[Submitted on 22 Jul 2026]
Title:From Discharge Notes to Patient Understanding: Persona-Grounded, Open-Ended Simulation of LLMs as Discharge Educators
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Abstract:Hospital discharge education is an interactive teaching task: a clinician adapts a discharge plan to a patient's literacy, recall, and personality. Existing LLM evaluations target static or artifact-generation tasks and do not measure patient understanding under open-ended dialogue. We introduce DischargeBench, a persona-grounded simulation in which a candidate LLM educator conducts a multi-turn session with a Virtual Patient, while an Education Monitor Agent regulates patient realism without modifying the educator, protecting the evaluation signal. We curate MIMIC-IV-Ext-DischargeBench, 477 cases over 24 ICD chapters with persona axes (personality, education level, health literacy, past-medical-history recall) for stratified analysis. Each simulation is scored on four axes -- Conversation Quality, Topic Checklist, Comprehension, and Factual Consistency -- by an LLM-as-a-Judge aligned against physician annotations. Across closed- and open-source LLMs, aggregate scores conceal clinically relevant variation across ICD chapters and patient personas; difficult personas expose coverage failures, comprehension gaps, and reduced source-answer agreement. LLM evaluation for discharge education should center patient understanding, not text quality or answer accuracy alone.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2609.20827 [cs.CL]
(or arXiv:2609.20827v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.20827
arXiv-issued DOI via DataCite
Submission history
From: Won Seok Jang [view email] [v1] Wed, 22 Jul 2026 13:24:24 UTC (5,705 KB)
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