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Comparing Machine Learning Models and Human Raters When Ranking Medical Student Performance Evaluations.

Jonathan Kibble¹, Jeffrey Plochocki²

¹Both authors are with University of Central Florida College of Medicine. is Professor of Medical Education; and.

Journal of Graduate Medical Education

|August 28, 2023

Summary

A new rubric reliably ranked Medical Student Performance Evaluations (MSPEs). Machine learning models detected positive sentiment but could not reliably rank the MSPEs for residency program evaluations.

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Area of Science:

Medical Education
Natural Language Processing
Residency Admissions

Background:

Medical Student Performance Evaluations (MSPEs) are lengthy narrative summaries.
Evaluating numerous MSPEs presents challenges for residency programs.

Purpose of the Study:

Develop a rubric to assess MSPE narratives.
Compare machine learning models (MLMs) for ranking MSPEs by positivity.

Main Methods:

30 de-identified MSPEs were manually scored using a new rubric.
Faculty faculty assessed MSPEs based on ACGME competencies.
3 commercial MLMs performed global sentiment analysis on MSPEs.
Correlation analysis compared faculty and MLM rankings.

Main Results:

Faculty interrater reliability for the rubric was high (ICC=0.864).
Faculty rankings showed strong correlation (r=0.758).
MLMs detected positive sentiment in all MSPEs but showed no significant correlation in rankings among themselves or with faculty.
The rubric was feasible and added minimal time.

Conclusions:

The rubric provides reliable scoring and ranking of MSPEs.
MLMs accurately identify positive sentiment but lack ranking reliability for MSPEs.