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Automated scoring of patient notes using natural language processing (NLP) offers reliable and feasible feedback for medical students. This automated short answer grading (ASAG) system enhances learning in case-based medical education.

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Area of Science:

  • Medical Education
  • Natural Language Processing
  • Psychometrics

Background:

  • Scoring postencounter patient notes (PNs) provides valuable student performance insights but is resource-intensive.
  • Advances in NLP and machine learning enable automated short answer grading (ASAG) for PNs.

Purpose of the Study:

  • To evaluate the psychometric characteristics and reliability of an ASAG system for scoring PNs.
  • To assess the feasibility and annotation requirements for implementing ASAG in a new case.

Main Methods:

  • A feed-forward neural network algorithm was used to train the ASAG system on PNs from standardized patient (SP) cases.
  • Faculty annotation of 10 PNs per case was required to tune the system.
  • ASAG ratings were compared to human ratings using item analysis and inter-rater reliability (IRR) metrics (kappa).

Main Results:

  • ASAG scores showed variability in differentiating learner performance and high IRR (kappa = .83) compared to human ratings.
  • The system demonstrated high item discrimination and internal consistency reliability (Cronbach's alpha = .65–.77).
  • Tuning the ASAG system was cost-effective, with faculty time costing approximately $928 per case.

Conclusions:

  • NLP-based ASAG for PNs is reliable and psychometrically sound for learner feedback.
  • Minimal phrase-level annotations are needed to tune the system for new cases, enhancing feasibility.
  • ASAG has significant potential for improving feedback in case-based medical education.