Large language model discharge summary preparation using real-world electronic medical record data shows promise.

Journal: Internal medicine journal
Published Date: May 28, 2025

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.

Authors

  • Lewis Hains
    University of Adelaide, Adelaide, South Australia, Australia.
  • Oliver Kleinig
    University of Adelaide, Adelaide, SA.
  • Ashwin Murugappa
    Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.
  • Samuel Gluck
    Neurology Department, Royal Adelaide Hospital, Port Road, Adelaide, SA, 5000, Australia.
  • Jarrod Marks
    Division of Medicine, Lyell McEwin Hospital, Adelaide, South Australia, Australia.
  • Toby Gilbert
    Neurology Department, Royal Adelaide Hospital, Port Road, Adelaide, SA, 5000, Australia.
  • Stephen Bacchi
    Faculty of Health and Medical Sciences, Adelaide Medical School, University of Adelaide, Adelaide, SA 5000 Australia.

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