Healthcare Technology Innovation Adoption - Electronic Health Records and Other Emerging Health Information Technology Innovations PDF
Healthcare Technology Innovation Adoption - Electronic Health Records and Other Emerging Health Information Technology Innovations PDF
Healthcare Technology Innovation Adoption - Electronic Health Records and Other Emerging Health Information Technology Innovations PDF
Tugrul U. Daim
Nima A. Behkami
Nuri Basoglu
Orhun M. Kök
Liliya Hogaboam
Healthcare
Technology
Innovation Adoption
Electronic Health Records and
Other Emerging Health Information
Technology Innovations
Innovation, Technology, and Knowledge
Management
Series Editor
Elias G. Carayannis
George Washington University
Washington, D.C., USA
Healthcare Technology
Innovation Adoption
Electronic Health Records
and Other Emerging Health Information
Technology Innovations
Tugrul U. Daim Nima A. Behkami
Department of Engineering Merck Research Laboratories
and Technology Management Boston, MA, USA
Portland State University
Portland, OR, USA Orhun M. Kök
Ernst and Young Advisory
Nuri Basoglu Istanbul, Turkey
Department of Industrial Design
İzmir Institute of Technology
Urla, Izmir, Turkey
Liliya Hogaboam
Department of Engineering
and Technology Management
Portland State University
Portland, OR, USA
1
We define sustainable entrepreneurship as the creation of viable, profitable, and scalable firms.
Such firms engender the formation of self-replicating and mutually enhancing innovation networks
and knowledge clusters (innovation ecosystems), leading toward robust competitiveness
(E.G. Carayannis, International Journal of Innovation and Regional Development 1(3), 235–254,
2009).
2
We understand robust competitiveness to be a state of economic being and becoming that avails
systematic and defensible “unfair advantages” to the entities that are part of the economy. Such
competitiveness is built on mutually complementary and reinforcing low-, medium-, and high-
technology and public and private sector entities (government agencies, private firms, universities,
and nongovernmental organizations) (E.G. Carayannis, International Journal of Innovation and
Regional Development 1(3), 235–254, 2009).
3
The concepts of robust competitiveness and sustainable entrepreneurship are pillars of a regime
that we call “democratic capitalism” (as opposed to “popular or casino capitalism”), in which real
opportunities for education and economic prosperity are available to all, especially—but not
only—younger people. These are the direct derivatives of a collection of topdown policies as well
as bottom-up initiatives (including strong research and development policies and funding, but
going beyond these to include the development of innovation networks and knowledge clusters
across regions and sectors) (E.G. Carayannis and A. Kaloudis, Japan Economic Currents, p. 6–10
January 2009).
v
vi Series Foreword
Books that are part of the series explore the impact of innovation at the “macro”
(economies, markets), “meso” (industries, firms), and “micro” levels (teams, indi-
viduals), drawing from such related disciplines as finance, organizational psychol-
ogy, research and development, science policy, information systems, and strategy,
with the underlying theme that for innovation to be useful it must involve the shar-
ing and application of knowledge.
Some of the key anchoring concepts of the series are outlined in the figure below
and the definitions that follow (all definitions are from E.G. Carayannis and
D.F.J. Campbell, International Journal of Technology Management, 46, 3–4, 2009).
Global
Systemic Mode 3 Quadruple Democracy Democratic
macro level helix of capitalism
knowledge
Structural and
organizational Knowledge Innovation Entrepreneurial Academic
meso level clusters networks university firm Global/local
Sustainable
entrepreneurship
4
E.G. Carayannis, Strategic Management of Technological Learning, CRC Press, 2000.
viii Series Foreword
Elias G. Carayannis
Preface
Healthcare costs have been increasing dramatically over the last years. This volume
explores the adoption of health technology innovations designed to streamline the
service delivery and thus reduce costs and increase quality.
The first part reviews theories and applications for the diffusion of healthcare
technology innovations. The second and third parts focus on electronic health
records (EHR) which is the leading technology innovation in the healthcare sector.
The second part develops evaluation models and the third part analyzes an adoption
case. These models and the case provide a set of factors which need further attention
by those responsible for implementing such technologies.
ix
Contents
xi
xii Contents
3.6.4 Domain................................................................................. 56
3.6.5 Modeling Language ............................................................. 56
3.6.6 Tool ...................................................................................... 57
3.6.7 Simulation ............................................................................ 57
3.7 Modeling Technique Trade-Off Analysis for Proposed
HIT Diffusion Study ........................................................................ 57
3.7.1 Soft System Methodology ................................................... 60
3.7.2 Structured System Analysis and Design Method................. 61
3.7.3 Business Process Modeling.................................................. 61
3.7.4 System Dynamics (SD) ........................................................ 61
3.7.5 System Context Diagram and Data Flow Diagrams
and Flow Charts ................................................................... 62
3.7.6 Unified Modeling Language ................................................ 64
3.7.7 SysML .................................................................................. 66
3.8 Conclusions for Modeling Methodologies to Use ........................... 66
3.9 Qualitative Research, Grounded Theory, and UML ........................ 67
3.9.1 An Overview of Qualitative Research ................................. 67
3.9.2 Grounded Theory and Case Study Method Definitions ....... 68
3.9.3 Using Grounded Theory and Case Study Together ............. 70
3.9.4 Grounded Theory in Information Systems (IS)
and Systems Thinking Research .......................................... 71
3.9.5 Criticisms of Grounded Theory ........................................... 72
3.9.6 Current State of UML as a Research Tool and Criticisms ... 73
3.9.7 To UML or Not to UML ...................................................... 73
3.9.8 An Actual Example of Using Grounded Theory
in Conjunction with UML ................................................... 73
References ................................................................................................. 76
4 Field Test .................................................................................................. 83
Nima A. Behkami and Tugrul U. Daim
4.1 Introduction and Objective............................................................... 83
4.2 Background: Care Management Plus............................................... 84
4.2.1 Significance of the National Healthcare Problem ................ 84
4.2.2 Preliminary CMP Studies at OHSU..................................... 85
4.3 Research Design............................................................................... 86
4.3.1 Overview .............................................................................. 86
4.3.2 Objectives............................................................................. 86
4.3.3 Methodology and Data Collection ....................................... 87
4.3.4 Analysis................................................................................ 90
4.3.5 Results and Discussion ........................................................ 91
4.3.6 Simulation: A System Dynamics Model
for HIT Adoption ................................................................. 100
References ................................................................................................. 110
xiv Contents
Due to changing population demographics and their state of health, the healthcare
system in the United States is facing monumental challenges. For example patients
suffering from chronic illnesses account for approximately 75 % of the nation’s
healthcare-related expenditures. A patient on Medicare with five or more illnesses
will visit 13 different outpatient physicians and fill 50 prescriptions per year
(Friedman, Jiang, Elixhauser, & Segal, 2006). As the number of a patient’s condi-
tions increases, the risk of hospitalizations grows exponentially (Wolff, Starfield, &
Anderson, 2002). While the transitions between providers and settings increase, so
does the risk of harm from inadequate information transfer and reconciliation of
treatment plans. A third of these costs may be due to inappropriate variation and
failure to coordinate and manage care (Wolff et al., 2002). As costs continue to rise,
the delivery of care must change to meet these costs.
This has brought about a renewed interest from various government, public, and
private entities for proposing solutions to the healthcare crisis (Technology, health
care & management in the hospital of the future, 2003), which is helping fuel dif-
fusion research in healthcare. Technology advances and the new ways of bundling
technologies to provide new healthcare services is also contributing to interest in
Health Information Technology (HIT) research (E-Health Care Information
Systems: An Introduction for Students and Professionals, 2005). The promise of
applying technology to healthcare lies in increasing hospital efficiency and
accountability and decreasing cost while increasing quality of patient care
N.A. Behkami
Merck Research Laboratories, Boston, MA, USA
T.U. Daim (*)
Portland State University, Portland, OR, USA
e-mail: tugrul.u.daim@pdx.edu
(HealthIT hhs gov). Therefore it’s imperative to study how technology, in particu-
lar HIT, is being adopted and eventually defused in the healthcare sector to help
achieve the nation’s goals. Rogers in his seminal work has highlighted his concern
for almost overnight drop and near disappearance of diffusion studies in such fields
as sociology and has called for renewed efforts in diffusion research (Rogers, 2003).
Others have identified diffusion as the single most critical issue facing our modern
technological society (Green, Ottoson, García, & Hiatt, 2009).
According to the U.S. Department of Health and Human Services definition
Health Information Technology allows comprehensive management of medical
information and its secure exchange between health care consumers and providers
(HealthIT hhs gov). Information Communication Technology (ICT) and Health
Information Technology (HIT) are two terms that are often used interchangeably
and generally encompass the same definition. It is hoped that use of HIT will lead
to reduced costs and improved quality of care (Heinrich, 2004). Various policy bod-
ies including Presidents Obama’s administration (Organizing for America) and
other independent reports have called for various major healthcare improvements in
the United States by the year 2025 (The Commonwealth Fund). In describing these
aspirations, almost always a call for accelerating the rate of HIT adoption and diffu-
sion is stated as one of the top five levers for achieving these improvement goals
(Organizing for America). Hence it is of critical importance to study and understand
upstream and downstream dynamics of environments that will enable successful
diffusion of HIT innovations.
researchers that we don’t fully understand what it takes for successful innovations
to diffuse into the larger population of healthcare organizations.
Diffusion of Innovation (DOI) theory has gained wide popularity in the
Information Technology (IT) field, for example one study found over 70 IT articles
published in IT outlets between 1984 and 1994 that relied on DOI theory (Teng,
Grover, & Guttler, 2002). Framing the introduction of new Information Technology
(IT) as an organizational innovation, information systems (IS) researchers have
studied the adoption and diffusion of modern software practices, spreadsheet soft-
ware, customer-based inter-organizational systems, database management systems,
electronic data interchange, and IT in general (Teng et al., 2002). These studies have
been conducted at several levels: (1) at the level of intra-firm diffusion, i.e., diffu-
sion of innovation within an organization; (2) inter-firm diffusion at the industry
level; (3) overall diffusion of an innovation throughout the economy.
The main models used for diffusion of innovation were established by 1970. The
main modeling developments in the period 1970 onwards have been in modifying
the existing models by adding greater flexibility to the underlying model in various
ways. The main categories of these modifications are listed below (Meade & Islam,
2006):
• The introduction of marketing variables in the parameterization of the models,
• Generalizing models to consider innovations at different stages of diffusions in
different countries,
• Generalizing the models to consider the diffusion of successive generations of
technology.
In most of these contributions the emphasis has been on the explanation of past
behavior rather than on forecasting future behavior. Examining the freshness of
contributions; the average age of the marketing, forecasting, and OR/management
science references is 15 years, the average age of the business/economics reference
is 19 years (Meade & Islam, 2006). Scholars of IT diffusion have been quick to
apply the widespread DOI theory to IT but few have carefully analyzed whether it
is justifiable to extend the DOI vehicle to explain the diffusion of IT innovations too.
Similar critical voices have been raised recently against a too simplistic and fixed
view of IT (Robinson & Lakhani, 1975).
Figure 1.1 shows the research publications trend in HIT and Diffusion studies
(Behkami, 2009a, 2009b), which shows a steep increase in interest over the last few
years. While adopter attitudes, adoption barriers, and hospital characteristics have
been studied in depth, other components of DOI theory are under-studied. No
research had attempted to explain diffusion of innovation through dynamic capabili-
ties yet. There also have been less than a handful of papers forecasting diffusion
with system dynamics methodology. Figure 1.2 summarizes the frequency of
themes that emerged from a study that analyzed publications related to HIT
Diffusion. 80 % of the 108 articles examined were published between the years
2004 and 2009 (Behkami, 2009a).
1 Introduction to the Adoption of Health Information Technologies 7
700
600
articles not in PubMed
500
articles from PubMed (mostly Biomedical Informatics)
400
300
200
100
0
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Fig. 1.1 Cumulative trend of HIT diffusion research publications over the last three decades
Fig. 1.2 Number of published articles that address themes generated from review
References
Angst, C. (2007). Information technology and its transformational effect on the health care indus-
try. Dissertation Abstracts International Section A: Humanities and Social Sciences.
Ash, J., & Goslin, L. (1997). Factors affecting information technology transfer and innovation dif-
fusion in health care. Innovation in Technology Management—The Key to Global Leadership.
PICMET’97: Portland International Conference on Management and Technology
(pp. 751–754).
Assistant Secretary for Public Affairs. Process begins to define “meaningful use” of electronic
health records.
8 N.A. Behkami and T.U. Daim
Behkami, N. (2009a). Literature review: Diffusion & organizational adoption of healthcare related
information technologies & innovations.
Behkami, N. (2009b). Methodological analysis of Health Information Technology (HIT) diffusion
research to identify gaps and emerging topics in literature.
C.O.E.C.A.O. (1996). Telemedicine and I.O. Medicine. Telemedicine: A guide to assessing tele-
communications for health care. Washington: National Academies Press.
Cherry, B. (2006). Determining facilitators and barriers to adoption of electronic health records
in long-term care facilities. UMI Dissertation Services, ProQuest Information and Learning,
Ann Arbor, MI.
Cho, S., Mathiassen, L., & Gallivan, M. (2008). From adoption to diffusion of a Telehealth innova-
tion. Proceedings of the Proceedings of the 41st Annual Hawaii International Conference on
System Sciences (p. 245). Los Alamitos, CA: IEEE Computer Society.
Daim, T. U., Tarman, R. T., & Basoglu, N. (2008). Exploring barriers to innovation diffusion in
health care service organizations: An issue for effective integration of service architecture and
information technologies. Hawaii International Conference on System Sciences (p. 100). Los
Alamitos, CA: IEEE Computer Society.
E-Health care information systems: An introduction for students and professionals. San Francisco,
CA: Jossey-Bass, 2005.
Friedman, B., Jiang, H., Elixhauser, A., & Segal, A. (2006). Hospital inpatient costs for adults with
multiple chronic conditions. Medical Care Research and Review, 63, 327–346.
Green, L. W., Ottoson, J. M., García, C., & Hiatt, R. A. (2009). Diffusion theory and knowledge
dissemination, utilization, and integration in public health. Annual Review of Public Health, 30,
151–174.
HealthIT.hhs.gov: Home.
Heinrich, J. (2004). HHS’s efforts to promote health information technology and legal barriers to
its adoption.
Meade, N., & Islam, T. (2006). Modelling and forecasting the diffusion of innovation—A 25-year
review. International Journal of Forecasting, 22, 519–545.
Menachemi, N. (2006). Barriers to ambulatory EHR: Who are ‘imminent adopters’ and how do
they differ from other physicians? Informatics in Primary Care, 14, 101–108.
Merrill, M. (2009). HIMSS publishes ‘meaningful use’ definitions. Healthcare IT News.
Middleton, B., Hammond, W. E., Brennan, P. F., & Cooper, G. F. (2005). Accelerating US EHR
adoption: How to get there from here. Recommendations based on the 2004 ACMI retreat.
Journal of the American Medical Informatics Association, 12.
Organizing for America|BarackObama.com|Health Care.
Robinson, B., & Lakhani, C. (1975). Dynamic price models for new-product planning. Management
Science, 21, 1113–1122.
Rogers, E. (2003). Diffusion of innovations (5th ed.). New York: Free Press.
Technology, health care, and management in the hospital of the future. Praeger Publishers, 2003.
Teng, J., Grover, V., & Guttler, W. (2002). Information technology innovations: General diffusion
patterns and its relationships to innovation characteristics. IEEE Transactions on Engineering
Management, 49, 13–27.
The Commonwealth Fund—Health policy, health reform, and performance improvement.
U.S. Department of Health & Human Services. Centers for Medicare & Medicaid Services.
U.S. Department of Health & Human Services. HealthIT.hhs.gov: Health IT Policy Committee.
Wolff, J., Starfield, B., & Anderson, P. G. (2002). Expenditures, and complications of multiple
chronic conditions in the elderly. Archives of Internal Medicine, 162(20), 2269–2276.
Chapter 2
Background Literature on the Adoption of
Health Information Technologies
N.A. Behkami
Merck Research Laboratories, Boston, MA, USA
T.U. Daim (*)
Portland State University, Portland, OR, USA
e-mail: tugrul.u.daim@pdx.edu
The critical stakeholders in the Healthcare delivery system in the United States
include the providers, the government, the payers, the patients, and the suppliers.
In the following sections each of these categories of stakeholders is described in
more detail.
2 Background Literature on the Adoption of Health Information Technologies 11
The term Provider is used to refer to the source of care that provides treatment to
patients. It is important to differentiate between the two instantiations of the Provider
one as an Individual and another as an Organization. The individual Provider is for
example the Physician, Nurse, or someone with similar medical training that
provides often one-on-one care to the patient. The organization type of Provider is
the clinic or hospital which is the business unit housing the physician or nurse
whom provide the care.
Physicians are individuals who through training, experience, and certification are
allowed to provide care to patients with a variety of illnesses. A Physician can be a
general practitioner such a primary care physician or a specialist. Typically physi-
cians are employed by a hospital or clinic. Nurses similar to Physicians have been
through healthcare education and often under physician supervision (and at times
independently) are expected to provide care to patients. Medical Assistants (MA)
typically poses job-specific training mainly to assist physicians and nurses with
routine and less education dependent activities of providing care around the clinic.
During daily operations physicians, nurses, and MAs are typically consumers of
various forms of technology-based tools and they have been subjects of various
research studies (Dorr, Wilcox, Donnelly, Burns, & Clayton, 2005; Dorr et al.,
2006; Eden, 2002; Eley, Soar, Buikstra, Fallon, & Hegney, 2009; Ford, McAlearney,
Phillips, Menachemi, & Rudolph, 2008; Jha et al., 2007; May et al., 2001; Simpson,
2007; Wilcox et al., 2007). Research has shown that each of these types of individ-
ual provides based on attributes of their work place and/or their own personal char-
acteristics experience various levels of technology use. Their use of technology can
range from simply using electronic mail or calendars to sophisticated usages such
as design patient selection algorithms from EHR data.
Studying this type of stakeholder is critical since they are the daily users of tech-
nology and can have a profound effect on adoption of HIT Innovations. They can
also often act as the champion or decision makers when it comes to adopting an
innovation in their clinics or hospitals. As shown in Fig. 2.1 the providers provide
care to patients, are employed in the clinic, provide feedback to the IT vendors they
use products from, adopt & use HIT innovations and collaborate with other provid-
ers for providing care.
The hospital or clinic is where patients would receive care and they are type of a
provider. This type of provider can range from a single physician clinic in a rural
community to a large multi-system hospital in a large city. Research has shown that
12 N.A. Behkami and T.U. Daim
these two types of providers operate drastically different from one another and
when it comes to adoption of HIT they have different needs, barriers and
facilitators(David, 1993; Fonkych, 2006; Hikmet, Bhattacherjee, Menachemi,
Kayhan, & Brooks, 2008; May et al., 2001; Menachemi, 2007; Menachemi, Brooks,
& Simpson, 2007; Menachemi, Burke, & Brooks, 2004). In general hospitals can
have various attributes that distinguishes how they participate in the healthcare dev-
ilry ecosystem for example affiliation, tax status, number of beds, technology usage
culture, location, and more.
It is important to study this type of Provider separate from the individual Provider
such as a physician since their priorities are organizational where physicians are
individual contributors. For example a physician may feel that using an EHR at any
price is justified, while the priorities and budget conditions of the hospital may not
allow for that (Katsma, Spil, Light, & Wassenaar, 2007; Lobach, Detmer, &
Supplement, 2007). As shown in Fig. 2.1 the hospital employee’s physicians, pays
the HIT vendor for products and adopts innovations.
2.3.2 Government
The role of government in the health delivery system of the United States is enor-
mous (Aalbers, van der Heijden, Potters, van Soest, & Vollebergh, 2009; Bower,
2005; Cherry, 2006). Government plays this role in two ways (1) payer (meaning
providing insurance through Medicaid and Medicare (U S Department of Health
Human Services Centers for Medicare Medicaid Services) for the low income and
elderly) (2) policy setter and enforcer (Rosenfeld, Bernasek, & Mendelson, 2005).
As a payer the government expenditure for providing insurance through Medicare
alone reached $440 billion in 2007(Centers for Medicare Medicaid Services
National Health Expenditure Data). Such volume of business makes the government
have an active interest in cost reduction through adoption of HIT (HealthIT hhs
gov). As a policy setter, especially under the current Obama administration through
the American Recover Act (H.R. 1: American recovery and reinvestment act of
2009) the government of the United States has taken the driver seat to implement
Healthcare reform. Government hopes that much of this improved in care and
reduction in cost will be realized through meaningful use of HIT (Assistant Secretary
for Public Affairs) and faster and wider spread of technology adoption.
Research that have reviewed the role of government have found that it can posi-
tively influence and sometimes accelerate more effective HIT adoption (Fonkych,
2006). It is important to note that in the United State with a decentralized health
system the government influences the ecosystem both at the federal level and at the
regional/state levels. Hence when modeling the system it is critical to consider the
multiple perspectives. As shown in Fig. 2.1 the government pays providers, influ-
ences adoption decisions of providers, influences the physicians in general, invests
in support agencies, and encourages nationwide standards.
2 Background Literature on the Adoption of Health Information Technologies 13
The patient is one of the most critical actors in the healthcare delivery system.
Patients once ill seek care through providers. In 2006 Americans made a total of
902 million healthcare visits and 49 % were with primary care physicians
(Ambulatory medical care utilization estimates for 2006). Family or other care
givers are one of the main support networks for the patient. Research finds that
patients with family or a network are more likely to recover. As active participants
in the care process patients and their family/caregivers can be a large influencer for
HIT adoption by their providers or even use HIT themselves (Ash, 1997; Dorr et al.,
2005; Hersh, 2004; Leonard, 2004; May et al., 2001; Robeznieks, 2005a). The
patient family also uses HIT by using Personal Health Records (PHR) (Tang, Ash,
Bates, Overhage, & Sands, 2006). As shown in Fig. 2.1 this stakeholder pays pro-
viders for service, seeks care from physicians, can provide feedback to HIT ven-
dors, cares for patients, and use HIT innovations.
2.3.4 Payers
The payers are the stakeholders who pay for the care that the patients receive. They
fall in the three categories of the government, private insurance, and the patients
themselves. In 2006, 43 million Americans were enrolled in Medicare and 53 mil-
lion enrolled in Medicaid (Centers for Medicare Medicaid Services National Health
Expenditure Data). Medicare is an insurance program administered by the United
States government providing health insurance to people aged 65 and over, or indi-
viduals with disabilities. Similarly Medicaid provides insurance for low income
families (U S Department of Health Human Services Centers for Medicare Medicaid
Services).
By having Private health coverage people can protect themselves from finical
cost and guaranteed to have access to health care when needed (Claxton, 2002). In
order to make private healthcare affordable to individual citizens, payers pool the
risk of healthcare cost across large number of people. This affords individuals (usu-
ally through their employers) to pay a premium that is equal to the average cost of
medical care for the group of people. It is this spreading of the risk that makes
healthcare affordable to most people in the society.
Public sources of healthcare coverage include Medicare, Medicaid, federal and
state employee health plans, the military, and the Veterans Administration. Private
health coverage is primarily through employee sponsored benefit plans. Private
Citizen can also obtain individual health insurance from the free market in 2002;
about 12 million nonelderly people purchased health insurance on their own
(Claxton, 2002). Examples of health insurance coverage include commercial health
insurers, Blue Cross, and Blue Shield plans, Health Maintenance Organizations
(HMOs), Self-Funded Employee Health Benefit Plans.
14 N.A. Behkami and T.U. Daim
With such numbers and revenue it is not surprising that Payers exercise a lot of
power and leverage in the healthcare delivery system. In fact the change agents in
care delivery are often the demands of the payers instead those of the patients
(Healthcare payers and providers: Vital signs for software development, 2004).
Effectively payers are able to manipulate providers through such mechanisms as
co-payments and negotiated rates for procedures. It is this influence from payers
that is pushing hospitals to invest in Health IT. For example in order to deliver care
more efficiently integrating their various isolated repositories of patient data is a
priority for the payers. Providers fear that this push for investment in HIT can erode
their already thin revenues. However, it is believed that if the providers are able
show effective use of IT through meaningful usage, Payers would be willing to
compensate for infrastructure investment through future contract negations that
would be more favorable and provide more revenue for the providers (Healthcare
payers and providers: Vital signs for software development, 2004).
In context of the proposed research Suppliers are either the entities that build, sup-
port, or service the HIT innovation that are used by the providers and the patients and
sometimes paid for by the payers for the purpose of delivering patient care. For
example the General Electric Corporation is the vendor that builds one of the most
popular EHR on the market and in this case is considered a Supplier in the ecosystem.
Another type of Supplier is government organizations that support HIT use for pro-
viders, such as a Regional Health Information Organization (RHIO) discussed below.
HIT vendors develop and offer technical services for a variety of HIT applications
such as Health Records, e-prescribing, and others. Vendors typically specialize in
serving certain size physician practices. Their products are often licensed by physi-
cian or user. They charge maintenance and support fees and usually charge for prod-
uct upgrades. They offer some limited service policies and guarantees.
In case of products such as Electronic Health Records (EHR) a vendor’s product
may be certified for interoperability through the Certification Commission for
Health Information Technology (CCHIT) (Certified®, 2011). The vendors often
charge for their products to interface with other products or sources of information
at the adopting hospital. In some case third-party modules or components are bun-
dled with a product and the customer may need to pay for them separately.
Implementation and training services add to the adoption cost. Since usually adop-
tion requires a large investment from the provider, a healthy relationship is desired
2 Background Literature on the Adoption of Health Information Technologies 15
with the vendors. As shown in Fig. 2.1 vendors receive feedback from providers and
patients and try to stay competitive in the market place.
