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Exploring how feedback reflects entrustment decisions using artificial intelligence.

Brian C Gin1, Olle Ten Cate2,3, Patricia S O'Sullivan3,4

  • 1Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA.

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Summary

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Area of Science:

  • Medical Education
  • Artificial Intelligence in Healthcare
  • Clinical Competency Assessment

Background:

  • Clinical supervisors' entrustment decisions significantly impact trainee learning and competency development.
  • Entrustment frameworks are increasingly used in medical education assessment.
  • Understanding narrative feedback's role in entrustment is crucial for addressing learner needs.

Purpose of the Study:

  • To analyze characteristics of narrative feedback linked to supervisor entrustment decisions.
  • To explore how artificial intelligence (AI) and natural language processing (NLP) can identify feedback themes related to entrustment.
  • To determine how feedback informs trainee educational needs and goal-setting.

Main Methods:

  • Utilized AI and NLP on 3328 direct observation encounters of medical students.
  • Trained a deep neural network (DNN) to predict entrustment levels from narrative feedback.
  • Developed an explainable AI protocol to identify key thematic features in feedback.

Main Results:

  • Entrustment levels correlated with feedback detail (specific clinical task steps), feedback type (constructive/reinforcing), and task type (procedural/cognitive).
  • Supervisors provided specific performance details to justify both high and low entrustment ratings.
  • Feedback focused on concrete performance aspects rather than general trainee qualities.

Conclusions:

  • Narrative feedback emphasizing specific clinical competencies, rather than general traits, guides effective goal-setting.
  • The identified feedback characteristics align with factors influencing entrustment decisions.
  • NLP offers a valuable methodology for future research on entrustment and feedback in medical education.