# DischargeSim: A Simulation Benchmark for Educational Doctor-Patient Communication at Discharge

arXiv: https://arxiv.org/abs/2509.07188 · HTML: https://mlsanity.com/paper/2509.07188/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 49 references that are in the map.
- First posted on arXiv 8 Sep 2025.

## Builds on (most cited first, 19 of 19)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [HealthBench: Evaluating Large Language Models Towards Improved Human Health](https://mlsanity.com/paper/2505.08775/) (2025)
- [Large Language Models Are State-of-the-Art Evaluators of Translation Quality](https://mlsanity.com/paper/2302.14520/) (2023)
- [ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge](https://mlsanity.com/paper/2303.14070/) (2023)
- [MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning](https://mlsanity.com/paper/2311.10537/) (2023)
- [MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data](https://mlsanity.com/paper/2304.08247/) (2023)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments](https://mlsanity.com/paper/2405.07960/) (2024)
- [HuatuoGPT, towards Taming Language Model to Be a Doctor](https://mlsanity.com/paper/2305.15075/) (2023)
- [Unsupervised Commonsense Question Answering with Self-Talk](https://mlsanity.com/paper/2004.05483/) (2020)
- [A Comprehensive Analysis of the Effectiveness of Large Language Models as Automatic Dialogue Evaluators](https://mlsanity.com/paper/2312.15407/) (2023)
- [StoryER: Automatic Story Evaluation via Ranking, Rating and Reasoning](https://mlsanity.com/paper/2210.08459/) (2022)
- [PaniniQA: Enhancing Patient Education Through Interactive Question Answering](https://mlsanity.com/paper/2308.03253/) (2023)
- [Comparing Two Model Designs for Clinical Note Generation; Is an LLM a Useful Evaluator of Consistency?](https://mlsanity.com/paper/2404.06503/) (2024)
- [MedQA-CS: Objective Structured Clinical Examination (OSCE)-Style Benchmark for Evaluating LLM Clinical Skills](https://mlsanity.com/paper/2410.01553/) (2024)
- [VeriFact: Verifying Facts in LLM-Generated Clinical Text with Electronic Health Records](https://mlsanity.com/paper/2501.16672/) (2025)
- [MCQG-SRefine: Multiple Choice Question Generation and Evaluation with Iterative Self-Critique, Correction, and Comparison Feedback](https://mlsanity.com/paper/2410.13191/) (2024)

## Built on by (most cited first, 7 of 7)

- [Automated Benchmark Generation from Domain Guidelines Informed by Bloom's Taxonomy](https://mlsanity.com/paper/2601.20253/) (2026)
- [MentalHospital: A Virtual Environment for Evaluating Psychiatric Clinical Encounters](https://mlsanity.com/paper/2607.08257/) (2026)
- [Rethinking Patient Education as Multi-turn Multi-modal Interaction](https://mlsanity.com/paper/2604.14656/) (2026)
- [MedGame: Storytelling Gamification Empowered by Large Language Models for Medical Education](https://mlsanity.com/paper/2607.21570/) (2026)
- [Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies](https://mlsanity.com/paper/2608.05993/) (2026)
- [LiveSim: Simulating Environment-Shaped Users in Multi-Agent Live-Stream Ecosystems](https://mlsanity.com/paper/2608.26849/) (2026)
- [DischargeBench: Pre-Deployment Evaluation of LLM Discharge Educators through Persona-Grounded Patient Simulation](https://mlsanity.com/paper/2609.20827/) (2026)

## Abstract (arXiv)

Discharge communication is a critical yet underexplored component of patient care, where the goal shifts from diagnosis to education. While recent large language model (LLM) benchmarks emphasize in-visit diagnostic reasoning, they fail to evaluate models' ability to support patients after the visit. We introduce DischargeSim, a novel benchmark that evaluates LLMs on their ability to act as personalized discharge educators. DischargeSim simulates post-visit, multi-turn conversations between LLM-driven DoctorAgents and PatientAgents with diverse psychosocial profiles (e.g., health literacy, education, emotion). Interactions are structured across six clinically grounded discharge topics and assessed along three axes: (1) dialogue quality via automatic and LLM-as-judge evaluation, (2) personalized document generation including free-text summaries and structured AHRQ checklists, and (3) patient comprehension through a downstream multiple-choice exam. Experiments across 18 LLMs reveal significant gaps in discharge education capability, with performance varying widely across patient profiles. Notably, model size does not always yield better education outcomes, highlighting trade-offs in strategy use and content prioritization. DischargeSim offers a first step toward benchmarking LLMs in post-visit clinical education and promoting equitable, personalized patient support.