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A cognitive bias in diagnostic reasoning and its remediation by the "2-Dimensional Approach"
Hisashi Shimozono1, Nobutoshi Nawa1, Makoto Takahashi1
1Department of Medical Education Research and Development.
Abstract:
This article was migrated. The article was marked as recommended. Background Misdiagnoses are associated with various cognitive biases, which are difficult to reduce even if physicians think about clinical cases deliberately. We propose a new "2-Dimensional Approach" that combines two perspectives in diagnostic reasoning: organs (brain, heart, lung, intestine/liver, kidney) and systems (metabolic, endocrine, blood/tumor, infection, immune, circulatory, nervous systems). Systems involve throughout the whole body and can be pathogenesis of diseases. As a result of diseases, organs show abnormal vital signs and symptoms. We investigated: (1) whether each individual resident has a bias for either organs or systems when they diagnose clinical cases, and (2) whether the 2D Approach can reduce such a bias. Methods We randomly assigned PGY1 residents (n=105) to either the 2D Approach group (n=45) or a control group (n=60). After attending either a lecture on the 2D Approach or a non-related lecture, residents were asked to diagnose two clinical cases. We divided each diagnosis into one of the two categories, either "organs" or "systems". We investigated whether each resident would diagnose the two cases into the same category, either organs or systems (i.e., a bias for either organs or systems). Results The participants in the control group tended to diagnose the two cases into the same category, either organs or systems (OR: 5.63, 95% CI: 1.62-21.7, p=0.0030, Fisher's exact test). In the 2D Approach group, the category of diagnoses for the two cases were not related to each other (OR: 2.14, 95% CI: 0.50-9.81, p=0.33). Conclusion There is a bias for either organs or systems when residents diagnose clinical cases, suggesting that organs and systems are different perspectives in diagnostic reasoning. By combining these 2 different perspectives in a 2-dimensional matrix, the 2D Approach reduces this bias.
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