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Utilizing Natural Language Processing of Narrative Feedback to Develop a Predictive Model of Pre-Clerkship

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Natural language processing (NLP) can streamline medical education feedback review. NLP models accurately predict student performance, aiding competency committee reviews by analyzing narrative feedback efficiently.

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Area of Science:

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

Background:

  • The Feinberg School of Medicine uses a portfolio assessment system for reviewing narrative feedback for pre-clerkship learners.
  • This review process is time-consuming and labor-intensive.
  • Natural language processing (NLP) offers a potential solution for improving efficiency.

Purpose of the Study:

  • To develop and validate a predictive model using NLP to assess pre-clerkship student performance from narrative feedback.
  • To assist medical school competency committees in their review processes.

Main Methods:

  • An iterative and inductive approach was used to analyze narrative feedback.
  • Words and phrases were manually grouped into topics predictive of performance.
  • Qualitative techniques, including member checking and iterative revision, were employed.

Main Results:

  • Sixteen topic groups demonstrated predictive power for student performance.
  • The optimal model combined topic groups, word counts, and categorical ratings.
  • The model achieved an AUC of 0.92 on training data and 0.88 on test data.

Conclusions:

  • A tailored NLP approach, incorporating qualitative methods, is crucial for analyzing medical education narrative feedback.
  • Standard NLP packages are insufficient for predicting student outcomes in this context.
  • The developed model provides a useful and salient tool for competency committee reviews.