The Stakeholders described in the previous sections each have multiple attributes.
For example an attribute of the Hospital as a stakeholder maybe its affiliation; is it
affiliated with an academic university or is it purely for profit organization. These
attributes determine how a stakeholder participates and influences the healthcare
delivery ecosystem. Table 2.1 summarizes the critical attributes associated with
each healthcare system stakeholder extracted from research literature.
While stakeholders and their attributes determine some of the characteristics of the
healthcare delivery system there are other factors that also influence the ecosystem. The
categories of these factors include: Barriers & Influences, theories & methodologies,
policy making, ecosystem characteristics, adopter attitudes, market competition, inno-
vation champions, clinic workflow, timing, modeling, infusion, and social structures.
16 N.A. Behkami and T.U. Daim
Hospitals that place a high priority on patient safety can more easily justify the
cost of Computerized Physician Order Entry (CPOE). Outside the hospital, finan-
cial incentives and public pressures encourage CPOE adoption. Dissemination of
data standards would accelerate the maturation of vendors and lower CPOE costs
(Poon et al., 2004). Adoption of functionalities with financial benefits far exceeds
adoption of those with safety and quality benefits (Poon et al., 2006). The ideal
COPE would be a system that is both customizable and integrated with other parts
of the information system, is implemented with maximum involvement of users and
high levels of support, and is surrounded by an atmosphere of trust and collabora-
tion (Ash, Lyman, Carpenter, & Fournier, 2001).
Lack of clarity about the value of telehealth implementations is one reason
cited for slow adoption of telemedicine (Cusack et al., 2008). Others have looked
at potential factors affecting telehealth adoption (Gagnon et al., 2004) and end
user online literature searching, the computer-based patient record, and electronic
mail systems in academic health sciences centers in the United States (Ash,
1997). Successful diffusion of online end user literature searching is dependent
on the visibility of the systems, communication among, rewards to, and peers of
possible users who promote use (champions) (Ash, 1997). Adoption factors on
RFID deployment in healthcare applications have also been researched (Kuo &
Chen, 2008).
Technology and Administrative innovation adoption factors that have been iden-
tified include the job tenure, cosmopolitanism, educational background, and organi-
zational involvement of leaders (Kimberly & Evanisko, 1981). Hospitals that
adopted a greater number of IT applications were significantly more likely to have
desirable quality outcomes on seven Inpatient Quality Indicator measures
(Menachemi, Saunders, Chukmaitov, Matthews, & Brooks, 2007). Factors found to
be positively correlated with PSIT (patient safety-related IT) use included physi-
cians active involvement in clinical IT planning, the placement of strategic impor-
tance on IT by the organization, CIO involvement in patient safety planning, and the
perception of an adequate selection of products from vendors (Menachemi, Burke,
& Brooks, 2004).
Patient’s fears about having their medical records available online is hindering,
not helping the push for electronic medical records. Specific concerns include com-
puter breaches and employers having access to the records(Robeznieks, 2005b)
Public sector support is essential in five main aspects of child health information
technology, namely, data standards, pediatric functions in health information
systems, privacy policies, research and implementation funding, and incentives for
technology adoption(Conway, White, & Clancy, 2009).
Financial barriers and a large number of HIT vendors offering different solu-
tions present significant risks to rural health care providers wanting to invest in
HIT (Bahensky, Jaana, & Ward, 2008). The relative costs of the interventions or
technologies compared to existing costs of care and likely levels of utilization are
critical factors in selection (Davies, Drummond, & Papanikolaou, 2001). Reasons
for the slow adoption of healthcare information technology include a misalign-
ment of incentives, limited purchasing power among providers, and variability in
2 Background Literature on the Adoption of Health Information Technologies 19
the viability of EHR products and companies, and limited demonstrated value of
EHRs in practice (Middleton, Hammond, Brennan, & Cooper, 2005). Community
Health Centers (CHC) serving the most poor and uninsured patients are less likely
to have a functional EHR. CHCs cited lack of capital as the top barrier to adoption
(Shields et al., 2007). Increasing cost pressures associated with managed-care
environments are driving hospitals’ adoption of clinical and administrative IT
systems as a means for cost reduction (Menachemi, Hikmet, Bhattacherjee,
Chukmaitov, & Brooks, 2007).
There is a gap in our knowledge on how regulatory policies and other national
health systems attributes combine to impact on the utilization of innovation and
health system goals and objectives. A study found that strong regulation adversely
affects, access to innovation, reduces incentives for research-based firms to develop
innovative products and leads to short- and long-term welfare losses. Concluding
that policy decision makers need to adopt a holistic approach to policy making, and
consider potential impact of regulations on the uptake and diffusion of innovations,
innovation systems and health system goals (Atun, Gurol-Urganci, & Sheridan,
2007). Recommendations have been made to stimulate adoption of EHR, including
financial incentives, promotion of EHR standards, enabling policy, and educational,
marketing, and supporting activities for both the provider community and health-
care consumers (Blumenthal, 2009; Middleton et al., 2005). Proposed manners on
how the government should assist are a reoccurring topic (Bower, 2005).
Economic issues for health policy and policy issues for economic appraisal have
concluded that a wide range of mechanisms exist to influence the diffusion and use
of health technologies and that economic appraisal is potentially applicable to a
number of them (Drummond, 1994). Other conclusions calls for greater Centers for
Medicare and Medicaid Service (CMS) involvement and reimbursement models
that would reward higher quality and efficiency achieved (Fonkych, 2006). Medicare
should pay physicians for the costs of adopting IT and assume that future savings to
Medicare will justify the investment. The Medicare Payment Advisory Commission
2 Background Literature on the Adoption of Health Information Technologies 21
Academic affiliation and larger IT operating, capital, and staff budgets are associ-
ated with more highly automated clinical information systems (Amarasingham
et al., 2008). Despite several initiatives by the federal government to spur this devel-
opment, HIT implementation has been limited, particularly in the rural market
(Bahensky et al., 2008). Study of a small clinic found that the EHR implementation
did not change the amount of time spent by physicians with patients. On the other
hand, the work of clinical and office staff changed significantly, and included
decreases in time spent distributing charts, transcription, and other clerical tasks
(Carayon, Smith, Hundt, Kuruchittham, & Li, 2009).
Health IT adoption for medication safety indicate wide variation in health IT
adoption by type of technology and geographic location. Hospital size, ownership,
teaching status, system membership, payer mix, and accreditation status are associ-
ated with health IT adoption, although these relationships differ by type of technol-
ogy. Hospitals in states with patient safety initiatives have greater adoption rates
(Furukawa, Raghu, Spaulding, & Vinze, 2008). Another study examined geographic
location (urban versus rural), system membership (stand-alone versus system-
affiliated), and tax status (for-profit versus non-profit) and found that location is
systematically related to HIT adoption (Hikmet, Bhattacherjee, Menachemi,
22 N.A. Behkami and T.U. Daim
Kayhan, & Brooks, 2008). Others studies have also considered hospital characteris-
tics (Jha, Doolan, Grandt, Scott, & Bates, 2008; Koch & Kim, 1998).
Although top information technology priorities are similar for all rural hospitals
examined, differences exist between system-affiliated and stand-alone hospitals in
adoption of specific information technology applications and with barriers to infor-
mation technology adoption (Menachemi, Burke, Clawson, & Brooks, 2005).
Hospitals adopted an average of 11.3 (45.2 %) clinical IT applications, 15.7 %
(74.8 %) administrative IT applications, and 5 (50 %) strategic IT applications
(Menachemi, Chukmaitov, Saunders, & Brooks, 2008).
There are concerns that psychiatry may lag behind other medical fields in adopt-
ing information technology (IT). Psychiatrists’ lesser reliance on laboratory and
imaging studies may explain differences in data exchange with hospitals and labs,
concerns about patient privacy are shared among all medical providers (Mojtabai,
2007). Some innovations in health information technology for adult populations can
be transferred to or adapted for children, but there also are unique needs in the pedi-
atric population (Conway et al., 2009).
The diffusion of health care technology is influenced by both the total market share
of care organizations as well as the level of competition among them. Results show
that a hospital is less likely to adopt the technology if Healthcare Maintenance
Organization (HMO) market penetration increases but more likely to adopt if HMO
competition increases (Bokhari, 2009). Increasing cost pressures associated with
managed-care environments are driving hospitals’ adoption of clinical and adminis-
trative IT systems as such adoption is expected to improve hospital efficiency and
lower costs (Menachemi, Hikmet et al., 2007).
Deployment of health information technology (IT) is necessary but not suffi-
cient for transforming U.S. health care. The strategic impact of information tech-
nology convergence on healthcare delivery and support organizations have been
studied (Blumberg & Snyder, 2001). Four focus areas for application of strategic
management have been identified: adoption, governance, privacy and security,
and interoperability (Kolodner, Cohn, & Friedman, 2008). While another found
little that strategic behavior or hospital competition affects IS adoption
(McCullough, 2008).
A study looking at strategic behavior of EHR adopters found that the relevance
of EHR merely focuses on the availability of information at any time and any place.
This implementation of relevance does not meet end-users’ expectations and is
insufficient to accomplish the aspired improvements. In addition, the used participa-
tion approaches do not facilitate diffusion of EHR in hospitals (Katsma, Spil, Ligt,
& Wassenaar, 2007).
There is a need for the tight coupling between the roles of both the administrative
and the clinical managers in healthcare organizations in order to champion adoption
and diffusion and to overcome many of the barriers that could hinder telemedicine
success (Al-Qirim, 2007b). Survey of chief information officers (CIOs), the indi-
viduals who manage HIT adoption effort, suggests that the CIO position and their
responsibilities varies significantly according to the profit status of the hospital
(Burke, Menachemi, & Brooks, 2006).
Acting as aids to change-agents in healthcare settings Clinical engineers can
identify new medical equipment, review their institution’s technological posi-
tion, develop equipment-selection criteria, supervise installations, and monitor
24 N.A. Behkami and T.U. Daim
Determining the right time for adoption and the appropriate methods for calculating
the return on investment are not trivial (Kaufman, Joshi, & O’Donnell, 2009).
Among the practices without an EHR, 13 % plan to implement one within the next
12 months, 24 % within the next 1–2 years, 11 % within the next 3–5 years, and
52 % reported having no plans to implement an EHR in the foreseeable future
(Simpson, 2000). The relationship between the timing of adoption of a technologi-
cal innovation and hospital characteristics have been explored (Poulsen et al., 2001).
Key factors that influence sustainability in the diffusion of the Hospital Elder
Life Program (HELP) are Staff experiences sustaining the program recognizing the
need for sustained clinical leadership and funding as well as the inevitable
modifications required to sustain innovative programs can promote more-realist
(Bradley, Webster, Baker, Schlesinger, & Inouye, 2005).
2 Background Literature on the Adoption of Health Information Technologies 25
2.5.11 Infusion
Innovation attributes are important predictors for both the spread of usage (internal
diffusion) and depth of usage (infusion) of electronic mail in a healthcare setting
(Ash & Goslin, 1997). In a study two dependent variables, internal diffusion (spread
of diffusion) and infusion (depth of diffusion) were measured. Little correlation
between them was found, indicating they measured different things (Ash, 1999).
Study of organizational factors which influence the diffusion of end user online lit-
erature searching, the computer-based patient record, and electronic mail systems in
academic health sciences centers found that Organizational attributes are important
predictors for diffusion of information technology innovations. Individual variables
differ in their effect on each innovation. The set of attributes seems less able to pre-
dict infusion (Ash, 1997).
(HTA) findings within their organizations, and what factors influence how and when
they communicate their findings to members or other organizations (Fattal &
Lehoux, 2008).
There are three perspectives that are part of Linstone’s Multiple Perspectives meth-
odology: Technical (T), Organizational (O), and Personal (P) (Linstone, 1999).
In the T perspective the technology and its environment are viewed as a system.
The T perspective is a rational approach to viewing the problem and it represents a
quantitative approach to viewing the world in terms of for example alternatives,
trade-offs, optimization, data, and models (Linstone, 1999).
The O perspective is concerned with less technical matters and more what affects
organizations can have. The O perspective also describes the culture that has helped
form and connects the organization or a society. For example an example of an item
from this view could be fear of staff in a company about making errors in their
work. The O perspective can help by identifying pressures on the technology,
insights into societal abilities to absorb a technology and increase abilities to facili-
tate organization’s support for technology.
According to Linestone, the P perspective can be the hardest view to define and
should include any matters relating to individuals that are not included in other
views. In general the P perspective helps us better understand the O perspective.
Individuals matter and they can sometimes bring changes to organization with less
effort than the whole institution would; the P perspective identifies their character-
istic and behavior. Perspectives are dynamic and change over time; they also can
conflict or support each other. Table 2.2 shows a summary of characteristics for
each Linestone perspective (Linstone, 1999).
2
Numerous sources emphasis the importance of modeling business processes and the
relevant ecosystems, however there seems to be a lack of guidance on how to best
capture these architectures. Documenting a model is an important sub-disciple of
software engineering. Architecture allows us to concentrate on the components and
relationship at a relevant yet manageable level. Dividing a complex problem into
parts allows groups to participate in solving a problem. In general documenting
systems serves three important purposes: as a means of education by using it to
introduce people to the system, a tool for communication between stakeholders and
provides appropriate information for analysis.
A view represents elements and relationships amongst them within a system. When
documenting a model a view highlights dimensions of the system architecture while
hiding other details. Various authors have recommended specific views that should be
employed when documenting software architectures including: Zachman Framework
(The Zachman Framework), Reference Model for Open Distributed Processing
(RM-ODP) (Reference model of open distributed processing Wiki), Department of
Defense Architecture Framework (DoDAF) (DoDAF Architecture Framework Version
2 0), Federal Enterprise Architecture (Federal Enterprise Architecture), and Nominal
Set of Views (ANSI/IEEE 1471). In particular “4 + 1” approach to architecture by
Philippe Kruchten of the Rational Corporation (Kruchten, 1995) has been influential;
used in system building it uses four views (Logical, Process, Development, and
Physical) with a fifth view (Scenarios) that ties the other four together. While these are
beneficial views, they may not be useful in every system and the ultimate purpose is to
separate concerns and document the model for a variety of stakeholders (Bachmann
et al., 2001).
factors have been group into T-O-P perspectives; showing how the various factors
relating to HIT Diffusion can fit into views and the proposed research.
Consistent with Linstone methodology if a factor was related to technology and
its focus was an artificial construct it was placed under the T column. If the factor
was from an institutional view and its system focus was social it was placed under
O column. If the factor was related to an individual or self with a psychological
focus it was placed in the P column. Table 2.3 shows the combinations of stakehold-
ers and perspectives being considered in this research. Table 2.4 lists each factor in
30 N.A. Behkami and T.U. Daim
its relevant T-O-P perspective column; at this time they are combined for all the
stakeholders, in the future factors can be separated by stakeholder.
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Chapter 3
Methods and Models
N.A. Behkami
Merck Research Laboratories, Boston, MA, USA
T.U. Daim (*)
Portland State University, Portland, OR, USA
e-mail: tugrul.u.daim@pdx.edu
Awareness
+
Capabilities
Awareness
+
Condition Adoption N
+
Capabilities
In researching the HIT diffusion phenomena using system thinking this proposed
research has two overarching goals. One is “to understand” and the other is “to
improve.” To understand means and refers to all the activities related to
40 N.A. Behkami and T.U. Daim
investigating and later describing the problem space. To improve means and refers
to all the activities to use the description and use it to improve the existing condi-
tion or problem. Naturally various research traditions, tools, techniques, and theo-
ries can be used to assist in achieving these two goals (Forrester, 1994). Figure 3.6
shows the phases of research model building using system thinking that are appro-
priate for the proposed HIT diffusion study. “To understand” includes prototyping,
modeling, documenting, and communicating research models and findings. “To
improve” includes using documentation and communication, simulation, and
changing through new policy or theories. Inside each of the boxes in Fig. 3.6 the
artifacts used for that activity are listed. For example technology management
constructs, scientific theories, and research methods are tools for modeling. In the
following sections various methods and tools for modeling, simulation, theoriz-
ing, and research methods that were investigated as candidate for this research are
described and discussed.
3.3.1 An Innovation
3.3.1.2 Compatibility
Compatibility is referred to as how good of a fit the new innovation is with the cur-
rent structure of values, past experiences, and needs of candidate adopters. An idea
that is ill fit for an organization will face slower adoption rate or may never be
adopted. For an unfit innovation to be adopted by an organization, it requires the
culture and value structure of the adopters to change.
3.3.1.3 Complexity
3.3.1.4 Trialability
New innovations that can be tried within a restricted scope prior to adoption are said
to be trialable. The easier it is to try out a new idea, the higher the chance of its adop-
tion by potential participants. The concept of trial has become immensely popular
with software innovation. Many software vendors allow a close to full product dem-
onstration of their products over an extended period of time (usually 30 days). The
feeling of uncertainty inherent in adopters can be reduced by a trial of a new innova-
tion. The new learning can lead to a more rapid adoption
3.3.1.5 Observability
Observability is the extent that results of an adoption of a new innovation are notice-
able by other people. The more noticeable innovations are adopted more quickly.
Observability information is mostly communicated through peer-to-peer networks.
Based on a literature review for criticisms and limitations of diffusion theory, some
of the more recent issues are listed and described in this section:
Diffusion research is spreading from industrial settings to public policy setting
as well: DOI research was started in industrial and service settings and ever since
it has been concentrated in areas of study such as agriculture, manufacturing, and
electronics. Success in those fields has prompted applying DOI research in areas
such as public service and policy innovation, for example healthcare and education
(Nutley & Davies, 2000).
Diffusion of innovation is not as linear process as most researches suggest:
Traditional research has described the DOI process as one that flows through the fol-
lowing steps: research, creation, dissemination, and finally utilization. These steps
describe a more or less linear process. Studies have shown that in fact, often innovations
don’t spread throughout the population in such a manner and instead experience vari-
ous iterations and loops among the stages (Cousins & Simon, 1996). Therefore to have
a better understanding of the DOI process the entire picture needs to be evaluated.
Interests in diffusion research still remains high: Wolfe conducted a literature
review on diffusion of innovation from 1989 to 1994 and identified 6,240 articles on
this topic (Wolfe, 1994). A similar search was performed by Nutley from 1990 to
2002 that identified 14,600 articles (Nutley & Davies, 2000). This twofold increase
highlights the increasing research interest in this area. Increase may be contributed
to public policy, health, and energy and consumer diffusion research.
3 Methods and Models 43
According to Nutley (Nutley & Davies, 2000) to date Wolfe identifies the following
limitations in innovation research (Wolfe, 1994):
• Lack of specificity concerning the innovation stage upon which investigations
focus.
• Insufficient consideration given to innovation characteristics and how these
change over time.
• Research being limited to single-type studies.
• Researchers limiting their scope of inquiry by working within single theoretical
perspectives.
3 Methods and Models 45
Behavioral
Behavior
Intention
Subjective Norm
The TAM model is an adaptation of the TRA for the information technology (IT)
domain. How users reach the point to adopt a technology and use it is explained by
TAM. TAM hypothesizes that perceived usefulness and perceived ease of use are
3 Methods and Models 47
Peroeived Ease
of Use
Source: Davis et al. (1989), Venkatesh et. al. (2003)
Peroeived
Usefulness
Peroeived Ease
of Use
Capabilities to
Use Exists
the determinants for an individual’s intention to use a system or not as shown in the
top part of Fig. 3.9 (Davis, 1985, 1989; Davis, Bagozzi, & Warshaw, 1989).
Perceived usefulness is defined as the degree that an individual believes using a
technology would improve his/her performance. Perceived ease of use is defined as
the level an individual believes using a technology would bring him/her efficiently
by saving them effort for otherwise needed work. Perceived usefulness can also be
directly impacted by perceived ease of use.
In order to simplify the TAM model, researchers have removed the attitude
constrict from the original TRA (Venkatesh et al., 2003). In the literature various
efforts have been made to extend TAM which these efforts generally fall into one
of the following three categories: adding influential parameters from other related
models, adding brand new parameters to the model not found in other models,
and finally examining various influences on perceived usefulness and perceived
ease of use (Wixom & Todd, 2005). The relationship between usefulness, ease of
use, and system usage have been explored since the original work on TAM
(Adams, Nelson, & Todd, 1992; Davis et al., 1989; Hendrickson, Massey, &
Cronan, 1993; Segars & Grover, 1993; Subramanian, 1994; Szajna, 1994). Similar
to the limitations of TRA, TAM also assumes that intention to act is formed free
of limitations and constraints such as time, environment, and capability. In addi-
tion triviality and lack of practical value have been recently highlighted as limita-
tions of TAM (Chuttur, 2009). The original TAM has been extended to now
include social influence and instrumental processes in TAM2 (Viswanath, Morris,
Davis, & Davis, 2003).
48 N.A. Behkami and T.U. Daim
Attitude Toward
Act or Behavior
Behavioral
Subjective Norm Intention Behavior
Perceived
Behavioral
Source: Ajzen (1991)
Control
As explained earlier for the proposed study the methodology of choice is diffu-
sion theory, since it provides a macro-level view. However dynamic capabilities can
also be integrated with the TAM model. For example as shown in the bottom part of
Fig. 3.9 a new “capabilities to use exist” construct can be added to the classic TAM,
which would influence the existing “behavioral intentions to use” or “actual system
use” constructs. One of the main difficulties in this integration is that unlike diffu-
sion theory TAM does not provide a way to describe a time element.
The theory of planned behavior (TPB) model states that an individual’s behavior is
powered by behavioral intentions which are influenced by attitude, subjective norm,
and perceptions of ease of use as in Fig. 3.10 (Ajzen, 1985, 1991). The originating
field for this theory is psychology and it was proposed as an extension to TRA. Similar
to the components of TRA model: an individual’s positive or negative attitude toward
performing a behavior is defined as the attitude toward act or behavior. Subjective
norm is described as whether the individual’s environment and other people in it feel
it’s positive or negative for a behavior to be performed. Behavioral control is described
as an individual’s perception of how difficult it is to perform an act or behavior.
The unified theory of acceptance and use of technology (UTAUT) was developed to
explain the individual’s intentions in using an information system and its resulting
behavior as in Fig. 3.11. UTAUT was developed based on the combination of com-
ponents identified by previous models including theory of reasoned action, TAM,
motivational model, theory of planned behavior, a combined theory of planned
behavior/technology acceptance model, model of PC utilization, innovation
3 Methods and Models 49
Performance
Expectancy
Effort
Expectancy
Behavioral Use
Intention Behavior
Social
Influence
Fancilitating
Conditions
Voluntariness
Gender Age Experience
of Use
diffusion theory, and social cognitive theory. Its hypostasis that the four constructs
of performance expectancy, effort expectancy, social influence, and facilitating con-
ditions can explain usage intention and resulting behavior (Viswanath et al., 2003).
Gender, age, experience, and voluntariness of use were identified as other important
parameters in explaining usage and behavior (Viswanath et al., 2003).
Similar to the TAM, lazy user model (LUM) attempts to describe the process that
individuals use to select a solution for satisfying a need from a series of alternatives
(Collan & Tétard, 2007). LUM hypothesizes that from a set of available solutions
the user always attempts to select the one with the least amount of effort.
The model starts by assuming that the user has a need that is definable and
satisfiable. Then the set of possible solutions are defined by the user need. Each
solution in the set has its own characteristics which meet the user need in varying
degrees. The user state further determines the available solutions. For example,
to check an address for a restaurant an individual can use the Internet or a tele-
phone. But if this individual is driving and is without an Internet connection he/
she can either call the phone directory to get the restaurant phone number or
phone a friend for directions. Therefore as in this example, the user state is deter-
mined by the users and their situation characteristics at any given time.
The LUM model assumes that after the user need and user state have defined the
set of possible solutions the user will select a solution. Worth mentioning that if the
set is empty the user does not have a way to satisfy the need. The LUM hypothesizes
that the use will select a solution from the limited set based on lowest level of effort.
Effort is defined as aggregate of monetary cost + time needed + physical and/or
mental efforts necessary to satisfy the user need (Tetard & Collan, 1899).
As described in the earlier sections of this document dynamic capabilities are one of
the main constructs that are being proposed for extending diffusion theory for HIT
adoption. What is specifically referred to as dynamic capabilities is also generally
discussed by researchers through other explanations such as competencies, factors
of production, assets, and more. The roots of almost all of these variations can be
traced back to resource-based theory (RBT). Before deciding on dynamic capabili-
ties it was important to review and compare all the variations of so-called factors of
production. Almost any of the variations would be useable for the proposal, since
it’s merely intended to demonstrate the existence of organizational ability (capabil-
ity). However since adoption of HIT would require obtaining new abilities or recon-
figuring existing abilities, this is most consistent with the dynamic qualification of
dynamic capabilities.
3 Methods and Models 51
Firms’ outperforming other firms has been explained using two explanations in the
literature (Barney & Clark, 2007). The first is attributed to Porter (Porter, 1981;
Porter Michael, 1979) and is based on structure-conduct-performance (SCP) theory
from industrial organization economics (Bain, 1956). This perspective argues that a
firm’s market power to increase prices above a competitive level creates the superior
performance (Porter, 1981). The second explains superior performance through the
differential ability of those firms to more rapidly and cost effectively react to cus-
tomer needs (Demsetz, 1973). This perspective suggests that it is resource intensive
for firms to copy more efficient firms; hence this causes the superior performance to
persist between the haves and the have-nots (Rumelt & Lamb, 1984).
In RBD Barney acknowledges that these two explanations are not contradictory
and each applies in some settings. While also acknowledging the roll of market
power in explaining sustained superior performance, Barney chooses to ignore it
and instead focus on “efficiency theories of sustained superior firm performance”
(Barney & Clark, 2007).
