?
Confidentiality and Security of Patient Information
HIPAA, the Health Information Technology for Economic and Clinical Health
(HITECH), and state confidentiality, data security, and consumer protection laws are
highly relevant to electronic patient information. All breaches of confidentiality can
have significant consequences under state confidentiality and consumer protection
laws. Covered entities under HIPAA and HITECH face additional penalties under
federal law for breach of electronic protected health information (basically medical
and billing records). Federal civil penalties for HIPAA violations include up to
at least $50,000 for each violation, up to a $1.5 million maximum for identical
violations per calendar year. Federal criminal penalties for HIPAA violations can
reach $250,000 and 10 years imprisonment.
Fraud and Abuse
Fraud and abuse issues comprise another regulatory concern. Initially created to
prevent increased costs to federal healthcare programs, such as Medicare and
Medicaid, states have enacted similar laws with broader applications. While a
comprehensive legal analysis is beyond the scope of this chapter, there are several
areas for which legal advice should be sought to prevent violations. Such areas
include, but are not limited to:
• Anti-Kickback Statute: Prohibits the offering, inducing, or paying for referrals.
For example, it is illegal for a hospital to pay a physician providing telemedicine
services more than the fair market value to induce the physician to bring patients
to the hospital.
• Anti-Trust Law: Prohibits, among other practices, price-fixing between
providers. For example, physicians within a telemedicine network could violate
anti-price fixing laws.
• Physician Self-Referral/Stark Law: Prohibits physicians from referring patients

to entities in which the physician, or the physician’s family, has an ownership
interest.
• False Claims Act: Prohibits false or fraudulent claims from being submitted to
the government for payment. For example, telepsychiatrists could violate this
law by submitting a claim for telepsychiatry services rendered to a patient in a
state where the psychiatrist did not have a license, or otherwise failed to comply
with the law in the patient’s state.
One practical tip is to very carefully analyze, or have an attorney analyze any
arrangement involving telemedicine equipment or services for free or for less than
fair market value for potential fraud and abuse concerns.
Credentialing
If you will be providing telepsychiatry services through a hospital, the Centers

for Medicare and Medicaid and The Joint Commission allow credentialing by
proxy. This permits the credentialing and privileging decisions by the distant-site
hospital to be relied upon. While eliminating duplicative credentialing, the process
is involved and time-consuming.
Reimbursement
Compliance with governmental payment requirements is yet another obstacle.

Medicare has coverage for certain telemedicine services, currently limited to
patients at a medical facility (not the patient’s home) in specific geographic, rural
locations. Each state’s Medicaid is different in terms of coverage for telemedicine.
Most states provide some type of reimbursement for telemedicine services. As
illustrated by the Oklahoma discipline example discussed in Sect. 8.4.2, each
state’s Medicaid program can have rules related to telemedicine, such as requiring
informed consent to telemedicine, use of only approved telemedicine networks, etc.
Failure to comply with these requirements can lead to discipline by the licensing
board. As noted by the American Telemedicine Association (ATA) in its State
Telemedicine Legislation Tracking, [6] reimbursement for telemedicine services by
private insurers varies by insurer, and is required by a number of states.
Corporate Practice of Medicine
Many states prohibit corporations from practicing medicine and from hiring a
physician to provide professional services. The rationale behind this prohibition is
that only a physician can make medical judgments and corporations should not be
166 D. Vanderpool
allowed to control clinical decision-making. There are narrow exceptions, such as

for licensed hospitals. Moreover, physicians may be disciplined by the licensing
board for aiding a corporation in the practice of medicine. See the licensing board
discipline example in section “Prescribing”.
8.3.4 Professional Liability
Patient safety and patient satisfaction are key concerns in telepsychiatry, consistent
with all care delivery models. As evidenced by the examples in this chapter,
licensing boards are disciplining physicians for telemedicine activities. Currently
there is a paucity of reported medical malpractice lawsuits involving telepsychiatry.
Two legal database searches revealed only one relevant reported telepsychiatry
lawsuit, which is the White v. Harris case [7] discussed in Sect. 8.4.1. Of course,
other telepsychiatry lawsuits could have been brought, but they may have been
dropped or settled, so they are not publicly reported. Even expanding the search to
telemedicine lawsuits, no reported lawsuits were found addressing either the general
appropriateness of telemedicine or the appropriateness of the specific telemedicine
services provided. There were older cases involving internet prescribing based on
online questionnaires (as examples, see the Hageseth case [3] in section “Licensure”
and the Holzhauser case [8] in Sect. 8.4.6).
The laws related to telemedicine, and specifically telepsychiatry are developing
at a much slower rate than technology is developing. Plaintiff malpractice attorneys,
typically working on a contingency fee basis under which they only get paid if they
win, do not like to take cases that they are not confident they can win. So cases with
non-existent, developing, and contradictory law are not attractive to the plaintiff’s
bar.
Once the relevant law develops, producing court guidance for telepsychiatry,
there will be more malpractice cases involving telepsychiatry. Plaintiffs will have to
prove the same four elements as in any medical malpractice case – the psychiatrist
owed a duty to the patient, the psychiatrist was negligent (failed to meet the
standard of care), the patient suffered damages, and the damages were caused by
the psychiatrist’s negligence. Given that plaintiff has to prove all of these elements
in litigation, but not in a licensing board complaint, board complaints will continue
to be a risk faced by telepsychiatrists.
It is also important to confirm coverage for your specific telepsychiatry activities
under your professional liability insurance policy. The American Medical Asso-
ciation (AMA) recommends, in its Coverage of and Payment for Telemedicine
Report [9] “that our AMA encourage physicians to verify that their medical liability
insurance policy covers telemedicine services, including telemedicine services
provided across state lines if applicable, prior to the delivery of any telemedicine
service.”
8.4 How to Practice Telepsychiatry
8.4.1 Step 1: Determine Exactly What Type of Telepsychiatry

You Want to Practice (Fig. 8.1)
This determination requires an examination of many questions pertaining to poten-

tial telepsychiatry services, as shown on Fig. 8.1. What exactly do you want to do?
Do you only want to provide consultations? If so, to whom? Providing consultations
to other psychiatrists is the least risky in terms of professional liability. With a true
consultation, the consultant does not prescribe or write orders, and the psychiatrist
receiving the consultation is free to accept or reject the consultant’s opinion.
Providing consultations to other physicians, such as pediatricians and primary care
physicians is still very low risk. Maybe you are interested in providing consultations
to non-physician providers such as nurse practitioners, psychologists, and social
workers. True consultations are still low risk, but with non-physicians, the issue
is whether they can truly ignore your opinion. The relationship may be viewed
as a supervisory one, which increases your liability risk. Or maybe you want to
provide consultations to patients. This is riskier, as it may be difficult for patients
to understand that you are rendering services, but not treating. You should manage
expectations and make your limited role clear so the individual understands that
you are not treating. However, even when steps are taken to clarify your role as a
consultant, there is no inoculation against potential professional liability risk.
#2: What technology do you want to use

#1: What exactly do you want to do?
to provide these services?
Consultation Emergency Correctional Video

Treatment Other Telephone E-mail
Only Evaluations Services conferencing
To?
Through a facility or
established program?
Non-Physician
Physicians Patients
Providers
Yes No
You are responsible for

choice of technology
TO DO: Determine
appropriate technology
Fig. 8.1 Preliminary determinations for telepsychiatry [10]

168 D. Vanderpool
Case Example
At the request of a 14-year old patient’s treatment team, the psychiatrist
performed a 90 minute consultation via telemedicine. In the signed informed
consent document, the psychiatrist explicitly stated that the scope of his
services was limited. At the conclusion of the consult, the psychiatrist
offered recommendations for the patient’s treatment, specifically regarding
medication, as requested by the treatment team. Ten months later, the patient
suicided by a variety of medications, none of which had been recommended
by the psychiatrist. The psychiatrist was among the many defendants named
in a lawsuit subsequently brought by the patient’s family. Prior to trial, the
trial court granted the psychiatrist’s motion for summary judgment, holding
that no duty existed on the part of the psychiatrist at the time of the death. The
patient’s family appealed this dismissal of the consulting psychiatrist from the
lawsuit. The state Supreme Court reversed the trial court, holding that through
the consultation, a limited doctor-patient relationship was established, and
therefore the psychiatrist assumed a duty to act in a manner consistent with
the standard of care and to not harm the patient. The Court noted that it was
too early in the case to determine the scope of the psychiatrist’s duty and the
standard of care, and left it to the trial court to continue with the case against
the psychiatrist and the other defendants [7].
Do you want to treat patients? Patient selection is key, as telepsychiatry is not

appropriate for every patient. Technology’s suitability for a specific patient will
depend on the needs of that patient. There are many options for treating patients,
including remotely seeing patients who are in a facility, such as a clinic or hospital.
In this model, there may or may not be another clinician in the room with the
patient. At the other end of the spectrum, the patient may be sitting alone at
home. Telepsychiatry may be the only contact, or the telepsychiatry visits may be
supplemented with in-person visits. If the patient is not seen through a facility, how
will the patient’s needs be met remotely? Does the patient have access to other local
mental health professionals? How will the full continuum of care be provided?
Do you want to provide emergency evaluations? You will need to be familiar with
all applicable laws. State laws may prohibit telepsychiatrists in different states from
evaluating for civil commitment. Even remote evaluations done by a psychiatrist in
the same state as the patient may not met legal requirements, such as those requiring
two psychiatrists to perform in-person evaluations (see Pinal County Mental Health
[11] in 8.4.6).
Do you want to provide telepsychiatry services to jails and prisons? This
is a well-established use of telepsychiatry. Or do you have a different type of
telepsychiatry practice in mind? You will need to check the applicable state
law – statutes, regulations, and licensing board policies. For example, for those
considering remote supervision, some states do not allow out of state physicians
to supervise in-state providers.
8.4.2 Step 2: Determine How You Want to Practice

Telepsychiatry (Fig. 8.1)
As shown on Fig. 8.1, you’ll also need to determine what method you want to use.
Most – but not all – states define telemedicine to include videoconferencing, but
exclude treatment delivered via e-mail, telephone, and fax. If you want to practice
telepsychiatry via videoconferencing, is it through a relationship with a healthcare
facility or other established telemedicine program (university, correctional facility,
etc.)? If so, there may be less risk, as facilities tend to have greater resources
and policies and procedures, addressing, for example, continuity of care and
emergencies. If you are not practicing via a formal telemedicine program, there are
basically two delivery models to consider. The first is to provide services through an
established online telepsychiatry provider, and the second is to do it on your own.
When considering utilizing an online telepsychiatry provider, there are additional
concerns related to the service that need to be addressed. As always, the technology
used must be appropriate for clinical and regulatory purposes. You would need a
Business Associate Agreement, by which the vendor agrees to have administrative,
physical, and technical safeguards to protect patient information. Also, the FSMB,
in its Model Policy for the Appropriate Use of Telemedicine Technologies in the
Practice of Medicine, [12] has extensive requirements when online services are
utilized. These requirements include disclosures (related to services provided, finan-
cial interests, qualifications of physicians, ownership, etc.), as well as mechanisms
for patients to access personal health information, provide feedback, and register
complaints. The FSMB model policy also includes a prohibition of “advertising
or promotion of goods or products from which the physician receives direct
remuneration, benefits, or incentives (other than the fees for the medical care
services).”
When utilizing telepsychiatry without the involvement of a technology vendor
or company that offers telepsychiatry, the psychiatrist is responsible for the choice
of appropriate technology that is effective for providing the intended care. In
determining what the appropriate technology is, there are standards that must be
reviewed. Ensure that the clinical and legal requirements for telepsychiatry can
be met so that you will meet the standard of care with that particular type of
technology. For example, confirm that the bandwidth and resolution are sufficient
to allow an adequate assessment and evaluation of side effects such as tics. The
ATA has issued Core Operational Guidelines for Telehealth Services Involving
170 D. Vanderpool
Provider-Patient Interactions [13] which includes many technical standards, such

as bandwidth, resolution, and frames per second. The ATA guidelines include other
useful recommendations, such as ensuring the telepsychiatry platform used does not
include social media functions that notify users when anyone on a contact list logs
on.
Compliance with HIPAA and state law requirements for the security and
confidentiality of patient information is essential. To ensure the appropriateness of
the technology to be utilized, know and comply with:
• HIPAA and similar state confidentiality law
• State telemedicine statutes
• State telemedicine regulations, policies, guidelines
• Payer requirements, including those of state Medicaid programs
Licensing Board Example

The Oklahoma Board of Medical Licensure and Supervision had a
telemedicine policy that required, among other things, use of a telemedicine
network that meets all technical and confidentiality standards as required
by state and federal law, and written consent from the patient stating their
agreement to participate in telemedicine. The state Medicaid program pro-
vided for reimbursement of telemedicine visits if, among other things, HIPAA
and state privacy requirements were maintained and followed at all times,
and the network used was on the list of Medicaid-approved telemedicine
networks. Three different individuals complained about Dr. Trow to the
Oklahoma licensing board. Two complained about his prescribing, and the
third complainant, from the state Medicaid program, alleged that the physician
was practicing telemedicine via Skype on Medicaid members but Skype
is not a Medicaid-approved network. Dr. Trow admitted this, but said he
thought it was his employer’s responsibility to ensure these requirements
were met. The Medicaid representative also complained that he prescribed
controlled substances without an in-person evaluation and that he failed to
get patients’ consent to the use of telemedicine. Dr. Trow was found guilty of
nine counts of unprofessional conduct, but nothing in terms of inappropriate
use of technology. However, following this case, the medical board issued
rules stating that internet contact such as web-based video does not meet
the equipment requirements, and therefore an actual face-to-face patient
encounter is required, and the technology used must be HIPAA compliant
[14].
As previously mentioned, HIPAA’s Privacy Rule requires a Business Associate

Agreement from any third party that creates, receives, maintains, or transmits
patient-identifying information. Note that some technologies never store the data,
but are a mere conduit, and are therefore not a Business Associate under HIPAA.
It is important to read the fine print related to any specific technology, such as the
privacy policy, to ensure messages are not stored if a technology vendor claims to
only be a conduit. If messages are stored for any amount of time, no matter how
brief, the vendor is not a conduit and must sign a Business Associate Agreement.
In addition to the Agreement promising to protect the security, confidentiality, and
integrity of patient information, HIPAA also requires business associates to notify
physicians of any breach of their patient information, utilize encryption, have audit
trails, etc.
Also be aware of and comply with pertinent professional organizations’ standards
and guidelines as they are part of any standard of care determination. Such
professional organizations include the AMA, American Academy of Child and
Adolescent Psychiatry (AACAP), American Psychiatric Association (APA), FSMB,
and the ATA.
Given the complicated and extensive nature of these legal requirements, it is
likely that consultation with a health information technology professional and a
healthcare attorney will be necessary. Taking the proper steps before getting started
will benefit you as well as your patients.
8.4.3 Step 3: Address Licensure (Fig. 8.2)
Once you have determined what you want to do and how you want to do it, you
can move to the legal hurdles (Fig. 8.2), the first one being licensure. Will you
be providing telepsychiatry services to patients located in a different state? If so,
contact the medical board in the patient’s state to determine if you need a license
from that state. Why is it so important to determine if licensure is required in
the state where the patient is located? The New York State Office of Professional
Medical Conduct in its Statement on Telemedicine [15] answered it best by stating
“The practice of medicine in New York State by someone not authorized to practice
in New York state may constitute the illegal practice of a profession, subject to
investigation and prosecution by the state attorney general.”
8.4.4 Step 4: Address In-Person Examination and Prescribing

Requirements (Fig. 8.2)
Even if you are only providing care via telepsychiatry to patients in states in which
you are already licensed, you must understand the boards’ position on remote
treatment and evaluation, as shown on Fig. 8.2. As mentioned above, states and
licensing boards reacted to online prescribing from questionnaires by enacting
172 D. Vanderpool
Credentialing The standard

