PatientHub: A Unified Framework for Patient Simulation

2026-02-12

AI Summary

This work addresses the lack of standardization in existing patient simulation methods, which exhibit incompatible data formats, prompt templates, and evaluation metrics, thereby severely hindering reproducibility and fair comparison. To overcome this limitation, we propose the first standardized and modular patient simulation framework that unifies the definition, composition, and deployment of simulated patients, enabling cross-method evaluation and seamless integration of custom metrics. Built upon a large language model–based role-playing architecture, the framework offers high flexibility and interoperability with diverse simulation strategies and assessment mechanisms. We demonstrate its effectiveness by successfully reproducing multiple representative approaches and rapidly developing two novel simulators, thereby validating the framework’s extensibility and capacity to accelerate research and development. The code is publicly released.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Search and Optimization: Sampling/Simulation-based Search
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data generation and simulation

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.

Problem

• patient simulation
• standardization
• reproducibility
• evaluation metrics
• framework

Innovation

• unified framework
• patient simulation
• standardization
• modular architecture
• cross-method evaluation