发表机构
Monash University; Peking University; Shantou University Medical College; The University of Hong Kong（莫纳什大学; 北京大学; 汕头大学医学院; 香港大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究通过分析210名医学生的1030条GenAI VP胸痛病例对话，采用三层分析揭示高分临床推理的过程模式，为医学教育提供教师可解释的过程证据。
AI 中文摘要
病史采集是一项基于对话的临床推理任务，学习者需在问诊过程中收集、整理并整合患者信息。生成式AI虚拟患者（GenAI VPs）可让重复的病史采集练习实现规模化，并保留完整的逐轮对话记录。然而，这些日志直接用于教学存在困难：完整的转录内容对教师日常审阅而言过于繁琐，而最终分数则无法体现学习者是否跟进了患者线索、核对了不确定性，或利用总结来指导后续提问。本研究探讨编码后的GenAI VP对话是否能提供教师可解释的临床推理过程证据。我们分析了210名二年级医学生在五周胸痛病例中的1030条GenAI VP对话。每次问诊由教师采用评估完整病史采集对话的评分标准打分，并按每周中位数将各周的问诊分为高分或低分两类。为解释评分表现如何反映在对话过程中，我们对同一编码后的对话数据应用了三层分析：行为流行度、采用认知网络分析（Epistemic Network Analysis）的局部共现，以及采用转换网络分析（Transition Network Analysis）的序列转换。高分问诊涉及更多病史采集活动，但差异并非仅关于数量；高分问诊更常将信息收集与症状探究同沟通、核对、组织及综合相联系，总结与组织行为更常导向验证或以机制为导向的跟进。这些发现表明，对GenAI VP对话日志的分层分析可揭示与高分病史采集相关的过程模式，并支持医学教育中以过程为核心的反馈。
英文摘要
Medical history taking is a dialogue-based clinical reasoning task in which learners must gather, organise, and integrate patient information while the consultation unfolds. Generative AI-powered virtual patients (GenAI VPs) make repeated history taking practice scalable and preserve full turn by turn dialogue. However, these logs are educationally difficult to use directly. Complete transcripts are too detailed for routine teacher review, whereas final scores obscure whether learners followed up patient cues, checked uncertainty, or used summaries to guide later questioning. This study examined whether coded GenAI VP dialogues can provide teacher-interpretable process evidence of clinical reasoning. We analysed 1{,}030 GenAI VP dialogues from 210 second-year medical learners across five weeks chest-pain cases. Each consultation was teacher-scored using a rubric assessing the full history taking dialogue, and consultations were classified within each week as high- or low-rated using the weekly median score. To explain how rated performance was reflected in the dialogue process, we applied three analytic layers to the same coded dialogue data: behavioural prevalence, local co-occurrence using Epistemic Network Analysis, and sequential transition using Transition Network Analysis. High-rated consultations involved more history taking activity, but differences were not simply about volume. High rated consultations more often connected information gathering and symptom exploration with communication, checking, organisation, and synthesis. Summarising and organising moves more often led to verification or mechanism-oriented follow-up. These findings show how layered analysis of GenAI VP dialogue logs can reveal process patterns associated with high rated history taking and support process-focused feedback in medical education.