Licensure Others
Issues of care
General Rule: Services General Rule: The standard In-person exam requirement
are provided where Will you be providing of care does not change Prescribing requirements
patient is located services through a facility? with technology
Fraud and abuse issues
Reimbursement
Etc.
Will you be providing services Factors evidencing the
to patients out of state? applicable standard of care:
Yes No Statutes - federal and state
Regulations - federal and state
Court Opinions - federal and state
No Other material from regulatory
Yes No TO DO: credentialing agencies - federal and state
Determine issue Authoritative clinical guidelines
credentialing
Are you licensed in the requirement Policies and guidelines from
patient’s state? of facilities professional organizations
Journal articles/research
Yes Accreditation standards
No
Facility policies and procedures
Other
TO DO: Contact other
state’s medical bard to
see if license is required
TO DO: Contact all relevant medical boards

to determine requirements for inperson
evaluation/examination
General Rule: Ensure all relevant medical

boards allow the exact telemedicine
activities you want to do
Fig. 8.2 Legal hurdles for telepsychiatry
laws and regulations requiring an in-person examination prior to prescribing. Some

boards are starting to acknowledge that telemedicine is different from internet
prescribing. Some boards state that an in-person examination is not required. For
example, North Carolina Medical Board in its Telemedicine Policy Statement [16]
states the following:
“Licensees using telemedicine technologies to provide care to patients located in North
Carolina must provide an appropriate examination prior to diagnosing and/or treating the
patient. However, this examination need not be in-person if the technology is sufficient to
provide the same information to the licensee as if the exam had been performed face-to-
face.”
Other states say it depends, for example on where the patient is located.
According to the Texas Medical Board’s Rule §174.4, if the patient is not at an
official medical site, such as a hospital, there must be a face-to-face evaluation by
some physician. Or it could depend on prescribing. For example, the Rhode Island
Board of Medical Licensure and Discipline in its Guidelines for the Appropriate
use of Telemedicine and the Internet in Medical Practice, [17] states “the board
specifically highlights that prescribing controlled substances without an established
in-person physician-patient relationship is prohibited.”
8.4.5 Step 5: Address Other Relevant Legal Issues (Fig. 8.2)
As previously discussed, other potentially relevant legal issues include under-

standing and complying with the requirements for appropriate credentialing and
privileges, complying with reimbursement standards, and avoiding fraud and abuse
problems, as shown on Fig. 8.2.
8.4.6 Step 6: Determine the Standard of Care and How to Meet

or Exceed It When Practicing Telepsychiatry (Fig. 8.3)
Technology does not change the standard of care. As stated by the Florida Board of
Medicine in its Rule 64B8-9.0414, “Telemedicine equipment and technology must
be able to provide, at a minimum, the same information to the physician : : : which
will enable them to meet or exceed the prevailing standard of care for practice of
medicine.”
There are many practical issues to be considered related to use of the technology
and meeting the standard of care, such as framing yourself in the video display, and
gaze angle. Shore [18] has written on this topic and has compiled useful guidance for
many of the practical issues to ensure a professional telepsychiatry encounter. The
ATA, in its Practice Guidelines for Videoconferencing-Based Telemental Health,
[19] offers additional practical advice for the room setup (avoid a distracting
ISSUE: Can you meet the standard of care when providing services remoteyly?
Step 1: Identify all relevant factors concerning the applicable standard of care
(see LEGAL HURDLES chart)
Step 2: Consider care issuse not unique to telepsychiatry, including but not limited to:
Patient Evaluation
Informed consent to treatment
Documentation
Confidentiality
Release of records
Patient monitoring
Interim care
Follow-up care
Emergencies
Patient non-adherence
Re-evaluation of treatment
Other
Step 3: Consider additional care issues related to telepsychiatry
Patient Consent to Re-evaluation of Contingency

Lost abilities Data security Other
Selection telepsychiatry technology planning
Clinical Technology
Sight Hearing Smell Touch Other
Emergencies Failures
Fig. 8.3 Clinical hurdles for telepsychiatry [10]

174 D. Vanderpool
background), and lighting (ensure a well-lit room but do not position yourself
in front of a window). Also, your professional presentation (your appearance,
language, and demeanor) should match that utilized in a psychiatric office.
State licensing boards have been unanimous in stating that the standard of care
is the same whether the patient is seen in-person, or through technology-enabled
patient care. The physician retains the same responsibilities of obtaining informed
consent, ensuring the privacy of medical information, etc. While there is no single
standard of care for any given patient, there are factors that can evidence the
applicable standard of care for any clinical care issue, including:
• Federal and state laws, promulgated by legislatures
– Examples: HIPAA, CSA
• Federal and state regulations, promulgated by agencies
– Examples: HIPAA’s Security Rule, state regulations for prescribing via
telemedicine
• Federal and state court opinions
– In one example, the physician saw an advertisement for a company providing
prescriptions over the internet. Patients were required to provide prior medical
records for the past 2 years and a photo ID. This information was provided
to the physician who normally reviewed it the day before her telephone
consultation with the patients. The physician would consult with each patient,
typically for 20–30 minute. The licensing board permanently revoked her
license for prescribing controlled substances without personally examining
patients. The board specifically wrote in the decision that the physician failed
to contact anyone with the board to determine whether prescribing over the
internet was permissible in the state. The physician appealed, but the board’s
decision was upheld by the trial court and the appellate court [8].
– In another case, the appellate court held that a psychiatrist’s evaluation of
a patient via a remote video-conferencing system did not comply with the
state’s statutory requirement of conducting a complete physical examination
for involuntary treatment [11].
• Other materials from federal and state agencies
– Examples include prescribing guidelines and guidelines for utilizing telepsy-
chiatry in civil commitment evaluations
• Authoritative clinical guidelines; relevant examples include:
– AMA: Coverage of and Payment for Telemedicine [9]
– AACAP: Practice Parameter for Telepsychiatry with Children and Adoles-
cents [20]
– APA: Telepsychiatry via Videoconferencing [21]
– FSMB: Model Policy for the Appropriate Use of Telemedicine Technologies
in the Practice of Medicine [12]
– ATA:
• Core Operational Guidelines for Telehealth Services Involving Provider-
Patient Interactions [13]
• Video-Based Online Mental Health Services [22]
• Practice Guidelines for Videoconferencing-Based Telemental Health [19]
• Evidence-Based Practice for Telemental Health [23]
• Policies and guidelines
• Research and journal articles
Once you are familiar with all the relevant standard of care factors, then
consider the care issues common to all care – services rendered in-person or via
telepsychiatry. As indicated on Fig. 8.3, some of these common care issues include:
• Patient evaluation, including history and physical examination to establish the
diagnosis
• Informed consent to treatment
• Documentation
– There may be extra documentation for telepsychiatry sessions, such as the
location of the patient and the psychiatrist, type of equipment used and any
malfunction, who was present during the visit, etc. [21]
• Confidentiality – compliance with federal law and the law of both patient’s and
psychiatrist’s state
• Patient monitoring
• Follow-up care
– From the New York State Office of Professional Medical Conduct’s Statement
on Telemedicine [15]: “The physician, having established a relationship, has
the duty to be available for care when it is needed or to see that there is reliable
provision for care and advice. The fact that the advice or treatment occurred
via electronic media does not change the requirement for follow-up care.”
How will your telepsychiatry patients reach you between scheduled visits?
How does your patient report adverse effects of medication?
You also need to think through the additional patient care issues unique to
telepsychiatry:
• Patient selection
– Psychiatrists must evaluate whether telemedicine is an appropriate care deliv-
ery mechanism for a given patient. While children and adolescents may be
very comfortable with the technology, telepsychiatry may not be appropriate
for those with cognitive impairment. Moreover, technology is only a tool that
can address lost abilities (sight, smell, etc.) when treating patients remotely.
But not all abilities can be restored, so telepsychiatry will not be appropriate
for every patient.
176 D. Vanderpool
– Questions to ask yourself:

• What conditions do I routinely treat?
• Which of those conditions can I treat remotely?
• Will lost abilities be a problem?
• Is there someone local to assist as needed?
• Where is the patient receiving services?
• Can I treat this condition in this environment?
– Questions to ask yourself about an individual patient:
• Is he sufficiently tech-savvy?
• Is he stable?
• Do I trust the patient?
• How will the use of telemedicine impact the patient?
– Create distance?
– Put patient more at ease?
– Will there be sufficient privacy?
• Would it be difficult to terminate the treatment relationship?
• Informed consent to telepsychiatry
– According to the APA, patients must be given the option of not participating
in telepsychiatry [21]
• Lost abilities, including possibly:
– Sight
– Depending on your telepsychiatry model, you may not know where your
patient is physically located. Uncertainty about patient location can pose
significant challenges for responding to emergencies. Unless patients are in
a telemedicine facility, the patient should disclose their exact location at the
outset of every telepsychiatry session in case emergency services need to be
called.
• Data security – federal and state law must be complied with (e.g., 42 CFR Part 2
regulating the confidentiality of drug and alcohol treatment information)
• Contingency planning, including:
– Clinical emergencies – identify potential local collaborators to help with man-
aging emergencies, as well as being familiar with commitment procedures.
– Equipment failures – protocols are needed to identify specific steps to deal
with equipment failures and alternative methods should be available to
complete the session, such as by telephone.
Note that while this is a comprehensive list, it is not necessarily an exhaustive
list due to all the possible variables that may be involved in specific circumstances
with specific patients and situations.
8.4.7 Step 7: Evaluate the Quality and Effectiveness

of the Services Rendered via Telepsychiatry
As with psychiatric services delivered in-person, the appropriateness and efficacy

of treatment must be constantly considered. Part of this determination involves
evaluating the patient’s satisfaction and your satisfaction with the delivery of care
via telepsychiatry. As always, you should focus on ensuring that your treatment will
help the patient progress toward treatment goals.
It may be necessary to terminate if you determine that telepsychiatry is not
meeting the patient’s clinical needs or the patient is dissatisfied with the remote
treatment. In the event termination of the psychiatrist-patient relationship is nec-
essary, the patient should not be abandoned. Rather, the standard termination
process would need to be utilized – discuss the need for termination and treatment
recommendations with the patient, provide sufficient notice for the patient to find
a new provider, provide resources or referrals for future treatment, confirm with
termination letter, and offer to forward records at the direction of the patient.
8.5 In the Future
The use of telepsychiatry will continue to expand in the future, and we can expect
to see additional regulation, but also increased reimbursement.
8.5.1 States Will Continue to Address Telemedicine
States will continue to address the need for an in-person visit as a requirement
for a valid patient-physician relationship and as a requirement for prescribing
medications, including controlled substances. The issue of state licensure may
become moot if the FSMB’s draft Interstate Medical Licensure Compact [24] is
adopted by the states. Such a compact is not a national license, but rather an
agreement between many states allowing a physician licensed in one state to seek
an “expedited license” from one or more additional states. Licensing boards would
share disciplinary information and licenses can be revoked by any state in the
compact where the physician is treating patients.
8.5.2 Telemedicine Will Be Utilized More to Meet the Needs

of an Increasing Number of Patients with Insurance
The increase in telepsychiatry will be as a result of, among other things:

• Increased reimbursement for telepsychiatry services by Medicare, Medicaid, and
private health plans.
178 D. Vanderpool
• Continued promotion of telemedicine companies by health insurers for use by

their insured members [25].
• Promotion of telemedicine by non-physician providers, including allowing super-
vision across state lines.
8.5.3 Telepsychiatry Programs Will Be Accredited
The ATA will be launching its accreditation program for physicians providing
online, direct to consumer healthcare consultations. Accreditation will be based on
guidelines codifying best practices [26].
8.5.4 Telepsychiatry Will Continue to Move Beyond National

Borders
As more telepsychiatry is done internationally, there are additional regulatory

concerns to be addressed, such as licensure, privacy, and data protection. United
States-based physicians that are covered entities under HIPAA are responsible for
patient information sent to all Business Associates, whether in the United States
or abroad, but HIPAA is only enforceable in the United States. Also, be sure to
understand professional liability insurance coverage, or lack of coverage, when
treating patients located outside of the country.
8.6 Pearls of Wisdom – Risk Management Advice (Figs. 8.4

and 8.5)
Figure 8.4 provides a summary of the risks associated with telepsychiatry that have
been discussed above. Figure 8.5 provides an overview of strategies to manage
telepsychiatry risks. Before you start doing telepyschiatry:
• Determine what you want to do and how you want to do it, including the choice
of technology.
• Be sure you understand all of the relevant laws and other standard of care factors.
• Evaluate your ability to comply with all legal requirements – check with all
applicable licensure boards to ensure the appropriateness of what you want
to do and how you want to do it, including licensure requirements, in-person
examination requirements, prescribing requirements, etc.
• Understand the importance of the patient’s location – that is where services are
rendered, so you may need to be licensed there, you need to follow that state’s
laws, and you must know the patient’s location in case of a clinical emergency.
• Determine your ability to meet the standard of care, which is the same for
telepsychiatry as it is for in-person. Consider which tasks would be expected if
RISKS
Administrative Risks Clinical Risks Technical Risks
Compliance with state

All clinical risks not unique to Technical failure
licensure laws
telepsychiatry PLUS Appropriateness of
Compliance with Medicare,
Staying current with evolving technology choices
Medicaid regulations
standard of care HIPAA compliance
Credentialing Uncertainty of patient location
Compliance with federal and Contingency planning for
Continuity of care technical failure
state data security laws
Patient selection Etc.
Compliance with all applicable
Consent to telepsychiaty
state telemedicine laws
Lost abilities
Compliance with all state and
Contingency planning for
federal prescribing laws
clinical emergency
Lack of appropriate protocols
Etc.
Inappropriate clinical setting
Ownership and availability of
medical records
Professional liability insurance
coverage
Etc.
Fig. 8.4 Telepsychiatry risks [10]
RISK MANAGEMENT STRATEGIES
Collect Information Communicate Carefully Document
About relevant licensure With patient Contract with third party

laws With all treating providers vendor
About laws (treatment, Consent to telepsychiatry Business Associate
Protocols Agreement
telemedicine, etc.) from
patient’s state Etc. Clinical record
Protocols
About reimbursement Etc.
About HIPAA compliance
About telepsychiatry
technology set-ups
About professional liability
insurance coverage
From patient
From other providers
From state PM
Etc.
Fig. 8.5 Telepsychiatry risk management strategies [10]

180 D. Vanderpool
the encounter was taking place with you and the patient in the same room. Then
examine the ways in which the circumstances surrounding the arrangement –
including the particular technology to be used – are likely to impact your ability
to perform those tasks.
• Confirm coverage for telepsychiatry services, including to patients located out
of state (or out of the country, if applicable) with your professional liability
insurance company prior to providing telepsychiatry services.
Once you are providing services via telepsychiatry:
• Consider what abilities are lost when treating remotely.
• Carefully evaluate whether a particular form of telepsychiatry is appropriate for a
given patient, both at the beginning of the treatment relationship and periodically
as treatment progresses.
• Ensure the patient has a basic understanding of the technology used.
• Be sure to have an appropriate contingency plan for emergencies, including local
emergency services telephone numbers.
• Obtain appropriate consent from the patient after discussion, including risk of
confidentiality breach, and the chance that telepsychiatry may not be appropriate
for future treatment.
• Document adequately and ensure the confidentiality, security, integrity and
availability of the clinical record.
• Continually re-evaluate your satisfaction, as well as the patient’s satisfaction with
the remote treatment.
References
1. Brown WB. Rural telepsychiatry. Psychiatr Serv. 1998;49(7):963–4.

