Research

An analysis of the causes of adverse events from the

Quality in Australian Health Care Study

Ross McL Wilson, Bernadette T Harrison, Robert W Gibberd and John D Hamilton

MJA 1999; 170: 411-415

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Abstract - Introduction - Methods - Results - Discussion - Acknowledgements - References - Authors' details

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Administration and health services

The Quality in Australian Health Care Study (QAHCS), published in the

Journal in 1995,1 reported that 16.6% of hospital admissions were associated
with an iatrogenic patient injury, termed an adverse event (AE) (see Box 1).
This compares with the rate of 3.7% for AEs in the Harvard Medical
Practice Study (HMPS),2 and a rate of 17% in a more recent study with an
alternative observational method of determining AEs.3
Fifty per cent of the AEs in the QAHCS were judged to have a high
preventability score (4 or more on a scale of 1-6 of increasing likelihood of
preventability).1 The disability caused by these adverse events ranged from
temporary disability (fully resolved in one month) in 46.6% of AEs, to death
in 4.9% of AEs. Although recording AEs emphasises only the
"complications" of rather than the benefits derived from healthcare, AEs are
of great significance to individual patients as well as to the whole healthcare
system.
Abstract Objective: To examine the causes of adverse events (AEs) resulting from
healthcare to assist in developing strategies to minimise preventable patient
injury.
Design: Descriptions of the 2353 AEs previously reported by the Quality in
Australian Health Care Study (QAHCS) were reviewed. A qualitative
approach was used to develop categories for human error and for prevention
strategies to minimise these errors. These categories were then used to
classify the AEs identified in the QAHCS, and the results were analysed
with previously reported preventability and outcome data.
Results: 34.6% of the causes of AEs were categorised as "a complication of,
or the failure in, the technical performance of an indicated procedure or
operation", 15.8% as "the failure to synthesise, decide and/or act on
available information", 11.8% as "the failure to request or arrange an
investigation, procedure or consultation", and 10.9% as "a lack of care and
 attention or failure to attend the patient". AEs in which the cause was
cognitive failure were associated with higher preventability scores than those
involving technical performance. The main prevention strategies identified
were "new, better, or better implemented policies or protocols" (23.7% of
strategies), "more or better formal quality monitoring or assurance
processes" (21.2%), "better education and training" (19.2%), and "more
consultation with other specialists or peers" (10.2%).
Conclusion: The causes of AEs or errors leading to AEs can be
characterised, and human error is a prominent cause. Our study emphasises
the need for designing safer systems for care which protect the patient from
the inevitability of human error. These systems should provide new policies
and protocols and technological support to aid the cognitive activities of
clinicians.
Introduction An additional analysis of data from the Quality in Australian Health Care
Study (QAHCS)1 was undertaken in order to understand more fully the
causes of the adverse events (AEs) identified and to assist in developing
prevention strategies. Here, we describe the error or errors in the delivery of
healthcare which led to the AEs. This contrasts with our previous report,1
which focused on the patient characteristics associated with AEs and the
nature and consequences of the AEs.
Methods The method of determining AEs in the QAHCS has been described
previously.1 The AEs were re-examined with the specific goals of
determining the causes for, or the underlying errors leading to, each AE. In
addition, strategies that were judged to have the potential to prevent AEs
were recorded.

To obtain this information the first and subsequent review forms (RF1 and
RF2 forms1) collected during the QAHCS were re-examined. The source
material for these forms had been the hospital medical records, but neither
the hospitals nor the medical records were revisited in this analysis.

Categories for the causes of the AEs were devised by an iterative process
during a three-day workshop. For this, we sought additional expertise in
clinical epidemiology and qualitative research methods. Using these
categories, the AEs recorded on the review forms were assessed by three of
the senior medical specialists who had originally reviewed the medical
records in the QAHCS.

All the material from each AE was reviewed by only one reviewer, as the
agreement between the medical reviewers in determining the presence or
absence of an AE during the QAHCS was 80% (kappa, 0.55). A proforma
was completed which asked the reviewer to identify the error and then
classify it by human cause and preventive strategy. All AEs were also
categorised into some of the processes of clinical care. Results for the
"delay", "treatment" and "investigation" categories are presented. The
categories were not mutually exclusive. These data were then entered into a
 database, merged with the original data from the QAHCS for each case and
analysed. Two of the original total of 2353 AEs were missed in this review;
thus, results are given for 2351 AEs.

