医生在儿科出院总结生成中表现优于大型语言模型
Andrew P Bain1,2, Averi Wilson1,3,4, Janet Webb1,5,4
1Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas.
Hospital pediatrics
|January 21, 2026
概括
医生撰写的出院摘要在质量上被评为高于儿科临床医生未经编辑的大型语言模型 (LLM) 生成的摘要. 进一步的研究应该探索医生修改的LLM文档,以提高质量和减少工作量.
科学领域:
- 医疗信息学 医疗信息学
- 临床文档 临床文档
- 医疗保健中的人工智能
背景情况:
- 儿童医院医生面临着越来越多的患者复杂性和工作量.
- 大型语言模型 (LLM) 显示了自动化临床文档的潜力,包括放药摘要.
- 评估LLM生成的临床文档的质量对于评估其实用性至关重要.
研究的目的:
- 为了比较未经编辑的LLM生成的出院摘要与医生授权的出院摘要的质量.
- 评估临床医生对LLM生成与医生撰写的儿科出院摘要的看法.
主要方法:
- 对35个未经编辑的LLM生成和35个医生撰写的出院摘要进行了比较评估.
- 儿童医院医生和初级保健儿科医生使用医生文档质量仪器 (PDQI) -9-匿名对摘要进行了评分.
- 对每个摘要收集了临床风险组 (CRG) 和停留时间.
主要成果:
- 医生撰写的摘要从儿童医院医生和初级保健儿科医生获得了更高的整体质量评分.
- 通过LLM生成的摘要比医生撰写的摘要要长得多.
- 较高的CRG分数与医生撰写摘要的质量分数下降相关.
结论:
- 与未经编辑的LLMs相比,医生制作了更高质量的出院摘要.
- 未来的研究应该调查医生修改的LLM文档,以寻找质量和效率的潜在改进.
- 对医生工作量和幸福感的LLM的影响需要进一步研究.
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