2. Hilty DM, Ferrer DC, Parish MB, Johnston B, Callahan EJ, Yellowlees PM. The effectiveness
of telemental health: a 2013 review. Telemed J E Health. 2013;19(6):444–54.
3. Hageseth v. Superior Court, 150 Cal.App.4th 1339 (2007).
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GRPOL_Telemedicine_Licensure.pdf
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aEo7nsEwnrg=&DocumentExtensionMIME=PDF
6. 2014 state telemedicine legislation tracking [Internet]. Washington, DC: American
Telemedicine Association; 2014 [cited 2014 July 30]. Available from: http://www.
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sfvrsn=48
7. White v. Harris, 190 Vt. 36 (2011).
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9. Coverage and payment for telemedicine [Internet]. Chicago: American Medical Association;
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x-pub/coverage-payment-telemedicine.pdf
10. Professional Risk Management Services, Inc. (PRMS). Various PRMS risk management
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11. In re Pinal County Mental Health No. MH-201000076, 226 Ariz. 131 (2010)
12. Model policy for the appropriate use of telemedicine technologies in the prac-
tice of medicine [Internet]. Euless: Federation of State Medical Boards; 2014 [cited
2014 July 30]. Available from: http://www.fsmb.org/Media/Default/PDF/FSMB/Advocacy/
FSMB_Telemedicine_Policy.pdf
13. Core operational guidelines for telehealth services involving provider-patient interac-
tions [Internet]. Washington, DC: American Telemedicine Association; 2014 [cited 2014
July 30]. Available from: http://www.americantelemed.org/docs/default-source/standards/
core-operational-guidelines-for-telehealth-services.pdf?sfvrsn=4
14. Order [Internet]. Oklahoma City: Oklahoma Board of Medical Licensure and Supervision;
2013 [cited 2014 July 30]. Available from: http://ctel.org/2013/10/ok-state-medical-board-
what-is-a-%E2%80%9Cface-to-face%E2%80%9D-telemedicine-visit/
15. Statement on telemedicine [Internet]. Albany. New York State Office of Professional Medical
Conduct; 2014 [cited 2014 July 30]. Available from: https://www.health.ny.gov/professionals/
doctors/conduct/telemedicine.htm
16. Telemedicine position statement [Internet]. Raleigh: North Carolina Medical Board; 2010
[cited 2014 July 30]. Available from: http://www.ncmedboard.org/position_statements/detail/
telemedicine
17. Guidelines for the appropriate use of telemedicine and the internet in medical practice
[Internet]. Providence: Rhode Island Board of Medical Licensure and Discipline; 2014 [cited
2014 July 30]. Available from: http://www.health.ri.gov/publications/guidelines/provider/
AppropriateUseOfTelemedicineAndTheInternetInMedicalPractice.pdf
18. Shore JH. Telepsychiatry: videoconferencing in the delivery of psychiatric care. Am J
Psychiatry. 2013;170:256–62.
19. Practice guidelines for videoconferencing-based telemental health [Internet]. Washington,
DC: American Telemedicine Association; 2009 [cited 2014 July 30]. Available from: http://
www.americantelemed.org/resources/standards/ata-standards-guidelines/videoconferencing-
based-telemental-health#.U9kqAONdURI
20. Myers K, Cain S. Practice parameter for telepsychiatry with children and adolescents. J Am
Acad Child Adolesc Psychiatry. 2008;47(2):1468–83.
21. Telepsychiatry via videoconferencing. Resource document 980021. Washington, DC: Ameri-
can Psychiatric Association; 1998.
22. Video-based online mental health services [Internet]. Washington, DC: American Telemedicine
Association; 2013 [cited 2014 July 30]. Available from: http://www.americantelemed.org/
resources/standards/ata-standards-guidelines/practice-guidelines-for-video-based-online-
mental-health-services#.U9lF5-NdURI
23. Evidence-based practice for telemental health [Internet]. Washington, DC: American
Telemedicine Association; 2009 [cited 2014 July 30]. Available from: http://www.
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for-telemental-health#.U9lGNONdURI
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Boards; 2014 [cited 2014 July 30]. Available from: http://www.fsmb.org/Media/Default/PDF/
Advocacy/Compact%20Draft%20Language%20July%202014.pdf
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2014 July 14 [cited 2014 July 30]. Available from: http://www.bloomberg.com/news/2014-07-
14/the-doctor-will-click-on-you-now.html
26. ATA developing accreditation of online medical services [Internet] 2013 Dec 9 [cited
2014 July 30]. Available from: http://www.americantelemed.org/news-landing/2013/12/09/
ata-developing-accreditation-of-online-medical-services#.U9lNnONdURJ
Chapter 9
Social Media
John S. Luo and Brian N. Smith
Abstract Social media use on the Internet has become the predominate activity
online. It incorporates elements of Web 2.0, a construct where an architecture of
participation, collective intelligence, and collaboration define how the website is
to be used. While Facebook, LinkedIn, and Twitter dominate the social media
landscape, healthcare and health information are now using these social media
tools. The boundary between personal versus professional social media has blurred,
where patients, providers, and healthcare organizations navigate and utilize all of
these tools for both personal and professional reasons. Privacy online is slowly
becoming extinct. In mental health, ethics still rule what type of searching behavior
is appropriate. Patients now have access to a plethora of health tools, ranging
from provider and hospital ratings, to peer support and even open access to health
information posted by patients about their condition. Use of these social media tools
and health information is no longer a domain of the tech savvy young as now seniors
go online with increasing frequency and utilizing broadband Internet access.
Keywords Anxiety • Bipolar disorder • Blogging • Cellular phone • Cognitive

therapy • Cooperative behavior • Dancing • Depression • Drug interactions •
Electrocardiography • Electronic mail • Exploratory behavior • Friends • Infor-
mation dissemination • Leadership • Malpractice • Mental health • Neuroleptic
malignant syndrome • Paranoid disorders • Paroxetine • Patient care • Patient-
centered care • Physicians • primary care • Psychiatry • Public health • Self-
help groups • Social media • Social networking • Social support
J.S. Luo, M.D. ()

Health Sciences, Professor of Clinical Psychiatry, UCLA David Geffen School of Medicine,
Los Angeles, CA, USA
e-mail: johnluomd@gmail.com
B.N. Smith, Ph.D.
VA National Center for PTSD, Boston University School of Medicine, Boston, MA, USA

184 J.S. Luo and B.N. Smith
9.1 Introduction
Nowadays, use of the Internet has become practically synonymous with use of
social media online. According to the Pew Internet and American Life project
survey in 2013, 73 % of online adults use a social networking of some kind [1].
This survey noted that while new services such as Pinterest [2] and Instagram [3]
have become popular, Facebook remains the dominant social networking platform
with over one billion active users since 2012 [4]. Social media is inclusive of
various web-based tools such as blogs, wikis, video sharing sites, people search
engines, and social bookmark services, all of which help people to engage with
each other and share information in so many different ways. While Facebook,
LinkedIn [5], and Twitter [6] may still dominate, more websites are always on the
horizon and they will capture users to interface in new ways. The term “Web 2.0” is
commonly associated with such web-based applications that facilitate community
and interactive information sharing. These tools are making it easier than ever
before to find information, resources, and contacts, and to interact with others
around these sources in meaningful ways. In the context of health and health care,
the term “Health 2.0” is used to describe the application of these participation-
enhancing tools – such as health care blogs, patient support sites, patient friendly
drug interaction tools, and social networking services – by all actors in health care.
From the scientists seeking innovative therapies, to physicians and nurses providing
treatment, to the patients receiving care, we are all participants in health care. In
this chapter we aim to not only review these participation-enabling technologies
and discuss their implications for behavioral health providers, but to also provide
useful guidelines for when, why, and how to get the most out of these innovative
tools. We will also provide cautionary warning for situations in which use of
some technologies may be ill advised. Upon reading this chapter we hope you
will agree that while there are certainly some risks and temptations with respect
to these technologies that are best avoided, the benefits of these innovative tools –
to the extent that they empower patients and health professionals, foster information
sharing and community, and facilitate engagement across the spectrum of health
care – can be quite powerful.
9.2 Background
9.2.1 Web 2.0: Fostering Interactivity, Engagement,