Four sources contribute to theoretical underpinnings of RBD (Barney & Clark,
2007): (a) distinctive competencies research, (b) Ricardo’s analysis of land rents, (c)
Penrose 1959 (Penrose, 1959), and (d) studies of antitrust implications of economics.
Of the four parts only distinctive competencies and Penrose’s work are related to this
proposed research and will be explained in more detail in the following subsections.
A firm’s distinctive competencies are the characteristics of the firm that enable it to
implement a strategy more efficiently than other firms (Hitt & Ireland, 1985a, 1986;
Hrebiniak & Snow, 1982; Learned, Christensen, Andrews, & Guth, 1969). One of
52 N.A. Behkami and T.U. Daim
the early distinctive competencies that researchers identified was “general manage-
ment capability.” The thinking was that firms that employ high-quality general man-
agers often outperform firms with “low-quality” general managers. However it is
now understood that this perspective is severely limited in explaining performance
difference among firms. First, the qualities and attributes that constitute a high-
quality general manager are ambiguous and difficult to identify (a platter of research
literature has shown that general managers with a wide array of styles can be effec-
tive). Second, while general management capabilities are important it’s not the only
competence critical in the superior performance of a firm. For example a firm with
high-quality general managers may lack the other resources ultimately necessary to
gain competitive advantage (Barney & Clark, 2007).
In the work The Theory of the Growth in 1959 Penrose attempted to understand the
processes that lead to firm growth and its limitations (Penrose, 1959). Penrose
advocated that firms should be conceptualized as follows: first, an administrative
framework that coordinates activities of the firm and second, as a bundle of produc-
tive resources. Penrose identified that the firm’s growth was limited by opportuni-
ties and the coordination of the firm resources. In addition to analyzing the ability
of firms to grow Penrose made two important contributions to RBD (Barney &
Clark, 2007). First, Penrose observed that the bundle of resources controlled can be
different from firm to firm in the same market. Second, and most relevant to this
research proposal, Penrose used a liberal definition for what might be considered a
productive resource including managerial teams, top management groups, and
entrepreneurial skills.
Four seminal papers constituted the early work on RBT; these included Wernerfelt
(1984), Rumelt (1984), Barney (1986), and Dierickx (1989) (Barney, 1986; Dierickx
& Cool, 1989; Rumelt & Lamb, 1984; Wernerfelt, 1984). These papers made it pos-
sible to analyze firm’s superior performance using resources as a unit of analysis.
They also explained the attributes resource must have in order to be source of sus-
tained superior performance.
Using the set of resources a firm holds and based on the firm’s product market
position Wernerfelt developed a theory for explaining competitive advantage
(Wernerfelt, 1984) that is complementary to Porters (Porter, 1985). Wernerfelt
labeled this idea resource-based “view” since it looked at the firm’s competitive
advantage from the perspective of the resources controlled by the firm. This method
argues that the collection of resources a firm controls determines the collections of
product market positions that the firm takes.
3 Methods and Models 53
Around the same time as Wernerfelt, Rumelt published a second influential paper
that tried to explain why firms exist based on being able to more efficiently generate
economic rents than other types of economic organizations (Rumelt & Lamb, 1984).
An important contribution of Rumelt to RBD was that he described firms as a bun-
dle of productive resources.
In a third paper similar to Wernerfelt, Barney recommended a superior perfor-
mance theory based on attributes of the resources a firm controls (Barney, 1986;
Wernerfelt, 1984). However Barney additionally argued that a theory based on
product market positions of the firms can be very different than the pervious and
therefore a shift from resource-based view to the new RBD (Barney & Clark, 2007).
In a fourth paper Dierickx and Cool supported Barney’s argument by explaining
how it is that the resources already controlled by firm can produce economic rents
for it (Dierickx & Cool, 1989).
While RBD was shaping into its own other research streams were developing theories
about competitive advantage that have implications to this proposed research since
they were also looking at competencies and capabilities. The most influential were
the theory of invisible assets by Itami and Roehl (1987) and competence-based theo-
ries of corporate diversification (Hamel & Prahalad, 1990; Prahalad & Bettis, 1986).
Itami described sources of competitive power by classifying physical (visible)
assets and invisible assets. Itami identified information-based resources, for exam-
ple technology, customer trust, and corporate culture, as invisible assets and the real
source of competitive advantage while stating that the physical (visible) assets are
critical to business operations but don’t contribute as much to source of competitive
advantage. Firms are both accumulators and producers of invisible assets and since
it is difficult to obtain them having them can lead to competitive advantage. Itami
classified the invisible assets into environment, corporate, and internal categories.
Environmental information flows from the environment to the firms such as cus-
tomer information. Corporate information flows from the firm to its ecosystem such
as corporate image. Internal information rises and gets consumed within the firm
such as morale of workers.
In another parallel research stream Teece and Prahalad et al. (Prahalad & Bettis,
1986; Teece, 1980) had started looking at resource-based logic to describe corporate
diversification. Prahalad in particular stresses the importance of sharing intangible
assets and its impact on diversification. Prahalad and Bettis called these intangible
assets the firm’s dominant logic, “a mindset or a worldview or conceptualization of the
business and administrative tools to accomplish goals and make decisions in that busi-
ness.” Hamel and Prahalad (1990) extended dominate logic into the corporation “core
competence” meaning “the collective learning in the organization, especially how to
coordinate diver production skills and integrate multiple streams of technologies.”
54 N.A. Behkami and T.U. Daim
For the purposes of this proposal the various forms of factors of production have
been extracted from literature and presented here in Table 3.2. The table includes
the name of the view, its source, and some brief notes.
During research when modeling ecosystems or problem domains for the purposes
of system analysis a variety of complementary and sometimes redundant methods
exist. Choosing the right combination is important and is a multistep process. First
the need for problem analysis or modeling has to be clear. Second a set of alterna-
tive solutions needs to be developed and third well-suited combination of tools
needs to be picked to demonstrate the problem/solution. In order to be able to
effectively execute these three steps the researcher needs to be familiar with the
tools of the trade. Figure 3.12 shows the building blocks of these tools and the
relationships among them. A description of each of these building blocks follows
in this section.
3.6.1 Model
3.6.2 Diagram
3.6.3 View
3.6.4 Domain
A modeling language is an artificial language that describes a set of rules which are
used to describe structures of information or systems. The rules are what provide
meaning and description to the various artifacts, for example in a graphical
3 Methods and Models 57
3.6.6 Tool
In a general sense a tool is an object that interfaces between two or more domains.
It enables a useful action from one domain on another. For example, a system
dynamics model which is a tool from the engineering domain can act as an interface
for a problem in the healthcare domain.
3.6.7 Simulation
For the proposed HIT diffusion study the following modeling needs can be
identified:
• Decompose the HIT adoption ecosystem into actors, behaviors, etc.
• Look at the HIT adoption and diffusion process from various perspectives.
• Look at the behavior such as relationships and data exchanged between the
actors.
• Document the model.
• Simulate or forecast over time.
58 N.A. Behkami and T.U. Daim
• Prototype.
• Communicate the model.
In each row of Table 3.3 the needs mentioned above are shown with more detail.
The columns list the domain or field that would be used to satisfy that need. It is
effectively a need vs. solution matrix which describes for example UML will be
used to prototype structure.
Table 3.4 is an exhaustive list of potential modeling techniques, methodologies,
and tools from software/systems engineering and technology management relevant
to analyzing and simulating models. Members of list that were more relevant to the
research are described in detail in the following sections and they include soft sys-
tem methodology (SSM), structured system analysis and design method (SSADM),
business process modeling (BPM), system dynamics, system context diagrams
(SCD), data flow diagrams (DFDs), flow charts, UML, and Systems Modeling
Language (SysML). These tools were examined for applicability in detail before
deciding to use the combination listed in Table 3.3.
3 Methods and Models 59
In systems and software engineering BPM is the activity of describing the enter-
prise processes for analysis. BPM is often performed to improve process effi-
ciency and quality and often involves information technology. Newly arriving
applications from large-platform vendors make some inroads for allowing BPM
models to become executable and capable of use for simulations (Smart, Maddern,
& Maull, 2008).
In system dynamics after creating a CLD, the next step is to create a stock and flow
diagram. Stocks are accumulations (they characterize the state of the system) and flows
are rate of accumulation or depletion over time. Stocks can create delays by accumulat-
ing difference in inflow versus outflow. Figure 3.14 shows a stock and flow diagram for
a Bass diffusion model. Figure 3.15 shows a sample output for adoption rates from the
stock and flow diagram in Fig. 3.14. And Fig. 3.16 is a snippet of the differential equi-
tations (the behind the scene parts) of the same system dynamics model.
SCD are used to represent external objects or actors that interact with a system
(Kossiakoff & Sweet, 2003). An SCD illustrates a macro view of a system under
investigation showing the whole system with its inputs and outputs related to exter-
nal objects. This type of diagram is system centric with no details of its interior
3 Methods and Models 63
Potential
Adopters
Adopters
Adoption Rate "A"
"P" R
B "AR"
+
+ Word of Total Large
Market Practice Population
Saturation Mouth
+ "N"
+ Adoption from
Institutional word of -
Adoption from Mouth
Advertising in +
Conferences + + Adoption
+ B Fraction
"i"
Advertising Market
Effectiveness Saturation Contact Rate
"a" "c"
10
100
0
0
0 10 20 30 40 50 60 70 80 90 100
Time (Month)
Adoption from Advertising in Conferences : Current
Adoption from Institutional word of Month : Current
The set of diagrams listed here describe the elements that are in the system being
modeled (Unified Modeling Language—Wikipedia, the free encyclopedia):
• Class diagram: describes the structure of a system by showing the system’s
classes, their attributes, and the relationships among the classes.
• Component diagram: depicts how a software system is split up into compo-
nents and shows the dependencies among these components.
• Composite structure diagram: describes the internal structure of a class and the
collaborations that this structure makes possible.
• Deployment diagram: serves to model the hardware used in system implemen-
tations, and the execution environments and artifacts deployed on the hardware.
• Object diagram: shows a complete or partial view of the structure of a modeled
system at a specific time.
• Package diagram: depicts how a system is split up into logical groupings by
showing the dependencies among these groupings.
• Profile diagram: operates at the metamodel level to show stereotypes as classes
with the <<stereotype>> stereotype, and profiles as packages with the <<pro-
file>> stereotype. The extension relation (solid line with closed, filled arrow-
head) indicates what metamodel element a given stereotype is extending.
These sets of diagrams listed here illustrate the things that happen in the system that’s
being modeled (Unified Modeling Language—Wikipedia, the free encyclopedia):
• Activity diagram: represents the business and operational step-by-step workflows
of components in a system. An activity diagram shows the overall flow of control.
66 N.A. Behkami and T.U. Daim
3.7.7 SysML
The difference between qualitative and quantitative research is man; selecting the
appropriate methodology depends on the objectives and preferences of the
researcher. Largely selecting qualitative or quantitative depends on the variables of
available time, familiarity with research topic, access to interview subjects and data,
research data consumer preference, and relationship of researcher to study subjects
(Hancock & Algozzine, 2006).
Quantitative methods can be appropriate when resources and time are limited.
Since these methods use instruments such as surveys to quickly gather specific vari-
ables from large groups of people for example political preferences these instru-
ments can produce meaningful data in a short amount of time even for small
investments. However for collecting data qualitative methods require individual
interviews, observations or focus groups which require a considerable investment in
time, and resources to adequately represent the domain being studied.
In case little is known about a situation qualitative research is a good starting
methodology, since it attempts to investigate a large number of factors that may be
influencing a situation. However quantitative methods typically investigate the
impact of just a few variables. For example often a holistic qualitative approach can
investigate an array of variables about a problem and later serve as a starting point
for a comparative quantitative study.
Quantitative research can often be performed with minimal involvement from
participants. In case access to study subject is difficult a quantitative approach is pre-
ferred. In distinction, difficulties of delays in access to participants for observations or
focus group, and types of qualitative research, could slow down the researcher efforts.
68 N.A. Behkami and T.U. Daim
Grounded theory (GT) and case study method are often used independently or
together to study social and technological systems. In order to select the appropriate
methodology and especially for this proposed HIT diffusion research it’s important
to understand the definition of GT and case study. They both have been used in
conjunction with UML to study information systems among others.
Case study method can be used to study one or more cases in detail and its
fundamental research question is the following: “What are the characteristics of this
single case or of these comparison cases?” (Johnson & Christensen, 2004). A case
study is often bounded by a person, a group, or an activity and is interdisciplinary.
Once classification of case study types includes the following (Stake, 1995):
1. Intrinsic case study—only to understand a particular case.
2. Instrumental case study—to understand something at a more general level than
the case.
3. Collective case study—studying and comparing multiple cases in a single
research study.
In a case study approach for data collection multiple methods such as interviews
and observations can be used. The final output of a case study is a rich and compre-
hensive description of the case and its environment.
3 Methods and Models 69
Where case study is detailed account and analysis of one or more cases, grounded
theory is developed inductively and bottom-up. GT’s fundamental research question
is the following: “What theory or explanation emerges from an analysis of the data
collected about this phenomenon?” (Johnson & Christensen, 2004). Grounded the-
ory is usually used to generate theory and it can also be used to evaluate previously
grounded theories. The following are important characteristics of a grounded theory
(Johnson & Christensen, 2004):
• Fit (i.e., Does the theory correspond to real-world data?)
• Understanding (i.e., Is the theory clear and understandable?)
• Generality (i.e., Is the theory abstract enough to move beyond the specifics in the
original research study?)
• Control (i.e., Can the theory be applied to produce real-world results?).
70 N.A. Behkami and T.U. Daim
Grounded theory is a general method of analysis that can accept quantitative, quali-
tative, or hybrid data (Glaser, 1978); however it has mainly been used for qualitative
researcher (Glaser, 2001). When using grounded theory and case study together
care has to be taken as principles of case study research do not interfere with the
emergence of theory in grounded theory (Glaser, 1998). As Hart (2005) points out,
Yin (1994) states “theory development prior to the collection of any case study data
is an essential step in doing case studies.” While Yin’s statement is valid for some
types of case study research, it violates the key principle of open-mindedness (no
theory before start) that is in grounded theory. Therefore when combining grounded
theory and case study the researcher has to explicitly mention which method is driv-
ing the investigative research.
Supporting the close relationship of GT and case study Hart (2005) in his own
research found that reasons for using grounded theory were consistent with reasons for
using case study research set forth (Benbasat, Goldstein, & Mead, 1987; Hart, 2005):
1) the research can study IS in a natural setting, learn the state of the art, and generate theo-
ries from practice.
2) The researcher can answer the questions that lead to an understanding of the nature and
complexity of the processes taking place.
3) It is an appropriate way to research a previously little studied area.
Various researchers have identified generated theory grounded in case study data
as a preferred method (Eisenhardt, 1989; Lehmann, 2001; Maznevski & Chudoba,
2000; Orlikowski, 1993; Urquhart, 2001). Cheryl Chi calls combing grounded the-
ory and case studies a “theory building case study” (Chi. Method-Case Study vs
Grounded Theory) and Eisenhardt (1989) identifies the following strength for using
case data to build grounded theories:
1. Theory building from case studies is likely to produce novel theory; this is so
because “creative insight often arises from juxtaposition of contradictory or par-
adoxical evidence” (p. 546). The process of reconciling these accounts using the
constant comparative method forces the analyst to a new gestalt, unfreezing
thinking and producing “theory with less researcher bias than theory built from
incremental studies or armchair, axiomatic deduction” (p. 546).
3 Methods and Models 71
2. The emergent theory “is likely to be testable with constructs that can be readily
measured and hypotheses that can be proven false” (p. 547). Due to the close
connection between theory and data it is likely that the theory can be further
tested and expanded by subsequent studies.
3. The “resultant theory is likely to be empirically valid” (p. 547). This is so because
a level of validation is performed implicitly by constant comparison, questioning
the data from the start of the process. “This closeness can lead to an intimate sense
of things’ that ‘often produces theory which closely mirrors reality” (p. 547) [4].
Various researchers have criticized grounded theory. The earliest riff is a contro-
versy that developed among the originators. Strauss has further developed GT
(Strauss & Corbin, 1998), while Glaser (1992) criticized this version for violating
basic principles. Others have proposed a newer multi-GTM that would integrate
empirical grounding, theoretical grounding, and internal grounding (Goldkuhl &
Cronholm, 2003).
Other problems with GT include how to deal with large amounts of data, since
there is no explicit support for where to start the analysis (Goldkuhl & Cronholm,
2003). The open-mindedness in the data collection phase can lead to meaninglessly
diverging amount of data (Goldkuhl & Cronholm, 2003). Another is that GT practi-
tioners are advised to discard pre-assumptions they hold, so the real nature of the
study field comes out. GT researchers are encouraged to avoid reading literature
until the completion of the study (Rennie, Phillips, & Quartaro, 1988). Ignoring
3 Methods and Models 73
existing theory can lead to duplicating effort for theories or constructs already
discovered elsewhere (Goldkuhl & Cronholm, 2003). Lack of adequate illustration
technique is yet another weakness of GT (Goldkuhl & Cronholm, 2003).
Current issues in UML research concern with the extent and nature of UML use and
UML usability. One study found that the use of UML by practitioners varies and
non-IT professionals are involved in the development of UML diagrams (Dobing &
Parsons, 2005). The study concluded that the variation in use was contrary to the
idea that UML is a “unified” language.
Another study while acknowledging the popularity that UML has gained in sys-
tem engineering felt “it is not fulfilling its promise” (Batra, 2009). Others have
stated that UML is too big and complicated (Siau & Cao, 2001); suffers from vague
semantics (Evermann & Wand, 2006) and steep learning curve (Siau & Loo, 2006);
and doesn’t allow for easy interchange between diagrams and models. At a higher
level some have highlighted that it is difficult to model a correct and reliable appli-
cation using UML, and to understand such a specification (Peleg & Dori, 2000).
Others have claimed that UML is low in usability because it requires multiple
models to completely specify a system (Dori, 2002) and have proposed another
methodology, namely the object process methodology (OPM) (Dori, 2001).
A study used the hierarchical coding procedure offered by GTM with UML to create
the requirements for an organization’s enterprise application. Figure 3.18 summa-
rizes the coding procedures of GTM that were incorporated into the requirements
74 N.A. Behkami and T.U. Daim
engineering process for the enterprise application (Chakraborty & Dehlinger, 2009).
For this example the study chose a “high-level description for a university support
system comprising of: a student record management system (SRMS), a laboratory
management system, a course submission system and an admission management sys-
tem” (Sommerville, 2000). Recall from earlier sections that grounded theory coding
processes are done in three steps of open coding, axial coding, and selective coding.
In this step the transcript of interview or case is read line by line. The text is broken
down into concepts. Concepts are any part of textual description that the researchers
believe are descriptive of the system being studied. Table 3.6 shows the concepts
extracted after this study applied GTM to a subsystem of the university support
system (SRMS). The preliminary concepts are highlighted in bold. The open coding
led to the identification of other supporting information as expressed in UML shown
in Fig. 3.19.
The goal of this step is to organize the concepts identified during open coding into
a hierarchical relationship. First the higher order categories are sorted out and later
sub-categories add more descriptive information. The process is continued until all
3 Methods and Models 75
Subsystem
-Student record
Management system
Student Classes/courses
User Interfaces
Fig. 3.19 Axial coding-description of the SRMS (Chakraborty & Dehlinger, 2009)
categories have been associated. Figure 3.20 shows the result of this process
expressed in UML.
The pervious step of axial coding has provided description for each of the subsys-
tems present in the problem space. Selective coding integrates the categories and
descriptions from the individual subsystems into an overall description of the sys-
tem. Figure 4.1 shows this final description derived from grounded theory and pre-
sented with UML.
76 N.A. Behkami and T.U. Daim
Fig. 3.20 System description after selective coding (Chakraborty & Dehlinger, 2009)
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Chapter 4
Field Test
N.A. Behkami
Merck Research Laboratories, Boston, MA, USA
T.U. Daim (*)
Portland State University, Portland, OR, USA
e-mail: tugrul.u.daim@pdx.edu
Today care for patients with complex healthcare needs is in a state of crisis in the
USA. The aging population, lifestyle shifts, and environmental factors have led to
rapid increases in numbers of patients who suffer from complex illnesses while the
healthcare system struggles to adapt. Treatment for patients with complex needs
succeeds when their needs are known, their care is well coordinated, and their
healthcare team is able to make clinical decisions based on the systematically avail-
able evidence. Tools, such as better health IT systems and robust financial incen-
tives, can facilitate improved quality of care.
Patients suffering from chronic illnesses account for approximately 75 % of the
nation’s healthcare-related expenditures. However these patients only receive the
appropriate treatment about 50 % of the time. Inadequacy of care is even more of a
problem for patients with multiple chronic illnesses. For example a patient on
Medicare with five or more illnesses will visit 13 different outpatient physicians
and fill 50 prescriptions per year (Friedman, Jiang, Elixhauser, & Segal, 2006). As
the number of a patients’ conditions increases, the risk of hospitalizations grows
exponentially (Wolff, Starfield, & Anderson, 2002). While the transitions between
providers and settings increase, so does the risk of harm from inadequate informa-
tion transfer and reconciliation of treatment plans. Such risks are a large part of the
reason patients like this account for 40 % of all Medicare costs. Wolff estimates
that a third of these costs may be due to inappropriate variation and failure to coor-
dinate and manage care (Wolff et al., 2002). As costs continue to rise, the delivery
of care must change to meet these costs. Components identified as important
include better planning on the part of providers and patients/families, both in visits
and over time; better coordination and communication; and increased self-manage-
ment of conditions by patients and caregivers (Bodenheimer, Wagner, & Grumbach,
2002a, 2002b).
Two changes to healthcare teams that can provide this systematic approach are
nurse-based care management and health information technology (Dorr, Wilcox
et al., 2006; Shojania & Grimshaw, 2005; Shojania et al., 2006). A meta-analysis
for redesign for patients with diabetes showed that nurse care managers and team
reorganization were the most successful quality improvement techniques; infor-
mation technology alone was only moderately successful (Shojania et al., 2006).
A care management model for depression in older adults (who tend to have more
complicated depression and concurrent illnesses) demonstrated broad success
(Steffens et al., 2006; Rubenstein et al., 2002). Patients with schizophrenia bene-
fitted from care management with HIT using the Medical Informatics Network
Tool (Young, Mintz, Cohen, & Chinman, 2004). The CMP team and others have
shown that reduction in hospitalization visits can occur in models focused on older
adults with complex needs (Dorr, Brunker, Wilcox, & Burns, 2006; Counsell
et al., 2007).
4 Field Test 85
Care Management
Technology
-Access
-Best Practices
-Communication
dissemination of CMP in more than 75 clinics across the country has led to a deep
understanding of the barriers and benefits of such HIT. Barriers include the need to
integrate systems, difficulty communicating with the entire team, and representa-
tion of workflow.
4.3.1 Overview
The chart below shows the steps used in conducting the field study. Using a litera-
ture review a preliminary framework and model were produced. Next data was
collected using mix methods and various tools were used for analysis and later
validation (Fig. 4.3).
4.3.2 Objectives
Objective 2: Demonstrate that dynamic capabilities theory can be used and how to
meaningfully extend diffusion of innovation theory.
Objective 3: Use software and system engineering methods including 4 + 1 view for
perspectives and UML to demonstrate documentation and analysis.
Objective 4: Build and run a small simulation of the DOI theory extension using
system dynamics. The simulation will be used to demonstrate the validity of the
new diffusion framework.
The methodology used for the research design is an exploratory case study. The case
study method is chosen because the proposed research needs to know “how” and
“why” HIT adoption/diffusion program has worked (or not). Such questions deal
with operational links needing to be traced over time, rather than mere frequencies
or incidences. The next three subsections describe the data collection tools used and
the last explains the sampling for the field study.
88 N.A. Behkami and T.U. Daim
Readiness Assessment
For the Readiness Assessment sample data from four sites in Oregon and one in
California who currently participate in the OHSU CMP trail were reviewed. This
section provides a brief description of each location and its affiliated organizations.
The Oregon clinics are members of the Oregon Rural Practice and Research
Network (ORPRN), which is a statewide network of primary care clinicians, com-
munity partners, and academicians, dedicated to research into delivery of healthcare
to rural residents and research to reduce rural health disparities. ORPRN includes
42 rural primary practices which care for over 166,000 patients (ORPRN). The fol-
lowing individual clinics participated in providing data: Lincoln City Medical
Center, Eastern Oregon Medical Associates, OHSU Scappoose Family Health
Center, and Klamath Open Door Family Medicine.
The fifth study participant is HealthCare Partners (HCP), LLC, a management
service organization that manages and operates medical groups and independent
physician networks nationally. The organization serves more than 500,000 patients,
of whom more than 100,000 are older adults. HealthCare Partners Medical Group
(HCPMG) has been recognized by health plans and business groups for its medical
leadership, the high quality of medical care delivered, operational effectiveness, and
high rates of patient satisfaction. HCPMG employs 500+ primary care and specialty
physicians who care for patients in Los Angeles County and north Orange County,
California, through 40 neighborhood offices, five urgent care centers, two medical
spas, an ambulatory surgery center, and an employer on-site office (Health Care
Partners Medical Group).
4.3.4 Analysis
Using open coding and focused methods of Thematic Analysis the author created
themes from the data (Bailey, 2006), including recurring patterns, topics, theories,
viewpoints, and concepts. Rogers’ diffusion of innovation theory and dynamic
capability theory and TAM and adoption barriers and influences were used to guide
the coding. Figure 4.4 shows the workflow used for analysis. Figure 4.5 shows a
sample of the coding artifacts created.
