Volume 4, Issue 2

Background: The Improving Diagnosis in Health Care report from the National Academies of Sciences, Engineering and Medicine (NASEM) provided an opportunity for many groups to reflect on the role they could play in taking actions to improve diagnostic safety. As part of its own process, AHRQ held a research summit in the fall of 2016, inviting members from a diverse collection of organizations, both inside and outside of Government, to share their suggestions regarding what is known about diagnosis and the challenges that need to be addressed. Content: The goals of the summit were to learn from the insights of participants; examine issues associated with definitions of diagnostic error and gaps in the evidence base; explore clinician and patient perspectives; gain a better understanding of data and measurement, health information technology, and organizational factors that impact the diagnostic process; and identify potential future directions for research. Summary and outlook: Plenary sessions focused on the state of the new diagnostic safety discipline followed by breakout sessions on the use of data and measurement, health information technology, and the role of organizational factors. The proceedings review captures many of the key challenges and areas deserving further research, revealing stimulating yet complex issues.

Diagnostic error is a serious public health problem to which knowledge gaps and associated cognitive error contribute significantly. Identifying diagnostic approaches to common problems in ambulatory care associated with more timely and accurate diagnosis and lower cost and harm associated with diagnostic evaluation is an important priority for health care systems, clinicians, and of course patients. Unfortunately, guidance on how best to approach diagnosis in patients with common presenting complaints such as abdominal pain, dizziness, and fatigue is lacking. Exploring diagnostic practice variation and patterns of diagnostic evaluation is a potentially valuable approach to identifying best current diagnostic practices. A “diagnostic path” is the sequence of actions taken to evaluate a new complaint from first presentation until a diagnosis is established, or the evaluation ends for other reasons. A “big data” approach to identifying diagnostic paths from electronic health records can be used to identify practice variation and best practices from a large number of patients. Limitations of this approach include incompleteness and inaccuracy of electronic medical record data, the fact that diagnostic paths may not represent clinician thinking, and the fact that diagnostic paths may be used to identify best current practices, rather than optimal practices.

Background: A 2015 National Academy of Medicine report on improving diagnosis in health care made recommendations for direct action by hospitals and health systems. Little is known about how health care provider organizations are addressing diagnostic safety/quality. Methods: This study is an anonymous online survey of safety professionals from US hospitals and health systems in July–August 2016. The survey was sent to those attending a Leapfrog Group webinar on misdiagnosis (n=188). The instrument was focused on knowledge, attitudes, and capability to address diagnostic errors at the institutional level. Results: Overall, 61 (32%) responded, including community hospitals (42%), integrated health networks (25%), and academic centers (21%). Awareness was high, but commitment and capability were low (31% of leaders understand the problem; 28% have sufficient safety resources; and 25% have made diagnosis a top institutional safety priority). Ongoing efforts to improve diagnostic safety were sparse and mostly included root cause analysis and peer review feedback around diagnostic errors. The top three barriers to addressing diagnostic error were lack of awareness of the problem, lack of measures of diagnostic accuracy and error, and lack of feedback on diagnostic performance. The top two tools viewed as critically important for locally tackling the problem were routine feedback on diagnostic performance and culture change to emphasize diagnostic safety. Conclusions: Although hospitals and health systems appear to be aware of diagnostic errors as a major safety imperative, most organizations (even those that appear to be making a strong commitment to patient safety) are not yet doing much to improve diagnosis. Going forward, efforts to activate health care organizations will be essential to improving diagnostic safety.

Background: This article reports on the findings of 12,278 laboratory related safety events that were reported through the British Columbia Patient Safety & Learning System Incident Reporting System. Methods: The reports were collected from 75 hospital-based laboratories over a 33-month period and represent approximately 4.9% of all incidents reported. Results: Consistent with previous studies 76% of reported incidents occurred during the pre-analytic phase of the laboratory cycle, with twice as many associated with collection problems as with clerical problems. Eighteen percent of incidents occurred during the post-analytic reporting phase. The remaining 6% of reported incidents occurred during the actual analytic phase. Examination of the results suggests substantial under-reporting in both the post-analytic and analytic phases. Of the reported events, 95.9% were reported as being associated with little or no harm, but 0.44% (55 events) were reported as having severe consequences. Conclusions: It is concluded that jurisdictional reporting systems can provide valuable information, but more work needs to be done to encourage more complete reporting of events.

Background: Contemporary medicine has expressed concern about lay incursions into the diagnostic process buttressed by commonly available medical information on line. Even while the world wide web is a new structure, there is a long historical precedent for this concern. With the emergence of scientific medicine in the late 19th century came a strong belief in the role of diagnosis, not only to explain disease symptoms but also to differentiate the physician from a range of other unreliable practitioners. Along with this focus on diagnosis came also a concern expressed by doctors about patients’ inclination to self-diagnose, or to propose candidate diagnoses for the problems that ailed them. Methods: This paper uses Zerubavel’s social patterning method. Using material written by doctors from the late 19th until the mid-20th century, I explore comments about, and attitudes towards, self-diagnosis. Results: Three areas of concern about self-diagnosis are expressed by doctors. First, self-diagnosis produces anxiety in the patient. Second, it interferes with doctor-patient relationship. Finally self-diagnosis is commonly linked to commercial interests. Conclusions: Contemporary concerns about self-diagnosis are part of an ongoing social pattern, which simultaneously promotes diagnosis as means for explaining disease but also protests when the diagnostic explanations originate with the patient.

Background: Misinterpretation of medical images is an important source of diagnostic error. Errors can occur in different phases of the diagnostic process. Insight in the error types made by learners is crucial for training and giving effective feedback. Most diagnostic skill tests however penalize diagnostic mistakes without an eye for the diagnostic process and the type of error. A radiology test with stepwise reasoning questions was used to distinguish error types in the visual diagnostic process. We evaluated the additional value of a stepwise question-format, in comparison with only diagnostic questions in radiology tests. Methods: Medical students in a radiology elective (n=109) took a radiology test including 11–13 cases in stepwise question-format: marking an abnormality, describing the abnormality and giving a diagnosis. Errors were coded by two independent researchers as perception, analysis, diagnosis, or undefined. Erroneous cases were further evaluated for the presence of latent errors or partial knowledge. Inter-rater reliabilities and percentages of cases with latent errors and partial knowledge were calculated. Results: The stepwise question-format procedure applied to 1351 cases completed by 109 medical students revealed 828 errors. Mean inter-rater reliability of error type coding was Cohen’s κ=0.79. Six hundred and fifty errors (79%) could be coded as perception, analysis or diagnosis errors. The stepwise question-format revealed latent errors in 9% and partial knowledge in 18% of cases. Conclusions: A stepwise question-format can reliably distinguish error types in the visual diagnostic process, and reveals latent errors and partial knowledge.