and Community
The concept of Web 2.0 has morphed into what we do online as our use of the
Internet has become practically synonymous with social media use. Nonetheless,
a review of the concept of Web 2.0, and the significant frame shift on how users
interact on the Internet has evolved over the last 5 years is important to understand in
9 Social Media 185
order to put the new cultural norms of the Internet into context. It is fitting then, that
the most illustrative example of Web 2.0 provides us with its definition. Wikipedia
indicates that Web 2.0 is a term that describes web sites that use technology ‘beyond
the static pages of earlier web sites’, and refers to ‘changes in the way Web Pages are
made and used’ [7]. The platforms that were and are still often associated with Web
2.0 are those that are built upon an architecture of participation, where tools make it
easy for end users to provide value and connect to their peers as well as the overall
community. Perhaps the most central byproduct of this underlying structure is the
ability to harness the power of collective intelligence. Web 2.0 refers to the shared
approach and attitude of facilitating collaboration, harnessing network effects, and
providing the resulting collective intelligence back to the end user and community
as a whole.
Web 2.0 is essentially the difference between using the Encyclopedia Britannica
Online [8] and Wikipedia. The former, while it can provide a great deal of
information on a given topic, does not harness and allow users to benefit from
the collective intelligence and insights of the “community,” a quality that is at
the core of Wikipedia. One can readily anticipate the flipside of the argument as
well, as willingness to contribute to Wikipedia’s ever-growing encyclopedia does
necessarily imply “expert” status – anyone with access to a computer and the desire
to submit entries or edits to Wikipedia can have their information posted. This col-
laborative participation is how the trust in the collective process has developed into
mainstream Internet use. Millions of people have made contributions to Wikipedia’s
ever-expanding project, and not only does the community work to continuously
build upon the database, they also efficiently update incomplete, incorrect, or biased
information. As a result, Wikipedia, as a resource, becomes more robust over time, a
process that will continue as long as people are motivated to contribute their exper-
tise via the platform. It is this engagement with the collaborative process and infor-
mation exchange which has contributed to a fundamental shift in how the public has
begun to trust the collective process online as much as the expert opinion if not more.
Among the Web 2.0 social technologies that have seen the most attention, the
most powerful and ubiquitous are those that offer some form of social networking.
It is difficult to argue against the popularity of social networking sites, which, at their
core, provide tools for connecting people to other people. Only the search engine site
Google [9] ranks above social media sites such as Facebook [10] and Twitter [11] as
seen on Quantcast’s monthly top sites accessed by people in the United States [12].
As is often the case with technology, social applications are often first adopted by
younger audiences, and for predominately casual uses. The first social networking
site to attract a significant following was Friendster [13], which was conceptualized
and launched in the spring of 2002 by computer programmers as a tool to help
people find friends through their friends. Fast-forward 12 years later, and we see
that hundreds of millions of people are using social networking platforms to connect
as seen by the traffic monitored by Quantcast. Not only is the number of users
staggering, the amount of time people use social media sites such as Facebook is
astounding. Statistics to validate that statement are not needed here because we all
have seen for ourselves how people have become consumed with adding posts and
reading them. That Facebook is able to command such loyal attention on a scale that
is in the hundreds of millions is a testament to the power and potential inherent in
many social technologies.
9.2.2 Health Care-Based Internet Tools Go Health 2.0
It probably does not come as a surprise that these Web 2.0 technologies (i.e., wikis,
social networks, blogs, and social site sharing networks) have found their way into
the world of healthcare. Over the last few years as we have seen these new web-
based technologies enable information search, collaboration, and community, the
Web 2.0 revolution is being applied to empower patients and facilitate information
sharing. Patients who used to use the Internet to connect primarily through email
discussion lists have transitioned to using these robust tools to build communities
around their health interests. This phenomenon is not only limited to patients
as health professionals are also harnessing these technologies to connect and
collaborate as well. Wikis, which are websites that are designed to allow users to
collaborate on content, have been built for a multitude of health and health care
domains (e.g., helping communities prepare for public health emergencies).
WikiHow, for example, has a mental health section that provides tips for
promoting good mental health [14] and the Psychology Wiki [15] is a resource
for psychologists, covering many different psychological topics organized in a
textbook-like structure.
In addition to enhancing search and building community, these technologies and
the companies behind them are promoting engagement, information sharing, and
patient empowerment. It is no wonder then, that use of social media related websites
has become the predominate activity on the Internet.
9.3 Social Networking
Social networking sites are based on a very simple concept – they are designed
to allow users to connect and communicate with those friends and peers that
they already have, and to also find new contacts, peers, romantic relationships,
collaborators and so on through those they already know. As explored previously,
this concept is now a ubiquitous phenomenon on the Internet. Facebook started
out as a social networking site based primarily on university campuses to network
students and eventually faculty; however, it reached its fame by opening up its
system to anyone, and to allow people to connect around personal, professional,
and commercial interests. With easy to navigate applications for sharing pictures,
videos, comments, and blog entries, Facebook’s popularity took off when the
platform’s programming interface (API) was opened up to third party developers in
2007. These programmers have created games and other socially based applications
9 Social Media 187
such as quizzes to develop an online sense of connection to one’s friends and family.
In addition, numerous groups have been created to also foster a sense of belonging.
One can connect, for example, with others who appreciate news outlets like National
Public Radio, television shows such as Dr. Who, or thousands upon thousands
of other entities – movies, sports teams, restaurants, vacation destinations, artists,
political affiliations, support groups, the list goes on and on. If you can think of
it, there may already be a Facebook group or page dedicated to it. The site also
allows users to sift through existing connections to find new ones based on profile
characteristics, common connections (i.e., friends), and similar interests, thereby
facilitating new contacts.
Other current popular social networking sites include Pinterest [16], Instagram
[17], and Twitter [18]. Pinterest helps its members share photos and infographics
from different websites by saving them on ‘boards’, which function as digital
version of a corkboard. Instagram, which was purchased by Facebook in 2012,
facilitates sharing photos created on their smartphones and stylized with filters [19].
While these two sites are extremely popular, their use remains primarily personal
and not professional in nature. On Pinterest, a search using the tags ‘md’ and
‘psychiatry’ has many interesting boards, including a board from Sharon Packer,
MD, which then links back to her practice website [20]. Instagram tends to be used
primarily by hospitals, such as NewYork-Presbyterian Hospital, which are linked
to their Facebook accounts and provide a more personal and intimate connection
to the facility [21]. Twitter is a very simple but effective micro-blogging platform
that allows users to send and receive ‘tweets’, which are text-based messages of
no more than 140 characters that can be sent via twitter.com, a cell phone, or any
number of other Twitter applications. Each user decides which other users they wish
to follow, which enables the user to control the information that they see in their
twitter stream, which is the continuous feed of all of the tweets from those that one
has elected to follow. Twitter is a popular forum for many medical professionals,
who send tweets regarding topics of interest. For example, Psychiatry Rounds is
a Twitter account that serves as a professional social network for psychiatrists to
discuss and share ideas as well as network [22]. Dr. Gabriela Cora’s Twitter account
has almost 2,000 followers, a diverse group including professionals, organizations,
groups, and individuals [23]. These social media sites demonstrate how mental
health professionals are using these new mediums for outreach, education, and
engagement in discussions that promote mental health.
Although Facebook, Twitter, Pinterest, and Instagram are social networking sites
primarily for personal use, there are a number of social networking sites dedicated
to professional use as well. The most well-established professional networking
site is LinkedIn, with over 300 million members worldwide [24]. This site was
initially adopted by the technology-based working sector, but has now grown to
encompass practically all professional industries such as healthcare, architecture,
search firms, etc. The primary use of this site has been to connect to colleagues
but to then leverage the degrees of separation to establish new connections in the
context of finding jobs, collaborators, and references. There used to be all too
many professional social network sites, but over the last 5 years, many of them
have folded as LinkedIn has dominated the professional networking sector, much as
how Facebook dominates personal social networking. It is a highly recommended
strategy to create a profile on LinkedIn, and perhaps one to two other specific social
network sites such as MedicalMingle [25] or Therapy Networking [26].
In the past, the dichotomy of having separation between personal and profes-
sional social networking site use was recommended in order to maintain a boundary
for personal versus professional use. Now, it has become widely accepted that
there is an advantage in having a professional focused social network account on
Facebook as well as on LinkedIn. The key aspect has been to have these various
professional focused social networking accounts link back to a professional website,
either for a private practice or to one’s university or hospital based profile page.
It is recommended that in order to maintain privacy and to keep the personal and
professional channels from getting mixed up, create two accounts on Facebook,
one for personal use and the other for professional use. The primary advantage of
having a professional social network profile on both Facebook and LinkedIn is that
these pages are often indexed and searched by the various Internet search engines.
By having a link on your social networking account to your professional website,
you will have more traffic without much marketing effort. These professional
connections are ideal for referrals and new business ventures. Many recruiters often
search through these sites to find candidates for their job openings. Therefore, it
helps mental health professionals to provide a complete profile with details on
leadership, administration, and experience that will enable the recruiter to contact
you with a more likely job of interest.
Just as Facebook’s popularity has grown, so has the comfort that both patients
and physicians have developed using social networking-based websites. Indeed,
the social networking phenomenon is enabling patients, health providers, and other
stakeholders to efficiently share information and experiences in every health context
imaginable – from health and disease to treatment and recovery, patients, scientists,
and health providers are utilizing these tools to connect, mobilize communities,
and filter information. There are now even a few reports of healthcare providers
in other fields who have chosen to “friend” or connect to their patients [27, 28].
In those instances, the providers were not in the field of mental health, and the
reasons why patients wanted to connect with their doctors seem innocuous enough.
One patient was thinking about going to medical school, and had contacted her
former medical student, now a resident, on that simple issue. Patients also found
that being connected to their doctor on Facebook was convenient in asking for
medication refills or scheduling an appointment, which bypasses the hit or miss
of whether the doctor was available since on Facebook your friends currently online
are made known to you. Patients even commented that seeing personal matters such
as the doctor’s videos of his children dancing made them feel more connected to
their provider. However, in the field of psychiatry and psychology, such personal
information and privacy are much different matters.
In mental health, privacy is a critical parameter, as many patients would not
enter into treatment or disclose the very issues that torment them without that
sense of privacy. Scott G. McNealy, chief executive officer of Sun Microsystems,
9 Social Media 189
Inc. has been quoted in1999, stating that on the Internet “You already have zero
privacy. Get over it” [29]. Indeed, the plethora of search engines and specific
individual information mining sites such as PeekYou [30], Zabasearch.com [31], and
Pipl.com [32] search for information on numerous sites including public records,
Amazon.com, Facebook.com, and many others. It is rather illuminating and perhaps
even frightening to see what private information is available on the Internet such as
birthdays, wish lists, pictures, and comments posted on a web site many years ago.
However, just because absolute privacy is perhaps a lingering memory it does not
imply that the principles of privacy no longer apply to mental health care on the
Internet.
Privacy of personal information is critical to the therapeutic relationship in
behavioral healthcare. Patients in psychotherapy who know all too much about their
therapist may have difficulty with transference, and discover that they struggle more
with their issues. Providers who search for more information about their patients
may uncover lies or other unrelated matter that will change the perspective and focus
of the therapy goals. As therapists begin and continue to explore the connectivity
inherent with Health 2.0 applications, they are advised to remain cognizant of just
how public the Internet is, and to strive to maintain clear distinctions between
their professional and personal lives online. While it can be advantageous to
provide professional information to current and prospective patients online (e.g.,
your medical specialties, hospital affiliations, whether or not you are taking new
patients, as well as highlight online resources that you believe to be useful), it would
not be advisable to share content that is of a personal nature. This includes, for
example, photos of yourself or family, lists of “friends,” and specific updates as
to where you might be spending your weekend. This is not to say that behavioral
therapists are forbidden to join sites like Facebook or Twitter, but rather that those
in mental health professions should consider the importance of boundaries. You
could, for example, limit Facebook connections to just family and close friends,
and set up the privacy controls on the platform to ensure that your information is
only accessible by those to whom you are directly connected. When patients make a
“friend request” to a therapist on any social networking site, privacy and boundaries
are the primary reasons to consider declining the request. It is far too difficult on
these social networking sites to create settings that prevent patients, for example,
from accessing specific pictures or reading certain comments made with regards to
blog postings, and many users have no idea that these adjustments were possible,
and allowed default settings of general access to remain. Facebook has a tendency to
readjust its privacy controls, and even with simplified options, these are difficult to
use and often underutilized. In general, it is recommended that when a patient makes
a ‘friend request’, discussing the privacy matters in person with the patient while
politely declining the request is important to avoid the perception of abandoning or
ignoring the patient.
Similarly, if a therapist is comfortable – perhaps even excited – about the utility of
micro- blogging tools like Twitter for information sharing, he or she could choose
to limit posts to those that are professional in nature. Many health professionals
have adopted this strategy, choosing not to share personal information via Twitter
(e.g., such as where they might be having dinner that night) and instead using it to
share and receive professional content, such as news of exciting research findings,
or tips for managing stress. In fact, a number of therapists have incorporated
their Twitter posts directly into their professional websites, which is a clever and
relatively simple way to keep the content on a website dynamic and fresh.
Just as mental health professionals are advised to maintain boundaries when it
comes to their own personal information and accessibility, it is similarly important
to respect the privacy of patients. Consider the following question: do you think
that “Googling” a patient would be a positive or a negative strategy vis-à-vis
the therapeutic process? One possibility is that the therapist could glean some
information that might help the treatment, such as evidence of specific rumination
or paranoia, or the discovery of improved functioning (i.e., behaviors) in some
domain following a set of targeted therapy sessions. On the other hand, looking
for information not explicitly disclosed by the patient can also be seen as a violation
of trust. As such, it has been suggested that, before searching for information online
regarding a patient, therapists first consider the reason for doing so. That is, is
information being sought in an effort to help the patient in some way, or is the
therapist merely “researching” to satisfy his or her own curiosity? If the answer is
the former, the therapist could address the boundary issue by being upfront with the
individual prior to searching for information online, and ask how they would feel
about online information being sought in an effort to inform the therapeutic process.
If the patient agrees, the therapist could consider reviewing any pertinent findings
obtained with the patient. The American Psychiatric Association Ethics Committee
considers providers who have searched for information on their patient to satisfy
their curiosity to have committed an ethical violation [33]. The key element that
makes searching for information an ethical violation is that finding such information
does not contribute to patient care and serves another purpose. In some instances,
searching for information about a patient does make clinical sense. For example,
when the patient makes a grandiose statement and there are no other sources of
collateral information, it may be necessary to determine if that information is true
by checking information on the Internet.
9.4 Provider Ratings
Nowadays, the wealth of health information on the Internet now includes opinions
by patients and others regarding their professionals. In the past, word of mouth
or lists of providers from the insurance panel were the traditional method for
finding behavioral healthcare providers. For many patients, the starting point may
be their primary care physician, who will then refer the patient on to someone
they know. One of the challenges is that for many primary care physicians, their
network primarily consists of specialty colleagues to whom they frequently refer
patients such as cardiology, rheumatology, and gastroenterology. Oftentimes, this
network was established via contacts made through graduate school, postgraduate
9 Social Media 191
training, local healthcare provider society, or just because they are in the same health
professional building. In these circumstances, it is often the case where a primary
care provider would ask colleagues for recommendations regarding mental health
providers. To remedy this situation, a virtual network via social networking sites
such as LinkedIn or Doximity [34] as well as continued efforts to expand a referral
network in person make sense for the mental health practitioner.
Today, patients can search physician and therapist rating sites to see what others
had to say about their experience. These sites include RateMDs [35], DrScore [36],
Vitals [37], HealthGrades [38], UCompareHealthCare [39], and LifeScript [40],
where patients post comments both in free form as well as give ratings on scales
regarding aspects such as professionalism, punctuality, helpfulness, knowledge,
and quality. None of these ratings have been studied to produce validity, although
HealthGrades does search through malpractice databases, public state medical board
disciplinary action records, and board certification agencies to create an award
called ‘Healthgrades Honor Roll” for those providers with valid board certification
and no record of disciplinary action or malpractice lawsuits.
One of the problems for providers is that there is little recourse for negative
reviews. This stance is typical of most ratings sites, which state that they serve
as a forum not an arbiter of opinions. Some sites will remove comments or
ratings determined to be unconstructive or merely lambasting the provider. Yelp
has developed notoriety in offering to ‘downgrade’ and displace negative reviews in
return for purchasing ads for better placement of the business on their site [41]. A
significant fact to consider by both behavioral health providers as well as potential
patients is that many of these reviews are done anonymously. Few patients actually
give their real name or other identifying information in order to maintain privacy.
The adage “caveat emptor” comes to mind in determining whether anonymously
provided information has much merit. In addition, the majority of patients who do
rate their healthcare providers are typically extremely dissatisfied or hopefully quite
happy with their provider.
Another downside to provider ratings sites is that there are too many of these sites
out there, and patients often do not know where to turn to find accurate or helpful
information. Even the most ‘liked’ physician or provider has about 30 ratings on
a particular review site, with many sites averaging only two or three per provider.
Although an online reputation is important to maintain, a broader perspective, such
as the attitude that one negative review out of many positive ones is likely to not
drive future patients away, may preserve sanity and decrease anxiety and paranoia.
Furthermore, the reality is that the majority of referrals still come in traditional ways,
from providers or other satisfied patients, as well as from search engine hits on the
practice website. In addition, another strategy is to decrease the search ranking of
the provider rating site as many patients today just enter the provider’s name into
the search engine versus checking a specific provider rating site. This downgrade
of the search ranking can be accomplished by having many other sites linked to
your primary professional site as well as creating additional content for the Internet,
such as postings on other health related sites. It also may be helpful to know what
sites containing information about you are being viewed. As such, we suggest that
health professionals periodically check the online landscape to see what kinds of
information on them might be out in the public domain, and hence easily accessible
by others. One way to accomplish this task is to set up a search alert in popular
search engines such as Google [42] and Yahoo [43], which will then notify you via
e-mail on what terms and what pages were viewed.
9.4.1 Health Tools
Searching for health and medical information online has been commonplace
for a number of years, as more and more people turn to online resources for
insight. Recent research indicates that the use of the Internet for access to health
information in this country reached 59 % in 2013, up from 25 % in 2000 [44].
One of the problems facing patients today is that there is too much information,
both good and erroneous, contradictory and confusing, as well as misleading
available on the Internet. To address this issue, specific health search engines, such
as Medstory, Healia, and Healthline were developed to search specific medical
databases, healthcare websites, and use a specialized health-related taxonomy to
improve the relevancy of the search findings. While the efforts of these sites were
helpful in finding health information, traffic through them were limited due to the
popularity of the search engine portals Google and Microsoft’s Bing. Medstory’s
technology was incorporated into Bing Health, which then became integrated into
the search results, thereby making the technology invisible to the user [45]. Many
patients are reading about other patients’ accounts with medications and types of
therapies, which inform their decision making about compliance or follow-through
on recommendations by behavioral health providers. Although traditional sources
of health information on the Internet such as the National Institutes of Health [46],
Medscape [47], PsychCentral [48], and now Wikipedia are still utilized, it behooves
the behavioral healthcare practitioner to check out what patients are viewing that
may potentially shape their actions.
In addition to general information, specific tools are now available on the Internet
to help and perhaps stimulate the consumer to consider behavioral health services.
The Depression and Bipolar Support Alliance offers confidential screening tools
for mania, depression, and anxiety [49]. MoodGym is an online-based cognitive
therapy program to help prevent and cope with depression [50]. Patients are using
the web site DoubleCheckMD [51] as well as the popular medication program
ePocrates [52] to determine if there are drug interactions among their medications
to be concerned about. One source of confusing information is the result of various
drug interaction programs available online. For example, in checking the interaction
between paroxetine and risperidone, DoublecheckMD will highlight the need to
monitor blood sugar, platelet counts, and white blood cell counts, as well as
checking EKG for abnormal heartbeats, but it does not comment on how paroxetine
with its 2D6 cytochrome P450 enzyme inhibition may slow down the metabolism of
risperidone. The drug interaction program of Epocrates has identified this potential
9 Social Media 193
increase in risperidone levels, and Epocrates then reminds providers about the
increased risk of the adverse effects as well as neuroleptic malignant syndrome.
Although it is nearly impossible for patients and providers to check all of the various
mental health tools available on the Internet, it makes sense to ask patients what
health information and health tools on various web sites they have been visiting
in order to determine the relevancy of the information they are considering. By
engaging the patient in a discussion of the information they have found online in
a confident, non-accusatory, and open manner, behavioral healthcare providers are
providing patient centered care and establishing that they are open to learning about
the concerns of their patients. This process helps engender trust that the provider
has the expertise to help patients determine whether the information they have
discovered in the Health 2.0 era is relevant to their health needs.
9.4.2 Peer Support
Of all of the Health 2.0 applications that we have seen to date, among the most
powerful have been those that bring support to those who need it most. When
faced with uncertainty, we turn to peers for support. In the context of health, where
the stakes can be quite high, people are particularly motivated to seek out others
like them – people that have faced or are facing the same types of illnesses and
health situations that they are themselves facing. Fortunately, thanks to the Web 2.0
movement, patients have at their disposal an ever growing arsenal of online tools
and networks to provide what can be otherwise elusive insight and support. Sites like
MedHelp [53], PatientsLikeMe [54], and DailyStrength [55] are providing powerful
tools and dynamic communities to empower patients and foster a sense of belonging
and community among those facing illness. Armed with a basic understanding of
the sites and tools that are available, mental health providers will be better able
to understand the experience of their patients who turn to these communities for
help, as well as be able to facilitate patients reaching those resources that may offer
the most benefits. Mental health professionals, as well as any health care provider,
understand and appreciate the value of social support, and the importance of not
feeling isolated or alone. Given that patients (and we are all patients at some point)
are turning to these platforms, it is suggested that those providing therapy at least
have a basic understanding of the online communities that are available to patients
seeking further support and insight. While this section will certainly not cover all
or even most of the online peer communities available for patients, several dynamic
communities will be highlighted.
PatientsLikeMe, founded in 2004 by three MIT engineers, is considered by many
to be one of the most creative and high impact companies in the patient support
domain. Their tools are designed to help those diagnosed with “life-changing
diseases” by allowing patients to share and discover the outcome based on a number
of disease categories. As an example, patients who have been diagnosed with major
depression may be interested in going to their Mood Conditions community to see
data on the kinds of treatments being used by thousands of other patients who have
been fighting depression. Here they would be able to see information regarding
efficacy and side effects for a multitude of treatments, as well as learn about
how behavioral changes like quitting smoking and getting physical exercise may
impact their symptoms. Not only is this information readily available for patients,
the anonymized data that is generated via the PatientsLikeMe community helps
researchers learn how these diseases act in the real world, thereby facilitating the
potential discovery of novel treatments.
Of the many entities that are offering health-related peer support, among those
with the longest staying power to date has been MedHelp, which has been a reliable
destination for medical information and support for patients since 1994 – well
before there was talk of “Web 2.0” technologies. One of the significant advantages
of MedHelp is the active presence of medical experts who moderate many of the
forums and wikis on the site. As such, their dynamic community consists of patients
and physicians working together. MedHelp has taken this collaborative approach
even further by establishing partnerships with some of the most reputable health
care institutions in the world, such as the Cleveland Clinic, Johns Hopkins, and The
Mount Sinai Medical Center, among others. As a result of these partnerships, not
only can patients post questions to the community of members, they can also utilize
any number of “Ask a Doctor” forums, where they are able to ask questions of
medical specialists from MedHelp’s partnering institutions.
Another Health 2.0 site that allows patients to get information from experts is
DailyStrength. DailyStrength has created hundreds of support groups for people
facing a number different disease conditions. Like MedHelp, DailyStrength has
combined efforts with other reputable healthcare institutions, such as the Centers
for Disease Control. Not only can patients find support from peers within the
DailyStrength community facing the same illnesses that they have faced, medical
professionals are also available for advice and consultation. WebMD [56] a pioneer
in the world of online health and medical information also provides tools that allow
patients to interact around medical content and interests, along with their expert-
vetted medical information.
In addition to these and many other Health 2.0 sites that offer peer support
for patients, Ning [57] is one network service provider that has taken a different
approach. Through the Ning platform, anyone can essentially create their own social
networking site, and establish a community for whatever interest they may wish to
connect around. Literally millions of networks have been created on Ning, many
of which are privately branded. While Ning is not a Health 2.0 company per se,
countless communities have been created around medical conditions, diseases, and
other health-related interests. Private and public groups have been formed around
topics such as addictions, anxiety disorders, Asperger syndrome, cancer support,
autism, obsessive compulsive disorder and on it goes.
Patients are not the only players in the healthcare industry benefiting from Web
2.0 tools and technologies. Just as PatientsLikeMe and many other community-
based platforms offer resources and communities to patients, companies like Sermo
[58], Medscape Connect [59], and Doximity provide technologies to help facilitate
9 Social Media 195
networking and information sharing among medical professionals. Sermo, which