After iterating over the themes that emerged from the collected data I was able to
group them into eight categories that affected the HIT diffusion process for
CMP. They included:
• Needs and drivers
• Barriers
• Outcome measures
• Influences
• Capabilities
• Adoption decision
• Adoption success criteria
• Awareness of innovation versus actual adoption timeline
92 N.A. Behkami and T.U. Daim
Based on the extracted constructs a process of the adoption from the clinic per-
spective was created as shown in Fig. 4.6. The innovation process seems to start for
the clinics based on “Drivers” or “Needs.” A driver for example is something
like the need to more efficiently manage clinic workflow. Eventually these needs
drive the clinic to adopt the HIT innovation in this case CMP offered by OHSU. Then
there are “Barriers” and “Influences,” which are negative and positive reinforce-
ments, respectively. Barriers can discourage both the “Drivers” and the “Adoption
Decision” in a negative way. For example lack of funding at the clinic for buying an
expensive software system can be an example of a barrier. Influence reinforces both
the “Drivers” and the “Adoption Decision” and it’s a positive force. For example
government reimbursement for using HIT in the form of extra revenue for clinic
seems to be an example of a positive influence on the HIT adoption process.
Another theme that emerged from the data which is directly fed related to the
adoption decision is “Adoption Success Criteria.” This is how a clinic defines
whether adopting CMP was successful or not. These criteria were either mecha-
nisms created by the clinic itself or government- or payer-supported “Outcome
Measures” that described adoption goals and the progress towards them. In time
these “Outcome Measures” can either become barriers or influences either for the
same adopter or future adopters; this is similar to the “confirmation” stage that
Rogers defined in Diffusion of Innovation.
In all based on the data collected it was clear that the clinics didn’t adopt as soon
as they became aware of CMP and once they decided to adopt, often they didn’t
know what to do and how to go about adopting it. This is where the theme of
“Capabilities” comes to light in the adoption process. For example having a nurse
that was properly trained and skilled in care management to oversee the program
was a capability needed and recommended by OHSU for successful adoption.
As evident from Fig. 4.6 needing “Capabilities” directly became a factor in the
4 Field Test 93
Based on the interviews I was able to build a structural diagram of the stakeholders
and actors involved in the CMP diffusion ecosystem as shown in Fig. 4.9. The nota-
tion used for the diagram is a UML class diagram that shows the static aspects of the
important objects in the system. As seen in Fig. 4.7 each object is represented as a
rectangle box. In the top section of each rectangle is the name of the object and in
the second subsection is the attributes of that object. A stakeholder or actor is con-
sidered to be a type of an object. The arrows between object boxes as in Fig. 4.8
show the relationships among objects. It’s worth mentioning that these links don’t
represent behavior, which will be shown using dynamic types of UML diagrams in
later sections of this document. The lines with an arrow at the end show a general-
ization relationship meaning for example as in Fig. 4.8 a physician is a type of
provider and so are nurses and institutional providers (clinic). This notation allows
us to analyze these objects as part of the whole while keeping their specializations
in mind. The dotted lines between objects represent a link and not a hierarchical
relationship like the other line types (Fig. 4.9).
Physician
Instituational Provider
-Education Nurse
-Comfort with Technology -Size
-Specialization -Location
-Role -Technology
94 N.A. Behkami and T.U. Daim
The ecosystem is made up of five major packages of objects as shown in the top part
of Fig. 4.10 as a UML component diagram. These packages include the provider,
government, innovation supplier, care seeker, and payer packages. Being able to
identify and correctly group these objects is useful in studying the diffusion/adop-
tion process. This eventual categorization will be one of the benefits and unique
contributions of the proposed research HIT diffusion research.
There are a range of activities that occur at the clinic for adoption of CMP, which
require analysis. These include adoption, rejection, dissemination, developing
capabilities, implementation, usage, reconfirmation, developing capabilities, and
4 Field Test 95
Adoption
Implementation
Government Provider
Rejection
Usage
Dissemenation Reconfirmation
Supplier
Payer
managing capabilities. In Fig. 4.11 these are expressed in a UML use case diagram
notation. Within the scope of the field test subset of these activities including the
knowledge stage and developing capabilities stage are evaluated in more detail in
the following sections.
The UML sequence diagram in Fig. 4.12 was created and shows the stakeholders
and sequence of actions that shape the “Knowledge Stage” of Rogers’ diffusion
process. The “HIT Innovation Supplier” (in this case OHSU for CMP) attends a
“Conference” such as the Annual AGA Conference (American Geriatrics
Association) where a “Physician” comes to their presentation and becomes aware of
the innovation (CMP) at the conference. If the “Physician” decides that CMP may
be useful for their clinic, they go back and inform the “Clinic” that they work at
about CMP including the “Nurses,” “CEO” (or other administrative decision maker),
and other “Physician(s).” The interactions of these multiple stakeholders over time
forms the “Knowledge Stage” of Rogers’ Diffusion Theory. Having this model, with
such level of detail, allows us to examine the precise participants and decision points
and examine the time elements of CMP adoption and diffusion processes.
The UML sequence diagram in Fig. 4.13 was created from data collected and shows
the stakeholders and sequence of actions that shape the “Dynamic Capability
Development Stage” for adoption of CMP. Once a potential adopter gains knowl-
edge of an innovation and later decided to adopt the innovation, it goes into the loop
4 Field Test 97
No
Develop or Buy Reject
Capability Innovation
(Receive Payments)
What the sequence diagram in the previous section couldn’t show about alternative
paths for decisions can be illustrated in Fig. 4.14 using a UML activity diagram. The
happy path is down the middle of the diagram where when the clinic decides to adopt
CMP it already has the three needed capabilities (CMP software, a nurse care man-
ager, and a way to get paid by payers). In that case it can quickly move down the
middle and adopt CMP and therefore is less likely it would reject the innovation
(CMP). However what’s more interesting about this graph based on the interviews
with experts and users is the alternate paths the scenario can take. If some of the three
needed capabilities are not in place the adoption has to wait until those remaining
capabilities are either built or bought, before true adoption happens. This supports
the objective of the proposed research that awareness alone is not enough as described
in Rogers to move to next step of adoption. Meaning after knowledge of innovation
capabilities need to be developed or bought to truly adopt an innovation.
Recall from earlier sections of this document that various researchers have attempted
to classify capabilities or competencies necessary for competitive advantage,
namely Barney Figure 4.15 and Itami Figure 4.16. Similar to their works, based on
the data collected from my feasibility study a classification of dynamic capabilities
for HIT adoption (CMP) can be generated (Fig. 4.17).
4.3.5.4 Limitations
While the purposed model is flexible and could accommodate studying various
types of organizations (hospitals), patients, or providers the following are some of
the limitations:
• The proposed model is a qualitative-based descriptive case study. What it tries to
do is to understand and bound the problem for one case. Therefore the findings
4 Field Test 99
HIT Adoption
Capabilities (CMP)
CMP EHR
Software Integration
Reimbursement Patient Panel
Payment Processing Management
Training
between CMP capabilities (unless directly interfacing with CMP) and other hos-
pital systems for example billing, electronic health record, disease registry, etc.
• This research does not look at the internals of the process required for acquiring
capabilities; it’s treated as a black box. Existence of (or lack of) these capabili-
ties, interfacing with them, and their timing are of most importance to the
proposal.
• Although due to its sophistication the CMP product at OHSU in many ways is a
perfect HIT innovation to study, but it mostly targets older adults and extremely
sick patients. A healthier target population such as professional workers less than
40 years of age may have unique influences on the HIT adoption and diffusion
process that may not be highlighted in this choice of application to study.
• Similar to using multi-perspective to represent stakeholder and views, in classi-
fication of capabilities for HIT innovation (CMP) it could be beneficial to use
levels. For example a small clinic may need a subset of capabilities that a larger
hospital would need for adoption. Using multi-levels would be a constructive
endeavor for future research.
Actual behavior of the real-world model for this report is based on two theories and
two examples:
• Diffusion of innovation theory by Rogers.
• Bass diffusion model with modeling disease epidemics example (Sterman &
Sterman, 2000).
• Bass diffusion model with cable TV penetration in US households (Sterman &
Sterman, 2000).
“Diffusion is the process in which an innovation is communicated through
certain channels over time among the members of a social system” (Rogers &
Rogers, 2003). This special type of communication is concerned with new ideas.
It is through this process that stakeholders create and share information together in
order to reach a shared understanding. Some researchers use the term “dissemina-
tion” for diffusion that are directed and planned. In his classic work (Rogers &
Rogers, 2003) Rogers identifies four main elements in the diffusion process that are
virtually present in all diffusion research: (1) an innovation, (2) communication
channels, (3) over time, and (4) social systems.
The diffusion and adoption of new ideas and new products often follows S-shaped
growth patterns. Adoption of new technologies spreads as those who have adopted
them come into contact with those who haven’t and persuade them to adopt the new
system. The new believers in turn then persuade others. An example of the Bass
diffusion model for adoption of cable TV (Sterman & Sterman, 2000) by house-
holds can be used as a reference for health IT model. The example identified the
following important factors in a household’s decision to subscribe to cable TV:
• Favorable word of mouth from existing subscribers
• Positive experience viewing cable at the homes of friends and family
• Keeping up with the Joneses
• Feeling hip because of consuming on cable only knowledge
Similarly adoptions of HIT applications depend on favorable word of mouth
from hospitals or clinics that currently use the HIT product. Also positive empirical
and financial evidence through industry publications shows that the HIT application
improved patient care and financials of the clinic.
102 N.A. Behkami and T.U. Daim
In this Bass style model as seen in Fig. 4.18 potential adopters were broken down
into large and small practices. Small practice is enticed by large government reim-
bursement to adopt and is assumed not to be affected by word of mouth or advertis-
ing for adoption. It’s important to mention that word of mouth may affect the choice
of HIT vendor for adoption in a small clinic, but nonetheless act of adoption is for
certain and it’s this part that is of interest to this report.
The model in this report captures some of the important variables that have been
identified through a literature review and interviewing a physician. The model
includes three stocks:
• Small Practice Potential Adopters “SP” represents the number of small clinic
that have not adopted health IT.
• Large Practice Potential Adopters “LP” represents the number of large clinics
that have not adopted health IT.
• Adopters “A” represents the number of small and large clinics that have adopted
health IT.
In this model potential adopters are grouped into small and large practice. The
small practices will be receiving a $40,000 reimbursement check from the OBAMA
stimulus package for adopting health IT. Large practices will not receive any stimu-
lus and they will continue adopting health IT per their business and strategic plans.
Adoption rates “LAR” and “SAR” represent number of clinics adopting per time for
large and small practices, respectively:
1. LAR = Adoption from advertising + adoption from word of mouth
(a) Adoption from advertising = a × SP
(b) Adoption from word of mouth = c × i × LP × A/N
2. SAR = Adoption from government stimulus = j × LP
Adoption for large clinic can occur from two sources:
3. Adoption_from_Advertising = Large_Potential_Adopters × Advertising_
Effectiveness
4. Adoption_from_word_mouth = contact_rate × adoption_fraction_i × (adopters/
total_population)
Adoption for small clinics can happen only because of:
5. Adoption_from_Government_ incentive = Small_Potential_Adopters × Adoption_
fraction_j
Total adopters:
6. Adopters “A” = SAR + LAR
4.3.6.3 Assumptions
Adoption
Fraction
"j"
+
Adoption from Govermnet
Small Practice Incentive
$40k
initial values, etc.), and time scale errors. Process of isolating errors include doubting
frame of mind, outside doubters, walkthrough, and hypothesis testing techniques.
The goal of this activity is to find scenarios that cause the model to fail so that we
can isolate and correct errors. Table 4.3 shows the scenarios tested for and their
results.
Outside Doubters
The model was shown to an engineering graduate student. The student knew and
understood the modeled system and its intended operation, but it was not involved
in its construction. Model passed outside doubter check, and future additions were
suggested.
Walkthroughs
The modeler explained the model’s logic to a small group of individuals who are
familiar with the system being modeled; they included a physician and a health-
care researcher. Model passed walkthrough and three items were highlighted: (1)
the Bass model of diffusion was the correct theory to apply and (2) healthcare
systems and policies are much more complicated than the current model; however
this is an acceptable and promising first pass at modeling heath IT adoption
(Table 4.4).
106 N.A. Behkami and T.U. Daim
Hypothesis Testing
To fully exercise the model hypothesis tests with various conditions were
developed.
Tornado Diagram
Having verified the model, it is validated against reference behavior pattern (RBP),
comparing the conceptual model to reality. In validating the health IT adoption
model the two validation “paradigms” of rational and practical are suitable fits.
The model fits the rational (conceptual) paradigm by being believable and one is
able to reason about its structure/assumptions/logic. The model fits the practical
paradigm because it meets its intended goal, to understand how quickly hospitals
may adopt HIT (under optimistic conditions). The learning realized from the model
justifies its development cost.
Earlier in this report in the RBP we identified two theories of diffusion, with two
real-world examples of innovation adoption. Using a multi-perspective approach
(of modeler, technical evaluator, and user) based on the models conceptual validity,
operation validity, and believability were able to validate that the correct model has
been built.
Conceptual Validity
The created model exhibits the concepts identified by Rogers’ classical theory on
Diffusion of Innovation (Rogers & Rogers, 2003). Theory states that Diffusion of
Innovation includes communicating messages. This communication requires chan-
nels by which messages move from one individual or unit to another. The context of
the information sharing determines the experience of the communication and
whether ultimately the receivers adopt the innovation. According to Rogers adoption
evaluations can be objective or subjective. However they are often subjective based
on information reaching the individual through other communication channels.
Communication can occur between hemophilic or heterophilic individuals.
Homophily refers to how similar two interacting individuals are based on their
beliefs, education, etc. Heterophily is the opposite and refers to how different from
each other interacting individuals are.
Two individuals that are homophilous are able to create more meaningful com-
munications. One of the barriers in innovation of diffusion is that participants are
very heterophilous. For example an inventor with an engineering background often
has difficulty communicating merits of his or her innovation to investors or poten-
tial nontechnical users.
108 N.A. Behkami and T.U. Daim
Time is involved in three stages: (1) the time that passes between first knowledge
and adoption or rejection of an innovation, (2) the earliness or lateness that an
individual adopts compared to the group, (3) innovation rate of adoption, which is
the number of people that adopt it during a particular period of time.
Operational Validity
Believability
When an innovation is introduced and the adopter population is zero, the only
source of adoption will be external influences such as advertising. The advertising
effect will be largest at the state of the diffusion process and steadily diminish as the
pool of potential adopters is depleted. Figure 4.21 shows the behavior of the Bass
model for CMP. The total population N is assumed 2600 hospitals. Advertising
effectiveness, a, and the number of contacts resulting in adoption from word of
mouth, ci, were estimated to be 0.005 per year and 0.16 per year, respectively. The
contribution of adoption from advertising is small in general and on a decline after
the first year, as seen in Figs. 4.22 and 4.23. Adoption through word of mouth peeks
after the second year.
4 Field Test 109
3,000
4,000
1,000
0
0 6 12 18 24 30 36 42 48 54 60
Time (Month)
Adopters "A" : Current
20
200
40
0
0
0
0 6 12 18 24 30 36 42 48 54 60
Time (Month)
Adoption from Advertising in Conferences : Current
Adoption from Government Small Practice Incentive $40k : Current
Adoption from Institutional word of Mouth : Current
2,000
500
1,000
0
0
0
0 6 12 18 24 30 36 42 48 54 60
Time (Month)
Adopters "A" : Current
Potential Adopters "P" : Current
Small Practice Potential Adopters "S" : Current
110 N.A. Behkami and T.U. Daim
This report presented an SD model to study the HIT adoption process in the US
healthcare system. Using a system dynamics view brings a fresh and much-needed
means for studying the adoption process. The overview of the model does not show
an unexpected dominant loop and more work remains to be done to benefit more
comprehensive conclusions.
4.3.6.8 Limitations
The presented model includes several limitations that should be addressed in future
work in order to improve the representation of the system. For example the model
does not explicitly reflect the interests of patients, payers, the high-tech industry,
etc. The proposed model is valuable in providing a common ground for interested
research parties and presenting an overall view of the system. By expanding the
model a simulation for evaluating policies and strategies can be obtained, which is
a main objective of developing system dynamics theory.
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4 Field Test 111
Rubenstein, L., Parker, L., Meredith, L., Altschuler, A., dePillis, E., Hernandez, J., et al. (2002).
Understanding team-based quality improvement for depression in primary care. Health
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Chapter 5
Conclusions
Despite the fact that diffusion theory was introduced several decades earlier, we still
don’t seem to truly understand how the phenomenon impacts our society. In recent
years many researchers, including Rogers, the father of diffusion theory, have called
for renewed interest in diffusion research. One domain as discussed in this proposal
which can benefit from better understanding of diffusion is the field of healthcare,
specifically improvements in understanding adoption and diffusion process for
health information technology (HIT). Due to various factors including changing
demographics, the US healthcare delivery system is facing a crisis; and having real-
ized this government and private entities are pouring support into advocating HIT
adoption-related research amongst other initiatives.
One such research that would help with this agenda is the research proposed in
this study. This study has shown that indeed an extension of Rogers’ diffusion the-
ory using the extension of dynamics capabilities can help further our understanding
of what it takes for successful innovations to diffuse in the US Healthcare industry.
This report started by proposing a dynamic capability extension to diffusion theory.
Then it was reasoned for why diffusion theory rather than other adoption theory, due
to its macro-level property rather than micro, is the appropriate theory for the pro-
posed study. It was also shown that how dynamic capabilities as a one manifestation
of “factors of production” originating from the strategic management field can be
used to further characterize the adoption/diffusion decision and its life cycles.
This study also shows that use of a case study or grounded theory types of quali-
tative research is necessary to do an exploratory study of the problem. It’s through
this type of research that we hope to gain in-depth understanding of situation and
meaning for those involved. In future research the results of such mostly qualitative-
based research can be inputs for hybrid or purely quantitative method research on
the same topics and in the same field, after the problem and what’s really going on
have been structured a little more with qualitative methods. Additionally in this
report various system modeling tools were compared and contrasted for purposes of
analysis, documentation, and communication of research findings. It was shown that
for this research the use of the Unified Modeling Languages (UML) is a productive fit.
UML benefits from having constructs for both showing static and dynamics aspects of
the system. UML also supports multi-perspective views of the problem which was
also shown here to be essential for understanding HIT diffusion innovation.
In addition to comparing and discussing various methodologies, theories, and
aspects of the problem in this document, the proposed research was accompanied
and verified for demonstrability and validity by conducting a field study at Oregon
Health & Science University with its Care Management Plus team. CMP, a HIT-
based innovation, is an ambulatory care model for older adults and people with
multiple conditions; components of CMP include software, clinic business pro-
cesses, and training. The field study was conducted using site readiness survey and
expert interviews. The data collected was analyzed using thematic analysis includ-
ing open and focus coding. Models were created using diffusion and dynamic capa-
bility theory and they were documented using multi-perspectives and the UML’s
structural and behavior diagrams. A system dynamics model based on Bass diffu-
sion model was also created and demonstrated. And in conclusion conducting the
field study was able to demonstrate that the research objectives (generally for pro-
posal and specifically for field study) were met.
Objectives 1 and 2 were about showing that DOI and dynamic capabilities can be
combined in a meaningful manner:
Objective 2: Demonstrate that dynamic capability theory can be used and how to
meaningfully extend diffusion of innovation theory.
This objective was demonstrated based on the model constructed from site data
collection as described in Fig. 5.1, where it’s the clinic need(s) that drives them to
consider adopting an innovation. And this need and decision have barriers and/or
influences that can affect them in a negative or positive way. Additionally as that
same figure shows whether a clinic has the needed capabilities to adopt or not
becomes a pressure point as either an positive influence (in case they already have
the capabilities) or a barrier (in case clinic doesn’t have the needed capability yet).
In further support of the Objective 2, Fig. 5.2, a depiction of the “dynamic capa-
bility development stage” shows the sequence and time frame of acquiring capabili-
ties prior to truly adopting an innovation. These two points mentioned indeed
validate and support the second objective which helps in drawing the picture in
Fig. 5.3 that demonstrates how dynamic capabilities can be used to meaningfully
extend diffusion of innovation theory.
5 Conclusions 115
HIT Adoption
Capabilities (CMP)
CMP EHR
Software Integration
Reimbursement Patient Panel
Payment Processing Management
Training
modeled using word of mouth and advertising. A complete set of system dynamics
components were developed including causal loop diagram (CLD) (Fig. 5.12) and
stock and flow system dynamic model in Vensim software (Fig. 5.13). The model
was extensively validated and verified using popular methods. Verification was per-
formed with the techniques of doubting frame of mind, outside doubter, walk-
through, hypothesis testing, and tornado diagram testing. Model was validated
using conceptual validity, operational validity, and the believability test. Figure 5.14
an S-curve of adopter population along with Figs. 5.15 and 5.16 growth curves
showing adoption rates were outputted by the model. The generate model and its
outputs show that it’s possible to effectively model the HIT adoption and diffusion
process in a good enough way so that we can experiment with scenarios and
forecasting. In future research this model can be extended to integrate dynamic
capabilities.
5 Conclusions 119
Adoption
Implementation
Government Provider
Rejection
Usage
Dissemenation Reconfirmation
Supplier
Payer
In conclusion all objectives of the research proposal were met and demonstrated
through preparation of this document. Along with the results of the included feasi-
bility field study it’s verified that indeed there is a need for extension of Rogers’
theory. Dynamic capabilities are a good fit candidate integrating with Rogers’ diffu-
sion theory and extending it. Additionally the combination of the presented theories
and methods in this document can assist healthcare stakeholders understand their
problems and solution more efficiently as they set new policies and investment for
their support.
5 Conclusions 121
No
Develop or Buy Reject
Capability Innovation
(Receive Payments)
Adoption
Fraction
"j"
+
Adoption from Govermnet
Small Practice Incentive
$40k
3,000
4,000
1,000
0
0 6 12 18 24 30 36 42 48 54 60
Time (Month)
Adopters "A" : Current
20
200
40
0
0
0
0 6 12 18 24 30 36 42 48 54 60
Time (Month)
Adoption from Advertising in Conferences : Current
Adoption from Government Small Practice Incentive $40k : Current
Adoption from Institutional word of Mouth : Current
2,000
500
1,000
0
0
0
0 6 12 18 24 30 36 42 48 54 60
Time (Month)
Adopters "A" : Current
Potential Adopters "P" : Current
Small Practice Potential Adopters "S" : Current
5 Conclusions 123
References
Osterweil, L. J. (1987). Software processes are software too. In Proceedings of the 9th International
Conference on Software Engineering (p. 13).
Osterweil, L. J. (1997). Software processes are software too, revisited: an invited talk on the most
influential paper of ICSE 9’, paper presented to the International Conference on Software
Engineering. In Proceedings of the 19th International Conference on Software Engineering,
Boston.
Part II
Evaluating Electronic Health Record
Technology: Models and Approaches
This part reviews electronic health records and considers technology assessment
scenarios for multiple purposes. These are the following:
(a) The adoption of EHR with focus on barriers and enablers.
(b) The selection of EHR with focus on different alternatives.
(c) The use of EHR with focus on impacts.
The exploration will assume that the adoption, selection, and use of EHR relate
to the ambulatory EHR accepted in small practices.
The first section will highlight the gaps each scenario will address and list match-
ing research goals and research questions.
The second section will describe a research project matching each objective
above. In each case, we will explain the methodology of choice, describe other
methods that may also be considered, and list the reasons to justify the methodology
we are choosing. We will develop a preliminary model for each research and list the
theories behind.
The third section will explain what kind of data we will need and how we will
acquire it. We will consider the following in this section:
(a) The required data size in terms of number of data points, respondents, or
experts.
(b) Data access issues such as sample size or access to experts.
The fourth section will explain the types of analyses to be done for each scenario.
We will consider the following in this section:
(a) Types of metrics used to measure accuracy.
(b) Validity and reliability in each case.
Chapter 6
Review of Factors Impacting Decisions
Regarding Electronic Records
Let’s explore the gaps found in the literature that relate to adoption of EHR with
focus on enablers and barriers.
• The impact and significance of implementation barriers and enablers (financial,
technical, social, personal, and interpersonal) have not been satisfactorily
studied.
• Significance of the relationship of factors of perceived usefulness, perceived ease
of use, and perceived benefits on attitude toward using EHR in ambulatory set-
tings has not been adequately shown with global studies.
• Lack of studies in the USA involving TAM models and research on a global
scale.
• Lack of quantitative studies in EHR adoption toward small ambulatory settings.
Palacio, Harrison, and Garets (2009) provided a research that documented an
increased adoption of EHR in the US hospitals through the period of 2005–2007.
The authors also indicate potential barriers of HIT implementation as cost, lack of
financial incentives for providers, and the need for interoperable systems.
A systematic literature review on perceived barriers to electronic medical record
(EMR) adoption identified eight categories (financial, technical, time, psychologi-
cal, social, legal, organizational, and change process, Boonstra & Broekhuis, 2010).
The study is bibliographical and explorative in nature, and the barriers are not tested
for significance rather interpreted as guidelines for EMR adopters and policy mak-
ers and as a foundation for future research.
Taxonomy of the primary and secondary barriers is listed in Table 6.1 below
(Boonstra & Broekhuis, 2010):
Boonstra and Broekhuis (2010) also noted that barriers in primary categories
vary significantly between small and large practices, since small practices face
greater difficulties overcoming those barriers. Those differences may greatly
impact the focus and the effort needed to overcome financial, technical, and time
barriers.
