The simulation of realistic clinical interactions plays a pivotal role in advancing clinical Large Language Models (LLMs) and supporting medical diagnostic education. Existing approaches and benchmarks rely on generic or LLM-generated dialogue data, which limits the authenticity and diversity of doctor-patient interactions. In this work, we propose the first Chinese patient simulation dataset (Ch-PatientSim), constructed from realistic clinical interaction scenarios to comprehensively evaluate the performance of models in emulating patient behavior. Patients are simulated based on a five-dimensional persona structure. To address issues of the persona class imbalance, a portion of the dataset is augmented using few-shot generation, followed by manual verification. We evaluate various state-of-the-art LLMs and find that most produce overly formal responses that lack individual personality. To address this limitation, we propose a training-free Multi-Stage Patient Role-Playing (MSPRP) framework, which decomposes interactions into three stages to ensure both personalization and realism in model responses. Experimental results demonstrate that our approach significantly improves model performance across multiple dimensions of patient simulation.

翻译：真实临床交互的模拟在推进临床大语言模型（LLM）发展和支持医学诊断教育方面起着关键作用。现有方法和基准依赖于通用或LLM生成的对话数据，这限制了医患交互的真实性与多样性。本研究提出了首个中文患者模拟数据集（Ch-PatientSim），该数据集基于真实临床交互场景构建，旨在全面评估模型在模拟患者行为方面的性能。患者模拟基于五维人物画像结构构建。针对人物画像类别不平衡的问题，部分数据集通过小样本生成进行增强，并经过人工验证。我们评估了多种前沿大语言模型，发现大多数模型生成的回答过于正式且缺乏个体个性。为克服这一局限，我们提出了一种免训练的多阶段患者角色扮演（MSPRP）框架，该框架将交互过程分解为三个阶段，以确保模型回答兼具个性化与真实性。实验结果表明，我们的方法在患者模拟的多个维度上显著提升了模型性能。