As Large Language Models increasingly power role-playing applications, simulating patients has become a valuable tool for training counselors and scaling therapeutic assessment. However, prior work is fragmented: existing approaches rely on incompatible, non-standardized data formats, prompts, and evaluation metrics, hindering reproducibility and fair comparison. In this paper, we introduce PatientHub, a unified and modular framework that standardizes the definition, composition, and deployment of simulated patients. To demonstrate PatientHub's utility, we implement several representative patient simulation methods as case studies, showcasing how our framework supports standardized cross-method evaluation and the seamless integration of custom evaluation metrics. We further demonstrate PatientHub's extensibility by prototyping two new simulator variants, highlighting how PatientHub accelerates method development by eliminating infrastructure overhead. By consolidating existing work into a single reproducible pipeline, PatientHub lowers the barrier to developing new simulation methods and facilitates cross-method and cross-model benchmarking. Our framework provides a practical foundation for future datasets, methods, and benchmarks in patient-centered dialogue, and the code is publicly available via https://github.com/Sahandfer/PatientHub.

翻译：随着大型语言模型日益赋能角色扮演应用，患者模拟已成为培训咨询师和扩展治疗评估的重要工具。然而，现有研究呈现碎片化：既有方法依赖于互不兼容、非标准化的数据格式、提示词和评估指标，阻碍了研究的可复现性与公平比较。本文提出PatientHub，一个统一且模块化的框架，用于标准化模拟患者的定义、组合与部署。为展示PatientHub的实用性，我们以案例研究形式实现了若干代表性患者模拟方法，展示本框架如何支持标准化的跨方法评估及自定义评估指标的无缝集成。我们进一步通过原型开发两种新型模拟器变体，证明PatientHub的可扩展性，并阐明其如何通过消除基础设施开销来加速方法开发。通过将现有工作整合至统一的可复现流程中，PatientHub降低了开发新模拟方法的技术门槛，并促进了跨方法与跨模型的基准测试。本框架为未来以患者为中心的对话数据集、方法与基准测试提供了实用基础，相关代码已通过https://github.com/Sahandfer/PatientHub公开。