arXiv 2604.197742026-03-27cs.CLcs.AI

LLM生成的出院小结在荷兰学术医院中的阶段1实施显示高采用率

Phase 1 Implementation of LLM-generated Discharge Summaries showing high Adoption in a Dutch Academic Hospital

Department of Health Information Office, Data & Digitalization, University Medical Center（健康信息办公室、数据与数字化化大学医院）；Department of Trauma Surgery / Orthopedics, University Medical Center（创伤外科/骨科大学医院）；Department of Orthopaedic Surgery, Massachusetts General Hospital（骨科手术科马萨诸塞总医院）；Harvard Medical School Orthopaedic Trauma Initiative（哈佛医学院骨创伤倡议）；Department of Orthopaedic Surgery and Sports Medicine, Amsterdam UMC location University of Amsterdam（骨科手术与运动医学部门，阿姆斯特丹UMC地点大学）；Department of Orthopaedic Trauma, Flinders University Medical Centre（骨创伤部门，弗林德斯大学医疗中心）；Department of Otolaryngology – Head and Neck Surgery, University Medical Center（耳鼻喉科-头颈外科大学医院）；Department of Pediatrics, Pediatrics Nephrology, Beatrix Children’s Hospital, University Medical Center（儿科、儿科肾病学，比西特儿童医院，大学医院）

Nettuno Nadalini, Tarannom Mehri, Anne H Hoekman, Katerina Kagialari, Job N Doornberg, Tom P van der Laan, Jacobien H F Oosterhoff, Rosanne C Schoonbeek, Charlo… 展开作者

Nettuno Nadalini, Tarannom Mehri, Anne H Hoekman, Katerina Kagialari, Job N Doornberg, Tom P van der Laan, Jacobien H F Oosterhoff, Rosanne C Schoonbeek, Charlotte M H H T Bootsma-Robroeks

备注：: The methods section is located after the discussion in this manuscript

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AI总结：

本研究评估了集成到电子健康记录中的LLM生成出院小结草案的效果，结果显示高采用率和文档时间减少，支持进一步实施。

摘要：

撰写出院小结以传递医疗信息是一个重要但耗时的过程，可以通过大型语言模型（LLMs）来协助。本项前瞻性混合方法试点研究评估了集成到电子健康记录（EHR）中的LLM生成出院小结草稿的效果。在为期9周的临床实践中，由21名居民和4名医生助理共生成了379份出院小结。LLM生成的文本在58.5%的病例中被复制，可识别的LLM内容可追溯到29.1%的最终出院小结。值得注意的是，86.9%的用户自报文档时间减少了，60.9%的用户自报行政工作量减少了。试点阶段后的使用意愿很高（91.3%），支持进一步实施此用例。准确测量用户在出院小结上的文档时间仍具挑战性，但未来对LLM辅助文档的外在评估将是必要的。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。