用大模型模拟病人对抗与辩解两种难沟通场景，提升医学生实战能力。
Modeling Challenging Patient Interactions: LLMs for Medical Communication Training
- 通过提示工程构建具备情绪特征的虚拟病人，模仿真实对话行为。
- 医生评分显示虚拟病人真实感高（3.7~3.8分），情绪特征与设定一致。
- 适合医学教育、临床培训，尤其需提升沟通技巧的医护人员使用。
有效患者沟通在医疗中至关重要，但传统培训缺乏对多样化、挑战性人际互动的接触。本研究提出利用大语言模型（LLMs）模拟基于萨提尔模型的“控诉者”和“合理化者”两类患者沟通风格，并支持多语言以适配多元文化背景，提升医疗从业者可及性。通过先进提示工程，包括行为提示、作者注释与固执机制，开发出具备细腻情感与对话特征的虚拟患者（VPs）。医疗专业人员评估显示，其真实性得分分别为：控诉者 $3.8 /pm 1.0$，合理化者 $3.7 /pm 0.8$（5分制量表）。情感分析揭示：控诉者表现出疼痛、愤怒与焦虑，合理化者则呈现沉思与平静，与预设详细病史一致。情感分数（0–9分）进一步验证差异——控诉者为负向（$3.1 /pm 0.6$），合理化者更趋中性（$4.0 /pm 0.4$）。结果表明，LLMs能有效复现复杂沟通模式，为医学教育提供变革性工具，助力医学生应对临床挑战，增强同理心与诊断能力。该方法具备可扩展、低成本优势，为未来医疗培训创新奠定基础。
原文摘要 · Abstract (English)
Effective patient communication is pivotal in healthcare, yet traditional medical training often lacks exposure to diverse, challenging interpersonal dynamics. To bridge this gap, this study proposes the use of Large Language Models (LLMs) to simulate authentic patient communication styles, specifically the "accuser" and "rationalizer" personas derived from the Satir model, while also ensuring multilingual applicability to accommodate diverse cultural contexts and enhance accessibility for medical professionals. Leveraging advanced prompt engineering, including behavioral prompts, author's notes, and stubbornness mechanisms, we developed virtual patients (VPs) that embody nuanced emotional and conversational traits. Medical professionals evaluated these VPs, rating their authenticity (accuser: $3.8 \pm 1.0$; rationalizer: $3.7 \pm 0.8$ on a 5-point Likert scale (from one to five)) and correctly identifying their styles. Emotion analysis revealed distinct profiles: the accuser exhibited pain, anger, and distress, while the rationalizer displayed contemplation and calmness, aligning with predefined, detailed patient description including medical history. Sentiment scores (on a scale from zero to nine) further validated these differences in the communication styles, with the accuser adopting negative ($3.1 \pm 0.6$) and the rationalizer more neutral ($4.0 \pm 0.4$) tone. These results underscore LLMs' capability to replicate complex communication styles, offering transformative potential for medical education. This approach equips trainees to navigate challenging clinical scenarios by providing realistic, adaptable patient interactions, enhancing empathy and diagnostic acumen. Our findings advocate for AI-driven tools as scalable, cost-effective solutions to cultivate nuanced communication skills, setting a foundation for future innovations in healthcare training.
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