arXiv:2507.16947cs.CL2025-07被引 37

AI助手帮医生减少误诊和治疗错误,真实世界效果显著

AI-based Clinical Decision Support for Primary Care: A Real-World Study

  • AI助手嵌入临床流程,仅在需要时提醒,不干扰医生决策
  • 使用AI后诊断错误降16%,治疗错误降13%,每年可避免超5万例错误
  • 一线医生普遍认可其价值,75%认为改善“显著”,适合基层医疗推广

我们评估了大型语言模型驱动的临床决策支持工具在真实医疗环境中的影响。与肯尼亚内罗毕的初级保健诊所网络Penda Health合作,研究了名为AI Consult的工具,该工具通过识别潜在的病历记录和临床决策错误,为医生提供安全网。该系统融入医生工作流,仅在必要时触发,保障医生自主权。我们在15家诊所开展质量改进研究,对比了39,849次患者就诊中,有无使用AI Consult的医生的表现。独立医师评审发现,使用AI Consult的医生诊断错误减少16%,治疗错误减少13%。以Penda Health为例,年均可避免2.2万次诊断错误和2.9万次治疗错误。对使用AI Consult的医生调查显示,所有医生都认为其提升了医疗质量,其中75%表示改善“显著”。成功应用依赖于与临床流程匹配的部署方式及主动推广策略。本研究展示了基于LLM的临床决策支持工具在真实世界中减少错误的潜力,并提供了负责任落地的实践框架。

原文摘要 · Abstract (English)

We evaluate the impact of large language model-based clinical decision support in live care. In partnership with Penda Health, a network of primary care clinics in Nairobi, Kenya, we studied AI Consult, a tool that serves as a safety net for clinicians by identifying potential documentation and clinical decision-making errors. AI Consult integrates into clinician workflows, activating only when needed and preserving clinician autonomy. We conducted a quality improvement study, comparing outcomes for 39,849 patient visits performed by clinicians with or without access to AI Consult across 15 clinics. Visits were rated by independent physicians to identify clinical errors. Clinicians with access to AI Consult made relatively fewer errors: 16% fewer diagnostic errors and 13% fewer treatment errors. In absolute terms, the introduction of AI Consult would avert diagnostic errors in 22,000 visits and treatment errors in 29,000 visits annually at Penda alone. In a survey of clinicians with AI Consult, all clinicians said that AI Consult improved the quality of care they delivered, with 75% saying the effect was "substantial". These results required a clinical workflow-aligned AI Consult implementation and active deployment to encourage clinician uptake. We hope this study demonstrates the potential for LLM-based clinical decision support tools to reduce errors in real-world settings and provides a practical framework for advancing responsible adoption.

临床决策AI医疗大模型真实世界研究

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