Our analysis provides the frequency of occurrence of each of the categories

of causes of AEs, together with the proportion in each category resulting in
permanent disability (including death) and the proportion with high
preventability.
Results Human error categories
Box 2 shows the frequency of occurrence of each of the human error
categories, and the proportion of the AEs in each category judged to have
permanent disability and high preventability. Of the 2351 AEs, 1922
(81.8%) were associated with one or more human error categories. As the
error categories were not mutually exclusive, the 1922 AEs were associated
with 2940 causes. "Complication of, or failure in, the technical performance
of an indicated procedure/operation" was the most frequent cause of AEs;
examples of this category are shown in Box 3A.

Human errors associated with categories of failure of cognitive function

were the next most frequent cause of AEs (Box 2). These included "Failure
to synthesise, decide and/or act on available information", "Failure to
request or arrange investigation, procedure or consultation", and
"Misapplication of, or failure to apply, a rule; or use of a bad or inadequate
rule".

The most frequent error category, "complication of, or failure in, the
technical performance of an indicated procedure/operation", had a lower
proportion of AEs with permanent disability (14.2%). The next five most
frequent human error categories all had a high proportion of AEs with
permanent disability (25% or more) (Box 2). This pattern was also seen in
the proportions of AEs with death as the outcome: 2.2% in the first category,
and 8% or more in each of the next five categories.

Of the 1201 AEs having high preventability, 9 (0.7%) were not associated
with a human error category; for the remaining 1192 AEs, 2051 causes were
identified (Box 2).

Delay categories
The importance of timeliness to the quality of healthcare led to further
analysis of all AEs to ascertain the nature and role of delay in their causation
(Box 4A).

Delays contributed to 20.0% of AEs: of these, delays in diagnosis accounted

for 56.8% and treatment delays for 40.6%. Diagnostic delay was usually the
failure to make, or attempt to make, a diagnosis of a patient's condition
 rather than just providing symptomatic or even no treatment. Treatment
delay was when the diagnosis had been made but there was a delay in
initiating specific therapy. Examples of AEs in the delay category are
included in Box 3B.

The AEs with delay categories were judged to have very high preventability
(86%-90%) compared with the average (51.2%) for all AEs (Box 4A).

Treatment categories
AEs categorised as caused by a treatment error were also analysed (Box 4B).
In 19.6% of all AEs, treatment error contributed to the cause. The majority
of AEs in this group fell into the categories of "no or inadequate treatment"
(51.5%), or "wrong or inappropriate treatment" (27.4%). As with AEs
caused by delay, these AEs were judged to have much higher preventability
than the average for all AEs. Examples of AEs involving treatment errors are
shown in Box 3C.

Investigation categories
Analysis of the AEs caused by patient investigation issues is shown in Box
4C, and examples are given in Box 3D. There was a problem with clinical
investigation in 10.7% of AEs. Paralleling the results in the treatment
category, most (78.6%) of these AEs were in this category because an
investigation was not done, rather than the investigation being inappropriate
(3.6%), or not acted upon (15.5%). Consistent with other AEs that are
attributed to cognitive failure, there was a very high percentage of these AEs
rated as high preventability.

Strategies for preventing AEs

When describing AEs, preventability refers to the identification of an
avoidable error that led to the adverse event. This is not to say that the error
could be avoided on every occasion, and that the adverse event would not
occur. Rather, it implies that, with the current state of knowledge and
technology, it is possible to identify and avoid that particular error, and
hence reduce the probability of an AE. The reviewers were making a
judgement, having identified the error, on the particular strategy for a change
in the healthcare system that could have prevented the AE. The outcomes of
these judgements are given in Box 5.

Nineteen (1.6%) of the 1201 high preventability AEs did not have a
prevention strategy category. Of the 2613 prevention strategies identified in
the 1182 AEs with high preventability, 24.7% (646) were for "better
education and training", 20.9% (545) were for "new or better implemented
policies or protocols" and 18.6% (486) were for "more or better formal
quality monitoring or assurance processes".
Discussion AEs are important to patients, healthcare providers and to the custodians and
funders of health services. One estimate of the national cost to the Australian
healthcare system of just the additional hospital bed-days (as a result of the
AEs identified in 19921) is in excess of $800 million dollars per year.4 This
estimate ignores any subsequent hospital admissions and out-of-hospital
healthcare expenses, loss of productivity of the patients involved, and long
term community costs of permanent disability from AEs. It also ignores the
benefits received from healthcare.

