Large Language Model Assistant for Emergency Department Discharge Documentation

Abstract

Importance: Emergency department (ED) discharge documentation is time-consuming and often incomplete. Objective: To develop a large language model (LLM) assistant that generates ED discharge notes and to evaluate its effectiveness on documentation quality and workflow efficiency. Design, Setting, and Participants: This comparative effectiveness study, which was conducted at a 2400-bed tertiary care hospital in South Korea, consisted of 2 primary phases: a development phase and sequential validation of the LLM assistant. In the randomized sequential prospective validation, 6 emergency physicians first wrote discharge notes manually (session 1), then edited LLM-generated drafts after a 1-hour washout period (session 2). Three independent physicians evaluated 300 note sets (each containing a manual note, an LLM draft, and an LLM-assisted note). For model development and validation, patient records from ED visits between September 1, 2022, and August 31, 2023, were used. The inclusion criteria encompassed adult patients (aged ≥17 years) and pediatric patients with nondisease conditions (eg, trauma, poisoning, or burns). Emergency physicians selected 592 representative cases for training and 50 for validation. Exposure: A commercially available text generation transformer model was used as a core LLM, fine-tuned using the 592 training cases. Two distinct processing pipelines were implemented within the LLM assistant due to different input data: (1) for patients managed solely by emergency physicians, using the ED initial record and prescription list, and (2) for those requiring specialty consultations, using the ED initial record and consultation request form. Main Outcomes and Measures: Quality of notes using 4C metrics (completeness, correctness, conciseness, and clinical utility) on a Likert scale ranging from 1 to 5 and time taken to complete the notes manually and with the LLM assistant. Results: Of the 50 test cases, the mean (SD) patient age was 57.7 (23.1) years, and 28 patients (56%) were female. LLM-assisted notes achieved higher scores than manual notes in completeness (4.23 [95% CI, 4.17-4.28] vs 4.03 [95% CI, 3.96-4.09]), correctness (4.38 [95% CI, 4.33-4.42] vs 4.20 [95% CI, 4.14-4.26]), conciseness (4.23 [95% CI, 4.18-4.28] vs 4.11 [95% CI, 4.05-4.17]), and clinical utility (4.17 [95% CI, 4.11-4.23] vs 3.85 [95% CI, 3.78-3.91]) (all P < .001). When compared with LLM drafts, LLM-assisted notes excelled in conciseness (4.23 vs 3.98 [95% CI, 3.91-4.04]; P < .001) and maintained equivalent clinical utility (4.17 vs 4.16 [95% CI, 4.11-4.21]; P > .99), but scored lower in completeness (4.23 vs 4.34 [95% CI, 4.29-4.39]; P = .001) and correctness (4.38 vs 4.45 [95% CI, 4.41-4.49]; P < .001). The median documentation time per note dropped from 69.5 (95% CI, 65.5-78.0) seconds for manual notes to 32.0 (95% CI, 29.5-36.0) seconds for LLM-assisted notes (P < .001). Conclusion: In this comparative effectiveness study, use of an on-site LLM assistant was associated with reduced writing time for ED discharge notes compared with manual note-taking, without compromising documentation quality, representing a critical advancement in the use of artificial intelligence for clinical practice.

The authors' abstract, as published at the source. JAMA Network Open, 2025 · DOI ↗