EHRNavigator用AI代理实现跨异构病历的精准问答，临床可用。
EHRNavigator: A Multi-Agent System for Patient-Level Clinical Question Answering over Heterogeneous Electronic Health Records
- 设计多智能体系统，协同处理异构病历中的多模态数据
- 在真实医院场景中达86%准确率，响应时间符合临床要求
- 兼顾实用性与可解释性，适合医疗AI落地部署
临床决策越来越依赖电子健康记录（EHR）中及时且上下文相关的患者信息，但现有自然语言问答（QA）系统大多仅在基准数据集上评估，限制了实际应用价值。为克服这一局限，我们提出EHRNavigator，一个基于AI代理的多智能体框架，可在异构和多模态的EHR数据上实现患者级别的问题回答。我们在公开基准数据集和机构数据集上，在包含多样数据模式、时间推理需求和多模态证据融合的真实医院条件下评估其性能。通过定量分析和临床医生验证的病历审查，EHRNavigator展现出强泛化能力，在真实病例中达到86%的准确率，同时保持临床可接受的响应时间。结果表明，EHRNavigator有效弥合了基准评估与临床部署之间的差距，提供了一种鲁棒、自适应且高效的现实世界EHR问答解决方案。
原文摘要 · Abstract (English)
Clinical decision-making increasingly relies on timely and context-aware access to patient information within Electronic Health Records (EHRs), yet most existing natural language question-answering (QA) systems are evaluated solely on benchmark datasets, limiting their practical relevance. To overcome this limitation, we introduce EHRNavigator, a multi-agent framework that harnesses AI agents to perform patient-level question answering across heterogeneous and multimodal EHR data. We assessed its performance using both public benchmark and institutional datasets under realistic hospital conditions characterized by diverse schemas, temporal reasoning demands, and multimodal evidence integration. Through quantitative evaluation and clinician-validated chart review, EHRNavigator demonstrated strong generalization, achieving 86% accuracy on real-world cases while maintaining clinically acceptable response times. Overall, these findings confirm that EHRNavigator effectively bridges the gap between benchmark evaluation and clinical deployment, offering a robust, adaptive, and efficient solution for real-world EHR question answering.
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