Table 6.1 Taxonomy of the primary and secondary barriers (Boonstra & Broekhuis, 2010)
Primary Secondary
category Primary barriers category Associated barriers
Financial • High start-up costs Psychological • Lack of belief in EMRs
• High ongoing costs • Need for control
• Uncertainty about return
on investment (ROI)
• Lack of financial
resources
Technical • Lack of computer Social • Uncertainty about the
skills of the physicians vendor
and/or the staff • Lack of support from
• Lack of technical training other external parties
and support • Interference with
• Complexity of the system doctor-patient
• Limitation of the relationship
system • Lack of support from
• Lack of customizability other colleagues
• Lack of reliability • Lack of support from
• Interconnectivity/ the management level
standardization
• Lack of computers/
hardware
Time • Time to select, purchase, Legal • Privacy or security
and implement the system concerns
• Time to learn the system
• Time to enter data
• More time per patient
• Time to convert the
records
Organizational • Organizational size
• Organizational type
Change process • Lack of support from
organizational culture
• Lack of incentives
• Lack of participation
• Lack of leadership
6 Review of Factors Impacting Decisions Regarding Electronic Records 129
While the study by Lorenzi et al. (2009) reviews the benefits and the barriers of
EHR in ambulatory settings, it does not address EHR models, or the barriers associ-
ated with interconnectivity of EHR. The authors indicate that more research is
needed in those fields.
A group of Canadian researchers (McGinn et al., 2011) conducted a systematic
literature review of EHR barriers and facilitators. The review categorized the stud-
ies based on the user groups (physicians, healthcare professionals, managers, and
patients), while the differences of clinic size and type of setting and the factors that
are particular to each type were not discussed. The study, though, is interesting in
the sense of general ranking of the factors and commonalities in studies of those
factors. Technical issues are at the top of the list, while organizational factors are not
that common (McGinn et al., 2011). The ranking (from most to least common) is
shown in Table 6.2.
The three studies, mentioned in McGinn et al. (2011) related to ambulatory care,
were exploratory and/or qualitative in nature.
Table of categories of studies examined through literature review is shown in
Table 6.3.
Electronic health records have been a topic of research in various countries
throughout the world, some with high rates of adoption and implementation and
others with low ones. While researching and working on my independent studies, I
have found a number of studies in foreign countries (Bates et al., 2003; Rosemann
et al., 2010; Were et al., 2010). High transition to EHR technology was reported in
Australia, New Zealand, and England through financial support and incentives,
evidence-based decision support, standardization, and strategic framework (Bates
et al., 2003).
Those studies give a possibility to engage a similar research or test a certain
framework here in the USA while studying adoption of EHR by small ambulatory
clinics. In Table 6.4, I have summarized some of those important studies.
The US research in EHR adoption lacks rich involvement of TAM with structural
equation modeling, especially in ambulatory care. While researching EHR adoption
130 L. Hogaboam and T.U. Daim
Table 6.4 Summary of studies and a variety of methodologies and analyses used
Authors Country Study
Ludwick and Canada Lessons-learned study from EHR implementation in seven
Doucette countries. Concluded that systems’ graphical user interface design
(2009) quality, feature functionality, project management, procurement,
and user experience affect implementation outcomes. Stated that
quality of care, patient safety, and provider-patient relations were
not impacted by system implementation
Aggelidis and Greece Examined the use of health information technology acceptance
Chatzoglou with the use of modified and extended TAM. Facilitating
(2009) conditions (new computers, support during information system
usage, and financial rewards) was the main factor that positively
impacted behavioral intention. Perceived usefulness and ease of
use were the most important factors of direct influence on
behavioral intention. Anxiety during system use shown to be
reduced by facilitating conditions, perceived usefulness, and
self-efficacy
Melas et al. Greece Researchers implemented confirmatory factor analysis (CFA),
(2011) structural equations modeling (SEM), and multi-group analysis of
structural invariance (MASI) in a study of examining the intention
to use clinical information systems in Greek hospitals. The results
showed direct effect of perceived ease of use on behavioral
intention to use
Chen and Taiwan Modified TAM was used for IT acceptance research. Confirmatory
Hsiao (2012) factor analysis for reliability and validity of the model and SEM
for causal model estimation were used. According to the results of
the study, top management support had significant impact on
perceived usefulness while project team competency and system
quality significantly impact perceived use
Hung, Ku, Taiwan Modified TBP was used and results indicated that physicians’
and Chien intention to use IT was significantly impacted by attitude,
(2012) subjective norm, and perceived behavior control. Studied
impactful factors included interpersonal influence, personal
innovativeness in IT, and self-efficacy
Cheng (2012) Taiwan The researchers looked at IT adoption by nurses in two regional
hospitals with extended TAM, where the other factors impacting
intention to use consisted of learner-system interaction, instructor-
learner interaction, learner-learner interaction, and flow
Paré and Canada The study concluded that IT sophistication and perceived
Sicotte (2001) usefulness of clinical applications are moderately to highly
correlated while no relationship was found between the level of
sophistication and perceived usefulness of administrative
applications
Moores France The researchers found that there are differences in significant
(2012) impacts depending on the experience of the users while applying
extended and modified TAM in studying adoption of clinical
management system by hospital workers
(continued)
132 L. Hogaboam and T.U. Daim
programs in use, which indicates highly fragmented market, which authors note as
a barrier to proliferation. Statistical analysis involving demographic data was per-
formed and linear regression was utilized to analyze the variance in EHR/EMR
interest and the amount of willingness to pay (Valdes et al., 2004).
One important study was done to assess intensive care unit (ICU) nurses’ accep-
tance of EHR technology and examine the relationship between EHR design, imple-
mentation factors, and user acceptance (Carayon et al., 2011). This study was
regional (northeastern USA) and local to the medical center and nurses working in
four ICUs. It tested only two major components of TAM: usability (ease of use) and
usefulness. Three functionalities of EHR (computerized provider order entry
(CPOE), the electronic medication administration record (eMAR), and nursing doc-
umentation flow chart) were studied using multivariate hierarchical modeling. The
results showed that EHR usability and CPOE usefulness predicted EHR acceptance
while looking at the periods of 3 and 12 months after implementation (Carayon
et al., 2011).
6 Review of Factors Impacting Decisions Regarding Electronic Records 133
In the study of EHR selection based on different alternatives, certain gaps emerge
from the body of literature:
• A comprehensive decision-making model of EHR selection in small ambulatory
settings has not been successfully introduced and/or implemented.
• Combination of elements of human criteria (perceived usefulness and ease of
use), financial, technical, organizational, personal, and interpersonal criteria in
one decision-making model has not been performed.
• There is a lack of large-scale studies in the USA using HDM for EHR selection
for small ambulatory setting.
Ash and Bates (2005) indicate that comprehensive national surveys with a high
response rate are not available, and data in their study comes from the industry
resources that may have some vested interests in EHR usage or selection. The
authors also indicate that small practices are less likely to adopt comparing to
larger ones with various adoption gaps between the types of practices (pediatric,
internal medicine, etc.) Another interesting aspect provided by the authors is that
there is a considerable amount of international experience (for example, Sweden,
the Netherlands, and Australia) that the USA can gain insights from (Ash &
Bates, 2005).
134 L. Hogaboam and T.U. Daim
In the selection of EHR, the decision makers should consider factors that are
environmental (financial and safety, social, and behavioral), organizational, per-
sonal, and technical (for example, ability of systems to interoperate with each other)
in nature (Ash & Bates, 2005).
Study by Lorenzi et al. stresses the need for flexible change management strategy
for EHR introduction in a small practice environment while detailing the EHR
implementation through stages of decision, selection, pre-implementation, imple-
mentation, and post-implementation (Lorenzi et al., 2009).
One important study about the attitudes of physicians toward EHR implementa-
tion was performed by Morton and Wiedenbeck using the framework grounded in
diffusion of innovations theory and TAM while being conducted at the University of
Mississippi Medical Center (UMMC) (Morton & Wiedenbeck, 2009) The research-
ers acknowledged that their findings might not be generalized to other physician’s
offices, since the study was limited to one large healthcare system; however, they
revealed an overwhelming need for customizable and flexible EHR products
(Morton & Wiedenbeck, 2009).
One important observational study on selection of EHR software discussed chal-
lenges, considerations, and recommendations for identifying solutions mainly tar-
geted toward small practices and presented findings on installation, training, and use
of EHR software as well as a detailed industry analysis of over 200 vendors and
their offerings (Piliouras et al., 2011). According to their analysis, successful EHR
system implementation has certain aspects (Piliouras et al., 2011):
• The American Recovery and Reinvestment Act (ARRA) government mandates
knowledge and conformance.
• Application of techniques in operations management, systems analysis, and
change management.
• Learning EHR software.
• Secure information technology infrastructure installation and maintenance.
• Establishment of backup and disaster recovery procedures and processes.
Piliouras et al. (2011) also describe major challenges and recommendations:
1. Conforming to ARRA mandates.
2. Adherence to industry best practices.
3. Installation and maintenance of secure IT infrastructure.
4. Learning complex software:
(a) Availability and quality of training.
(b) Quality software design.
EHR systems could be either of a “client-server” or a “service-in-a-cloud” infra-
structure with the latter one, with data maintained on dedicated vendor facilities and
accessed over the Internet, having capability of reducing capital outlay for computer
and network infrastructure and associated upgrades and allowing expenditures to be
6 Review of Factors Impacting Decisions Regarding Electronic Records 135
monetized as a fixed monthly expense (Piliouras et al., 2011). At the same time, the
practice needs to make sure that the vendor could satisfy the following criteria:
• Access privileges
• Regulatory compliance
• Data location
• Data segregation
• Data recovery
• Monitoring and reporting
• Vendor viability
The key differences between the two types of EHR software infrastructure, taken
from small practice’s office view/interest, are described in Table 6.5.
Cloud computing in healthcare IT, particularly for EHR, also should not be con-
sidered as a single concept with the same privacy and security concerns. Zhang and
Table 6.5 Two types of EHR software infrastructure (Piliouras et al., 2011)
Infrastructure type
Feature Service-in-a-cloud Client-server
Location of system code Remote (mainly at Local (mainly at doctor’s
and execution vendor’s premise) office)
System data control Less More
Same vendor system Easier Harder and more complex
migration/extension
Security More Less
Hardware requirements Fewer More
Response time Depends on the Internet Depends on the system
service provider (ISP), network maintenance and
provisioning, and EHR vendor configuration
Reliability Depends on the Internet service Depends on the system
provider (ISP), network maintenance and
provisioning, and EHR vendor configuration, backup,
and recovery process
Remote access via the Easy Possible with extra
Internet security measures
Maintenance Easier Harder
Data synchronization for Easier Harder
clinic with multiple offices
Data backup and disaster Easier and cheaper Requires extra expense and
recovery technical support
Initial cost Lower Higher
Total life cycle cost Lower Higher
(3–5 years)
136 L. Hogaboam and T.U. Daim
Liu (2010) provide taxonomy of healthcare clouds, stressing those issues of privacy
and security (Table 6.6).
A very recent qualitative phenomenological study (ten interviews with physi-
cians) in south-central Indiana looked into physician’s view and perceptions of
EHR, which could help in the study of EHR selection (Hatton, Schmidt, & Jelen,
2012). Most reported and filtered challenges and benefits (Hatton et al., 2012) are
shown in Table 6.7.
Roth et al. (2009) also studied EHR use, and stated that many EHR users may not
always use EHR fully, but only a fraction of EHR capabilities. Some of the features
and possibilities for documentation or structured recording of information may be
ignored, opted out, or dismissed at the beginning of setup and use and the data may
not be easily accessible through the automated extraction schemes when needed.
Free text fields (commonly used for patients’ complaints) require natural language
processing software. While a lot has been accomplished in the area of natural lan-
guage parsing and identification, many challenges still remain in the area of detec-
tion of targeted clinical events from free text documents (Roth et al., 2009). Through
6 Review of Factors Impacting Decisions Regarding Electronic Records 137
the focus groups, participating in the study, the researchers learned that providers
want EHR that requires less complexity—a minimum of keystrokes, mouse clicks,
scrolling, window changes, etc. While the flexibility that accommodates various
data entry styles has been built in, it could complicate data extracting accuracy and
efficiency (Roth et al., 2009).
Below are the gaps, found through an extensive literature review of EHR impacts:
• The use of EHR in ambulatory settings and impact on quality of healthcare have
not been adequately studied.
• The magnitude of the impacts from EHR use in the small ambulatory setting has
not been adequately studied.
• The effects of user satisfaction and quality impacts in ambulatory settings are not
adequately analyzed with quantitative measures.
138 L. Hogaboam and T.U. Daim
• There is a lack of large-scale studies in the USA using HDM for EHR impacts in
small ambulatory setting.
While the attention of greater quality of care always persists, with research
focus on how providers, patients, and policies could affect factors that influence the
quality of care, despite high investments (over 1.7 trillion annually) and increased
healthcare spending, the USA ranks lower compared to other countries on several
health measures (Jung, 2006; Girosi, Meili, & Scoville, 2005). Jung listed specific
benefits of HIT in regard to quality of care:
• Medical error reduction (improved communication and access to information
through information systems could have a great impact in this area).
• Adherence support (the decision support functions embedded in EHR can show
the effect of HIT on adherence to guideline-based care and enhancing preventive
healthcare delivery (Dexter et al., 2004; Overhage, 1996; Jung, 2006).
• Effective disease management (potential to improving the health outcomes of
patients with specific diseases).
Jung (2006) also explained that while efficiency is a complex concept, some
efficiency savings have been reported by researchers as a result of HIT adoption as
reduction in administrative time (Wong, 2003; Jung, 2006) and hospital stays.
Positive effects on cost were documented as:
• Improved productivity
• Paper reduction
• Reduced transcription costs
• Drug utilization
• Improved laboratory tests.
Additional benefits, reported by several (Bates et al., 1998; Agarwal, 2002; Jung,
2006), were as follows:
• Improved patient safety (from safety alerts and medication reminders of EHR
system).
• Improved regulatory compliance (record keeping and reporting compliance with
federal regulations including Health Insurance Portability and Accountability
Act (HIPAA)).
Increased emphasis on preventive measures and early detection of diseases,
primary care, intermittent healthcare services, and continuity of care are prevalent
in our ever-changing healthcare domain (Tsiknakis, Katehakis, & Orphanoudakis,
2002). Information and communication technologies are taking lead in this dynamic
environment with the need for improved quality of healthcare services and cost
control (Tsiknakis et al., 2002). Another important trend in the healthcare system is
movement toward shared and integrated care (integrated electronic health record—
iEHR), growth of home care through sophisticated telemedicine services (facili-
tated by intelligent sensors, handheld technologies, monitoring devices, wireless
technologies, and the Internet), which pushes the need for EHR that supports qual-
ity and continuity of care (Tsiknakis et al., 2002). While the researchers enlisted a
6 Review of Factors Impacting Decisions Regarding Electronic Records 139
number of valuable benefits, they would need to be examined and the relationships
of EHR impacts and their significance would need to be studied further. The envi-
sioned benefits are listed in Fig. 6.1 and Table 6.8.
A systematic review by Goldzweig lists only a few studies of commercial health
IT system use with reported results and experiences of the impacts of EHR imple-
mentation (Goldzweig et al., 2009). In one of the studies described in their publica-
tion, authors concluded that EHR implementation (EpiCare at Kaiser Northwest)
had no negative impact on quality of care: measures of quality like immunizations
and cancer screening did not change (Goldzweig et al., 2009). In the second study
of implementation of a commercial EHR in a rural family practice in New York, the
authors report various financial impacts (average monthly revenue increase due to
better billing practices), clinical practice satisfaction, as well as the support of the
core mission of providing care.
Agency for Healthcare Research Quality defined quality healthcare as “doing the
right thing at the right time in the right way to the right person and having the best pos-
sible results” (Agency for Healthcare Research Quality 2004 in Kazley & Ozcan, 2008).
One important retrospective study in the USA by Kazley and Ozcan looked at
EMR impacts on quality performance in acute care hospitals (Kazley & Ozcan,
2008). Retrospective cross-sectional format with linear regression is used in order
to assess the relationship between hospital EMR use and quality performance
(Kazley & Ozcan, 2008). The authors concluded that there is a limited evidence of
the relationship between EMR use and quality. There are some interesting observa-
tions made by the authors toward measuring quality and they describe it as a multi-
140 L. Hogaboam and T.U. Daim
faceted and complex construct, which may grow and change. Ten process indicators
related to three clinical conditions, acute myocardial infarction, congestive heart
failure, and pneumonia, are used to measure quality performance based on their
validity (Kazley & Ozcan, 2008). The authors noted that they didn’t measure such
elements of quality as patient satisfaction and long-term outcomes and that EMR
implementation and practice should be further explored.
Leu et al. (2008) performed a qualitative study with in-depth semi-structured inter-
views to describe how health IT functions within a clinical context. Six clinical
domains were identified by the researchers: result management, intra-clinic
communication, patient education and outreach, inter-clinic coordination, medical
management, and provider education and feedback. Created clinical process diagrams
could provide clinicians, IT, and industry with a common structure of reference while
discussing health IT systems through various time frames (Leu et al., 2008).
6 Review of Factors Impacting Decisions Regarding Electronic Records 141
Results of 2003 and 2004 National Ambulatory Medical Care Survey indicated
that electronic health records were used in 18 % of estimated 1.8 billion ambulatory
visits in the USA for years 2003 and 2004 (Linder et al., 2007). The researchers
stated that despite the large number of patient records, the sample size was small for
some of the used quality indicators. The study didn’t identify the implementation
barriers for such low computerized registry use but outlined 17 ambulatory quality
indicators, and while some quality indicators showed significance for quality of
care, the researchers didn’t find consistent association between EHR and the quality
of ambulatory care. The main categories (Linder et al., 2007) of researched indica-
tors were the following:
• Medical management of common diseases (EHR had positive effect on aspirin
use for coronary artery disease (CAD), but worse effect on antithrombotic ther-
apy for atrial fibrillation (AF))
• Recommended antibiotic use
• Preventive counseling
• Screening tests
• Avoiding potentially inappropriate prescribing in elderly patients
While it would be expected that EHR-extracted data would allow quality assess-
ment and other impact assessment without expensive and time-consuming process-
ing of medical documentation, some researchers (Roth et al., 2009) conclude that
only about a third of indicators of the quality assessment tools system would be
readily available through EHR with some concerns that only components of quality
would be measured, perhaps to the detriment of other important measures of
healthcare quality. The researchers provided a table of accessibility of quality
indicators (clinical variables), which have been narrated in Table 6.9.
A group of researchers looked into the problem of improving patient safety in
ambulatory settings and throughout this qualitative study developed a tool kit of
best practices and a collaborative to enhance medication-related practices and
patient safety standards (Schauberger & Larson, 2006). The list of best practices for
the inpatient setting was the following, with # 6, 10, and 3 being the top three pro-
cess improvements on best practices:
1. Maintaining accurate and complete medication list
2. Ensuring medication allergy documentation
3. Standardizing prescription writing
4. Removing all IV potassium chloride from all locations
5. Emphasizing non-punitive error reporting
6. Educating about look-alike, sound-alike drugs
7. Improving verbal orders
8. Ensuring safety and security of sample drugs
9. Following protocols for hazardous drug use
10. Partnering with patients
11. Notifying patients of laboratory results.
Figures 6.2, 6.3, and 6.4 summarize this chapter.
142 L. Hogaboam and T.U. Daim
Fig. 6.2 Research gaps, goals, and questions for the adoption of EHR with focus on barriers and
enables
Research Gaps Research Goals Research Questions
A comprehensive decision- Do criteria of perceived
making model of EHR usefulness and ease of use play
selection in small a significant role in EHR
ambulatory settings has not selection?
been successfully
Define a research
introduced and/or Do interpersonal factors matter
framework for EHR
implemented. in selection of EHR software?
selection in small
ambulatory settings.
Combination of elements of
human criteria (perceived Do financial factors impact the
usefulness and ease of decision-making of EHR
use), financial, technical, software in a significant way?
organizational, personal and Assess the
interpersonal criteria in one importance of criteria
decision-making model has and subcriteria and
the lower level of Do organizational factors
not been performed. strongly influence decision-
HDM through expert
judgment making in EHR selection
There is a lack of large- quantification process?
scale studies in the United
States using HDM for EHR Do personal factors of
selection for small productivity and privacy play an
ambulatory setting. important role in selection of
EHR software?
Fig. 6.3 Research gaps, goals, and questions for the selection of EHR with focus on different
alternatives
Fig. 6.4 Research gaps, goals, and questions for the use of EHR with focus on impacts
144 L. Hogaboam and T.U. Daim
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6 Review of Factors Impacting Decisions Regarding Electronic Records 149
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Chapter 7
Decision Models Regarding Electronic
Health Records
Liliya Hogaboam and Tugrul U. Daim
7.1 T
he Adoption of EHR with Focus on Barriers
and Enables
Modifications to the models and extensions also have roots in theoretical back-
ground and have proven to be effective in studying various cases of IT adoption
under various conditions. Knowledge of specific implementation barriers and their
impact and statistical significance on the improvement of EHR use could lead to the
creation of guidelines and incentives toward elimination of those barriers in ambula-
tory settings. Focused incentives, training, and education in the right direction could
speed up the process of adoption and use of computerized registries as well as
implementation of more sophisticated IT systems (Miller & Sim, 2004).
In their study of perceived behavioral control and goal-oriented behavior, Ajzen and
Fishbein proposed TRA (Ajzen & Madden, 1986). The fundamental point of TRA
is that the immediate precedent of any behavior is the intention to perform behavior
in question. Stronger intention increases the likelihood of performance of the action,
according to the theory (Ajzen & Madden, 1986). Two conceptually independent
determinants of intention are specified by TRA: attitude toward the behavior (the
degree to which an individual has favorable evaluation of behavior in mind or oth-
erwise) and subjective norm (perceived social pressure whether the behavior should
be performed or not, i.e., acted upon or not). TRA also states that the behavior is a
function of behavioral beliefs and normative beliefs, which are relevant to behavior
(Ajzen & Madden, 1986).
Atude
toward the
behavior
Inten on Behavior
Subjec
ve
norm
In 1985, Fred Davis presented his work that was centered toward improving the
understanding of user acceptance process for successful design and implementation
of information systems and providing theoretical basis for a practical methodology
of “user acceptance” through TAM, which could enable implementers and system
designers to evaluate proposed systems (Davis, 1985). Perceived usefulness and
perceived use are outlined to be the main two variables influencing attitude toward
using the system. Perceived usefulness is “the degree to which individual believes
that using a particular system would enhance his or her job performance.” Perceived
ease of use is “the degree to which an individual believes that using a particular
system would be free of physical and mental effort.” Davis also shows that per-
ceived ease of use has a causal effect on the variable of perceived usefulness (Davis,
1985; Davis & Venkatesh, 1996).
Conceptual framework from Davis is shown in Fig. 7.1.
His proposed model sheds light on the behavioral part of the concept, with over-
all attitude of a potential user toward system use being a main determinant of the
system’s use. On the other hand, perceived usefulness and perceived use are out-
lined to be the main two variables influencing attitude toward using the system.
Perceived usefulness is “the degree to which individual believes that using a particu-
lar system would enhance his or her job performance.” Perceived ease of use is “the
degree to which an individual believes that using a particular system would be free
of physical and mental effort.” He argues that system that is easier to use will result
in increased job performance and greater usefulness for the user all else being equal.
Davis also shows that perceived ease of use has a causal effect on the variable of
7 Decision Models Regarding Electronic Health Records 153
Users'
System Mo
va
on Actual
Features and to Use System Use
Capabili
es System
User Movaon
x1
Perceived
Usefulness
Atude Actual
x2 Toward Using System Use
Perceived
x3 Ease of Use
perceived usefulness (Davis, 1985; Davis & Venkatesh, 1996). While ease of use is
important with a lot of emphasis on user friendliness of the applications that increase
usability, no amount of ease of use could compensate for the reality of the useful-
ness of the system (Davis, 1993). Causal relationships in the model are represented
by arrows (Fig. 7.2). Attitude toward use is referred to as the degree of evaluative
effect that an individual associates with using the target system in his/her job, while
actual system use is the individual’s direct usage of the given system (Davis, 1985;
Davis & Venkatesh, 1996).
Described mathematically, TAM will look like this (Davis, 1985):
where
Xi is a design feature I, i = 1…n.
βi is a standardized partial regression coefficient.
ε is a random regression term.
TPB extends TRA by including the concept of behavioral control. The importance
of control could be observed through the fact that the resources and opportunities
available to individuals have to dictate to some extent the likelihood of behavioral
achievement (Ajzen & Madden, 1986). According to the TPB, a set of beliefs that
deals with the presence or absence of requisite resources and opportunities could
ultimately determine intention and action. The more opportunities and resources
individuals think they possess, the fewer obstacles they anticipate and the greater
their perceived control over behavior should be (Ajzen & Madden, 1986) (Fig. 7.3).
Holden & Karsh (2010) analyzed studies where TAM was used and compared
the percentage of variance explained by this theoretical framework. The percentage
varies from 30 to 70 %, but in most cases tested in healthcare, the percentage of
variance is higher than 40 %, which means that the model explains at least 40 % of
phenomenon.
The proposed framework for assessing EHR adoption in ambulatory settings has
elements of TAM, TRA, and TPA along with important elements described in the
literature that were frequently mentioned, showed significant relationships, or were
expressed in qualitative and quantitative way. This framework consists of barriers
and enablers, since some of those variables might have a positive influence on the
system use. The concepts of perceived ease of use and perceived usefulness and
subjective norm have been explained earlier in this part of the exam. The external
factors have been constructed through the comprehensive literature review during
the independent studies and the short and extended version of external element con-
structs is shown in Fig. 7.4.
Extended taxonomy is listed in Table 7.1.
