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Competency-Based Assessments: Leveraging Artificial Intelligence to Predict Subcompetency Content.

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A new Natural Language Processing (NLP) algorithm effectively categorizes anesthesiology trainee feedback into specific competencies. This tool rapidly organizes narrative evaluations for improved self-assessment in medical education.

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Area of Science:

  • Medical Education
  • Natural Language Processing
  • Competency-Based Education

Background:

  • Faculty feedback is crucial for trainee development in competency-based medical education.
  • Accurate categorization of narrative feedback is essential for effective progress tracking.

Purpose of the Study:

  • To develop and evaluate a Natural Language Processing (NLP) algorithm for categorizing narrative feedback.
  • To align feedback with Accreditation Council for Graduate Medical Education (ACGME) Milestone 2.0 subcompetencies.

Main Methods:

  • Analysis of 5,935 narrative evaluations from anesthesiology trainees.
  • Development and validation of an NLP model using data from multiple graduate medical education programs.
  • Performance evaluation using metrics such as AUC, PPV, sensitivity, and F1 scores.

Main Results:

  • The NLP model demonstrated good performance for professionalism, communication, and practice-based learning (AUC > 0.75).
  • Performance varied for medical knowledge and patient care (AUC 0.63-0.88), with poorer performance for systems-based practice (AUC 0.59).
  • The model rapidly processed hundreds of evaluations, generating organized reports for self-assessment.

Conclusions:

  • An NLP model was successfully developed to recognize and categorize anesthesiologist feedback across graduate medical education programs.
  • The model is a powerful tool for efficiently organizing large volumes of narrative feedback.
  • Operationalization in a self-assessment exercise demonstrates its practical utility in medical training.