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Evaluating Hospital Course Summarization by an Electronic Health Record-Based Large Language Model.

William R Small1,2, Jonathan Austrian1,2, Luke O'Donnell2

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Summary
This summary is machine-generated.

Physician-AI collaboration using large language models (LLMs) for hospital course (HC) summarization requires less editing than physician-only drafts. LLM-generated HCs were more complete, concise, and cohesive, though with increased confabulations.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Documentation

Background:

  • Hospital course (HC) summarization is a time-consuming task for physicians.
  • Large language models (LLMs) show promise for automating HC summarization.
  • The effectiveness of physician-LLM partnerships in electronic health record (EHR)-embedded workflows for drafting HCs is not well understood.

Purpose of the Study:

  • To compare the editing effort required by resident physicians to refine LLM-generated HCs versus physician-generated HCs.
  • To evaluate the quality of HCs based on the 4Cs criteria: completeness, conciseness, cohesiveness, and confabulation-free.

Main Methods:

  • A quality improvement study involving 10 internal medicine residents and 8 hospitalists.
  • Residents edited physician-generated and LLM-generated HCs for randomly selected admissions.
  • Editing effort was measured by the percentage of edits and semantic change, with comparative ratings on the 4Cs using A/B testing.

Main Results:

  • LLM-generated HCs required significantly less editing (31.5% vs. 44.8%) and semantic change (2.4% vs. 4.9%) compared to physician-generated HCs.
  • Hospitalists rated LLM HCs as more complete (P<.001) and similarly concise (P=.20) and cohesive (P=.60).
  • LLM HCs had more confabulations (P=.002), but overall composite scores were similar (P=.46).

Conclusions:

  • EHR-embedded LLMs can assist physicians in drafting HCs, requiring less editing to meet quality standards.
  • Physician-LLM partnerships show feasibility for improving HC generation efficiency and quality.
  • Further monitoring of LLM-generated HCs in clinical practice is warranted, particularly regarding confabulations.