The summarized taxonomy barriers and enablers are displayed in Fig. 7.5.
Mathematical description of the proposed model is presented below:
Atude
toward the
behavior
Perceived
behavioral
control
Financial
factors
Perceived
usefulness
Technical
factors
Subjective
Personal Norm
factors
Interpersonal
Influence
Financial
• Start-up costs (Boonstra & Broekhuis, 2010; Cresswell & Sheikh, 2012; Fonkych & Taylor, 2005; McGinn et al., 2011; Menachemi
& Brooks, 2006; Palacio et al., 2009; Shoen & Osborn, 2006; Simon et al., 2007; Valdes et al., 2004; Zaroukian, 2006)
• Ongoing costs Ash & Bates, 2005; Boonstra & Broekhuis, 2010; DePhillips, 2007; Martich & Cervenak, 2007; Police et al., 2011;
Witter, 2009);
• Financial uncertainties (lack of (Blumenthal, 2009; Chaudhry et al., 2006; Goldzweig et al., 2009; Menachemi et al., 2008)
tangible benefits; lack of financial
return, reimbursement)
• Lack of financial resources (in (Ash & Bates, 2005; Boonstra & Broekhuis, 2010; Bowens et al., 2010; Fonkych & Taylor, 2005; Goroll et al., 2008;
some sources referred to as lack Lorenzi et al., 2009; Palacio et al., 2009; Robert Wood Johnson Foundation, 2010; Shields et al., 2007; Simon et al.,
of capital, lack of funding, etc.) 2008; Shen & Ginn, 2012; Simon et al., 2007)
Technical factors
• Information quality (accuracy, (Bodenheimer & Grumbach, 2003; Chen & Hsiao, 2012; Cresswell & Sheikh, 2012; Kim & Chang, 2006; Liang et al.,
content, format, timeliness) 2011; Mores, 2012; Wu et al., 2007)
• Intensity of IT utilization (Angst et al., 2010; Bates et al., 2003; Blumenthal, 2009; Boonstra & Broekhuis, 2010; Bowens et al., 2010; Chen
intensity of IT utilization (data et al., 2010; Chen & Hsiao, 2012; Dünnebeil et al., 2012; Glaser et al., 2008; Goroll et al., 2008; Greenhalgh et al.,
security, documentation, technical 2009; Handy et al., 2001; Jian et al., 2012; Lorence & Churchill, 2005; Ludwick & Doucette, 2009; Menachemi &
support, complexity, Brooks, 2006; Miller & Sim, 2004; Ortega Egea & Román González, 2011; Palacio et al., 2009; Police et al., 2011;
customization, reliability, Rahimpour et al., 2008; Rind & Safran, 1993; Rosemann et al., 2010; Robert Wood Johnson Foundation, 2010; Simon
interconnectivity, interoperability, et al., 2007; Tsiknakis et al., 2002; Tyler, 2001; Valdes et al., 2004; Vedvik et al., 2009; Yoon-Flannery et al., 2008;
hardware issues) Zhang & Liu, 2010)
Social/organizational
• Top management support (André et al., 2008; Chen & Hsiao, 2012; Kim & Chang, 2006; Legris et al., 2003; Morton & Wiedenbeck, 2009; Yusof
et al., 2008)
• Project/team competency (Carayon et al., 2011; Chen & Hsiao, 2012; Chow et al., 2012a, 2012b; Yarbrough & Smith, 2007; Zaroukian, 2006)
• Process orientation (Chiasson et al., 2007; Dünnebeil et al., 2012)
• Standardization (Boonstra & Broekhuis, 2010; Cresswell & Sheikh, 2012; Glaser et al., 2008; Greenhalgh et al., 2009; Helms &
Williams, 2011; Holden & Karsh, 2010; Kazley & Ozcan, 2008; Kumar & Aldrich, 2010; Lanham et al., 2012;
Lapinsky et al., 2008; Leu et al., 2008; Lorenzi et al., 2009; Ludwick & Doucette, 2009; Matysiewicz & Smyczek,
L. Hogaboam and T.U. Daim
2009; Randeree, 2007; Tsiknakis et al., 2002; Tyler, 2001; Wagner & Weibel, 2005; Zaroukian, 2006)
• Staff reallocation/employment (Greenhalgh et al., 2009; Papatheodorou, 1990; Janczewski & Shi, 2002)
• Security/confidentiality/privacy (Alper & Olson, 2010; Angst et al., 2010; Ash & Bates, 2005; Boonstra & Broekhuis, 2010; Bowens et al., 2010;
concerns Dünnebeil et al., 2012; Morton & Wiedenbeck, 2009; Piliouras et al., 2011; Rind & Safran, 1993; Rosemann et al.,
2010; Tyler, 2001)
• Incentives (Ash & Bates, 2005; Bates et al., 2003; Beckett et al., 2011; Boonstra & Broekhuis, 2010; Cresswell & Sheikh, 2012;
Ford et al., 2006; Goldzweig et al., 2009; Greenhalgh et al., 2009; Kumar & Aldrich, 2010; Rosemann et al., 2010;
Schoen et al., 2006)
• Policy drawbacks and supports (André et al., 2008; ; Chen & Hsiao, 2012; Chumbler et al., 2011; Goroll et al., 2008; Miller & Sim, 2004; Schoen
et al., 2006; Simon et al., 2008; Vishwanath et al., 2009; Witter, 2009)
• Transience of vendors (Bates et al., 2003; Ford et al., 2006; Randeree, 2007)
• Workflow redesign (Boonstra & Broekhuis, 2010; Bowens et al., 2010; Chaudhry et al., 2006; Dixon et al., 2010; Furukawa, 2011; Goroll
et al., 2008; Lorenzi et al., 2009; Menachemi & Brooks, 2006; Miller & Sim, 2004; Zandieh et al., 2008; Zaroukian,
2006)
Personal
• Age, specialty, position, (Angst et al., 2010; Bergman-Evans et al., 2008; Chen & Hsiao, 2012; Egea & Gonzalez, 2011; Handy et al., 2001;
familiarity Jeng & Tzeng, 2012; Kim & Han, 2008; Miller & Sim, 2004; Morton & Wiedenbeck, 2010; Pai & Huang, 2011; Police
et al., 2011; Rahimpour et al., 2008; Rosemann et al., 2010; Vishwanath et al., 2009; Wu et al., 2007)
• Motivation (Beckett et al., 2011; Cresswell & Sheikh, 2012; Dixon, 1999; Frambach & Schillewaert, 2002; Greenhalgh et al.,
7 Decision Models Regarding Electronic Health Records
2009; Piliouras et al., 2011; Wu et al., 2007; Yarbrough & Smith, 2007; Yu et al., 2009)
• Productivity (Bowens et al., 2010; DeLia et al., 2004; Morton & Wiedenbeck, 2009; Yoon-Flannery et al., 2008)
• Personal innovativeness (Frambach & Schillewaert, 2002; Hung et al., 2012; Jeng & Tzeng, 2012; Moores, 2012; Vishwanath et al., 2009;
Yi et al., 2006)
• Self-efficacy (Chau & Hu, 2002; Chen & Hsiao, 2012; Chow et al., 2012a, 2012b; Cresswell & Sheikh, 2012; Dixon, 1999; Kukafka
et al., 2003; Legris et al., 2003; McFarland & Hamilton, 2006; Rahimpour et al., 2008; Wu et al., 2007; Wu et al.,
2009; Yu et al., 2009)
• Anxiety (Aggelidis & Chatzoglou, 2009; Cheng, 2012; Kukafka et al., 2003; Ludwick & Douchette, 2009; Storey & Buchanan,
2008; Wu et al., 2007; Yarbrough & Smith, 2007)
Interpersonal (Chang, 2012; Chen & Hsiao, 2012; Chiasson et al., 2007; Dünnebeil et al., 2012; Frambach & Schillewaert, 2002;
• Doctor-doctor Liu and Ma, 2005; Wu et al., 2007; Yang, 2004; Yarbrough & Smith, 2007; Yu and Gagnon, 2009; Yusof et al., 2008)
• Doctor-nurse
157
• Doctor-patient
158 L. Hogaboam and T.U. Daim
Impact
factors
transience of vendors
workflow redesign
HD1-D2: Intention to use EHR system is impacted by subjective norms and attitude
toward using EHR and PU.
HE: PEoU influences PU of EHR in small ambulatory settings.
HF: Positive intention to use EHR system translates into EHR use.
7.2 T
he Selection of EHR with Focus on Different
Alternatives
Human
factors
ergonomics
Organizaonal Organizational/
Information occupational/
systems issues in HIT social
innovaon psychology
Management &
organizational
change
management
Below are some figures depicting the bodies of knowledge surrounding organi-
zational issues in HIT innovation (Fig. 7.6) and theoretical approaches that concep-
tualize interaction between technology, humans, and organizations (Cresswell &
Sheikh, 2012) (Table 7.12).
Table 7.2 is the table of theoretical approaches that conceptualize interaction
between technology, humans, and organizations (Cresswell & Sheikh, 2012).
Table 7.3 shows some information derived from Table 31 of 2009 Oregon
Ambulatory EHR survey (Witter, 2009).
The model is shown in Fig. 7.7.
7.2.1 Criteria
Seven criteria were chosen based on the extensive literature review. Perceived use-
fulness and perceived ease of use are based on the elements of the TAM. Since the
above-described research indicates that the acceptance of the technology is based on
perceptions of users (physicians of small clinics with decision-making power in this
7 Decision Models Regarding Electronic Health Records 161
Table 7.2 Theoretical approaches of interaction between technology humans and organizations
Name of the theory Explanations and definitions
Diffusion of Focuses on how innovations spread in and across organization over time
innovations
Normalization process Describes the incorporation of complex interventions in healthcare
into the day-to-day work of healthcare staff
Sense making Assumes that organizations are not existing entities as such, but
produced by sense-making activities and vice versa; they discover
meaning of the status quo often by transforming situations into
words and displaying a resulting action as a consequence
Social shaping theory Views technology as being shaped by social processes and highlights
the importance of wider macro-environmental factors in influencing
technology
Sociotechnical Conceptualizes change as a nonlinear, unpredictable, and context-
changing dependent process, assuming that social and technical dimensions
shape each other in a complex and evolving environment over time
Technology acceptance Assumes that individual’s adoption and usage of the system are
model shaped by the attitude toward use, perceived ease of use, and
perceived usefulness
The notion of “fit” Accentuates that social, technological, and work process factors
should not be considered in isolation but in the appropriate
alignment with each other
Table 7.3 Organizations and clinicians not planning to implement EHR in Oregon in 2009
Percent of organizations and clinicians with no plan to implement Clinicians
an EHR/EMR All entities all entities
Total organizations and clinicians 626 2,313
Barriers
Security and privacy issues 18.1 % 11.2 %
Confusing number of EMR choices 0.3 % 0.1 %
Lack of expertise to lead or organize the project 19.5 % 16.6 %
No currently available EMR product satisfies our [needs] 18.2 % 20.8 %
Staff would require retraining 26.0 % 31.0 %
Expense of purchase 80.2 % 84.1 %
Expense of Implementation 58.6 % 68.4 %
Inadequate return on investment 36.1 % 29.8 %
Concern the product will fail 17.9 % 15.6 %
Staff is satisfied with paper-based records 34.8 % 25.9 %
Practice is too small 47.8 % 25.7 %
Plan to retire soon 17.3 % 7.7 %
Other 14.7 % 23.1 %
case), those criteria were included in the model. It is assumed that EHR systems
comply with ARRA mandates and have legal compliance.
Those seven criteria and subcriteria will also be reviewed and justified by the
experts in the field. Experts will be chosen from academia in the field of healthcare
and healthcare management and physicians.
162 L. Hogaboam and T.U. Daim
This criteria has its roots in TAM (Davis, 1989), and identifies the user’s perception
of the degree to which using a particular system will improve his or her perfor-
mance. The psychological origins of the concept are grounded in social presence
theory, social influence theory, and Triandis modifications to the TRA (Karahanna
& Straub, 1999). Perceived usefulness has been shown to have a great impact on
technology acceptance in healthcare (Chen & Hsiao, 2012; Cheng, 2012; Cresswell
& Sheikh, 2012; Despont-Gros et al., 2005; Kim & Chang, 2006; King & He, 2006;
McGinn et al., 2011; Melas et al., 2011; Morton & Wiedenbeck, 2009; Yusof et al.,
2008). The concepts of TAM and relative research have been instrumental in
explaining how beliefs about systems lead users to have positive attitudes toward
systems, intentions to use these systems, and system use (Karahanna & Straub,
1999).
With the concepts of perceived usefulness, the subcriteria that were selected
from the literature review included the following:
• Data security
The concept of data security has been brought up by many researchers as well as
the government (Alper & Olson, 2010; Bowens, Frye, & Jones, 2010; Chen
et al., 2010; Dünnebeil et al., 2012; Liu & Ma, 2005; Lorence & Churchill, 2005;
Rind & Safran, 1993; Tsiknakis, Katehakis, & Orphanoudakis, 2002; Vedvik,
Tjora, & Faxvaag, 2009; Yusof et al., 2008; Zhang & Liu, 2010). The concept of
7 Decision Models Regarding Electronic Health Records 163
data security, encryption, and secure storage has been described in the literature
review sections above. Differences of in-cloud vs. remote storage have been
discussed as having various security features.
• Interoperability
The system should be able to function well with other applications in the net-
work, local and shared. Alper and Olson (2012) note that interoperability is
important to improve and coordinate care delivery. While in the USA most
patients receive care from several providers, a lack of interoperability in the
network would mean that physicians do not have access to a complete record for
a patient and a “master record” might not exist or might not be complete at any
point in time (Alper & Olson, 2012). Different systems will provide various
levels of interoperability and the users may require more or less advanced sys-
tems for their clinics. A number of researchers stressed the importance of interop-
erability of the EHR system as expressed by administrators, physicians, and
other EHR users and the need to invest in improvements in it (Alper & Olson,
2012; Ash & Bates, 2005; Blumenthal, 2009; Blumenthal, 2010; Box et al.,
2010; Bufalino et al., 2011; Cresswell & Sheikh, 2012; Degoulet, Jean, & Safran,
1995; DePhillips, 2007; Dixon, Zafar, & Overhage, 2010; Dünnebeil et al., 2012;
Fonkych & Taylor, 2005; Furukawa, 2011; Glaser et al., 2012; Goldzweig, et al.,
2009; Goroll et al., 2008; Jian et al., 2012; Jung, 2006; Kazley & Ozcan, 2008;
Lapinsky et al., 2008; Mäenpää et al., 2009; McGinn et al., 2011; Palacio,
Harrison, & Garets, 2009; Tsiknakis et al., 2002; Yao & Kumar, 2013; Yoon-
Flannery et al., 2008; Zaroukian, 2006; Zhang & Liu, 2010)
• Customization
Customization is an extremely important concept, since various clinics with their
unique specializations, services provided, and clients/patients of various needs
have different needs in software customization as far as costs, complexities, and
training required are concerned. While some prefer a system that could be tai-
lored in a unique way, others may prefer a low-cost off-the-shelf product without
elaborate customization capabilities (Alper & Olson, 2012). The issue of cus-
tomization in EHR selection has been stressed by a number of researchers (Alper
& Olson, 2012; Ash et al., 2001; Cresswell & Sheikh, 2012; Degoulet et al.,
1995; Kim & Chang, 2006; Ludwick & Doucette, 2009; Menachemi & Brooks,
2006; Randeree, 2007; Roth et al., 2009; Witter, 2009; Zandieh et al., 2008).
• Reliability
Reliability is a complex issue as well, since a certain level of reliability of the
system and the vendor must be present for the successful use of the EHR. Thus,
Alper and Olson (2010) stated that the health information network that is able to
be aggregated with a reasonable degree of accuracy and reliability would improve
the ability to track known epidemics, and identify new epidemics or other threats
to public health such as bioterrorism or environmental exposures at an early
stage. Cresswell and Sheikh (2012) look at the lack of reliability of the system
from the view of system stability—software crashes, etc. Other researchers
164 L. Hogaboam and T.U. Daim
include the concept of reliability when they study healthcare IT and EHR in par-
ticular (Alper & Olson, 2010; Box et al., 2010; Cresswell & Sheikh, 2012;
Degoulet et al., 1995; Despont-Gros et al., 2005; Goroll et al., 2008; Liu & Ma,
2005; Mäenpää et al., 2009; Moores, 2012; Yusof et al., 2008; Zaroukian, 2006).
• Product life cycle
Generally product life cycle of software (EHR as well) is short (Goroll et al.,
2008); therefore, the physicians that are planning to acquire those systems should
look into the fact of how fast they would need to upgrade and change the system,
when it will become obsolete, and how long could it run and be supported after
being installed. It is closely tied with concepts of upgradability and system obso-
lescence. This concept is mentioned by a number of authors (Carayon et al.,
2011; David & Jahnke, 2005; DePhillips, 2007; Goroll et al., 2008; Hatton,
Schmidt, & Jelen, 2012; Randeree, 2007; Vedvik et al., 2009; Witter, 2009;
Zaroukian, 2006; Zhang & Liu, 2010).
Just like perceived usefulness, the concept of ease of use has been known from
Davis’s TAM (Davis, 1989) and it is the user’s perception of the extent to which
using a particular system would be free of effort. A large body of research has
shown that perceived ease of use significantly impacts technology acceptance and
influences user’s decision-making process (Ayatollahi et al., 2009; Carayon et al.,
2011; Chen & Hsiao, 2012; Cheng, 2012; Chow, Chan et al., 2012a, 2012b; Chow,
Herold et al., 2012b; Cresswell & Sheikh, 2012; Davis & Venkatesh, 1996; Despont-
Gros, 2005; Dixon, 1999; Dünnebeil et al., 2012; Garcia-Smith & Effken, 2013;
Jian et al., 2012; Karahanna & Straub, 1999; Kim & Chang, 2006; King & He,
2006; Legris et al., 2003; Liu & Ma, 2005; Melas et al., 2011; Vishwanath et al.,
2009; Yusof et al., 2008 and others).
The subcriteria for “perceived ease of use” are the following:
• Ease of data extraction/access
The EHR system could be packed with valuable data, but if it is not easy for the
user to access it (in a timely manner with not a significant amount of effort), the
value of that system to the user diminishes greatly. Easy access to information
facilitates communication and decision making in healthcare (Kim & Chang,
2006). Certain decision support tools could be enabled in EHR software for
improving physician’s ease of access to data (Bodenheimer & Grumbach, 2003).
The concept of accessibility and data extraction is studied in the context of health-
care management, IT acceptance, and software or application selection (Ayatollahi
et al., 2009; Chumbler et al., 2011; Dünnebeil et al., 2012; Furukawa, 2011;
Garcia-Smith & Effken, 2013; Leu et al., 2008; Mäenpää et al., 2009; Millstein &
Darling, 2010; Rind & Safran, 1993; Roth, 2009; Zhang & Liu, 2010).
7 Decision Models Regarding Electronic Health Records 165
• Search ability
System’s user should be able to search the system in a timely effortless manner
with acceptable and meaningful results. Search capabilities could be one of the
most important subcriteria as having a good-quality search engine with quick
searching capabilities could greatly benefit a small practice; however, some phy-
sicians may not feel like they need an elaborate searching system and may opt
out for software with a modest acceptable searching capabilities. Researchers
have noted the feature of good data mining or data search (Alper & Olson, 2010;
Ayatollahi et al., 2009; Palacio et al., 2009; Randeree, 2007).
• Interface
Convenient interface that is easy to use and adjust to is possibly one of the most
and first noticeable user-friendly features of the EHR system. However, the user
might not require a fancy interface and may need an interface that fits the need
of the clinic. A user interface that is poorly designed with fragmented screens and
multiple sign-ins can increase computer time and also lead to dissatisfaction
(Furukawa, 2011). Interface is a discussed topic in research and is often men-
tioned in phrases as “interface design” or “interface design quality” (Alper &
Olson, 2010; Ayatollahi et al., 2009; Becker et al., 2011; Cresswell & Sheikh,
2012; Davis, 1989; Degoulet et al., 1995; Despont-Gros, 2005; Ludwick &
Doucette, 2009; Melas et al., 2011; Moores, 2012; Valdes et al., 2004; Yusof
et al., 2008).
• Archiving
Archiving and storing of the data is also an important concept, since the quality
of archiving can impact quality of retrieval of information. Also, the ease of
archiving, or the simplicity of it, should benefit the physician, the patient, and the
clinic overall. The importance of archiving is captured in various research jour-
nals and reports (Alper & Olson, 2010; Chen et al., 2010; Goldberg, 2012;
Ludwick & Doucette, 2009; Mäenpää et al., 2009; Sanchez et al., 2013; Vedvik
et al., 2009; Wu et al., 2009; Zhang & Liu, 2010).
Aldrich, 2010; Martich & Cervenak, 2007; Menachemi & Brooks, 2005 2006;
Piliouras et al., 2011; Vedvik et al., 2009; Witter, 2009; Zaroukian, 2006).
Paré & Sicotte, 2001; Police et al., 2011; Randeree, 2007; Vishwanath et al.,
2009; Zaroukian, 2006; Zhang & Liu, 2010).
• Compatibility
Ensuring compatibility of the EHR system with current work practices, one of the
key beliefs that influence adoption—the extent to which the system fits or is com-
patible with the way the user likes it to work, is a necessary component of IT
acceptance (Moores, 2012). The system must fit the needs of the user; however,
some users may require higher degree of compatibility due to specialization of the
practice, certain procedures, and particular processes in place, while others may
not perceive it as such a deciding factor in EHR selection. Other researchers
stressed the importance of the compatibility issue (Aggelidis & Chatzoglou, 2009;
Alhateeb et al., 2009; Chow et al., 2012a, 2012b; Goroll et al., 2008; Helfrich
et al., 2007; Holden & Karsh, 2010; Hung et al., 2012; Kukafka et al., 2003; Pynoo
et al., 2011; Randeree, 2007; Shibl et al., 2013; Staples et al., 2002; Wu et al.,
2007; Yi et al., 2006; Zaroukian, 2006). Compatibility also is mentioned in diffu-
sion theory as one of the five characteristics of innovation that affect their diffu-
sion as innovation’s consistency with users’ social practices and norms (Dillon &
Morris, 1996). The other four are relative advantage (the extent to which technol-
ogy offers improvements over tools that are currently available); complexity
(innovation’s ease of use or learning); trialability (the opportunity of trying an
innovation before committing to use it); and observability (the extent to which the
outputs and gains of the new technology are clearly seen) (Dillon & Morris, 1996).
• Clinical data exchange
Clinical data exchange system gives the capability to move clinical information
electronically across organization while maintaining the meaning of the informa-
tion being exchanged (Li et al., 1998). Communication, standardization, fund-
ing, and interoperability are some of the main barriers for the global clinical data
exchange networks. While selecting EHR, the importance of clinical data
exchange system to the users of the EHR system would be very interesting to
assess. Other researchers that studied the importance of clinical data exchange or
included it as one of the important aspects of EHR use are the following: Bowens
et al. (2006), Dixon et al. (2010), Goroll et al. (2008), Jian et al. (2012), Mäenpää
et al. (2009), Miller and Sim (2004), and Moores (2012).
In addition to the technical and financial aspects of EHR selections, it is also impor-
tant to consider organizational aspect that plays a crucial role in a decision-making
process. Box et al. (2010) state that throughout health information technology imple-
mentation, success requires a careful balance of technical, clinical, and organiza-
tional factors. Cresswell and Sheikh (2012) dedicate an empirical and interpretative
review study on organizational issues in HIT adoption and implementation.
7 Decision Models Regarding Electronic Health Records 169
There is some empirical research that expresses concern about EHR systems infring-
ing on physicians’ personal and professional privacy and acting as management
control mechanisms (McGinn et al., 2011). Boonstra and Broekhuis (2010) also
discuss physician’s personal issues about the questionable quality improvement
associated with EHR and worry about a loss of professional autonomy. Pilouras
et al. (2011) note that some practitioners use personal references and place high
reliance on the experiences of other practices to help them make decision on which
package to select.
• Privacy issues
Privacy concerns have been some of the well-noted issues for physicians while
choosing an EHR system.
Issues of privacy are mentioned in numerous research articles (Angst et al.,
2010; Ash & Bates, 2005; Bates et al., 2003; Blumenthal, 2010; Bufalino et al.,
2011; Dephillips, 2007; Glaser et al., 2008; Goroll et al., 2008; Handy et al.,
2001; Kazley & Ozcan, 2007; Lorenzi et al., 2009; Lustria et al., 2011; Morton
& Wiedenbeck, 2010; Palacio et al., 2009; Randeree, 2007; Simon et al., 2007;
Tyler, 2001; Yoon-Flannery et al., 2008; Zheng et al., 2012).
• Productivity
Physicians’ concerns about losses in productivity and time have been discussed
throughout my literature reviews and in this part. Some users reported decrease
in productivity right after the implementation of an EHR system (Cresswell &
Sheikh, 2012). There are numerous research papers, especially qualitative stud-
ies, that recorded interviews with physicians and other users of the system,
describing issues of productivity with selection and implementation of an EHR
system (André et al., 2008; Boonstra & Broekhuis, 2010; Bowens et al., 2010;
Chaudhry et al., 2006; Davidson & Heineke, 2007; Ford et al., 2006; Hatton
et al., 2012; Mäenpää et al., 2009; McGinn et al., 2011; Morton & Wiedenbeck,
2009; Piliouras et al., 2011; Police et al., 2011; Storey & Buchanan, 2008; Yi
et al., 2006; Yoon-Flannery et al., 2008). According to a survey of Medical Group
Management Association Report, more than four out of five users of paper
records (78.3 %) believed that there would be a “significant” to “very signifi-
cant” loss of provider productivity during implementation and two-thirds
(67.4 %) had concerns about the loss of physician productivity after the transi-
tion period with EHR (MGMA, 2011).
