An analysis of the causes of adverse events from the Quality in Australian Health Care Study

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.

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".

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.

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University of Newcastle, Newcastle, NSW 2308.
Robert W Gibberd, PhD, Associate Professor, Department of Statistics; and Director of Health Services Research Group.
John D Hamilton, MB BS, FRCP, Professor of Medicine, Faculty of Medicine and Health Sciences.

Reprints: Dr R McL Wilson, Director of QARNS, Royal North Shore Hospital, Pacific Highway, St Leonards, NSW 2065.
Email: rwilsonATdoh.health.nsw.gov.au

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