Large language model discharge summary preparation using real‐world electronic medical record data shows promise
Year: 2025
Type: article
Abstract: The efficacy of large language models (LLMs) in discharge summary preparation using real clinical documentation remains novel. Our study aimed to test the efficacy of two LLMs to generate DC summaries which were scored using a validated discharge summary scoring metric. The models performed nearly identically, with the llama3:instruct model having a mean score of 19.1/31 (SD: 2.42) compared to 19.2/31 (SD: 3.48) when produced by llama3:70b. Using LLMs to aid in the generation of discharge summaries may help to reduce the overall clinical administrative workload.
Source: Internal Medicine Journal
DOI: 10.1111/imj.70073
Authors: Lewis Hains, Oliver Kleinig, Ashwin Murugappa, Samuel Gluck, Jarrod Marks, Toby Gilbert, Stephen Bacchi
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,570
Funder: University of Adelaide
OpenAlex ID: W4410823695