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Initial Development of an Automated Platform for Assessing Trainee Performance on Case Presentations.

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Summary
This summary is machine-generated.

Natural language processing (NLP) can automatically assess oral case presentations by analyzing transcripts for content and style. This method allows for objective ranking of physician trainees, potentially improving medical education feedback.

Keywords:
clinical roundsdeep learningintensive care unitmedical educationnatural language processing

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Area of Science:

  • Medical Education
  • Computational Linguistics
  • Health Informatics

Background:

  • Oral case presentations are vital for physicians and team-based care.
  • Objective assessment and feedback on these presentations during training are often lacking.

Purpose of the Study:

  • To evaluate the utility of natural language processing (NLP) for automated, objective assessment of oral case presentation quality.
  • To determine if NLP can extract meaningful data from presentation transcripts.

Main Methods:

  • Simulated oral case presentations from critical care fellows and an attending physician were transcribed.
  • Natural language processing (NLP) was used to convert transcripts into numerical representations.
  • Similarity between presentations was calculated based on these numerical values, and fellows were ranked accordingly.

Main Results:

  • The study analyzed 99 presentations totaling 260 minutes.
  • NLP successfully ranked fellows based on presentation similarity to the attending physician.
  • A discernible difference in similarity scores was observed between top-ranked and lower-ranked fellows.

Conclusions:

  • Natural language processing (NLP) offers a viable method for ranking oral case presentations based on similarity to a reference.
  • While further development is needed to translate scores into actionable feedback, NLP holds promise for enhancing medical education tools.