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Updated: Sep 20, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
Large language model discharge summary preparation using real-world electronic medical record data shows promise
Lewis Hains1, Oliver Kleinig1, Ashwin Murugappa1
1Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.
None:
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.
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