ELICITED：基于电子健康记录（EHR）的纵向交互式对话，用于信息获取类分诊评估与决策

中文摘要 AI 辅助

急诊科（ED）分诊要求临床医生快速识别需要立即关注的患者、确定谁可以安全等待，并对有限的临床资源进行优先级排序。然而，在患者就诊时，信息可能仅限于主诉和初始生命体征。临床重要细节，包括症状发作与进展、伴随症状、病史和药物使用情况，通常通过针对性对话获取。因此，有效的分诊要求临床医生识别信息缺口、提出适当的后续问题，并随着新证据的出现更新评估。大多数现有的急诊科基准测试从固定的临床快照评估病情严重程度预测。尽管这种形式在患者信息汇总后测量预测性能，但它无法捕捉分诊相关证据被引出和解释的交互过程。现有的医疗对话数据集支持临床沟通研究，但对话语句并不总是与电子健康记录（EHR）中的时间顺序事件相关联。我们引入EHR2Dial-Triage，这是一个基于MIMIC-IV-ED的智能对话生成框架和基准。该框架在明确的基于角色和时间的信息边界下构建分诊对话。每一条被接受的患者披露信息都与其支持的EHR事件以及该信息首次可用的对话回合相关联。EHR2Dial-Triage支持对信息引出、证据使用、五级急诊严重指数预测以及跨模型和患者角色的面向患者沟通的受控评估。它为将对话式分诊作为临床信息获取、推理和沟通的动态过程研究提供了结构化环境。

英文摘要

Emergency-department (ED) triage requires clinicians to rapidly identify patients who need immediate attention, determine who can safely wait, and prioritize limited clinical resources. At presentation, however, information may be limited to a chief complaint and initial vital signs. Clinically important details, including symptom onset and progression, associated symptoms, medical history, and medication use, are often obtained through focused conversation. Effective triage therefore requires clinicians to identify information gaps, ask appropriate follow-up questions, and update their assessment as new evidence becomes available. Most existing ED benchmarks evaluate acuity prediction from a fixed clinical snapshot. Although this formulation measures predictive performance after patient information has been assembled, it does not capture the interactive process through which triage-relevant evidence is elicited and interpreted. Existing medical dialogue datasets support the study of clinical communication, but dialogue statements are not always linked to temporally ordered events in the electronic health record (EHR). We introduce EHR2Dial-Triage, an agentic conversation-generation framework and benchmark grounded in MIMIC-IV-ED. The framework constructs triage conversations under explicit role-based and temporal information boundaries. Each accepted patient disclosure is linked to its supporting EHR event and the first dialogue turn at which it becomes available. EHR2Dial-Triage enables controlled evaluation of information elicitation, evidence use, five-level Emergency Severity Index prediction, and patient-facing communication across models and patient personas. It provides a structured setting for studying conversational triage as a dynamic process of clinical information acquisition, reasoning, and communication.

发表机构

• Ellipsis Health（埃利皮西斯健康公司）
• University of Michigan（密歇根大学）

机构由 AI 辅助整理，请以论文原文为准。