AI Managed Emergency Documentation with a Pretrained Model
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Abstract (arXiv)
This study investigates the use of a large language model system to improve efficiency and quality in emergency department (ED) discharge letter writing. Time constraints and infrastructural deficits make compliance with current discharge letter targets difficult. We explored potential efficiencies from an artificial intelligence software in the generation of ED discharge letters and the attitudes of doctors toward this technology. The evaluated system leverages advanced techniques to fine-tune a model to generate discharge summaries from short-hand inputs, including voice, text, and electronic health record data. Nineteen physicians with emergency medicine experience evaluated the system text and voice-to-text interfaces against manual typing. The results showed significant time savings with MedWrite LLM interfaces compared to manual methods.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 2 of its 16 references that are in the map. First posted on arXiv 17 Aug 2024.
Builds on
2 of 16 references.
References · 2
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2021Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarizationcited by 76
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.