PatientHub: A Unified Framework for Patient Simulation

TL;DR

PatientHub is a modular framework that standardizes patient simulation methods, enabling reproducibility, fair comparison, and accelerated development of new simulation techniques in healthcare dialogue applications.

Contribution

It introduces a unified, extensible platform for patient simulation that consolidates existing approaches and supports standardized evaluation and benchmarking.

Findings

Supports cross-method evaluation and benchmarking

Facilitates rapid development of new simulation variants

Provides a publicly available, reproducible pipeline

Abstract

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.