7 Decision Models Regarding Electronic Health Records 171
7.2.1.8 Methodology
Multi-criteria decision tools like Saaty’s Analytic Hierarchy Process (AHP) (Saaty,
1977) and HDM (Kocaoglu, 1983) have some important steps in the application
process:
1. Structuring the decision problem into levels consisting of objectives and their
associated criteria.
172 L. Hogaboam and T.U. Daim
In the study about impacts of EHR system use, it’s important to consider impact
factors found in the literature. For example, such effect factors were described by
DesRoches et al. in the New England Journal of Medicine (DesRoches et al., 2008)
with percentages of positive survey responses upon adoption of EHR. Those were:
• Quality of clinical decisions.
• Quality of communication with other providers.
7 Decision Models Regarding Electronic Health Records 173
INFORMATION
QUALITY
INTENTION USE
TO USE
USER
SATISFACTION
SERVICE
QUALITY
Fig. 7.8 Updated DeLone and McLean IS success model (DeLone & McLean, 2003)
IT-organizational fit model was presented in 1991 by Scott Morton and includes
both internal and external elements of fit. Model’s internal fit is attained through
combination and dynamic equilibrium of organizational components of business
strategy, organizational structure, management processes, and roles and skills, while
model’s external fit is achieved due to formulation of organizational strategy
grounded in environmental trends and market, industry, and technology changes
(Yusof et al., 2008). The enabler—IT—is shown to affect the management process,
also impacting organizational performance and strategy. IT-organizational fit model
(Yusof et al., 2008) is shown in Fig. 7.9.
In 2008, Yusof et al. combined elements of both models to create human–orga-
nization–technology fit (HOT-fit) framework and proposed it for applications in
healthcare while testing it with subjectivist, case study strategy approach, employ-
ing qualitative methods (Yusof et al., 2008). The researchers also presented exam-
ples (Table 7.5) of the evaluation measures of the proposed network. The HOT-fit
proposed framework is shown in Fig. 7.10.
In our research model we are going to use hierarchical decision modeling in
order to study impacts of EHR system as perceived by physicians of small ambula-
tory clinics. The criteria in the levels have been explained through the theoretical
background and literature sources. The methodology has been explained in detail
176 L. Hogaboam and T.U. Daim
during the use of HDM for the second study explained in this exam. Just like in the
previous model, the components of the model are arranged in an ascending hierar-
chical order. At each level, those criteria and subcriteria are compared with each
other using a pairwise comparison scheme (also explained in the previous study).
The questionnaire will be administered online through Qualtrics, and the results
will be put into PCM software for pairwise comparisons as well as Excel and pos-
sibly SPSS to analyze some additional demographic and other information (age,
gender, job position, years of experience, years of experience with EHR, type and
brand of EHR system implemented, year of implementation, number of implemen-
tation (first system or replacement)).
7 Decision Models Regarding Electronic Health Records 177
Fit
Influence
HUMAN
System Use
TECHNOLOGY
System
Quality User Satisfaction
Net Benefits
Information
Quality
ORGANIZATION
Service Structure
Quality
Environment
User Satisfaction
System Quality
Service Quality
Environment
System Use
Financiial
Structure
Clinical
Some open-ended questions will be asked in this questionnaire, since they may
provide important qualitative information and depending on the response rate will
be used for further descriptive or other statistical analysis, for example:
• How many clinical measures are reported by your system?
• What clinical measures are reported by your system? Please, at least name the
main five you use or perceive useful if there are too many to report.
• What are the three major benefits to your practice from EHR?
• What are the three main frustrations with your EHR?
• Are you happy with your EHR system (5-point Likert scale)? Why?
(Fig. 7.11)
178 L. Hogaboam and T.U. Daim
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Part III
Adoption Factors of Electronic Health
Record Systems
Today’s rapidly changing regulations, increasing healthcare costs, and most impor-
tantly globalization have made health record keeping an important issue. Electronic
health record systems are rising as a crucial and unavoidable way of record keeping
for healthcare. However as other information technology implementations, elec-
tronic health records also have their own adoption processes and diffusion factors.
The main goal of this study is to define a model to analyze adoption process of
electronic health record systems and to understand the diffusion factors.
Results of the study indicate that there are different factors affecting the adop-
tion process via a literature research and quantitative field survey. Model has been
tested and constructs have been grouped under intermediary, dependent, and exter-
nal factors.
Chapter 8
Adoption Factors of Electronic Health Record
Systems
8.1 Introduction
In Turkey, 36.8 % of the people over the age of 15 have health problems affecting
their daily activities (Turkstat Health Statistics, 2012a, 2012b). Seventy-six percent
of the healthcare expenditure in Turkey is conducted via government in 2011
O.M. Kök
PwC Strategy& Ernst and Young Advisory, Istanbul, Turkey
N. Basoglu
İzmir Institute of Technology, Urla, Turkey
T.U. Daim (*)
Portland State University, Portland, OR, USA
e-mail: tugrul.u.daim@pdx.edu
health information systems, and diffusion of electronic health records have been
analyzed. This study has researched and sought answers for the following topics:
• The technology diffusion process and factors affecting the technology adoption
• Health information system implementation and main barriers affecting the
implementation process
• Electronic health record evolution and main benefits of electronic health record
usage
• Electronic health record diffusion models and factors affecting the electronic
health record adoption process
input for decision support systems with their long-term storage functionality,
reliable data structure, and exceptional sharing capabilities (Hannan, 1999). Usage
of EHR may lead to reducing costs, enhancing higher quality of care, increased reli-
ability, and access to more accurate results (Kierkegaard, 2011). Changing policies,
healthcare payers and governments require more accurate, standardized, and
detailed data in order to clearly understand the situation, to develop statistics, and to
segment their customers (Gonzalez-Heydrich et al., 2000). Electronic health records
can play an important role to fulfill these requirements (Gonzalez-Heydrich et al.,
2000). Although there are many policies regulating the electronic health record and
healthcare information systems, they are not totally practiced (Ovretveit et al.,
2007). All countries are changing their system from paper-based records to elec-
tronic health records; however, only some of them could succeed in this operation
(Jahanbakhsh, Tavakoli, & Mokhtari, 2011). Health information technologies and
electronic health records are rising as a method to increase quality of care, produc-
tivity, and security (Jha, Doolan, Grandt, Scott, & Bates, 2008). Also EHR offers an
easy process for disease management processes with its functionalities and easy
sharing (Wright et al., 2009).
Some models have been defined to understand the behaviors of people in the adop-
tion process. The theory of reasoned actions (Fishbein & Ajzen, 1975), Technology
Acceptance Model (Davis, 1989), Technology Acceptance Model 2 (Venkatesh &
Davis, 2000), and unified theory of acceptance and use of technology (Venkatesh,
Morris, Davis, & Davis, 2003) can be taken as the most significant ones. Also most
of the researchers are taking these models as base asset and then specify their
researches on these.
The theory of reasoned action, which can be seen in Fig. 8.1, takes subjective
norm and attitude toward act as its main constructs. Subjective norm refers to “the
person’s beliefs that specific individuals or groups think he/she should or should not
perform the behavior and his/her motivation to comply with the specific referents”
(Fishbein & Ajzen, 1975); on the other hand, attitude refers to “the person’s beliefs
that the behavior leads to certain outcomes and his/her evaluations of these out-
comes” (Fishbein & Ajzen, 1975).
Attitude
Toward Act
Behaviroal
Behavior
Intention
Subjective
Norm
Perceived
Usefulness
Behavioral
Attitude
Intention
Perceived Ease
of Use
Experience Voluntariness
Subjective
Norm
Perceived
Image Usefulness
Behavioral
Job Relevance Attitude
Intention
Perceived
Ease of Use
Output
Quality
Result
Demonstability
Davis came up with the idea of the Technology Acceptance Model (1989).
Perceived usefulness and perceived ease of use are taken as the two main drivers. In
final behavioral intention brings the actual use result (Davis, 1989). This model’s
main purpose is to predict user adoption behavior toward the technological develop-
ments. Figure 8.2 explains how the Technology Acceptance Model (TAM) is struc-
tured (Davis, 1989). TAM can be considered a future step for the theory of reasoned
actions (Fishbein & Ajzen, 1975) and theory of planned behavior (Ajzen, 1991).
Venkatesh and Davis have made some additions to the Technology Acceptance
Model and developed a further model with new factors in 2000. Factors such as
experience and voluntariness affect the perceived usefulness. Also the perceived
ease of use has determinants such as subjective norm, image, job relevance, output
quality, and demonstrability (Venkatesh & Davis, 2000). In Fig. 8.3, TAM2 is
explained (Venkatesh & Davis, 2000).
194 O.M. Kök et al.
The unified theory of acceptance and use of technology (UTAUT) has been
defined by Venkatesh et al. as a combination of different adoption theories such as
the Technology Acceptance Model, theory of reasoned actions, and theory of
planned behavior (2003).
UTAUT (Fig. 8.4) has three direct determinants on behavioral intention to use
such as expectations from performance, expectations from effort, and the influence
of the social environment (Venkatesh et al., 2003). Intention to use and facilitating
conditions affect the use behavior (Venkatesh et al., 2003).
DeLone and McLean have proposed a model for information systems success
which correlates system quality and information quality with the actual system use
and user satisfaction (1992). Furthermore, it is stated that these categories are mul-
tidimensional and also affect both individual and organizational impact (DeLone &
McLean, 1992) (Fig. 8.5).
In 2003, the information systems success model has been updated, and new vari-
ables have been added: intention to use, net benefits, and service quality (DeLone &
McLean) (Fig. 8.6).
Performance
Expectancy
Effort Behavioral
Use Behavior
Expectancy Intention
Social
Influence
Facilitating
Conditions
Individual Organization
Impact al Impact
User
Information Satisfaction
Quality
Fig. 8.5 Information systems success model (DeLone & McLean, 1992)
8 Adoption Factors of Electronic Health Record Systems 195
Information
Intention to Use Use
Quality
Net Benefits
System Quality
User
Satisfaction
Service Quality
Fig. 8.6 Updated information systems success model (DeLone & McLean, 2003)
Image
Behavioral
Intention
Computer Level
Service Quality
Accesibility
Usefulness
Quality of Sup.
Attitude
Information Qua.
Compatibility
Cost
Social Influence
Understandibility
Self- Efficacy
Technological Context
Technological
Readiness
Certified EHR
Point-to-point
connection
technologies
Organizational Context
Control
Vertical Integration
Uncompensated
care burden
8.3 Framework
In order to develop a model and taxonomy, detailed literature review and semi-
structured interviews have been conducted. Constructs have been analyzed and then
grouped under four categories: external, intermediary, dependent, and demographic
categories.
Table 8.1 implies the constructs that have been gathered via literature review and
semi-structured interviews. (L) refers to a construct that has been gathered from
literature review. (I) refers to a construct that has been gathered from the semi-
constructed interviews.
Literature has been deeply researched and factors affecting the technology adop-
tion, health information system adoption and electronic health record adoption have
been analyzed. Table 8.2 refers to the subjects and articles of the literature research.
Thanks to the expert focus group and semi-structured interviews, some of the
constructs have been selected for a deeper analysis. These constructs have struc-
tured the base of our study. The list of constructs and their explanations are implied
in Table 8.3.
Table 8.4 lists the major constructs and the literatures that they have been implied
before.
There are dependent items which are affected by the external factors via the
intermediary factors:
Medical information functionality System’s functionality of providing required additional medical information to the users in the case of necessity
(FuncXMed)
AccessALL User’s access to all required information in patient records
Accuracy System’s capability to have accurate and sensitive information (Hayrinen et al., 2008)
Completeness System’s capability to have complete information (Ovretveit et al., 2007)
Up-to-dateness System’s capability to update information regularly
(continued)
201
Table 8.3 (continued)
202
Construct Explanation
Standardization System’ functionality to keep information aligned with national and international standards (Yoshihara, 1998)
Mobility System’s functionality to offer user accessibility from anywhere at any time. System’s degree to the user’s ease
of access to the information (Topacan, 2009)
Privacy unauthorized access System’s functionality to prevent unauthorized access but letting authorized users to access required information
(PrivacyUA) (Dobbing, 2001)
Medical information sharing User’s attitude to patient information being seen by other caretakers
(PrivacyMD)
Knowledge sharing User’s attitude to share medical information with co-workers for consultation (Ueckert et al., 2003)
Support quality The quality of the support provided by guidelines, system help functionality, vendor team and co-workers
Self-confidence Individual’s own skills own computer usage (Tanoglu, 2006)
Ease of learning System’s rate on how easily it can be learned (Holbrook et al., 2003)
Ease of use System’s rate on how it can be used with least effort (Davis, 1989)
Usefulness System’s positive effects on the enhancing individual’s work (Davis, 1989)
Attitude Individual’s positive or negative perception about the system (Fishbein & Ajzen, 1975)
Quality of care Rate of the productivity in the healthcare services including number of successful treatments, number of
successful diagnosis, etc. (Ludwick & Doucette, 2009)
Efficient use Rate on how the individual efficiently uses the system
Diffusion Rate on how the system is spread within the organization
Infusion Rate on how the individual uses the offerings of the system
Use density Rate on how focused the individual used the system
Satisfaction Rate on how happy the individual is on using the system
O.M. Kök et al.
8
Ease of learning Holbrook et al. (2003), Hayrinen et al. (2008), DeLone and McLean (2003)
Info Hayrinen et al. (2008), Yoshihara (1998), Ovretveit et al. (2007), Cayir (2010), Basoglu et al. (2009),
Jahanbakhsh et al. (2011), Wang et al. (2010)
Quality of care Ludwick and Doucette (2009), Hayrinen et al. (2008), Collins and Wagner (2005), Brown and Warmington (2002),
Cho et al. (2010), Tange et al. (1997), Dossler et al. (2010)
Self-confidence Tanoglu (2006), Davis (1989), Yu et al. (2009), Aggelidis and Chatzoglou (2009), Tung and Chang (2008)
Privacy Dobbing (2001), Ludwick and Doucette (2009), Haas et al. (2010), Safran and Golderberg (2000), Blobel (2006)
User interface Saitwal et al. (2010), Wang et al. (2010), Dobbing (2001), Polat (2010), Brown and Warmington (2002)
203
204 O.M. Kök et al.
Relationship among usefulness, ease of use, and attitude is explained in the TAM
(Davis, 1989) and TAM2 (Venkatesh & Davis, 2000):
H9: Privacy function of the system, which avoids unauthorized access to confiden-
tial patient data, positively affects the attitude.
H10: Caretaker’s attitude toward information sharing with his/her co-workers has in
impact on attitude toward system use.
H11: The system’s ease of learning has an impact on attitude toward system use.
Holbrook et al. stated that provided support on the system and ease of learning
of the system have an impact on the implementation of EHR systems (2003):
H12: Ease of use positively affects the satisfaction.
H13: Usefulness positively impacts the satisfaction.
H14: Electronic health record system’s integration with medical equipment posi-
tively affects the satisfaction.
H15: Usefulness significantly and positively impacts use density of the system.
H16: Attitude toward use significantly impacts the use density of the system
(Table 8.5).
In the second aspect, the relationship between external factors and intermediary
constructs will be analyzed:
H1: Ease of use positively affects usefulness.
H2: Information quality positively and significantly impacts usefulness.
H3: Flexibility of the system positively affects usefulness.
H4: Mobility of the system positively affects usefulness.
H5: Self-confidence of the user positively affects usefulness.
H6: Ease of learning of the system significantly and positively affects usefulness.
H7: User interface significantly and positively affects usefulness.
H8: The system’s functionality related to keeping dose information of the medica-
tion positively affects usefulness.
H9: The system’s ease of learning positively impacts the system’s ease of use.
H10: User interface of the system positively and significantly impacts the ease of
use of the system.
H11: Mobility of the system positively and significantly affects the system’s ease of
use.
H12: Information quality significantly affects the ease of use.
H13: Privacy measure for avoiding unauthorized access negatively affects the ease
of use (Table 8.6).
In the third model, factors affecting user’s efficient use of the system will be
analyzed:
H1: Task–technology fit of the system significantly and positively affects the effi-
cient use.
H2: User interface significantly and positively impacts the efficient use of the systems.
H3: User’s ability to access all required information positively affects the efficient
use of the system.
H4: The system’s functionality of offering basic medical information significantly
and positively impacts the efficient use of the system.
H5: Information quality in the system positively impacts the efficient use of the
systems.
H6: The system’s integration with other software significantly and positively
affects the efficient use of the system.
H7: The system’s functionality related to keeping dose information of the medica-
tion positively affects the efficient use of the system (Table 8.7).
206 O.M. Kök et al.
8.4 Methodology
This research study has started in September 2010. From that time, many inter-
views, surveys, literature research, and observations have been conducted to deeply
understand the topic and to develop hypotheses.
Firstly, literature research has been done between September 2010 and July
2011. Literature related to electronic health records, health information systems,
technology adoption models, and health technology adoption has been analyzed and
main constructs and variables have been extracted.
Furthermore, to combine the literature information between September 2010 and
December 2010, semi-structured interviews have been conducted with healthcare
employees who use electronic health record systems. Results of the literature
research and semi-structured interviews have been consolidated and published in
the PICMET 2011 Conference (Kok, Basoglu, & Daim, 2011). Also these studies
have helped us to develop hypotheses.
In the second phase of the study, we have conducted a focus group study with
information systems and medical experts. A construct list has been provided to
them to select their top preferences.
In the third phase, a pilot survey has been conducted with 15 participants to
check the reliability of the items in the survey.
In the fourth phase, in order to test our hypotheses, quantitative field survey
study has been completed with 301 participants (Table 8.8).
We targeted the doctors as our interview group as they are the main users of EHR
systems. However, there are other users of the systems such as administrations, nurses,
medical assistants, etc. These groups were not included in the face-to-face interviews.
Eight interviews were conducted and the factors have been analyzed with their
existence ratio: rate of the factor’s occurrence in total of the interviews.
Questions list can be found in Appendix 1.
After the definition of constructs, an expert focus group has been conducted in order to
prioritize the constructs. Figure 8.10 implies the expert focus group study example.
A focus group has been performed with eight experts. Participants were experi-
enced medical doctors and software development engineers. The expert focus group
questionnaire was based on Excel, which has been sent to the experts, and can be
found in Appendix 2. Studied constructs are listed in Table 8.9.
Before the quantitative field survey study, two pilot studies were conducted to
improve the field survey study’s quality and accuracy.
The first pilot study was conducted with three people with a survey of 65 ques-
tions. Participants have completed the survey with us and shared their comments
regarding the quality or wording of the questions that we have prepared. Also one
of the participants requested a question to be added.
208 O.M. Kök et al.
The second pilot study was shared via a web survey system. Fifteen people have
participated in the second pilot study. Results of the pilot study have been used as
an input for the reliability and factor analysis test in the Statistical Package for
Social Sciences (SPSS).
After the pilot study, the survey has been prepared in a web-based tool and shared
via e-mail through different channels. Initially three hospitals were targeted. Then,
with efforts of the Manisa City Health Department, the survey is shared with the
8 Adoption Factors of Electronic Health Record Systems 209
family practitioners of the city of Manisa. They have shown great participation, and
the quantitative field survey study has been applied to 301 people in total. Mostly
the participants were family health practitioners in the city of Manisa.
8.5 Findings
Easy sharing is the one of the other important factors. It is implied that unlike the
paper records, medical records can be shared easier and faster without making phys-
ical transaction such as photocopying (Safran & Golderberg, 2000).
Also interviewers told that sometimes they are exchanging information about
patients with their colleagues. Moreover, interviewers working in government
hospitals explained that some of the government hospitals have been using a com-
mon system and they can easily share files through them. This also brings out that
systems can be used for consultation and some EHR system can be developed
with this functionality. This can also be related with the doctor’s title and work
experience. One of the interviewers stated that:
For some specific cases I request consultation over the system from more experienced doc-
tors. Even for some cases I share the file over the system with other departments to consult
their opinion. (Brain Surgeon, 49)
Moreover, it stated that many organizations started to look for exchanging healthcare
data and patient data faster through networks as a result of the development in commu-
nications technologies (Ueckert, Maximilian, Goerz, Tessmann, & Prokosch, 2003).
So easier and accurate sharing is an important adoption factor of EHR systems.
It brings more flexibility than paper-based records.
User interface highly affects the usage of EHR systems. It defines the mental opera-
tions needed to be done and also the physical steps to take for completing a task
(Saitwal, Xuan, Walji, Patel, & Zhang, 2010).
In the in-depth interview we made, we gained the feedback that most of the users
have complaints about the UIs of the EHR systems. Some of the doctors stated that
they have difficulties to compare the results of the tests that they requested with
their pre-diagnoses and the patient complaints. Because all of these are kept in
different places in the system and from one UI, they can’t view them all.
Also one of the interviewers has stated that for some tasks she needs to deal with
many steps:
For some simple tasks even I need to go to 2–3 different UIs and have to click a few buttons.
(Female, 35)
Davis defined the perceived ease of use as “the degree to which a person believes
that using a particular system would be free of effort” (1989).
Perceived usefulness is defined as “extent to which a person believes that using the
system will enhance his or her job performance” (Davis, 1989).
It is modeled that if users believe that a system has high usefulness, users will
gain high performance when the system is used (Davis, 1989).
Use of EHR brings standardization of the medical terms in the use of medical
records. Even though standardization of the terms may cause problems in the begin-
ning of the adoption process, such as requiring assistance to enter standardized
names, in the long term, users will start to use it more efficiently. Also, for effective
statistics, standardized records are the main base asset (Yoshihara, 1998).
One of the interviewers stated that:
Electronic health records provide us to the chance to compare them with other patients and
to be able to get statistics. The data that I get is more qualified. (Male, Internist, 50)
Also standardization of the procedures might have a positive impact on the qual-
ity of the processes (Nowinski et al., 2007). Usage of EMR has distinctive changes
on the way that physicians keep their records (Bergman, 2007). From this stand-
point, we can say that getting easier statistics with standardized information is one
of the important adoption factors of electronic health records. We can assume that it
has positive interaction with the perceived usefulness.
Most of our interviewees have stated that EHR usage has many effects on the qual-
ity of care provided. EHR lets the user see the medical history of the patient consis-
tently. Physicians have access to see the past injuries of the patient and the treatments
that have been applied to him/her.
If physicians do not have the enough information about the medical history of the
patient, they would not be able to give the right decisions. The patient care process
also includes the process of getting data, turning it to information, and then using it
in the decision-making (Collins & Wagner, 2005). Keeping accurate and correct
information is important; otherwise, with wrong data, wrong clinician actions can
be taken on the patients (Brown & Warmington, 2002). It has been proven in many
studies that EHR has a positive effect on the quality of care.
212 O.M. Kök et al.
As gathered from both interviews and literature, EHR usage reduces the time spent
in the healthcare. Input time does not really decrease with the EHR usage, but time
spent for gathering the information and viewing the patient’s medical history occurs
much faster (Dobbing, 2001). Also it is stated that sometimes data entry takes a
little more time than the data entry on paper-based records (Shabbir et al., 2010).
The more customized the workflows of the system can be, the faster the user can
adapt to the system (Dishaw & Strong, 1999).
Our interviewees did not really give specific responses about the time that they
saved during the data entry. However, they specified that EHR usage really reduces
the time spent during the search of the records and also they spend less time when
they want to look for some specific information.
8.5.1.8 Functionality
Interviewees had a general opinion about EHR having many advantages with search
abilities than paper-based records. Users can easily and quickly search health
records over the system. In the old-fashioned way, doctors needed to search the files
manually between folders. However, our interviewees have stated that the EHR sys-
tem is not fully functional about search now:
If my patients have two names it’s hard to find and identify them I need another criteria to
be able to search. (Ear–nose–throat, 32)
With the increasing data in the EHR systems, search abilities will play a very
critical role to find the accurate and required information (Natarajan, Stein, Jain, &
Elhadad, 2010).
We can say that search abilities are an important factor in adoption of EHR. As
the search abilities are developed more, it would have more effect on the use of
8 Adoption Factors of Electronic Health Record Systems 213
EHR. EHR systems can offer different functionalities such as integration with other
required software (IntegrationSW); integration with medical devices, e.g., ultra-
sound (IntegrationHW); keeping limit dosage values for medicines (FuncDose);
containing basic health and diagnosis information to assist healthcare responsible
(FuncXMed); and critical ranges for lab results (FuncRange).
Medical records are essential for healthcare. Thus archiving plays a critical role:
With EHR system we gained a better archiving. We are the master of the data now. 10–15
years ago, I was giving my patients the reports, lab results and etc. about them. They needed
to archive them in their house by themselves. However mostly they were not able to keep
the records. They generally lost them and for next appointments they came to me without
any records. So this was limiting my knowledge about the patients’ background and the
treatments have been applied. Now I keep all the records in my computer and the data is
preserved. (Neurobiologist, 49)
Paper-based records bring high costs to save, keep, and then use again. Sometimes
they are transferred to different departments, and sometimes they are not returned;
thus the data get lost (Safran & Golderberg, 2001).
Keeping the medical data is very important also for healthcare. At least the health
information which can be used as input for clinical decision-making should be kept
and archived in systems (Estebaranz & Castellano, 2009). EHR history should be
recorded with its updates and also should be aimed to be kept long term as required
(Toyoda, 1998).
We found another specific item which is the medical assistant. Medical assistants
are the clerks in the hospital who are occupied for up to 2–3 doctors. They handle
the office work of the doctors. Some doctors stated that they let their medical
assistants keep their medical records.
Constructs gained from literature review and qualitative study have been com-
piled in Excel. Then the Excel file has been sent to the expert via e-mail.