is often cited as the largest physician-only network in the United States, provides
an online environment where licensed physicians can exchange ideas and clinical
observations in real time. Medscape Connect enables physicians to utilize a large
community of peers to discuss clinical and nonclinical topics, as well as search
through thousands of archived discussion posts. Doximity provides networking
with colleagues and employees at hospitals as well as free CME credits with
its partnership with the Cleveland Clinic. In addition to these social network-
based communities, there are a number of others focused on specific specialties,
geographies, and other professional interests. While there are certainly more sites
specifically geared toward patients, it is clear there are also a number of platforms
designed to foster connectivity within and beyond professional networks within
healthcare. Given the multitude of connections that exist between colleagues within
health systems, alumni groups, academic centers, and medical societies, it is
not surprising that more and more tools are being developed to allow medical
professionals to more efficiently utilize these valuable networks.
In sum, it is clear that there are a multitude of web-based resources available to
provide peer support for both patient and provider. The tools for connecting to others
with common interests are continuously becoming more robust, and they seem to be
on a ubiquity-approaching trend. Of course, the value and potential positive impact
of support from others cannot be understated; a point that may not be understood by
all medical providers, but is not likely lost on most behavioral health professionals.
Knowing what we know about social support, we can help others navigate toward
networks and communities that are likely to provide social resources for those who
could most benefit from.
9.5 Conclusion
Social media technologies are facilitating interactivity and community development

among all actors in the healthcare system. These trends of connecting, sharing
information, and participating are only going to become more common and robust as
additional innovations are developed. It is clear that the innovative technologies that
we are seeing now are not just for the young crowds. According to a recent survey
conducted by the Pew Research Center in April 2014, over 60 % of America’s
seniors now go online, and 50 % of them are broadband Internet users [60].
Another signal of the staying power of some of the most heavily used social
media platforms can be seen with the abundance of businesses, government offices,
professional societies, nonprofits, and academic centers that are using them to
facilitate their mission. It has become common for professional organizations to
establish a presence on Facebook or Twitter, and to use these technologies to
disseminate information and engage audiences. Health professionals are encouraged
to at least become familiar with these participation-enhancing tools as well, and we
hope that this chapter will serve that purpose for many. If you are an expert in some
area, why not find out what is being said on the topic on some of the widely used
wikis, social networks, and interactive forums, and perhaps even contribute to the
collective discussion? While there are certainly risks that should be avoided and
protective strategies that should be taken – particularly with respect to privacy –
psychiatrists and other behavioral health professionals can do themselves a great
service by becoming aware of these powerful tools, and, when applicable, helping
to make patients and colleagues aware of them.
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Chapter 10
Technology Tools Supportive of DSM-5:
An Overview
Nathaniel Clark, Theresa Herman, Jerry Halverson, and Harsh K. Trivedi
Abstract In order to have tools supportive of DSM 5, we first need to start with an
understanding of the validity and reliability of psychiatric diagnoses. Inherent to the
success of what can be built is the ability to maintain both the face validity and test
validity of the diagnostic schema. The authors begin this chapter with a discussion
regarding the development of DSM 5. They consider how technology can support
accurate diagnosis and treatment planning. In the planning of the DSM-5 revision,
attention was given to address concerns regarding previous editions. Research into
the validity and reliability of the DSM-IV diagnostic constructs revealed problems
regarding test-retest reliability. There was also the logistical challenge of accurate
data collection across thousands of patients and multiple centers, compilation and
analysis of that data in an expedient fashion, and the application of the most current
advances in statistical measures of reliability and validity. In summary, the logistical
challenges around creating and coordinating a multi-site system for surveying and
collecting data across thousands of patients and hundreds of providers, research
coordinators, and analysts was solved with the involvement of REDCap. The
technological tool to assist with data collection and a central data management
function elevated psychiatry beyond the ancient system of one provider to one
patient, and created a wealth of possibilities for how to use this data beyond the
research for DSM 5.
N. Clark, M.D. ()

e-mail: nathaniel.clark@Vanderbilt.Edu
T. Herman, M.D., M.B.A.
Office of Quality, Patient Safety, and Risk Prevention, Vanderbilt University Medical Center,
Nashville, TN, USA
J. Halverson, M.D.
Rogers Memorial Hospital, University of Wisconsin School of Medicine and Public Health,
Madison, WA, USA
H.K. Trivedi, M.D., M.B.A.

200 N. Clark et al.
Keywords Antisocial personality disorder • Anxiety • Diagnosis • differential •

Diagnostic and statistical manual of mental disorders • Goals • International
classification of diseases • Obsessive-compulsive disorder • Psychopathology •
Psychotic disorders • Schizophrenia • Substance-related disorders
At Vanderbilt University Medical Center, an important refrain from our information

technology (IT) colleagues is that IT solutions cannot fix underlying flaws in process
or function. For IT to be innovative, useful, and easily adopted—the key is to have a
well-functioning and reliable process. To that end, in order to have tools supportive
of DSM 5, we first need to start with an understanding of the validity and reliability
of psychiatric diagnoses. Inherent to the success of what can be built is the ability
to maintain both the face validity and test validity of the diagnostic schema. We
begin this chapter with a discussion regarding the development of DSM 5. We then
consider how technology can support accurate diagnosis and treatment planning.
10.1 Background/History
Throughout the history of psychiatry, the foundation of diagnosis has remained

consistent: observation, clinical interview, and judgment [1]. The earliest tools
available to begin the categorization and classification for psychiatric illness
consisted of the observation of a disturbance in behavior [2]. The identified
psychiatric patient was then interviewed, focusing on the nature and etiology of
the apparent behavior. Finally, it was incumbent upon the expert diagnostician to
utilize knowledge of existing psychiatric diagnostic systems to apply them in a
common sense fashion to the patient whom had been observed and interviewed. The
obvious limitations of this approach consisted of how to demonstrate the reliability
and validity of a psychiatric diagnosis as a construct. More specifically, how would
we know that the constellation of symptoms that this expert called bipolar disorder,
for example, would indeed be diagnosed as bipolar disorder by the next astute
clinician? In addition, how would we know that what they are describing really was
bipolar disorder, and not simply two experts concurrently misdiagnosing borderline
personality disorder?
Psychiatric classification, or the creation of diagnostic systems, arose from this
problem. Emil Kraeplin developed the earliest major diagnostic system, Com-
pendium der Psychiatrie, in 1883 [3]. His diagnostic nosology pioneered several
concepts fundamental to future psychiatric diagnostic systems. First was that
psychiatric illness was to be held as a disease of the brain and nervous system.
In addition to its pathology beginning in the brain and nervous system, he posited
that psychiatric illness was naturally occurring and degenerative. In regards to
specific disease conditions and differential diagnosis, he defined manic-depression
and dementia praecox as distinct illnesses. Kraeplin developed successive versions
of his textbook and was working on the ninth edition when he died in 1926 [3].
10 Technology Tools Supportive of DSM-5: An Overview 201
10.2 Development of the Diagnostic and Statistical

Manual (DSM)
In the United States, the major diagnostic classification system emerging in the
twentieth century was the Diagnostic and Statistical Manual (DSM) [4]. The DSM
represented an effort to form consensus in the US around diagnostic validity and
reliability. It developed out of an 80-year history in the US focused on the gathering
of statistics on mental health diagnosis, stemming from a recording in the 1840
census of the frequency of “idiocy” and “insanity” and also establishing how
many were “at public or private charge” [5]. By 1952, the American Psychiatric
Association published the first edition of the Diagnostic and Statistical Manual as
an offshoot of the International Classification of Diseases sixth edition (ICD-6) [4].
In 1994, the DSM-IV was published, with a major revision in 2000—the DSM-IV
TR. As a result of the concerns about a new edition of the DSM in the wake of
the publication of the DSM-III in 1980, the DSM-IV was designed with a number
of “procedural safeguards : : : instituted to minimize arbitrary and idiosyncratic
revisions” [6].
These safeguards consisted of process oriented changes: (1) expert advisers
were appointed to each of the DSM diagnostic workgroups on illness categories,
(2) methods conferences were utilized to review methodological issues facing the
development of the DSM, (3) specific change criteria were developed for the
diagnoses under review, and (4) a balanced review of literature with inclusion
of the body of evidence both supporting and opposing the diagnostic constructs
that had been developed in the DSM III [6]. These reviews were intended to be
“descriptive, comprehensive, explicit, and systematic. The goal [was] not to generate
the data to argue for a certain position, but rather to provide a fair, balanced, and
descriptive summary of the literature” [6]. This review then forms the foundation of
the three-step process of the empirical review that leads directly to the development
of diagnostic criteria, as well as the inclusion or exclusion of diagnostic constructs
from the manual.
To complete the process, the work groups developed a standard that reanalysis
of existing but unanalyzed data sets and ultimately field trials are required [7].
The DSM-IV field trials were conducted for 12 diagnostic constructs: Antisocial
Personality Disorder, Autism and Pervasive Developmental Disorders, Disruptive
Behavior Disorders (including Conduct Disorder, Attention Deficit Hyperactivity
Disorder [8], and Oppositional Defiant Disorder), Insomnia, Major Depression
and Dysthymia, Mixed Anxiety-Depression, Panic Disorder, Obsessive-Compulsive
Disorder, Posttraumatic Stress Disorder, Schizophrenia, Somatization Disorder, and
Substance Use Disorders [9]. Practically speaking, the field trials represented an
effort to capture how psychopathology appeared to the clinician. An effort was
made to ensure a variety of clinical sites, generally between 5 and 10 in number,
as well as a mix of new and follow-up patients. Definitions also included the use of
standardized assessment tools such as the Yale-Brown Obsessive Compulsive Scale,
202 N. Clark et al.
the Fixity of Beliefs Scale and the Structured Clinical Interview for DSM Disorders
(SCID) for patients being assessed for obsessive compulsive disorder [10].
Despite these advances in the development of DSM-IV, concerns regarding the
validity and reliability of the diagnostic system arose. Specific concerns were raised
regarding the validity of the distinction between mania and schizoaffective disorder
[11] and between depression and anxiety [12]. The validity of the construct of
specific diagnoses were also questioned, such as for bipolar disorder [13] and
seasonal affective disorder [14]. The news was not all bad, as evidence and research
emerged supporting diagnostic constructs from the DSM-IV, such as for Psychotic
Depression [15]. More fundamental research was also conducted which investigated
the diagnostic structure and hierarchy of the DSM-IV as well as the diagnostic
reliability and validity of Axis V [16].
10.2.1 Development of the DSM-5
In the planning of the DSM-5 revision, specific attention was given to address
concerns regarding previous editions. Research into the validity and reliability of
the DSM-IV diagnostic constructs revealed problems regarding test-retest reliability
[17]. There was also the logistical challenge of accurate data collection across
thousand of patients and multiple centers, compilation and analysis of that data in
an expedient fashion, and the application of the most current advances in statistical
measures of reliability and validity. The process leading up to the field trials of the
DSM 5 was similar to that of the DSM-IV.
In 1999, an initial planning conference involving thought leaders and experts
in research was convened to determine the research direction heading into DSM
5 development [18]. Interestingly, leaders of the DSM-IV process typically were
excluded so as to foster creativity and an objective look at the challenges which
had arisen in the previous edition. Work groups were convened, research agendas
were developed and set, and white papers were ultimately published in “A Research
Agenda for DSM 5” [19] and “Age and Gender Considerations in Psychiatric
Diagnosis” [20]. Thirteen conferences were subsequently held in which specific
research questions were addressed and presented by content experts; these findings
were also subsequently published [21].
A task force was convened to review the DSM-IV, compare the findings with
the research generated by the research conferences, and this group developed the
drafts of the DSM 5 [18]. Concurrently, from 2010 through 2012, field trials were
undertaken [22]. There were two phases of field trials; the first consisted of Field
Trial Testing in Large, Academic Medical Centers in which 11 Academic Centers
participated. Data collection took approximately 10 months to complete. The second
phase consisted of Field Trial Testing in Routine Clinical Practices, which were
composed of solo and small group practices, randomly selected from an AMA
Database of physicians. Data collection for this group took approximately 8 months.
The challenges for this version of the DSM included the greater number of
patients, large academic medical centers participating, and the exponentially greater
number of routine clinical practices taking part in the field trials. The design
group also planned to take advantage of improved statistical technology developed
subsequent to the publication of the DSM-IV. The complexity of this project greatly
eclipsed that of the previous edition. The technology tools supportive of this updated
process (data collection, sampling strategy, and data analysis) are described in
several publications by the implementation workgroup [23–25].
There were 11 identified sites, 7 adult and 4 pediatric [23]. There were 2,246
patients enrolled in the field trials throughout the study. In contrast with the typical
prospective double blind randomized studies characteristic of pharmaceutical trials,
the field trials were designed to test the reliability of diagnosis in the “real world”—
the degree to which two examiners could agree on a diagnosis across a variety of
settings ranging from the solo practitioner to the large academic medical center, and
in a patient population in which comorbidity was common.
The methodology used to simulate the use of the manual by the clinician
included: clinical interview approaches versus structured research interviews; sep-
arate interviewers interviewing the same patients at short intervals of separation;
inclusion and stratification of patients into multiple diagnostic groups for study
to account for comorbidity; assessment of “cross-cutting symptoms” which could
indicate another diagnosis, or dimension to the present diagnosis [24], and assess
if diagnoses held up under these conditions. 86 % of all enrolled patients were
interviewed twice, and a total of 279 clinicians of varied disciplines were involved
in the total study. 33 total diagnoses were tested, on average about two trials per
diagnosis, and each site studied approximately 5 diagnoses [23].
Furthermore, the DSM 5 Field Trial utilized centrally designed protocols that
required uniformity of data collection and data analysis. The project’s main
challenge in terms of data collection placed a significant strain on available
technology. The main areas of tension included central protocol development and
implementation, data collection, and ongoing monitoring of the field trials, with the
potential for rapid assimilation and interpretation of the data. These project needs
required the use of an adaptable, widely distributable data capture system, ideally
using internet protocols for availability and ease [23].
The study designers elected to use The National Institutes of Health-funded
Research Electronic Data Capture system (REDCap) developed through Vanderbilt
University [23]. The REDCap system, described by the designers in a seminal
article [26], is an open source program developed to solve problems around sharing
research data with multiple collaborators at multiple sites via high-speed network
sharing, while maintaining a high degree of security. The conceptualization of
research while incorporating the REDCap tool involves the Informatics team at
the outset of the project design. They would demonstrate how REDCap works,
including use of the web interface, security, validation, statistical export, and a data
collection strategy for the project. Case report forms are created in a format familiar
to the researcher, typically an excel spreadsheet, which are populated by the research
204 N. Clark et al.
team around the specific goals, data, and other project requirements. This then forms
the foundation of a web-based electronic data collection application [26].
The next step in project design involves the user interface. Data on study variables
can be entered into the web-based application by text field, drop down menu, or
other .html object design, and then exported either in part or by whole for analysis.
According to the designers:
“[The project] uses PHP C JavaScript programming languages, and a MySQL
database engine for data storage and manipulation. Hardware and software require-
ments are modest, and the system runs on Windows/IIS and Linux/Apache web
server environments” [26].
Each project also contains significant and flexible data useful to the project
as a whole outside of the data collection and analysis. This includes a log of all
data transfers, researcher rights, and any ancillary forms required such as consents.
REDCap was developed and released within the Vanderbilt University research
environment in 2004, and at the time of the paper [26] included 204 active projects,
and the total number of subjects in all databases exceeded 17,000. In 2006, the
REDCap project was released in a pilot program to partner institutions, which had
grown to 27 total partners in 2009. According to the REDCap website [27], as of
2014, the total number of projects was 72,643, and the REDCap Consortium was
composed of 1,108 institutional partners from 83 countries. The group had also
developed an Online Designer via a web interface for easier access for remote
partners, and they note that both surveys and databases can be created.
The designer group emphasizes the flexibility and portability of REDCap in
terms of requirements. The hardware requirements are listed as a Web Server with
PHP, MySQL Database Server, and SMTP email server (installed if emails need
to be sent directly out of REDCap), and an optional file server [26]. These can be
running on the same or separate computer[s]. There are no hard requirements for
processing speed, memory, clock speed, or hard drive space, as the total program
space is approximately 10 MB. 20 GB total are recommended to be dedicated to
the web server and MySQL, which should suffice for approximately a year of use.
Being open source, there is no cost for the program [26].
Prior to the implementation of the REDCap system for the entire DSM 5 field
trials, two pilot studies were done at Johns Hopkins in the Community Psychiatry
Outpatient Program, and in the Child and Adolescent Outpatient Program at the
Johns Hopkins Bayview Medical Center [28]. These pilot studies took place over
an 8 week period with between 10 and 20 patients per stratum, and, identical to the
eventual field trials, included two study visits for test and retest validity separated
by between 4 h and 2 weeks. On the adult side, the pilot included Major Depressive
Disorder, Bipolar Disorder, Schizophrenia, Schizoaffective Disorder. On the Child
and Adolescent side, the pilot included Major Depressive Disorder, Disruptive
Mood Dis-regulation Disorder, Oppositional Defiant Disorder, Generalized Anxiety
Disorder. The total number of adult patients was 100, and the total pediatric patients
was 50.
The REDCap system could flexibly be developed to suit the needs of the study
or survey. In pilot field trials, the main modifications were around permitting the
embedded research coordinator to oversee the study progress and contained a
program that facilitated communication of the data entered in real time, as well
as, in communicating the data with a central management system. The types of data
that the patients (or parents) could enter were composed of self-administered and
clinician scored metrics. These included proposed DSM 5 cross cutting symptom
measures, the World Health Organization Disability Assessment Schedule II, and
the Personality Inventory for DSM-5. Similarly, clinician driven data consisted of
the proposed DSM 5 metrics for Suicide Risk in Teens, Suicide Concerns in Adults,
Psychosis, Early Development and Home Background, Clinical Utility and a 6 item
World Health Organization Disability Assessment Schedule [28].
Anonymous subject tracking was managed by assigning Patient Identification
Numbers (PID) and Clinician Identification Numbers (CID). The CID data was used
as a login for the system, and permitted data entry, access to reports and blindness to
the ratings of other clinicians. The Research Coordinator was permitted access to a
Patient Research Screening Form and to patient tracking forms, and the component
on REDCap was designed to allow the Coordinator to identify additional fields to
make the recruitment process more efficient. The Research Coordinator assisted
patients and parents in the self-administered section of the assessment, and then
in real time was able to download the results and make them available for study
clinicians to review prior to the diagnostic assessment of the patient.
The pilot study results regarding the inclusion of REDCap was felt to be
successful, with findings that most clinicians responding that a single 2–3 h training
session combined with sufficient practice would result in feeling comfortable
managing the data entry system. The REDCap system by necessity compelled
clinicians to enter data via a checklist to qualify or disqualify for diagnoses and
most study participants felt that greater automation in the entry of data would be
helpful. Finally, at the time of the pilot study, a feature of REDCap which makes
it attractive for study design—the coordination and communication of workflow
between patients, clinicians, and research coordinator—was not available, and a
second calendar system was implemented to help with the logistics of scheduling
to enhance recruitment [28].
In summary, the logistical challenges around creating and coordinating a multi-
site system for surveying and collecting data across thousands of patients and
hundreds of providers, research coordinators, and analysts was solved with the
involvement of REDCap. The technological tool to assist with data collection and
a central data management function elevated psychiatry beyond the ancient system
of one provider to one patient, and created a wealth of possibilities for how to use
this data beyond the research for DSM 5. It remains to be seen if the flexibility of
this instrument could be further utilized across providers and entities to assist with
diagnosis, treatment, and research.
206 N. Clark et al.
10.3 Using Technology Tools in Support of DSM-5

