Differential Diagnosis: Correctly Putting the Pieces of the Puzzle Together

Approaching diagnostic messiness through spiderweb strategies: Connecting epistemic practices in the clinic and the laboratory

Scientific and medical practice both relate to and differ from each other, as do discussions of how to handle decisions under uncertainty in the laboratory and clinic respectively. While studies of science have pointed out that scientific practice is more complex and messier than dominant conceptions suggest, medical practice has looked to the rigour of scientific and statistical methods to address clinical uncertainty. In this article, we turn to epistemological studies of the laboratory to highlight how clinical practice already has strategies for dealing with messiness. We draw on Hans-Jörg Rheinberger's Toward a History of Epistemic Things, in which he invokes the metaphor of a spider's web to explain the role of tacit practices in experimental biochemistry for helping practitioners manage messiness. We argue that diagnostic practices in clinical medicine employ similar, albeit codified, procedures to evaluate epistemic significance, ensure sensitivity to the unforeseen, and allow focused grounds for action. We consider three practices: (a) the pre-set structure of medical records, ensuring broad coverage in initial anamnesis, (b) the use of lists of differential diagnoses and ongoing 'anchoring and adjusting' as inquiry progresses, and (c) shared decision-making as an occasion to synthesize empirical evidence and reopen inquiry for potential missed information. We end by suggesting that while philosophy of medicine may learn from laboratory epistemology, the sciences may learn something from medical practice.

Utility of mobile learning in Electrocardiography

Aims

Mobile learning is attributed to the acquisition of knowledge derived from accessing information on a mobile device. Although increasingly implemented in medical education, research on its utility in Electrocardiography remains sparse. In this study, we explored the effect of mobile learning on the accuracy of electrocardiogram (ECG) analysis and interpretation.

Methods and results

The study comprised 181 participants (77 fourth- and 69 sixth-year medical students, and 35 residents). Participants were randomized to analyse ECGs with a mobile learning strategy [either searching the Internet or using an ECG reference application (app)] or not. For each ECG, they provided their initial diagnosis, key supporting features, and final diagnosis consecutively. Two weeks later, they analysed the same ECGs, without access to any mobile device. ECG interpretation was more accurate when participants used the ECG app (56%), as compared to searching the Internet (50.3%) or neither (43.5%, P = 0.001). Importantly, mobile learning supported participants in revising their initial incorrect ECG diagnosis (ECG app 18.7%, Internet search 13.6%, no mobile device 8.4%, P < 0.001). However, whilst this was true for students, there was no significant difference amongst residents. Internet searches were only useful if participants identified the correct ECG features. The app was beneficial when participants searched by ECG features, but not by diagnosis. Using the ECG reference app required less time than searching the Internet (7:44 ± 4:13 vs. 9:14 ± 4:34, P < 0.001). Mobile learning gains were not sustained after 2 weeks.

Conclusion

Whilst mobile learning contributes to increased ECG diagnostic accuracy, the benefits were not sustained over time.