用大模型自动生成出院小结，医生使用率高且省时。
Phase 1 Implementation of LLM-generated Discharge Summaries showing high Adoption in a Dutch Academic Hospital
- 将大模型嵌入电子病历，自动生成出院小结初稿。
- 58.5%的病例直接复制生成内容，86.9%医生感觉写病历时间减少。
- 适合临床医生快速完成文书工作，推动智能辅助落地。
撰写出院小结以传递医疗信息是重要但耗时的工作，可由大语言模型（LLM）辅助完成。本前瞻性混合方法试点研究评估了集成于电子健康记录（EHR）的LLM在生成出院小结初稿方面的应用。在9周内，21名住院医师和4名医师助理在本学术医院共生成379份出院小结。其中58.5%的病例直接复制了模型生成内容，可追溯到最终出院信的模型内容占29.1%。值得注意的是，86.9%的使用者自述文档时间减少，60.9%表示行政负担减轻。试点结束后继续使用的意愿高达91.3%，支持该应用场景的进一步推广。准确测量用户撰写出院小结的时间仍具挑战性，但未来外在评估中必不可少。
原文摘要 · Abstract (English)
Writing discharge summaries to transfer medical information is an important but time-consuming process that can be assisted by Large Language Models (LLMs). This prospective mixed methods pilot study evaluated an Electronic Health Record (EHR)-integrated LLM to generate discharge summaries drafts. In total, 379 discharge summaries were generated in clinical practice by 21 residents and 4 physician assistants during 9 weeks in our academic hospital. LLM-generated text was copied in 58.5% of admissions, and identifiable LLM content could be traced to 29.1% of final discharge letters. Notably, 86.9% of users self-reported a reduction in documentation time, and 60.9% a reduction in administrative workload. Intent to use after the pilot phase was high (91.3%), supporting further implementation of this use-case. Accurately measuring the documentation time of users on discharge summaries remains challenging, but will be necessary for future extrinsic evaluation of LLM-assisted documentation.
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