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Not All EPAs Are Created Equal: Fixing Sampling Bias With Utility Modeling.

Phillip Jenkins1, Ali Oran1, Carolyn C Chang1

  • 1Department of Surgery, OHSU, Surgical Data and Decision Sciences Lab, Portland, Oregon.

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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.

Keywords:
artificial intelligencecompetency-based medical educationentrustable professional activitieshuman-centered designsurgical educationutility model

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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.