Imagine conferring with your clinician colleagues and being handed a plateful of all of your missed and delayed diagnoses. But, imagine further that, rather than a nightmare of ghosts returning to haunt you in the form of malpractice claims, sanctions by regulatory boards, insurers pouncing on needless expenditures or hordes (yes, there would be large numbers) of angry finger-pointing patients and families, the experience would instead bring a dream of supportive feedback and learning. Imagine the ways such an idealised non-threatening consultation and conference might be designed to minimise defensiveness and maximise introspection, learn lessons, and rethink habits and standard practices. Rather than prompting incredulous exclamations of “you missed that?!” or “what were you thinking?!”, the process would generate engagement and scrutiny of office and hospital workflow and diagnostic testing practices, realistically grappling with time and cost trade-offs, pressures, uncertainties and diagnostic challenges that practicing clinicians face every day. In short, your plateful of missed diagnoses would initiate a process that combines the best elements of a fun and informative morbidity and mortality conference, an expert second opinion from a generous colleague and productive quality improvement consultation.

I suspect the average clinician could care less about diagnostic ‘triggers’ or a new study to increase their positive predictive value. However, no professional could fail to see the appeal of the ultimate form of continuing medical education imagined above—learning practical lessons from one's own cases and discussing with trusted colleagues the ways care could be improved. How to get there from here poses a fundamental challenge, one that the study by Singh et al on diagnosis error ‘triggers’ in this issue of BMJ Quality and Safety attempts to address.1

The authors, a research team based at the DeBakey Veterans Affairs Medical Center and Baylor College of Medicine in Houston, have continued to refine their approaches to reliably and efficiently screen for diagnostic error cases.1 2 Using the comprehensive longitudinal electronic medical record in the US Veterans Health Administration hospitals (and now another electronic medical record in a second health system), they took advantage of two powerful features of electronic records that will increasingly change the paradigm of quality improvement and health services research: (a) the ability to perform simple or even complex queries to select samples of patients meeting specified criteria and (b) the ability to efficiently retrieve and review electronic patient records identified by the screening criteria. Thus, the next decade of ‘chart review’ (which, as the authors point out, is essential for evaluating potential diagnosis error cases), with this quantitatively eased burden of case selection and review, holds the promise of facilitating qualitative leaps forward in what we can learn from the details of clinical care processes and outcomes.

As the authors and the literature repeatedly point out, diagnostic errors are important but understudied, largely because of difficulties in defining and detecting such errors.3 4

Can generic electronic screens cast an effective and efficient net to pick out/up charts to review for errors in diagnosis with a sufficiently high likelihood of errors (ie, positive predictive value) to make the manual reviews worth the effort? In attempting to ease the burden of detecting diagnostic errors, the authors (and others) have sought to develop methods to more efficiently sift through the tens of thousands of encounters they wanted to screen for diagnostic errors. In the present study,1 they developed two relatively simple screens—admission to an acute care hospital in the 14 days following an index primary care visit, or a second broader screen that identified patients who presented to an emergency department, sought urgent care or had an unscheduled subsequent primary care visit, also in the 14 days following an index visit.

Their rationale, that an unexpected hospitalisation, emergency department visit or urgent/unscheduled clinic visit could result from a missed diagnosis during the initial visit, seems sound, particularly for acute problems. Unfortunately, it overlooks considerable numbers of other situations where diagnosis fails. In fact, it seems unlikely to pick up many of the cases that represent the leading causes of malpractice claims in primary care—missed and delayed diagnoses of cancer. Here the diagnostic failures unfold over a period of weeks and months, rather than hours or days, and, except in relatively rare circumstances (eg, progression of an undiagnosed colon cancer to the point of causing bowel obstruction), would not typically lead to a positive trigger such as hospitalisation within the subsequent 2 weeks. The authors acknowledge this limitation and have, in other studies,5 6 sought complementary screens to detect missed cancer diagnoses. But this shortcoming illustrates the fact that no one screen fits all when it comes to detection of diagnostic errors.

More subtle, but sobering, is the evidence from this study's data that their screen would fail to detect more than 90% of the missed diagnoses in their cohort. Using the trigger increased the yield from roughly 2% of unselected (‘control’) cases, to 20% in the trigger-positive sample (meeting the 14-day hospitalisation screening criteria). However, applying this 2% rate to their cohort of screen-negative patients means that while they picked up a total of 177 error cases in the ‘screen positive’ patients (using both sets of trigger screens), there would have been 1710 additional cases involving missed diagnoses—nearly 10 times as many error cases in the larger cohort overlooked with a negative screen. Depending on the purpose of the error screening, this may or may not be acceptable. Certainly, if I wanted to hear about all of my cases of diagnostic error, I would find my plate 90% empty. On the other hand, if an institution seeks a quick sample of cases to illustrate problems with diagnosis, using the selective triggers to target a manageable number of charts to review might prove quite helpful.

Another important question this sampling strategy raises is the representativeness of the cases found via the screen. In other words, are the 177 cases identified by the triggers and subsequent chart reviews, fairly similar to or systematically different from the 1710 overlooked cases? As mentioned above, to the extent that they overlook missed diagnoses more related to chronic or subacute diseases, the triggers may miss the identification of important system problems related to diagnostic processes. As long as the institution recognises the characteristics of different possible triggers, it could probably develop an efficient, trigger-based strategy for identifying substantially delayed diagnosis of cancers and other subacute illnesses, as opposed to more acute problems.