in Clinical Practice
The introduction of DSM-5 has provided an excellent opportunity for better

integration of technology into clinical practice to enhance patient care. Although
there is much promise to improving clinical flow and the quality of patient care
in psychiatric setting by integrating technology, there are aspects of psychiatric
practice that have made such integration inherently difficult. The relatively personal,
private and subjective nature of psychiatric care have made such integration a
challenge. The increasing complexity of systems of psychiatric care combined with
improvements in the technology used in psychiatric diagnosis and the proliferation
of more objective measures in clinical practice have lead to an increased desire to
integrate technology into clinical practice.
The introduction of DSM-5 and the included assortment of measures to help
quantify psychiatric illness and qualify improvement provide several opportunities
to use technology to help the DSM-5 be more clinically relevant than any of the
previous versions. The implementation of the data collection and coordination part
of the DSM 5 field trials suggest ways in which the current tools available for
clinicians could be made available to enhance accuracy and reliability of diagnosis,
as well as communication of diagnosis among treatment entities.
Several areas could be of interest to developers and clinicians. The first could be
developing tools to assist clinicians with diagnosis. This would be an opportunity
to increase specificity and reliability of psychiatric diagnoses given in clinical
setting and provide “decision-support” at the point of care for clinicians to lead
to a more accurate diagnosis. This diagnosis could then be bridged to a menu of
evidence based options based on accepted guidelines. Furthermore, these clinically
validated diagnoses could be used for registries or for the basis of performance based
measures.
These diagnoses could also be collected and used in psychiatric epidemiologic
databases and possibly treatment outcomes databases. The data could be used to
track and improve psychiatric care at the population level. Further, the specific
diagnosis information could then coordinate with a computerized medical record
and be used in communication with treatment entities outside of the home institution
or potentially outside of psychiatry (such as primary care or other subspecialties).
Second, the digital tools could be useful for assisting the clinician to make
co-morbid diagnoses as well as track response to treatment and evolution of the
psychiatric illness. DSM-5 rating scales for diagnostic assessment of other condi-
tions, such as functioning, degree of impairment, and suicide risk among others
could prove invaluable. If available at the point of care or if previously completed
by the patient, these rating scales could provide a valuable enhancement to the
clinical encounter. There would be many ways that the cross-cutting assessments
and rating scales that are provided in DSM-5 could be integrated in the clinical
encounter with technology including applications on mobile devices or integration
within the medical record. Having the results of these assessments and scales at the
point of care would tremendously benefit patient care by helping to make diagnoses
that may not be manifestly clear by the presentation as well as by helping to track
treatment progress. Data that can provide the clinician an objective signal regarding
symptom clusters that require more attention could help prevent adverse outcomes.
Practical Scenario
Mrs. X is a 45 y/o female that has been seeing her psychiatrist Dr. H for several
years. She has a history of Major Depressive Disorder which has proven to be
recurrent. She has been well maintained on medication and psychotherapy for
several years. Her current episode began shortly after she lost her job. Dr. H has
used symptoms scales filled out by Mrs. X on a tablet computer while she was in
the waiting room. This tablet had a program that integrated with Dr. H’s electronic
medical record so that was able to see her depression scores at this visit as compared
to the last visit. He was able to see that the depression scores were worse and was
able to identify that the current treatment plan may not be sufficient. He was also
able to evaluate depression symptom cluster scores and he noted that she has been
sleeping particularly poorly and had elevated scores in suicidal ideation. Dr. H was
able to tailor his interview to be sure to address both of those issues. Further he was
able to evaluate the insomnia scale that had also completed and was able to use that
information for purposes of the differential diagnosis of the trouble sleeping.
Technology could also lead to enhancements of clinical care with the use of
DSM-5 for interview support. The Cultural Formulation Interview (CFI) included
in DSM-5 could be integrated with an electronic medical record to not only enhance
the sensitivity and the accuracy of the interview but also with its documentation.
Thus the CFI may be used as an interview aid as well as a documentation template.
This may lead to enhanced effectiveness of care provided.
Housing rating scales and fields with inputtable results from diagnostic inter-
views within REDCap also suggests that greater diagnostic accuracy is possible.
The transition from the DSM-IV to the DSM 5 is potentially fraught with confusion
regarding the nuances of diagnosis from one edition to another, but with the safety
net of the diagnostic criteria embedded in the REDCap interface, omitting elements
of diagnosis is less likely. This would be a helpful IT solution to ensure that practices
do not incur such coding risk to avoid penalties if billing and documentation are
audited.
It is also clear that REDCap is capable of managing the comorbidity implicit in
real world medical and psychiatric diagnosis from its handling of the comorbidity
data from the DSM 5 field trials. REDCap and other diagnostic tools could be a
valuable assistant in considering what diagnoses should be considered concurrently
given the potential overlap in diagnosis. The DSM 5 has also explicitly stated that
the text will be available as a subscription, and that this will make the book “readily
adaptable to future scientific discoveries and refinements in its clinical utility” [29].
While the DSM-IV underwent only one major revision (the DSM-IV TR in 2000),
the presence of a web-based subscription creates the possibility that changes may
occur more rapidly in the future, reinforcing the need to keep up to date with
208 N. Clark et al.
potential changes, and therefore making integration with a readily updateable survey
and database for diagnostic interview assists valuable to the busy clinician.
The converse to this scenario is also possible. If a centrally managed database
of information from clinical interviews is created across institutions, the amount
of data and ability for the tool to manage and analyze creates opportunities for
vast trials powered to examine progressively smaller variations and subtypes of
diagnosis in real world circumstances. In this way, the collection of interview data
could influence the development of an expert consensus text (the DSM), and a
discussion based format in which dynamic results could be assessed and viewed
by users throughout the world. For example, the development of a new designer
drug in a small region has been known to become disseminated widely throughout
the country causing public health problems. Some recent examples include synthetic
cannabis and bath salts [30].
Various studies have attempted to capture the public health impact, notably a
report from SAMHSA (Substance Abuse and Mental Health Services Administra-
tion) that abuse of synthetic cannabis accounted for 11,406 Emergency Room visits
in 2012 [31]. Could a nationally shared database of information regarding clinical
interviews and experiences have enabled physicians and policy makers to act more
rapidly to prevent the enormous impact of designer drugs in the US?
At the same time, substantial obstacles exist to the creation of a broadly available
data sharing technology tool. In the arena of medicine, privacy concerns regarding
the electronic mode of communication abound [32]. These concerns center around
the nature of privacy regulations, the feasibility of secure data exchange, the security
of personal devices and cloud computing, and social media. For example, the ability
to store large amounts of data in compact devices, or in data accessible via internet
connection such as a patient-provider connection website raises the possibility of
data loss or theft, which in the case of medicine would constitute a large breach in
confidentiality and privacy.
Given the role that technology has played in the development of the DSM 5, and
the potential for the effect on the field of psychiatry, tools currently available to the
psychiatric health care consumer and provider are affecting the current environment
of diagnosis and practice. In an article in Clinical Psychiatry News, [33] the board
of directors of the Anxiety and Depression Association of America (ADAA) is
developing a rating system for mental health apps available for smartphone, and
will be available on the associations website. For example, the National Center
for PTSD is distributing an app created by the National Center for Telehealth and
Technology, the Center for Deployment Psychology, and the National Center for
PTSD, entitled “Mobile App: Prolonged Exposure Coach” [34]. The app is intended
to be a companion tool for mobile devices that is intended to facilitate the evidence
based treatment for PTSD, Prolonged Exposure, and serve as an extender for the
therapist when not in session. The app features PTSD symptom tracking, psycho-
education videos including common reactions to trauma, recording and playback
of prolonged exposure treatment sessions, availability of homework forms and a
record of completed tasks, and an “interactive breathing retraining coach”. The app
is free, and privacy concerns are handled via a disclaimer with instructions on the
app’s webpage. Notably, the user is instructed that the data are only “as safe as the
phone/device itself”, and that storing or sharing data do not fall under HIPAA laws
until the data are transmitted or shared with a mental health provider.
Another area of interest is in substance abuse treatment, where several companies
have developed devices that either connect to smartphones/devices via the audio
jack or usb connection, or bluetooth, that will approximate a breathalyzer reading
[35]. These devices synchronize with an available app to track Blood Alcohol Levels
over time, include blood alcohol levels in texts, and gain a real-time approximation
of a blood alcohol level while drinking. Despite the comparative inaccuracy of
these devices compared to a roadside breathalyzer available to a police officer, their
relatively inexpensive cost may help them be more widely available. In addition, the
idea is less to gain a blood alcohol concentration of high accuracy, and more to give
the consumer objective information regarding blood alcohol rises and falls that may
help provide them with better decision-making ability. If such data were available
to a clinician, it may also aid in detection of substance related diagnoses apt for
treatment.
Electronic Health Records available from vendors also tout the benefits
of improved ability to enter rating scale data directly into patient records,
asserting that the benefits of rating scale presence in the medical record results
in improved quality outcomes and enhanced compensation from insurance, and
attributing the lack of common practice in community settings is around access
[http://www.patienttracemr.com/psychiatric-rating-scales/ for an example]. Vendors
that tie electronic records to a billing and coding system offer the additional
advantage of communicating rating scale results directly to an insurance company.
Rating scales are widely seen as helpful adjuncts in psychiatric diagnosis [36–38].
Overall, the sheer technology utilized in developing DSM-5 has greatly advanced
our field and the validity of our diagnoses. There are many additional benefits to be
derived from the greater adoption of technology to improve clinical care. While we
must balance the importance of confidentiality and avoid the creation of a cookbook
mentality to diagnosis and treatment, the potential for dramatically improving care
is difficult to argue.
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Chapter 11
Summary and Look Forward
Nancy M. Lorenzi and Naakesh A. Dewan
Abstract The goal of this book is to provide an overview of the important

components that touch the technological expansion. The use of computer tech-
nology is more prevalent and the future will bring different changes. Technology
innovations supportive of mental health care will be pervasive, continuous, and
disruptive. Clinicians will become “IT implementers” whether they practice in solo
offices, group practices, or large systems of care. There are a number of trends
emerging that will have a direct impact on the mental health professions. One trend
is that the scientific foundation for mental health is shifting and another is that
technology is becoming smaller and some technology is wearable. The role of blogs,
wikis, websites, podcasts, on-demand information, etc. will become more important
in educating and helping people. The digital age is well ensconced in our day-to-
day lives and our goal is that this book will help the reader understand the impact of
information on their mental health practices.
Keywords Cognitive therapy • Mental health • Mental health services

• Psychopathology • Psychotic disorders • Public health
The use of computer technology is more prevalent in our emerging digital world.
As you look at your practice world your use of information technology systems has
probably dramatically increased in the last few years. If the use of technology has
not increased, “fasten your seatbelts” as it will in the near future! Each chapter
in this book presents the multiple components that are key to being successful
with the more extensive use of the programs for information technology systems.
Incorporating what is needed into one book will be a major source of information
about the current issues and needs.
N.M. Lorenzi, Ph.D. ()

Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville,
TN, USA
e-mail: nancy.lorenzi@Vanderbilt.Edu
N.A. Dewan, M.D.
Behavioral Health, BayCare Medical Group, BayCare Health System, Clearwater, Florida, USA
e-mail: drnick@dewan.pro

