大型语言模型在术后医学的临床和经济影响:一个随机交叉试验
Yu He Ke1,2,3, Bernard Soon Yang Ong4, Liyuan Jin5,6
1Department of Anesthesiology, Singapore General Hospital, Singapore, Singapore. ke.yuhe@singhealth.com.sg.
NPJ digital medicine
|July 21, 2025
概括
一个新的AI聊天机器人PEACH (PErioperative AI CHatbot) 显示了改善手术前文档质量的潜力,并提供了显著的成本节省,尽管它没有减少医生整体文档时间.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 在外科手术期间的医学.
背景情况:
- 术前评估至关重要,但面临着时间,准则遵守和文档负担的挑战.
- 现有的工作流程往往导致在手术前收集和记录患者信息的效率低下.
研究的目的:
- 评估PEACH (PErioperative AI CHatbot) 系统对文档效率,质量,用户接受度和成本效益的影响.
- 评估在术前咨询中使用基于LLM的临床决策支持的可行性.
主要方法:
- 一个随机交叉试验,涉及新加坡总医院的住院医生.
- 评估文档的时间,质量指标 (包含问题列表),用户偏好和经济建模.
主要成果:
- PEACH并没有显著减少整体文档处理时间.
- 在中度复杂的病例和经验丰富的医生中观察到时间节省.
- 评估人员在57.1%的案例中更喜欢PEACH辅助的文档,并改进了问题列表的包含.
- 估计SGD197,501 (USD146,297) 的预计年度机构储蓄.
结论:
- 基于LLM的工具,如PEACH可以提高术前文档的质量,并提供经济价值.
- 进一步的研究和实施可以通过人工智能协助优化术后护理.
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