Not All EPAs Are Created Equal: Fixing Sampling Bias With Utility Modeling
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View abstract on PubMed
Summary
This summary is machine-generated.Entrustable professional activities (EPAs) assessments are unevenly completed, creating biases. Our new EPA assessment utility modeling corrects these biases and guides faculty to complete the most impactful assessments.
Area Of Science
- Medical Education
- Health Professions Education
- Competency-Based Medical Education
Background
- Entrustable professional activities (EPAs) are crucial for assessing resident readiness for practice.
- Manual initiation of EPA assessments leads to uneven completion rates and introduces biases.
- Variability in EPA assessment exists across individuals, specialties, and institutions.
Purpose Of The Study
- Introduce EPA assessment utility modeling to address biases in assessment completion.
- Provide a data-driven approach to correct for and avoid biases in EPA assessments.
- Inform faculty on the usefulness of EPA assessment opportunities and identify when they are most needed.
Main Methods
- Longitudinal analysis of general surgery EPA assessments across 37 institutions using an EHR-integrable platform.
- Power law curve fitting to measure skewing in EPA assessment counts.
- Bayesian network modeling and Monte Carlo simulations to quantify assessment impact and develop an assessment utility score.
Main Results
- EPA assessment counts exhibited significant skewing across EPA type, faculty, specialty, and resident.
- Top 4 EPA types accounted for 52.8% of assessments; top 15 faculty provided 33.5%.
- Top 2 specialties contributed 31.0% of assessments; top 20 residents received 20.1%.
Conclusions
- EPA assessments are heavily skewed, leading to biased representation of entrustment levels.
- An assessment utility framework is proposed to optimize EPA assessment timing, assessor selection, and prioritization.
- This data-driven approach aims to improve the measurement of competency-based medical education.
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