214 N.M. Lorenzi and N.A. Dewan
The purpose for this book is to prepare clinicians to understand, to critically

evaluate, and to embrace well-designed and validated technologies that have the
potential of transforming the access, affordability, and accountability of mental
healthcare. To reach that purpose we invited the most knowledgeable people to share
their knowledge so that the reader would become aware of the practical applications
of technology in mental health as well as research supporting information technol-
ogy tools, policy debates, and so forth. Each chapter contains either examples or
scenarios that are relevant to the current practice of mental health care.
The goal of this book is to provide you an overview of all the important
components that touch the technological expansion. The book begins with an
abbreviated overview of the past, present and future of policy issues in mental
health. It provides an excellent foundation for your current practice. Chapter 2
focuses on electronic health records outlining the issues within your practice and
also the interconnections beyond your practice. The leading change chapter will
take you through the various phases of implementing technology in your practice. It
includes the people and process components as well as the technology component
of the implementation.
Today we hear what is called Informatics, information-technology or software
or programs quite frequently. The next section of this book focuses on the digital
tools that are informatics-based used to aid in psychotherapy, cognitive training,
substance abuse and the treatment of adolescents. Chapters also outline coding
disease diagnoses, virtual reality, avatars in Second Life for innovative treatment
and computer programs to aid adolescents.
Currently technology is used for tele-psychiatry. Tele-psychiatry is the delivery
of psychiatric services via telemedicine. As Chap. 8 outlines that telemedicine is
the use of technology to enable the medical practitioner delivered medical services
remotely, with the physician and the patient in different locations, or the physician
consulting remotely with another provider.
11.1 The Future
The future will bring different changes. Technology innovations supportive of

mental health care will be pervasive, continuous, and disruptive in the first half of
the twenty-first century. Clinicians will need to become “IT implementers” whether
they practice in solo offices, group practices, or large systems of care.
Yearly goals and a plan for action will be essential requirements for success.
Hardware, software applications, or process engineers and market “makers” will not
have the same regulatory, liability, or economic constraints that traditional clinical
developers such as pharmaceutical or device makers have in healthcare. In fact,
purchasers of healthcare like consumers, and insurance companies or accountable
care organizations will have greater freedom to innovate than clinicians who are
held to a more conservative standard of patient safety and risk avoidance by society
and the institutions that monitor quality and standards of care.
11 Summary and Look Forward 215
Consumers will have access to “self-therapy” and virtual coaches, and automated
computerized eeg- guided magnetic stimulation devices to change cognitions,
behavior, emotional states, and neural functioning and circuitry. The clinician
must become an expert advisor of technology for the consumer and must strive
to increase “computer-assisted” or technology-assisted treatment. The regulatory
bodies such as accreditation organizations, licensing boards, and payers need to
innovate their processes to permit clinicians to innovate at a similar pace with
developers of technology. Vendors of technology will increase their “efficacy”
research automatically due to the speed and efficiency of data capture and analytics.
Clinicians will have outcome and predictive dashboards for each consumer and can
benchmark their clinical practices with an international risk adjusted norms. There
may come a time when clinicians spend half of their professional learning time
dedicated to technology use and implementation.1
The mass accumulation of neuroscience knowledge and applications, engineer-
ing advances, and technology innovations is coming at an ideal time in healthcare.
Technology is still too naïve and primitive to solve the issues that have plagued
modern healthcare for the past 50 years. Access, affordability, and accountability
are still utopian demands by society, and it may well take a half of a century to
respond to these demands. Clinicians must partner with their patients to accomplish
this utopian dream.
11.2 Trends
Fun futuristic vision from The Atlantic, titled The Extremely Personal Computer:
The Digital Future of Mental Health: “It’s 2018, and you’re not feeling your best.
Yesterday, on the phone with Comcast, you forgot your social security number, and
had to call your mom to get it : : : You fire up your PC and dig out your biomonitor
wrist strap. “Welcome back kiddo,” Regina, your therapist avatar, greets you. Regina
has shiny red hair and glasses, and the Australian accent of a Bond girl. “Let’s catch
up.” “I’m so sorry to hear what you’ve been through,” Regina says, eyes wide. “I
am here for you, ready to help you improve your mood and your mind.” Thomas
Insel, head of the National Institute of Mental Health, supported these efforts from
the start, as an example of what he described to the Royal Society of London in
2011 as psychiatry as “clinical neuroscience.” : : : We’re at an extraordinary moment
where the entire scientific foundation for mental health is shifting, with the 20th
century discipline of psychiatry becoming the 21st century discipline of clinical
neuroscience.”2
1
http://sharpbrains.com/blog/2012/10/04/the-digital-future-of-mental-health/ Oct 4, 2012 The
Digital Future of Mental Health By: SharpBrains.
2
The Extremely Personal Computer: The Digital Future of Mental Health. The Atlantic, October 3,
2012. http://www.theatlantic.com/health/archive/2012/10/the-extremely-personal-computer-the-
digital-future-of-mental-health/263183/
11.3 Clinical Neuroscience
There are a number of trends emerging that will have a direct impact on the mental
health professions. One trend is that the scientific foundation for mental health is
shifting. For example, mental health is moving to more to clinical neuroscience
based. A question for the future is, how will the therapy-practices change?
Clinical neuroscience is a branch of neuroscience that focuses on the fundamental
mechanisms that underlie diseases and disorders of the brain and central nervous
system. It seeks to develop new ways of diagnosing such disorders and ultimately
on developing novel treatments. Clinical neuroscientists – including psychiatrists,
neurologists and other medical specialists – use basic research findings to develop
diagnostic methods and ways to prevent and treat neurological disorders that affect
millions of people. Such disorders include addiction, Alzheimer’s disease, amy-
otrophic lateral sclerosis, anxiety disorders, attention deficit hyperactivity disorder,
autism, bipolar disorder, brain tumors, depression, Down Syndrome, dyslexia,
epilepsy, Huntington’s Disease, multiple sclerosis, neurological AIDS, neurological
trauma, pain, obsessive-compulsive disorder, Parkinson’s disease, schizophrenia,
sleep disorders, stroke, Tourette Syndrome, among many others.
11.4 There Is a Dynamic Technology Shift
Technology is becoming smaller and some technology is wearable. There will be

more technology to support the mental health professionals. For example, using a
neuro-cap for neuro-feedback or biomonitor/biometrics to help monitor the patient
could be readily available. In 2014 Myndbot technology was approved by the FDA
for anxiety treatment. Insurance companies have begun to pay for partial coverage.
In 2012 the National Institute of Health offered its first grants in the field of video
games as psychiatric intervention.
Virtual reality is one technology that has and can be used in therapy. Researchers
at the University of Southern California have used virtual reality to address war
wounds and combat related post-traumatic stress disorder. Mental health researchers
immerse patient “in simulations of trauma-relevant environments in which the
emotions intensity of the scenes can be precisely controlled by the clinicians, in
collaboration with the patient wishes.”3
Another form of virtual reality is Second Life, which is a 3D virtual world
where each person has an avatar and is able to socialize. The School of Medicine
at Vanderbilt University tested the possibility of nurse practitioners and physicians
from the Diabetes Center interacting with some of their patients by using Second
Life for counseling. In the virtual reality pilot Second Life was more successful for
3
Rizzo, A, Buckwalter, G, Forbell, E, et al. Virtual reality applications to address the wounds of
war. Psychiatric Annuals. 43(3):123–133. March 2013.
the people who were more familiar with technology. However, as people become
more familiar with technology this appears to be another way to work with patients.
11.5 Digital Technology for Prevention and Treatment
People are living longer and with the longer life there might be more geriatric mental
health issues that need to be addressed. Is it possible that technology will have a role
with the older population? How can technology help the mental health professional
help his/her patients gain a more holistic wellness program? What about prevention?
As computer programs become more sophisticated there will be a time when
people could get the needed answers through a computer. For example, what if a
computer could verbally help a person figure out how to put a product together or
what if the computer would ask the user enough questions to allow them to make a
better decision. A further “what if” is the possibility of a computer “talking” through
a problem with the user? [For those of you who have an iPhone, think about Seri
and how Seri can respond to such a wide range of questions today.] People need
help with decision making. Some of the issues that people go through today is that
they have made poor choices or poor decisions. Could this help mental health?
11.6 Education/Training
With all of the issues that need to be addresses we need a continuous supply of
well-trained mental health professionals. Can the use of information technology
accelerate training, especially for the patient therapy processes?
11.7 Public Health and Social Change Through Social Media
With all the school shootings and other acts of violence is there a role for technology
and the mental health professions in the future?4 Biofeedback, computer based
software programs can be now used to help children reduce test anxiety. Will
this spread to other places? Neuro-feedback for children with attention-deficit
/hyperactivity disorder. Will there be technology to actually change brain function?
Can any of this help the path of the “school shooters”? Wearable computers that can
help with depression or post-traumatic stress disorder will be more routine in the
future.
4
Randy Borum: Improving the clinical practice of violence risk assessment: technology guidelines
and training. American Psychologist September 1966 pages 945–956.
Can technology help to better connect the mental health professional to other
healthcare professionals? Will there be more Facebook type programs for the world
to use? Our phones keep us connected today and in the future the phones will offer
more and more connections. The number of apps are amazing. M-health is a word
used for the mobile health apps that are available for the “smart phones” today.
As a support network, e.g. Patients like me. http://www.patientslikeme.com/ On
June 18, 2014 there were about 2,000 results in 0.29 s! The following are the first
six listings.
Mental Health Counseling (Individual Therapy) Report for Patients : : :
Mental Health Counseling (individual therapy): Find the most comprehensive real -
world treatment information on Mental Health Counseling (individual therapy) : : :
www.patientslikeme.com/.../11845-mental-health-counseling-side-effects-and-
efficacy? : : : clipped from Google – 6/2014
Mental Health Nurse Practitioner Report for Patients Like You
Mental Health Nurse Practitioner: Find the most comprehensive real-world treat-
ment information on Mental Health Nurse Practitioner at PatientsLikeMe.
www.patientslikeme.com/...;/25400-mental-health-nurse-practitioner-side-
effects-and-efficacy clipped from Google – 6/2014
Art and Mental Health Charity (Volunteering) Report for Patients Like : : :
Art and Mental Health Charity (volunteering): Find the most comprehensive real-
world treatment information on Art and Mental Health Charity (volunteering) at
:::
www.patientslikeme.com/...;/16355-art-and-mental-health-charity-side-effects-
and-efficacy-and-efficacy clipped from Google – 6/2014
Mental Retardation Symptoms and Experiences Straight from : : :
Mental Retardation: Find the most comprehensive real-world symptom and : : :
help each other live better and uncover the best ways to manage your health today
: : : www.patientslikeme.com/conditions/1182-mental-retardation
Life Chart Method (Individual Therapy) Report for Patients Like You
Life Chart Method (National Institute of Mental Health prospective Life Chart
Methodology) allows for the daily assessment of mood and episode severity based
:::
www.patientslikeme.com/...;/10822-life-chart-method-side-effects-and-efficacy?
Post-traumatic Stress Disorder Symptoms and Experiences Straight : : :
Post-Traumatic Stress Disorder (PTSD), a mental health condition triggered by a
traumatic event, is characterized by many symptoms including flashbacks, : : :
www.patientslikeme.com/conditions/24-post-traumatic-stress-disorder
11.8 Future Challenge
With all of the increased use of technology will you face more patients who are
addicted to technology? Will there be an over reliance on technology instead of
face-to-face or verbal conversations? The role of blogs, wikis, websites, podcasts,
on-demand information, etc. will become more important in educating and helping
people.
11.9 Summary
The digital age is well ensconced in our day-to-day lives. It is our hope that the
chapters in this book and our predictions about the future will help the reader
understand the impact of information on their mental health practices.
Index
A mental health care, 149

Accountable Care Act (ACA), 143 clinical concerns, 143–144
Addiction treatment, CCT clinician assessments, 147
asynchronous and real-time (synchronous), doctor-patient relationship, 147–148
133–135 documentation, 142, 146–147
clinical assessments and interventions, family history, 146
124–125 federal policy implications, 142–143
computer-based psychosocial interventions functionality and user interface, 150
clinician/program-supported mental health providers, 154
technologies, 127–130 personal health information (PHI), 146
Internet-based psychotherapeutic primary care providers (PCPs), 146–147
interventions, 125 risk factors
self-management technologies, chat rooms, 146
125–127 cyberbullying, 145
digital communications, 123 sexting, 145
research and/or trends, 136–137 social media, 145–146
sensors, 135–136 safety and risk, 149
Smart phones, 131 in youth
SUD, 131–133 “Family Media Agreement”, 150–151
ADHD combined type (ADHD-C) Internet-based therapy, 152
co-morbidity, 107–108 mobile devices, availability, 151
compliance, 110 parental modeling of appropriate
hyperactivity, 109 technology use, 153
impulsivity, 109 patient-centered applications (“apps”),
neuropsychological tests, 109 152
ODD, 112 social media sites, age restrictions,
RAST, 108 153
RCT, 110 State/Trait Anxiety Inventory (STAI)
VSWM, 107 scores, 151
WM, 107 technology and media use, 150
Adolescent behavioral health care and treatment, 152–153
technology Adolescent psychiatry
child/adolescent patients, documentation AACAP, 20
“hedge phrases”, 149 psychiatric records, 154
“medical tweet”, 148 technology as field, 154

Health Informatics, DOI 10.1007/978-3-319-14109-1
222 Index
Adults development B
auditory attention, 101 Basic Number Screening test (BNST), 106,
brain activation, 101 107
cognitive failure, 101 Beating the Blues, 65, 72
computer-aided treatments, 64 Behavioral self-control training (BSCT), 126,
diagnosable disorders, 102 127
fMRI, 101 Behavioral self-control training program for
SNP, 100 Windows (BSCPWIN), 126
telepsychiatry, 70 Binomial effect size display (BESD), 83, 87
Affordable Care Act, 10 Biofeedback, 217
Alcohol, Smoking and Substance Involvement Bipolar disorder, 103, 200, 202, 204, 216
Screening Test (ASSIST), 129 Brain imaging
American Association for Technology in ADHD, 84
Psychiatry (AATP), 20 corpus callosum, 84
American Association of Child and Adolescent fMRI, 84
Psychiatrists (AACAP), 20, 153, motor networks, 95
171, 174 somatosensory, 95
American College of Medical Informatics striatum, 95
(ACMI), 16 BRAVE for Children-ONLINE, 60
American Health Information Management BSCPWIN. See Behavioral self-control
Association (AHIMA), 16 training program for Windows
American Medical Informatics Association (BSCPWIN)
(AMIA), 16 BSCT. See Behavioral self-control training
American Psychiatric Association (APA), 153, (BSCT)
171
American Recovery and Reinvestment Act,
143 C
Antisocial personality disorder, 201 Camp Cope-A-Lot condition (CCAL), 65
Anxiety Care Management tools. See Customer
computer-assisted treatment approaches, relationship management tools
64–65 (CRM)
management strategies, 60 CDCU. See College Drinker’s Check-up
and mood disorders, 59 (CDCU)
self-guided computerized treatments, Certification Commission for Health
59–60 Information Technology (CCHIT),
Attention deficit hyperactivity disorder 19
(ADHD) Chief Medical Information Officer (CMIO),
clinical populations, 82 22, 38–42, 44
combined and inattentive type, 103 clinical health IT leader, challenges, 44
co-morbid disorders, 103 systems’ capabilities, 38–39
fMRI, 84 systems’ limitations, 39–40
fronto-parietal system, 85 technology vs. process, 40–42
hyperactivation, 95 Chronic Disease Management (CDM), 52
hyperactive/impulsive, 104 Clinical communication technologies (CCT).
Memory for Goblins, 88 See Addiction treatment, CCT
neurobiology, 95 Clinical Decision Support (CDS), 43
RCT, 88–89 Clinical Language Understanding (CLU), 49
research design, 89, 90 Clinical neuroscience, 216
social deficits, 86 Clinical quality measures (CQMs), 27
strategy training, 89 Clinician identification numbers (CID), 205
VSWM, 89 Clinician/program-supported technologies,
Automated Working Memory Assessment, 127–130
102, 104 Clinton Health Security Act, 5–6
Index 223
Cognitive-behavioral therapy (CBT), 125, Diagnostic and statistical manual (DSM) of

