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Related Experiment Videos

Using artificial intelligence to uncover keywords associated with resident EPA entrustability.

Alexandra Z Agathis1, Joanna Yang2, Damien J Lazar1

  • 1Division of General Surgery, Department of Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

American Journal of Surgery
|April 26, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence and natural language processing analyzed resident evaluations to identify key factors in entrustability. This can help improve feedback and training for surgical residents.

Keywords:
Artificial intelligenceEPAEntrustable professional activityNatural language processingSurgical residency

Related Experiment Videos

Area of Science:

  • Medical Education
  • Artificial Intelligence in Medicine
  • Surgical Training

Background:

  • Entrustable Professional Activity (EPA) evaluations are fundamental for resident feedback and assessing practice readiness.
  • Current research lacks studies using AI to compare qualitative feedback with quantitative scoring in EPA evaluations.

Purpose of the Study:

  • To utilize artificial intelligence (AI) and natural language processing (NLP) to identify keywords associated with resident entrustability in surgical training.
  • To analyze discrepancies between attending and resident-assigned scores in EPA micro-assessments.

Main Methods:

  • A retrospective analysis of 1,000 resident EPA micro-assessments from 7/6/2023-12/3/2024 was conducted.
  • Natural language processing (NLP) models were applied to extract keywords related to resident entrustability.
  • Spearman's correlations were used to identify keywords and score discrepancies.

Main Results:

  • Key themes associated with lower entrustability included camera navigation, case progression, and retraction.
  • Residents described as "independent" and "safe" were rated with higher entrustability.
  • Attendings citing higher scores than resident self-ratings frequently mentioned complex pathologies and emotional aspects of patient care.

Conclusions:

  • An AI-based tool can be integrated into residency programs to identify characteristics of highly entrustable residents.
  • This approach offers a valuable roadmap for enhancing resident development and feedback systems.