Providing insights into how AEs occur can help in developing prevention
strategies to reduce the frequency and severity of patient injuries during
healthcare. Our review and analysis of the AE data from the QAHCS have
shown that the causes of AEs or errors leading to AEs can be characterised,
and that human error is a prominent cause.

It is important to recognise that human error is inevitable for even the best-
trained and best-qualified healthcare providers. Weed has recently pointed
out that the unaided human mind is incapable of performing consistently at
the necessary level to provide optimal healthcare.5 However, other studies6
have noted that the label "human error" is prejudicial and non-specific; it
may retard rather than advance our understanding of how complex systems
fail. It is postulated that within complex systems error is a symptom of
organisational problems, and this is likely to apply to healthcare. Therefore,
we need a healthcare-system response to error that moves the system
towards being as "failsafe" as possible rather than one that blames the
clinician who may have erred. Examples from the more frequently studied
area of adverse drug events7 would be decision-support technology for
antibiotic prescribing,8 with its demonstrated benefits, and electronic
prescribing to reduce prescribing and transcription errors in hospital.9

Our analysis identified broad functional categories that are linked to the
processes that make up the system of healthcare delivery and hence cut
across specialties, diagnosis-related groups (DRGs) and particular patient
groups. The sample size is large enough to provide useful information even
when several AEs could not be classified into the categories chosen, or
insufficient information was available to indicate cause. On the other hand,
several factors bias the information available for assessing AEs because of
an emphasis on procedures and short term outcomes and possible under-
reporting of the contribution of the supporting systems to the cause of the
AEs. Firstly, because the original data source was the hospital medical
record, the information available about AEs is biased towards the patient
involved and away from other potentially important contextual events at the
time. Further, the medical record often focuses more on the actions of
clinicians involved in direct or procedural patient intervention, and less on
the actions of other staff or systems with a more supportive role. These and
other factors will lead to an emphasis on procedures and short term
outcomes, and a possible under-reporting of the contribution of supporting
systems in causing AEs. Finally, information about subsequent or prior
hospitalisations is usually only available if the patient attended the same
hospital on all occasions.

Having acknowledged these potential limitations, cognitive failure (Box 2)

appears to have a role in 57% of all the causes of AEs, and most of the AEs
involved were judged to be of high preventability and to have caused
significant disability. These AEs were largely associated with errors of
omission rather than commission. Does this represent a minimum
"obligatory" error rate resulting from a combination of human error and our
healthcare system, and hence which cannot be improved? Our data are not
able to answer this question unequivocally, but we believe they show
sufficient opportunities for moving the system towards a failsafe mode to
suggest that the answer is no. Until recently there has been an under-
recognition of the role and responsibilities of the healthcare system and its
custodians in providing a "safe environment" using systems-improvement
tools.10

One response to these data should be to look at the factors in healthcare

delivery that may interfere with the cognitive or technical performance of
healthcare providers. Insufficient use of information technology to assemble
the necessary information at the time of decision-making may increase error.
Another important factor is fatigue, which has already been shown to
increase error in doctors.11 Sleep deprivation may have a much more
significant role in human error in healthcare than the current work-load
patterns pay heed to, but more research is needed. Other factors that may be
important include the level of supervision provided to junior staff, and the
pervasive effect of the culture of medical practice, which can unhelpfully
portray error as individual failure or deviation from perfection.12 Our study
method does not provide direct information about the role of these factors.

The high proportion of causes of AEs involving cognitive failure must

represent a manifestation of human error occurring in a system that is not
patient protective, if one accepts that these practitioners are appropriately
trained and competent by international standards. Our study provides clear
guidance on methods for improvement, with "new, better, or better
implemented policies or protocols" accounting for 24% of prevention
strategies identified for the AEs, "quality monitoring and assurance
processes" accounting for 21%, and "better education and training" for a
further 19%.

In summary, improvement is needed in the agreed processes of care,

supported by information systems that allow general dissemination of
current knowledge of diseases or treatments, and information on outcomes of
care for each patient, through appropriate quality processes. Simple
 examples are the availability of practice guidelines and protocols at the
point-of-care, and the use of automated reminders for patients and
practitioners when a particular test or follow-up is required. In addition,
having adequate patient "outcome" information in a form that can be
benchmarked is a powerful tool in identifying unacceptable variation.

Acknowledgements
We acknowledge the contributions of Professor B Armstrong, Professor W R
Runciman, Professor R Holland, Dr T Robertson and Dr A Hobbes.
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(Received 4 May 1998, accepted 20 Jan 1999)