127–128, 130 mental disorders
Cognitive remediation academic medical centers, 203
psychiatric rehabilitation, 85, 102 field trials, 202, 204
schizophrenia, 83 leaders and experts, 202
College Drinker’s Check-up (CDCU), pilot study, 205
126–127 planning conference, 202
Community Based Collaborative Care safeguards, 201
(CBCC), 20 SCID, 201–202
Community Mental Health Services Block task force, 202
Grant Program, 5 technology tools, 206–209
Community Reinforcement Approach (CRA), test-retest reliability, 202
125 web-based application, 204
Computer-assisted cognitive remediation Diffusion Tensor Imaging, 95
(CACR), 84 Digital technology for prevention and
“Computer-assisted” or technology-assisted treatment, 217
treatment, 215 Doctor patient relationship
Computer-assisted treatment approaches EHR system, 147–148
for anxiety disorders, 64–65 electronic medical record, 148
Camp Cope-A-Lot, 64, 66 Dopamine transporter gene (DAT1), 100
cost-effectiveness of programs, 67 Drinker’s Check-up (DCU), 61, 126
face-to-face interview, 72 Drug interactions, 21, 184, 192
for mental health difficulties, 66
mental health problems, 58
for mood disorders, 65–66 E
for obsessive-compulsive disorder, 65 Ecological momentary assessment (EMA),
Computer cognitive training. See 133–134
Neuroplasticity Education/training, 217
Computerized Physician Order Entry (CPOE), “EGetgoing”, web-based videoconferencing
49 intervention, 128
Computer-linked education, support, and EHR implementation
attention (CESA), 65 “Conversion/Go Live”, 34–35
Computer Stored Ambulatory Record disaster recovery, 33
(COSTAR), 14 downtime procedures, 32–33
Contingency-management (CM), 125, 130 hardware needs, 32
Continuity of patient care, 124, 169 post-implementation/ongoing, 35
Controlled substances, 170, 172, 174, 177 software addition/changes, 32
Customer relationship management tools testing, 33–34
(CRM), 52 training, 34
Cyberbullying, 145 work plan/staffing, development, 31–32
EHRs. See Electronic health records (EHRs)
EHR selection
D access, 24, 29–30
Dancing videos, 188 affordability, 29
Depression clinicians and administrators, 28
alcohol and cannabis, 130 data, 25–26
and anxiety, 192 documentation, 30
behavioral interventions, 90 functions and data, 23–24
computer-guided treatment, 61 hardware platforms, 26
“Facebook depression”, 145 implementation timeframes, 27
in older adults, 70 interfaces, 27
scores, 207 legal ownership, 29
sexting, 145 privacy/security, 29
symptoms, 71 software updates, 31
224 Index
EHR selection (cont.) “physician”, Social Security Act

support/manpower, 31 definitions, 143
system availability, 26–27 Functional resonance imaging (fMRI), 94–96,
team, 22–23 101
technical support, 30–31
testing, 29
training, 30 G
user and data accessibility, 24–25 Geographical momentary assessment (GMA),
vendor’s reputation, 28–29 134
volumes of data, 25 Good Days Ahead: The Multimedia Program
Electronic health records (EHRs), 142–144 for Cognitive Therapy, 65
academic institutions, 14
CCHIT, 19
clinical workforce totals (2012), 18
computer technology, organizations, 16 H
cost considerations, 27–28 Health 2.0, 186
e-prescribing, 21 Healthcare Information Management Systems
Health Information Technology, 17 Society (HIMSS), 16
history, 14–17 Health information exchanges (HIE), 40,
HL7 organization, 15–16 143
implementation (see EHR implementation) Health Information Technology for Economic
interoperability, 21–22 and Clinical Health (HITECH) Act,
Medicare provider, 21 17, 143
mental health providers, 18–19 Health insurance, 5–6, 8
MU program, psychiatrists participation, Health Insurance Portability and
18 Accountability Act (HIPAA),
organizations, 19–21 7, 142
practices, 22 Health IT optimization
selection (see EHR selection) clinical standardization
software vendors, 14–15 copy/paste/cloning, 45–46
specialty practice physicians, 17–18 inappropriate record access, 48
time/manpower resources, 22 managing notes, 46
Electronic mail, 6, 23 regulatory compliance, 47–48
Electronic medical records (EMR) voice recognition, 46–47
Clinical Decision Support (CDS), 43 CMIO role morphing (see Chief Medical
healthcare organizations, 42–43 Information Officer (CMIO))
Optimization Governance, 44 governing optimization, 42–44
“Organizational Life-Cycle” ICD 10, 49
build phase, 41 technologies
design phase, 41 care transitions and patient engagement,
Go-Live phase, 42 51–52
optimization phase, 42 CRM tools, 52
system selection phase, 41 data elements, 53–54
testing phase, 41–42 EMR optimization, 54–55
vendors, 39–40 EMR system selection and
E-prescribing, 21 implementation, 54
governance, 54
health management, 53
F home monitoring and data integration,
“Facebook depression”, 145 53
“Family Media Agreement”, 150–151 NLP and CLU, 54
Federal policy implications patient portals, 52–53
electronic health records (EHR), 142–143 physician IT leadership, 54
Index 225
work flow Medicaid providers, 5, 8, 17

Citrix vs. Native Apps, 51 Medical informatics
EMRs improve care and safety, 49–50 AMIA (see American Medical Informatics
physician portals, 50 Association (AMIA))
WIIFM, 50 CMIO, 22
Health Level 7 (HL7) organization, 15–16, 20 portable devices, 51
privacy, 174
Medicare providers, 8, 17, 21
I Mental health care
“iDisorders”, 145 early years (1975–1984)
Innovations IT context, 4
Anxiety Coach, 70 period hallmarks, 3–4
challenge, 219 policy context, 4
Dialetical Behavior Therapy (DBT) Coach, future (2015 and beyond), 9–10
70 information technology, 2–3
Helping the Noncompliant Child (HNC), 70 mental health policy, 2
Mobilyze, 70 middle years (1985–1994)
technology-enhanced HNC (TE-HNC), 70 IT context, 6
In-person examination, 171–172 period hallmarks, 5
Integrated Advanced Information Management policy context, 5–6
Systems (IAIMS) Awards, 14 modern era (2005–2014)
Interactive voice response (IVR) technology, IT context, 9
SUD period hallmarks, 7–8
assessment and reminder purposes, 132 policy context, 8
benefits, 133 recent years (1995–2004)
daily process methods, 131 IT context, 7
The Recovery Line IVR system, 132 period hallmarks, 6
traditional methods, 131–132 policy context, 6–7
treatment as usual (TAU), 132 Mental Health Information Technology
International classification of diseases, 201 (MHIT), 19–20
Internet-based CBT (I-CBT), 64–66 Mental health services, 4–6, 8, 20, 62, 175
iPhone, 217 Mental Health Statistics Improvement Program
(MHSIP), 4
L Mental Health Systems Act, 4
Leadership, 54, 188 Mental Retardation Facilities and Community
Learning disabilities (LD) Mental Health Centers Construction
cognitive failures, 113 Act, 2
hyperactivity and impulsivity, 113 M-health, 218
inattention/overactivity, 112 Micro-blogging platform, 187, 189
medication treatment, 112 Moderatedrinking.com (MD), 127
ODD, 112 Moderation Management (MM), 127
psychopharmacological treatment, 113 MoodGYM, 65
Liability legal Motivational enhancement system (MES), 129
DSM-IV diagnostic construct, 202 Motivational enhancement therapy (MET), 130
profession, 166 Motivational interviewing (MI), 125, 126,
psychiatric diagnoses, 206 128–129
and risk management, 70 MU. See Meaningful Use (MU) Program
self-guided computer, 63 MUMPS programming language, 14
Licensing board, 170 2014 Myndbot technology, 216
M N
Malpractice, 166, 191 National Aeronautics and Space
Meaningful Use (MU) Program, 17, 39 Administration (NASA), 61
226 Index
National Institute of Mental Health (NIMH) coaching program, 90

Child and Adolescent Service System internet interventions, 91
Program, 5 low intensity practitioners, 91
Community Support Program, 5 mood disorders, 90
deinstitutionalization, 4 Public health, 8, 58, 186, 208, 217–218
Neuro-feedback, 217
Neuroleptic malignant syndrome, 193
Neuroplasticity R
ADHD-C, 107–110 Research Electronic Data Capture (REDCap)
brain’s reaction, 92 system, 203–204
Cogmed coaching program, 91–92 Restricted academic situations task (RAST),
co-morbidity, 110–112 108
executive control hypothesis, 117
fMRI, 95
hippocampus, 92–93 S
inattention difficulties, 106–107 Schizophrenia
learning difficulties, 106–107 ADHD, 82–83
preschool children, 99–100 BESD, 83
psychiatric rehabilitation, 115 CACR, 84
psychological interventions, 94 cognitive remediations, 83
psychology, 89–91 computerized cognitive training, 83–86
research design, 82 functional outcomes, 85
synaptic plasticity, 93 neurocognitive domains, 83
training-induced plasticity, 96 neuropsychological performance, 84
transfer effects, 114 psychiatric rehabilitation, 85, 102
WM deficits, 88–89 (see also Attention social cognition, 84
deficit hyperactivity disorder social skills, 69
(ADHD)) and TBI, 99
Seasonal Affective Disorder (SAD), 103,
202
O Second Life, 68, 216
Obsessive-compulsive disorder (OCD), 60–61 Self-guided computerized treatment programs
Oppositional defiant disorder (ODD) for anxiety disorders, 59–60
comorbidity, 109, 111 benefits, 58–59
computerized cognitive training, 116 challenges, 62–63
inattention/overactivity, 112 cognitive-behavioral therapy (CBT), 59
for mental health difficulties, 62
for mood disorders, 61
P for obsessive-compulsive disorder, 60–61
Paroxetine, 192 parental consent for minors, 63
Patient Care System (PCS), 15 randomized controlled trials, 63
Patient-centered care, 148 for substance use, 61–62
Patient Protection and Affordable Care Act, 2, Self-Help for Alcohol and other drug use and
8 Depression (SHADE), 130
Patient satisfaction, 149, 166 Self-help groups
Personal health information (PHI), 146 CCT-based application, 126
Primary care, 6, 7, 15, 18, 21, 27, 128, 167, depression, 61
190, 206 materials and homework assignments, 65
Primary care providers (PCPs), 146 Self-management technologies
Psychiatric rehabilitation, 85, 115 behavioral self-control training (BSCT),
Psychopathology, 58, 66, 200, 201 126, 127
Psychotic disorders BSCPWIN, 126
ADHD, 89 CDCU, 126–127
behavioral interventions, 90 Drinker’s Check-up (DCU), 126
Index 227
MI techniques, 126 SUD. See Substance use disorders (SUD)

Moderation Management (MM), 127 Suicide, 63, 145, 149, 162, 168, 205, 206
self-administered interventions, 127 Supplemental Security Income Program, 5
Self-therapy, 215 Symposium on Computer Applications in
Sensors Medical Care (SCAMC), 16
eHealth, 136
in smartphones, 135
transdermal electrochemical sensors, 135 T
Shared Hospital Accounting System (SHAS), Telefacsimile, 159
15 Telemedicine, 3
Shared Medical Systems (SMS), 15 confidentiality and security, 164
Single nucleotide polymorphism (SNP), 100 credentialing and privileging decisions, 165
Smart phones documentation requirements, 163–164
benefits of EMA, 134–135 fraud and abuse, 164–165
SUD, 131–133 licensure, 162
Social Effectiveness Therapy for Children, practice of medicine, 161
68 prescription, 162–163
Social media reimbursement, 165
age restrictions, 153 Telepsychiatry, 58, 214
anxiety and paranoia, 190 acceptance-based behavioral therapy, 70
behavioral healthcare providers, 190 address licensure, 171
Facebook and Twitter, 185 administration of CBT, 69–70
health tools, 192–193 Business Associate Agreement, 169
interactivity and community, 195 clinical care issues, 174–176
negative reviews, 190 clinical efficacy and cost-effectiveness, 70
networking sites, 145 Core Operational Guidelines for Telehealth
peer support, 193–195 Services Involving Provider–Patient
physician and therapist rating sites, 190 Interactions, 169–170
web-based tools, 184 delivery models, 161
Social networking, 145, 184, 185 e-Getgoing, 70
Facebook’s popularity, 186–188 emergency evaluations, 168
Health 2.0 applications, 189 evidence supports, 160
Instagram, 187 HIPAA and state law requirements, 170
mental health professionals, 190 history, 160
micro- blogging tools, 189–190 Practice Guidelines for Videoconferencing-
personal vs. professional use, 188 Based Telemental Health, 173
Pinterest, 187 professional liability, 166
Twitter, 187 psychiatric services, 159
Software and Technology Vendors Association quality and effectiveness, 177
(SATVA), 7 randomized trial (RCT), 69
State/Trait Anxiety Inventory (STAI) scores, relevant legal issues, 172
151 technology’s suitability, 168
Structured Clinical Interview for DSM videoconferencing technology, 69
Disorders (SCID), 202 Teleradiology, 160
Substance Abuse and Mental Health Services The Medical Record (TMR), 14
Administration (SAMHSA), 5, 10, Traumatic brain injury (TBI)
20 acute attention deficits, 86
Substance-related disorders, 127, 209 ADHD, 82–83
Substance use disorders (SUD) attention rehabilitation, 88
IVR technology, 130–133 BESD, 87
microprocessor-based technology, 123 cognitive rehabilitation, 86, 88
psychosocial interventions, 125 communication deficits, 86
transcranial magnetic or deep brain efficaciousness, attention training, 87
stimulation, 124 research design, 87
228 Index
V W
Videoconferencing Web 2.0, 184–186
behavioral activation, 70 Web-based prevention program, 128
computer-aided intervention, 58 Web-based Therapeutic Education System
healthcare facility, 169 (TES), 130
technology (see Telepsychiatry) Wellstone-Domenici Mental Health Parity and
telemedicine, 161 Addiction Equity Act, 8
Virtual reality exposure therapy (VRET), Wireless technology, 160
67–68 Working memory (WM) training
Virtual reality programs anxiety disorder, 103
for anxiety disorders/phobias, 67–68 attentional stamina, 97
for mental health difficulties, 68–69 behavioral outcomes, 99
for post-traumatic stress disorder, brain plasticity, 97
67–68 Cogmed Claims and Evidence, 98, 99
skills, 67 co-morbidity, 103–105
Visual spatial short term memory (VSST), depressive disorder, 103
102 dopamine receptors, 92, 96
Visual spatial working memory (VSWM) frontal gyrus, 96
ADHD, 89 neuroplasticity, 92
Cogmed training program, 98 prefrontal association cortex, 96
Voice recognition software psychiatric rehabilitation, 102
EMRs, 46 RCT, 106
local profile, 47 TBI, 99
roaming profile, 47 VSWM, 97
training and practice, 47 VWM, 98

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Health Informatics

Mental Health Practice

ISSN 1431-1917 ISSN 2197-3741 (electronic)

Library of Congress Control Number: 2015932971

Springer Cham Heidelberg New York Dordrecht London

Printed on acid-free paper

Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www.

We achieve understanding within a circular movement

In his classic treatise on the phenomenology of mental illness, Karl Jaspers

Department of Psychiatry Laura J. Fochtmann

1 Past, Present, and Future Policy and IT Landscape

10 Technology Tools Supportive of DSM-5: An Overview.. . . . . . . . . . . . . . . . 199

Nathaniel Clark, M.D. Department of Psychiatry, Vanderbilt University School of

Neal R. Patel, M.D., M.P.H. Health Informatics Technologies and Services,

Keywords Deinstitutionalization • Electronic health records • Health insurance

R. Manderscheid, Ph.D. ()

© Springer International Publishing Switzerland 2015 1

1.2 Mental Health Policy

Mental health policy is a very imprecise concept. We must circumscribe it so that

1.3 Information Technology

Fig. 1.1 Dynamic relationships among IT hardware, software, and applications

1.4 The Early Years: 1975–1984

1.4.1 Period Hallmarks

This early period, 1974–1984, could be characterized as one of major transitions,

1.4.2 The Policy Context

1.4.3 The IT Context

1.5 The Middle Years: 1985–1994

1.5.1 Period Hallmarks

1.5.2 The Policy Context

Throughout this period, the de jure policy of community-based mental health

1.5.3 The IT Context

1.6 The Recent Years (1995–2004)

1.6.1 Period Hallmarks

1.6.2 The Policy Context

Community-based mental health care remained the predominant de jure policy

The hope of recovery—the lifelong process of regaining one’s life in the

1.6.3 The IT Context

1.7 The Modern Era (2005–2014)

1.7.1 Period Hallmarks

1.7.2 The Policy Context

1.7.3 The IT Context

Throughout the decade, efforts continued to implement electronic medical records

1.8 The Future (2015 and Beyond)

As we look to the future, it is advisable to seek a midground between what will be

workarounds necessary to accommodate federal law and regulation regarding the

L. Simon, M.D. ()

© Springer International Publishing Switzerland 2015 13

There are few industries where computer technology is needed as much as in

Lockheed and McDonnell Douglas, began developing software to fully automate

2.2 Status/Ongoing Problems

Gastroenterology 54.3 45.7

Percentage of eligible professionals awarded an incentive payment for 2012

Source: GAO analysis of CMS data

who received a meaningful use incentive payment in 2012. Although a number of

2.3 How to Proceed

involved in such areas as privacy/security, health information exchanges (HIEs), and

2.4 Decision to Use an EHR

2.5 Selection Preparation

2.6 Cost Considerations