Bolstering diagnostic reasoning skills with adaptive learning

Abstract

Purpose: Adaptive learning emerges when precise assessment informs delivery of educational materials. This study will demonstrate how data from Human Dx, a case-based e-learning platform, can characterize an individual's diagnostic reasoning skills, and deliver tailored content to improve accuracy.

Methods: Pearson Chi-square analysis was used to assess variability in accuracy across three groups of participants (attendings, residents, and medical students) and three categories of cases (core medical, surgical, and other). Logistic regression analyses were conducted to explore the relationship between solve duration and accuracy. Mean accuracy and duration were calculated for 370 individuals. Repeated measures analysis of variance (ANOVA) were used to assess variability for an individual solver across the three categories.

Results: There were significant differences in accuracy across the three groups and the three categories (p < 0.001). Individual solvers have significant variance in accuracy across the three categories. Shorter solve duration predicted higher accuracy. Patterns of performance were identified; four profiles are highlighted to demonstrate potential adaptive learning interventions.

Conclusions: Human Dx can assess diagnostic reasoning skills. When weaknesses are identified, adaptive learning strategies can push content to promote skill development. This has implications for customizing curricular elements to improve the diagnostic skills of healthcare professionals.