使用真实世界电子病历数据的大语言模型出院小结制备显示出前景。
Large language model discharge summary preparation using real-world electronic medical record data shows promise.
作者信息
Hains Lewis, Kleinig Oliver, Murugappa Ashwin, Gluck Samuel, Marks Jarrod, Gilbert Toby, Bacchi Stephen
机构信息
Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.
Division of Medicine, Lyell McEwin Hospital, Adelaide, South Australia, Australia.
出版信息
Intern Med J. 2025 May 28;55(7):1188-92. doi: 10.1111/imj.70073.
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.
大语言模型（LLMs）在利用真实临床文档进行出院小结编制方面的功效仍是新课题。我们的研究旨在测试两种大语言模型生成出院小结的功效，这些小结使用经过验证的出院小结评分指标进行评分。两个模型的表现几乎相同，llama3:instruct模型的平均得分为19.1/31（标准差：2.42），而llama3:70b生成的平均得分为19.2/31（标准差：3.48）。使用大语言模型辅助生成出院小结可能有助于减轻整体临床管理工作量。
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