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INTRODUCTION: Diagnostic errors in primary care contribute to increased morbidity and mortality, and billions in costs each year. Improvements in the way practicing physicians are taught so as to optimally perform differential diagnosis can increase patient safety and lower the costs of care. This study represents a comparison of the effectiveness of two approaches to CME training directed at improving the primary care practitioner's diagnostic capabilities against seven common and important causes of joint pain. METHODS: Using a convenience sampling methodology, one group of primary care practitioners was trained by a traditional live, expert-led, multimedia-based training activity supplemented with interactive practice opportunities and feedback (control group). The second group was trained online with a multimedia-based training activity supplemented with interactive practice opportunities and feedback delivered by an artificial intelligence-driven simulation/tutor (treatment group). RESULTS: Before their respective instructional intervention, there were no significant differences in the diagnostic performance of the two groups against a battery of case vignettes presenting with joint pain. Using the same battery of case vignettes to assess postintervention diagnostic performance, there was a slight but not statistically significant improvement in the control group's diagnostic accuracy (P = .13). The treatment group, however, demonstrated a significant improvement in accuracy (P < .02; Cohen d, effect size = 0.79). DISCUSSION: These data indicate that within the context of a CME activity, a significant improvement in diagnostic accuracy can be achieved by the use of a web-delivered, multimedia-based instructional activity supplemented by practice opportunities and feedback delivered by an artificial intelligence-driven simulation/tutor.
McFadden et al. (Fri,) studied this question.
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