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Quantifying Emergency Medicine Residency Learning Curves Using Natural Language Processing: Retrospective Cohort

Carl Preiksaitis1, Joshua Hughes1, Rana Kabeer1

  • 1Department of Emergency Medicine, Stanford University School of Medicine, 900 Welch Road, Suite 350, Palo Alto, CA, 94304, United States, 1 650-723-6576, 1 650-723-0121.

JMIR Medical Education
|December 9, 2025
PubMed
Summary
This summary is machine-generated.

Natural language processing (NLP) of clinical documentation reveals that emergency medicine (EM) residents gain significant new clinical topic exposure throughout their training, even into their fourth year. This supports a 4-year EM residency model for enhanced educational value.

Keywords:
clinical exposureelectronic health recordsemergency medicine residencygraduate medical educationlearning curvesnatural language processing

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Area of Science:

  • Medical Education
  • Emergency Medicine
  • Computational Linguistics

Background:

  • The optimal duration of emergency medicine (EM) residency training is debated, with potential standardization to 4 years.
  • Limited empirical data exists on resident clinical exposure accumulation and diagnostic reasoning depth.
  • Natural language processing (NLP) offers a novel method for comprehensive clinical experience quantification.

Purpose of the Study:

  • Quantify clinical topic exposure acquisition in EM residents over time.
  • Evaluate variations in exposure patterns among residents and graduating classes.
  • Assess changes in workload and case complexity to inform optimal program length.

Main Methods:

  • Retrospective cohort study of 62 EM residents and 244,255 ED encounters (2016-2023).
  • Utilized a retrieval-augmented generation NLP pipeline to map clinical documentation to the 2022 Model for Clinical Practice of Emergency Medicine (MCPEM) subcategories.
  • Analyzed cumulative topic exposure, coverage diversity, inter-resident variability, clinical complexity (ESI scores), and admission rates.

Main Results:

  • Residents gained the most new topics in PGY1, with exposure plateauing around 39-41 months, but with significant individual variation.
  • By PGY4, residents averaged 63.2% of MCPEM subcategories, a 9.9% increase over PGY3.
  • Annual case volume more than tripled from PGY1 to PGY4, with increased case complexity (lower ESI scores, higher ESI 1-2 cases).

Conclusions:

  • NLP provides a scalable, detailed method for tracking EM resident clinical exposure and progression.
  • Residents continue to acquire new experiences, including higher-acuity cases, into their fourth year.
  • A 4-year training model may offer additional educational value, emphasizing the need for individualized assessment due to learning trajectory variability.