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大型语言模型的基于模拟的评估在瘤学中启用了临床决策支持平台
Nesrine Lajmi1, Mehul Patel2, Gareth Obery3
1Roche Information Solutions, Roche Diagnostics, Indianapolis, IN.
JCO clinical cancer informatics
|January 28, 2026
概括
一个大型语言模型 (LLM) 支持的临床决策支持平台显著减少了生成临床摘要的时间. 这种LLM-CDS工具提高了效率和完整性,显示了瘤学工作流程的潜力.
科学领域:
- 在瘤学瘤学.
- 临床信息学 临床信息学
- 人工智能在医学中的应用
背景情况:
- 电子健康记录 (EHR) 的低效率导致临床医生的认知过载和倦怠.
- 将碎片化的患者数据合成连贯的摘要是一个关键但耗时的临床任务.
研究的目的:
- 评估一个大型语言模型 (LLM) 支持的临床决策支持 (LLM-CDS) 平台与模拟的EHR (SimEPR) 相比.
- 评估对工作流程效率和用户体验产生瘤板准备临床摘要的影响.
- 探索LLM-CDS在其他瘤工作流程中的适用性,如咨询准备和治疗规划.
主要方法:
- 一个参与者内部的模拟,涉及26名使用合成乳腺癌病例的瘤学家.
- 对于任务完成时间,LLM-CDS (可编辑的LLM生成摘要) 与SimEPR (手动编写) 的比较.
- 对摘要质量 (完整性,正确性,简洁性) 和用户体验 (可用性,认知负载,可接受性) 的评估.
主要成果:
- LLM-CDS显著减少了总结完成时间 (6:55比8:47分钟;P<.001).
- 总结的完整性高于LLM-CDS (3.93对3.13),而正确性和简洁性是相似的.
- 87%的参与者会推LLM-CDS,理由是预期的节省时间和有用性 (92%) 瘤委员会和咨询准备.
结论:
- 该LLM-CDS平台提高了临床总结的效率和完整性.
- 强大的用户接受度和感知到的时间节省表明了简化各种瘤学工作流程的潜力.
- 在减轻与EHR数据合成相关的临床医生负担方面,LLM-CDS显示出了前景.
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