@inproceedings{sabour-etal-2026-patienthub,
title = "{P}atient{H}ub: A Unified Framework for Patient Simulation",
author = "Sabour, Sahand and
Yam, NG Tsz and
Huang, Minlie",
editor = "Agrawal, Sweta and
Abad, Alberto",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-demos.76/",
pages = "806--817",
ISBN = "979-8-89176-461-3",
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 remains fragmented: existing approaches rely on incompatible, non-standardized profiles, prompts, and evaluation metrics, hindering reproducibility, fair comparison, and reuse. We introduce PatientHub, a unified and modular framework that standardizes the creation, simulation, and evaluation of LLM-based patients. Our framework provides 16 patient simulators, a graph-based orchestrator for multi-turn, multi-session interactions, and a configurable LLM-as-a-judge evaluator that supports multiple rubric types. Via our command-line interface, users can generate patient profiles, run simulations, and apply rubric-driven evaluation at the turn and session level. To demonstrate PatientHub{'}s utility, we compare several supported simulators under a shared interaction protocol and showcase its extensibility by prototyping a new simulator variant with minimal additional code. By consolidating existing work into a single reproducible pipeline, our framework eliminates much of the infrastructure overhead that currently fragments this research and accelerates the development of new methods. Our code and data are publicly available via https://github.com/Sahandfer/PatientHub."
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%0 Conference Proceedings
%T PatientHub: A Unified Framework for Patient Simulation
%A Sabour, Sahand
%A Yam, NG Tsz
%A Huang, Minlie
%Y Agrawal, Sweta
%Y Abad, Alberto
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-461-3
%F sabour-etal-2026-patienthub
%X 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 remains fragmented: existing approaches rely on incompatible, non-standardized profiles, prompts, and evaluation metrics, hindering reproducibility, fair comparison, and reuse. We introduce PatientHub, a unified and modular framework that standardizes the creation, simulation, and evaluation of LLM-based patients. Our framework provides 16 patient simulators, a graph-based orchestrator for multi-turn, multi-session interactions, and a configurable LLM-as-a-judge evaluator that supports multiple rubric types. Via our command-line interface, users can generate patient profiles, run simulations, and apply rubric-driven evaluation at the turn and session level. To demonstrate PatientHub’s utility, we compare several supported simulators under a shared interaction protocol and showcase its extensibility by prototyping a new simulator variant with minimal additional code. By consolidating existing work into a single reproducible pipeline, our framework eliminates much of the infrastructure overhead that currently fragments this research and accelerates the development of new methods. Our code and data are publicly available via https://github.com/Sahandfer/PatientHub.
%U https://aclanthology.org/2026.emnlp-demos.76/
%P 806-817
Markdown (Informal)
[PatientHub: A Unified Framework for Patient Simulation](https://aclanthology.org/2026.emnlp-demos.76/) (Sabour et al., EMNLP 2026)
ACL
- Sahand Sabour, NG Tsz Yam, and Minlie Huang. 2026. PatientHub: A Unified Framework for Patient Simulation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 806–817, Budapest, Hungary. Association for Computational Linguistics.