Experts were asked to determine the 20 most favorable constructs out of 51.
214 O.M. Kök et al.
The list had the Turkish meaning, English meaning, and explanation of the
construct:
• 12.5 % of the participants were female.
• 50 % of the participants had work experience over 20 years.
• Half of the participants were software experts and the other half were medical
experts (Table 8.12).
Participants had consistent responses. Age and ease of use constructs were selected
by all participants. Satisfaction, compatibility, usefulness, and accuracy were the
other significant constructs.
These results have been analyzed by us, and the responses are used as an input to
the pilot and quantitative field survey studies.
Detailed results can be viewed in Table 8.13.
The selection of constructs has been done and items for the pilot study have been
chosen.
After conducting reliability analysis and factor analysis, redundant items were elim-
inated. Table 8.15 shows the constructs and their related items for the quantitative
field survey study.
8
Reliability analysis has been conducted between the constructs. Generally, reli-
ability results were over 0.600 and items were considerably reliable. However, con-
structs such as mobility, security, and sharing had lower reliabilities. The main
reason for this situation is related to the low number of observations and low num-
ber of items in the test. These results have been ignored and constructs have been
kept same. Detailed results of the reliability analysis can be seen in Table 8.15.
8 Adoption Factors of Electronic Health Record Systems 217
Seven components have been extracted with the factor analysis for all items.
Detailed results for factor analysis of the pilot study can be found in Appendix 3.
Factor analysis results have also supported our hypotheses.
A study aimed to explore and understand factors affecting the adoption of electronic
health record systems. A web-based data collection tool has been used to gather
data via questionnaire from healthcare employees from different organizations with
different purposes.
Most of the respondents were university graduates (43.2 %), and majority of the
respondents were in the age between 36 and 45 (63.2 %). Systems in the respon-
dent’s work locations were mainly used for medical purposes. Doctors employed in
the family treatment centers constituted the majority of the respondents with 85.4 %
(Tables 8.16–8.18).
Responses from the survey have been evaluated with reliability analysis and factor
analysis. Validity of the constructs and reliability of the items have been investi-
gated with these studies. For multi-item constructs, lowest c. alpha value was calcu-
lated as 0.676. In general, c. alpha values were over 0.800 which show that the
consistencies of the items were relatively significant. However, constructs such as
support quality and flexibility have lower consistencies compared to the others
(Table 8.19).
Factor analysis has been conducted on all constructs. Ten main components have
been extracted. For intermediary construct group, one component was extracted with
70 % variance. For dependent construct group, one component was iterated with a
variance of 67 %. Finally, for external constructs, four components have been devel-
oped with a 57 % variance. Detailed factor analysis results can be seen in Appendix 3.
8.5.4.3 Descriptives
Descriptive statistics show us that participants do not have a certain decision about
information sharing with our colleagues. In average, they all find the electronic
health records software easy to learn, easy to use, and useful. They generally have a
positive attitude to the electronic health record software usage. They are mostly
satisfied with the software, and they believe that they are efficiently using the soft-
ware. Descriptive results of the summated constructs can be found in Table 8.20.
8 Adoption Factors of Electronic Health Record Systems 219
Obtained data has been analyzed using the IBM SPSS v20 software. Linear regres-
sion modeling has been chosen as the applied methodology. Results of the executed
regression model for dependent items are listed in Tables 8.21 and 8.22.
Based on the regression results, two models have been developed. One shows the
relationship between the external factors, intermediary factors, and dependent fac-
tors. The second model shows the relationship between the external factors and
efficient use. First model is implied in Fig. 8.11 and second model is implied in
Fig. 8.12 (Table 8.23).
Regression results show that usefulness and attitude are direct determinants of
quality of care with coefficients 0.55 (p < 0.001) and 0.24 (p < 0.001). Usefulness
(p < 0.001) and attitude (p < 0.01) explains 0.568 of the diffusion, respectively. On
the other hand, infusion is dependent on usefulness (p < 0.001) and EoU (p < 0.010).
Our hypothesis that attitude is dependent on PrivacyUA, PrivacyMD, and EoL was
not supported in the regression analysis. However, results showed that 0.710 of
attitude is dependent on usefulness with a coefficient of 0.68 (p < 0.001) and on EoU
with a coefficient of 0.20 (p < 0.001). The relationship between attitude, EoU, and
usefulness was also supported in Davis’s TAM model (Davis, 1989). Although EoU
(p < 0.001) and usefulness (p < 0.001) explain the 0.710 of satisfaction, analysis did
not imply that hardware integration (IntegrationHW) affects satisfaction. Usefulness
(p < 0.001) and attitude (p < 0.100) explain the 0.417 of use density (Table 8.24).
Information quality (b:0.30 p < 0.001), ease of use (b:0.20 p < 0.010), flexibility
of the software (b:0.14 p < 0.010), mobility of the software (b:0.14 p < 0.010), self-
confidence of the individual(b:0.11 p < 0.010), user interface of the software (b:0.15
p < 0.100), and dose functionality of the software (b:0.07 p < 0.100) explain the
0.752 of usefulness factor. Results also show similarities with other models. An
unsupported hypothesis was that privacy negatively affects ease of use and ease of
learning affects usefulness (Table 8.25).
Efficient use of the system is explained mainly with task–technology fit (b:0.27
p < 0.001) and user interface (b:0.28 p < 0.001) is then affected with AccessALL
(b:0.14 p < 0.002), medical information functionality of the software (b:0.09
p < 0.100), information quality (b:0.17 p < 0.010), integration of the system with
other software (b:0.11 p < 0.100), and dose functionality of the system (b:0.09
p < 0.100).
ANOVA analysis has been conducted on demographic values including age, entity,
goal, and education.
Significant results for ANOVA analysis based on age construct can be found in
Table 8.26. Participants are grouped under five different age categories: 18–25,
26–35, 36–45, 46–55, and 55+. It can be seen that participants in the age of 55+ are
more satisfied with their EHR system and use the system more densely. People in
8
EoL
User Int.
EoU
Infusion
Mobility
0,45***
Use Density
0,20**
Info
Attitude
Satisfaction
Flexibility
0,44*** Quality of
Usefulness 0,55*** Care
0,54***
Diffusion
Self 0,51***
Confidence
Use Density
Func Dose
p < 0,100 : *
p < 0,010 : **
p < 0,001 : ***
User Interface
Func Dose
FuncXMed
Access All
Info
p < 0,100 : *
p < 0,010 : **
IntegrationSW p < 0,001 : ***
the age between 26 and 36 have more self-confidence than other participants.
Participants in the age of 36–45 find their system easier to learn.
Significant ANOVA results for education (Table 8.27) show that participants
with a PhD have higher self-confidence than other participants and also they care
less about privacy issues.
ANOVA results for entity types show that (Table 8.28) participants from family
treatment centers are more satisfied with their system and they believe that their
system is aligned with their workflow. On the other hand, government and private
hospital participants stated that their systems are effectively integrated with diag-
nostic healthcare devices.
ANOVA results for software usage goal show that participants who use the sys-
tem for medical purposes find the system more useful and show a more positive
attitude to the usage of the system. On the other hand, participants who use the
system for management and finance purposes are more self-confident and keen on
information sharing. Whole results are implied in Table 8.29.
Sample clustering has been applied to the participants with two different construct
sets. Two-, three- , and four-group cluster analysis have been applied, and the four-
group analysis has given the most significant results in both sets. Case numbers have
been shown for each group in Table 8.30 for the first analysis.
The first cluster is the moderately satisfied cluster. They have an average attitude
and average satisfaction with most of the constructs. The second cluster is the least
satisfied cluster with low satisfaction rates. The third cluster is the totally satisfied
one with high satisfaction rates and positive attitude. They are also pleasant about
the general functionalities and specifications. The last cluster is the partially adopted
group. They are not pleasant about all the functionalities or specifications of the
system. Thus they are partially satisfied.
226 O.M. Kök et al.
Results of the first clustering can be seen in Fig. 8.13 and Table 8.31.
Second clustering has been done related to characteristics of the systems and
user behavior (Table 8.32).
The first group was the average systems. Their characteristics were fulfilling the
user expectations somehow. The second cluster was the least functional systems.
The third cluster was the moderate systems. They had similar performance to the
average system cluster; however, their performance was shown on different charac-
teristics. The fourth cluster was the capable systems. They had high-performance
characteristics in each area. Detailed results of the clustering can be seen in
Table 8.33 and Fig. 8.14.
At the end of the questionnaire, two open-ended questions were asked to the
participants regarding their requests for modifications and extra functionalities
related to the systems. The following quotes include selected responses from the
participants:
8 Adoption Factors of Electronic Health Record Systems 227
Currently we only have access to the patient records related to the family health centers. In
order to make a full assessment we need to see the whole medical history of the individual.
(Healthcare Practitioner)
We should be able to request laboratory tests, x-ray diagnosis and etc. for patient via
online channel from other institutions. Also the results should be delivered via same mod-
ule quickly and effectively. (Healthcare Practitioner)
The system should be integrated with the MEDULA (Social Insurance Medicine
System). Otherwise we can’t be able to see which medicines the patient has been prescribed
228 O.M. Kök et al.
EoU
9
Infusion 8 EoL
7
6
5
4
Diffusion 3 Usefulness 1
2
1
0 2
UseDensity Attitude 3
EfficientUse Satisfaction
QualityofCare
Flexibility
6.00
UserInterface Info
5.00
4.00
TTF 3.00 AccessALL
2.00 1
1.00
2
SupportQuality 0.00 KnowledgeShare
3
4
SelfConfidence Mobility
Security PrivacyMD
PrivacyUA
to and their dosages. This creates problems when we need to prescribe to the patient.
(Healthcare Practitioner)
These three quotes definitely show that caretakers require integration with other
healthcare institutions. Integration with other institutions will provide access to the
full medical history of the patients, and also the whole medical examination and
testing process will be kept in a common environment:
System has low response times. This creates delays in our caretaking process. (Healthcare
Practitioner)
In the user interface warnings should come up about the patient’s allergies, vaccine
deadline and etc. (Healthcare Practitioner)
230 O.M. Kök et al.
I can’t make changes in the past information sometimes mistakes or mistypes exist in
the recorded data. (Healthcare Practitioner)
These three comments raise the caretakers’ main problems regarding the sys-
tem’s performance or user interface. The last one discusses the data update mecha-
nism. However, that request needs a detailed and secure process map in order to be
successful since there are certain privacy, data quality, and security issues:
Sometimes properly working modules/functions of the systems are being altered due to
testing new functions. This creates problems as they also break the properly working mod-
ules. (Healthcare Practitioner)
This request is related with the updates in the system and their effects.
Developers should consider the ongoing work of the caretakers and system
updates should not go live without a proper testing period that does not affect
the live system:
A mobile version of this system should be developed since we often conduct on-site visits
to patient homes or villages out of the city center. (Healthcare Practitioner)
This quote is mainly aligned with the requirements of our era. Many software
offer mobile applications and mobile versions. After the main developments are
complete in the system, developers should consider the mobile version of the appli-
cations as the next step.
8.6 Conclusion
8.6.1 Limitations
This study had some limitations. First of all, it has been applied among three hospi-
tals and Manisa family health practitioners. Results may differ when the quantitative
field survey study has been applied in different geographic regions and among differ-
ent professionals. Secondly, all participants of the survey were using centralized
record systems. Ones that have their own individual systems for record keeping
might have different adoption factors. It would be sounder if we could recruit strati-
fied representative health professional samples from different health units of the
country such as state hospitals, university hospitals, private hospitals, primary health-
care facilities, and those who use specialized record systems such as a cancer regis-
try. As another restriction, the majority of our data come from the primary healthcare
facilities of Manisa in which the data were collected via an announcement from the
province health directorate of Manisa. This might positively bias the results.
8.6.2 Implications
During this study, main adoption factors of EHR system usage have been
analyzed.
Efficient use of the EHR system is found to be mainly related with the alignment
between the system’s workflow and the individual’s daily tasks. It can be stated that
the more the developers adapt their systems’ workflows to the individuals’ tasks, the
more efficiently their system will be used, or this can be considered vice versa. Also
efficient use of the system is found to be mainly dependent on the functionalities of
the system and its integration with other required software. Developers should focus
on offering more functionality with their system such as dose functionality and
medical critical value range. Other factors that developers or software architects
should take into account are information quality, user interface, and accessibility.
The information quality factor is considered a multi-construct factor in our study.
We defined information quality from completeness, accuracy, and up-to-dateness
aspects. Future studies may also include other aspects and take into account differ-
ent factors.
Quality of care was found to be an important factor during the whole research
since caretakers aim to offer the best care. The relationship between quality of care
and EHR systems is found to be usefulness of the system and the individual’s
attitude.
Infusion rate is found to be dependent on usefulness and ease of use of the sys-
tem. So developers should try to focus on creating systems which are found to be
more useful and easy to use.
Usefulness of the system is defined with information quality, flexibility, mobility,
user interface, and ease of use factors in the developed model. Moreover, the individual’s
232 O.M. Kök et al.
self-confidence is taken into account as an important factor. This shows that individuals
who have more computer experience will find the system more useful.
Ease of use of the system is found to be correlated with information quality, ease
of learning, mobility, and user interface of the system. We can say that software
developers should focus on the user interface of their product and make it easier to
learn with guidelines. Also this study proves that mobility is an important adoption
factor and should be considered with priority.
Outputs of this study and the developed model can be a really useful input for
further researches. More comprehensive or more detailed frameworks can be devel-
oped from this research.
8.7 Appendices
1. Adınız
2. Yaşınız?
3. Medikal Kayıt Sistemlerini daha önce kullandınız mı?
4. Medikal Kayıt Sistemlerini kullanmanın gerekli olduğunu düşünüyor musunuz
? Nedenleri nelerdir?
5. Medikal Kayıt Sistemlerinin kullanım kolaylığı hakkında ne düşünüyorsunuz?
6. Medikal Kayıt Sistemlerinin sizce sağladığı faydalar neledir?
7. Medikal Kayıt Sistemleri kullanmanız gerektiği durumlarda kayıtları kendiniz
mi tutuyorsunuz yoksa bu konuda daha yetkin kişilerden yardım mı alıyorsunuz?
8. Medikal Kayıt Sistemleri geliştirilirken hangi konulara dikkat edilmesi
gerektiğini düşünüyorsunuz?
9. Medikal Kayıt Sistemleri kullanırken aradığınız bilgiye ulaşmakta ne gibi zor-
luklar çekmektesiniz?
10. Hastalarınız medikal kayıtlarının dijital ortamda tutulduğundan haberdarlar mı?
11. Meslektaşlarınızla medikal kayıtları paylaşarak bilgi aktarımında bulunmakta
mısınız?
12. Medikal Kayıt sistemleri kullanırken teknolojik zorluklarla karşılaştınız mı?
13. Medikal Kayıt Sistemlerinde size göre bulunması zorunlu fonksiyonaliteler
nelerdir?
14. Medikal kayıtlarınızı kendiniz mi tutmaktasınız yoksa bu konuda medikal
sekreterler/asistanlarınızdan yardım aldığınız olmakta mıdır?
15. Medikal kayıtlarınızı başkalarına tutturdugunuz durumlarda, kayıtların önem
derecesi (ilgili hasta, operasyon, hastalık) bu kararı vermenizde etken oluyor mu?
8
1 2 3 4 5 6 7
Usef6 0.967 0.087 −0.151 0.147 −0.059 −0.096 −0.017
UserInterface1 0.943 0.183 0.036 0.205 0.062 0.139 0.107
EoU2 0.931 −0.120 0.001 0.091 0.075 0.080 −0.314
Usef4 0.918 0.071 0.152 0.059 −0.292 0.182 −0.082
EoU3 0.902 −0.001 0.033 0.230 −0.293 −0.159 −0.146
FuncXMed 0.868 0.294 0.041 0.064 −0.073 −0.303 0.238
EoU1 0.868 0.052 −0.148 −0.057 0.027 0.464 −0.058
UserInterface8 0.855 −0.250 0.273 0.291 −0.185 −0.112 0.019
UseDensity 0.826 0.209 −0.276 0.346 −0.251 −0.115 −0.038
EfficientUse 0.824 −0.506 −0.116 0.092 0.081 0.084 0.173
SupportQ1 0.789 −0.142 0.335 0.260 0.204 0.195 0.312
Usef1 0.774 0.012 −0.399 0.350 −0.035 0.344 −0.011
Infusion 0.765 −0.110 0.120 0.121 0.539 0.078 −0.276
Satisfaction2 0.750 −0.018 0.483 0.408 −0.065 −0.053 −0.175
Diffusion 0.710 −0.400 0.054 0.265 −0.249 0.448 0.016
TTF2 0.710 −0.369 −0.237 0.142 0.426 0.308 0.091
Completeness 0.670 0.257 −0.399 0.488 −0.255 0.129 0.078
UserInterface5 0.668 0.239 −0.116 0.566 0.327 0.233 −0.042
UserInterface6 0.595 −0.536 0.078 0.549 0.002 0.208 0.092
UserInterface2 0.543 −0.423 0.413 0.417 −0.211 0.341 0.144
Usef3 0.027 0.943 0.157 0.137 −0.247 −0.077 −0.016
QoCare1 0.249 −0.915 0.200 0.033 0.012 −0.038 −0.240
Attitude1 −0.065 0.913 −0.005 0.262 0.195 −0.134 0.194
TTF3 0.237 0.859 −0.166 0.368 0.201 −0.034 0.038
UptoDate 0.354 0.769 −0.283 0.294 −0.341 −0.032 0.006
SupportQ2 0.396 −0.684 0.442 −0.041 −0.401 0.098 −0.094
Attitude2 0.271 0.683 0.163 −0.027 0.111 −0.389 0.519
Flexibility2 0.374 −0.619 0.040 0.355 0.388 0.444 −0.018
SelfConfidence 0.128 0.605 0.145 0.506 −0.489 −0.320 −0.004
PrivacyUA −0.346 −0.581 0.385 0.011 0.383 0.393 0.304
IntegrationSW 0.327 −0.561 −0.456 −0.242 −0.295 −0.147 0.450
QoCare2 −0.120 −0.086 0.965 0.013 −0.118 0.171 0.058
(continued)
8 Adoption Factors of Electronic Health Record Systems 237
(continued)
Usef7 0.041 −0.035 0.965 0.004 0.187 −0.149 0.095
Consistency 0.317 0.247 0.826 0.265 −0.252 −0.065 −0.135
Mobility2 0.033 −0.375 0.822 −0.106 −0.276 0.113 0.289
Mobility3 0.254 0.487 0.727 0.041 −0.079 −0.395 0.074
FuncDose −0.148 −0.259 0.698 −0.266 0.350 0..174 −0.447
AccessALL −0.148 −0.259 0.698 −0.266 0.350 0.174 −0.447
UserInterface3 −0.184 −0.398 0.656 −0.257 −0.550 0.092 −0.017
Usef5 −0.264 −0.139 0.548 −0.358 0.536 0.389 0.210
Security1 0.244 0.232 0.086 0.833 0.094 −0.209 0.364
Satisfaction3 0.456 0.250 0.034 0.812 0.185 0.068 −0.175
EoL 0.258 0.388 −0.256 0.809 0.075 0.230 −0.067
Satisfaction1 0.584 0.102 0.160 0.771 0.037 −0.031 −0.159
Accuracy 0.634 0.008 −0.174 0.750 0.061 0.005 0.021
Standardization 0.127 −0.251 −0.433 0.543 0.363 0.396 0.388
FuncRange −0.251 0.010 0.238 0.068 0.934 0.002 0.050
PrivacyMD −0.044 0.313 −0.258 0.162 0.830 −0.286 0.193
TTF1 0.467 −0.147 −0.336 0.325 0.693 0.176 −0.176
Usef2 0.360 0.403 0.471 −0.005 −0.612 −0.333 0.033
Flexibility3 0.210 −0.164 0.310 0.087 −0.147 0.854 −0.273
Flexibility1 0.500 −0.007 −0.008 −0.004 0.180 0.844 −0.073
IntegrationHW 0.181 −0.623 0.269 0.130 −0.081 0.645 −0.260
UserInterface4 0.341 0.408 0.349 0.218 −0.001 −0.584 0.456
UserInterface7 0.435 −0.430 −0.497 0.064 0.084 0.568 0.210
QoCare3 0.506 0.102 0.431 −0.134 −0.299 −0.524 0.407
Mobility1 0.270 −0.041 0.141 −0.096 −0.029 −0.003 −0.946
KnowledgeShare −0.011 0.042 0.181 −0.374 0.069 −0.191 0.886
EoU4 −0.195 0.488 0.319 0.256 0.021 −0.313 0.677
Security2 0.182 0.388 −0.476 0.401 0.216 0.017 0.618
8.7.4 4. Factor Analysis Results
238
UserInterface1 0.323 0.711 0.331 0.164 −0.002 −0.003 −0.052 0.038 −0.050 0.068
UserInterface5 0.363 0.681 0.285 0.333 0.049 −0.015 −0.068 −0.019 0.038 0.108
EoU4 0.432 0.616 0.170 0.245 0.172 0.132 −0.086 0.015 0.048 0.142
UserInterface2 0.208 0.615 0.357 0.248 0.115 −0.010 0.214 0.079 0.103 −0.052
UserInterface4 0.317 0.589 0.128 0.354 0.146 0.040 −0.145 −0.045 0.019 0.107
Flexibility3 0.359 0.537 0.211 0.320 0.244 0.166 0.213 0.144 −0.039 −0.017
Flexibility1 0.327 0.510 0.099 0.147 0.054 0.278 0.176 −0.030 −0.197 −0.031
UserInterface8 0.355 0.509 0.189 0.212 0.154 0.007 0.119 0.158 0.204 −0.095
EoU1 0.465 0.485 0.410 0.207 0.024 −0.067 −0.116 0.129 −0.043 0.034
Mobility1 0.333 0.458 0.312 0.097 0.126 −0.123 0.179 0.315 −0.163 0.126
TTF2 0.147 0.200 0.745 0.262 0.128 0.052 0.278 0.042 0.088 0.029
TTF3 0.319 0.233 0.675 0.211 0.218 0.079 0.060 0.019 −0.085 0.029
EoU3 0.337 0.289 0.655 0.171 0.095 −0.031 −0.139 0.141 0.039 −0.032
EoL 0.121 0.287 0.650 0.178 0.051 0.083 −0.228 0.144 −0.023 0.029
TTF1 0.101 0.190 0.649 0.341 0.083 0.087 0.291 −0.014 −0.038 0.037
UserInterface7 0.287 0.221 0.632 −0.075 0.180 −0.050 0.015 −0.036 0.077 0.068
EfficientUse 0.237 0.324 0.454 0.305 0.123 0.160 0.163 0.216 0.250 0.036
Adoption Factors of Electronic Health Record Systems
PrivacyUA 0.059 0.046 0.356 0.199 0.318 0.074 0.256 −0.093 0.037 0.288
Accuracy 0.396 0.258 0.186 0.666 0.124 −0.049 0.018 0.014 0.073 0.004
Consistency 0.440 0.307 0.180 0.620 0.107 −0.017 −0.059 0.132 0.018 −0.068
Standardization 0.407 0.319 0.249 0.614 0.164 −0.019 −0.145 0.153 −0.008 0.074
Security1 0.263 0.262 0.156 0.610 0.052 0.098 0.149 0.021 −0.020 0.352
UptoDate 0.470 0.222 0.182 0.596 0.193 0.061 −0.033 0.052 0.021 −0.016
(continued)
239
Table 8.34 (continued)
240
1 2 3 4 5 6 7 8 9 10
Completeness 0.416 0.346 0.231 0.566 0.078 0.017 0.186 0.050 0.153 −0.038
Security2 0.300 0.309 0.180 0.551 0.139 0.075 −0.001 −0.091 −0.083 0.344
SupportQ1 0.351 0.406 0.202 0.501 0.208 0.075 0.117 0.074 −0.036 −0.050
Mobility2 0.307 0.300 0.090 0.159 0.715 −0.019 0.043 0.130 0.058 0.102
UserInterface3 0.066 0.007 −0.360 −0.137 −0.615 0.100 −0.037 −0.024 −0.024 −0.059
Mobility3 0.366 0.338 0.158 0.258 0.593 0.135 −0.038 0.185 0.024 0.015
FuncRange −0.023 0.135 −0.029 0.023 0.082 0.751 −0.036 0.076 0.054 −0.097
FuncDose 0.141 −0.126 0.145 0.008 −0.139 0.630 0.148 0.157 0.126 0.144
Flexibility2 0.192 0.136 0.370 −0.023 0.384 0.119 0.472 0.042 0.016 −0.029
AccessALL −0.015 −0.007 0.084 0.032 0.105 0.235 −0.051 0.781 0.128 −0.035
SupportQ2 0.298 0.250 0.054 0.355 0.124 −0.084 0.199 0.420 −0.034 −0.002
IntegrationSW −0.019 0.073 0.043 0.034 0.040 −0.050 0.020 0.149 0.737 0.146
IntegrationHW −0.172 −0.141 −0.106 −0.132 −0.022 0.369 −0.055 −0.012 0.547 −0.143
FuncXMed 0.100 0.041 0.128 0.168 0.086 0.344 0.136 −0.117 0.503 −0.222
KnowledgeShare 0.070 0.056 −0.056 0.162 0.048 −0.068 0.057 0.067 −0.014 0.792
PrivacyMD 0.126 −0.044 0.428 −0.183 0.112 0.024 −0.125 −0.196 0.039 0.536
Extraction method: principal component analysis. Rotation method: varimax with Kaiser normalization
a
Rotation converged in 13 iterations
O.M. Kök et al.
8 Adoption Factors of Electronic Health Record Systems 241
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