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Related Concept Videos

Discharge Summary Forms01:31

Discharge Summary Forms

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The discharge summary is crucial as it enables a smooth transition from a healthcare facility to a patient's home or another care setting. This critical document facilitates seamless continuity of care, ensuring patients receive the necessary support and attention.
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Related Experiment Video

Updated: Jan 6, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Large Language Model Assistant for Emergency Department Discharge Documentation.

Ji Woo Song1, Junseong Park2, Ji Hoon Kim2,3

  • 1Yonsei University College of Medicine, Seoul, South Korea.

JAMA Network Open
|October 21, 2025
PubMed
Summary

A large language model (LLM) assistant significantly reduced emergency department (ED) discharge note writing time. This AI tool improved documentation quality, marking a key advancement in clinical practice.

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Documentation Improvement
  • Emergency Medicine Informatics

Background:

  • Emergency department (ED) discharge documentation is a critical but time-consuming process.
  • Incomplete documentation can negatively impact patient care and hospital efficiency.
  • There is a need for innovative solutions to streamline ED documentation.

Purpose of the Study:

  • To develop and evaluate a large language model (LLM) assistant for generating ED discharge notes.
  • To assess the impact of the LLM assistant on documentation quality and workflow efficiency.

Main Methods:

  • A comparative effectiveness study was conducted at a tertiary care hospital.
  • A large language model (LLM) was fine-tuned using 592 ED cases and validated on 50 cases.
  • Emergency physicians completed notes manually and then edited LLM-generated drafts.

Main Results:

  • LLM-assisted notes demonstrated higher scores in completeness, correctness, conciseness, and clinical utility compared to manual notes.
  • The median documentation time per note decreased from 69.5 seconds (manual) to 32.0 seconds (LLM-assisted).
  • LLM-assisted notes showed improvements across all 4C metrics (completeness, correctness, conciseness, clinical utility) compared to manual notes (P < .001).

Conclusions:

  • An on-site LLM assistant significantly reduces the time required for ED discharge note completion.
  • The LLM assistant improves documentation quality without compromising key metrics.
  • This represents a significant advancement in leveraging artificial intelligence for clinical workflow efficiency.