PatientHub: A Unified Framework for Patient Simulation

[2602.11684] PatientHub: A Unified Framework for Patient Simulation Skip to main content Search Submit Donate Log in Computer Science > Computation and Language arXiv:2602.11684 (cs) [Submitted on 12 Feb 2026 ( v1 ), last revised 25 Aug 2026 (this version, v2)] Title: PatientHub: A Unified Framework for Patient Simulation Authors: Sahand Sabour , TszYam NG , Minlie Huang View PDF HTML (experimental) 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 this https URL . Comments: EMNLP 2026 Demo Paper Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) Cite as: arXiv:2602.11684 [cs.CL] (or arXiv:2602.11684v2 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2602.11684 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Sahand Sabour [ view email ] [v1] Thu, 12 Feb 2026 08:06:37 UTC (310 KB) [v2] Tue, 25 Aug 2026 00:17:55 UTC (257 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CL < prev | next > new | recent | 2026-02 Change to browse by: cs cs.AI cs.HC References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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