# EHRWorld: A Patient-Centric Medical World Model for Long-Horizon Clinical Trajectories

arXiv: https://arxiv.org/abs/2602.03569 · HTML: https://mlsanity.com/paper/2602.03569/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Placed in Medical and surgical world models, one of 11 directions in World models.
- Builds on 16 of its 25 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Placed in

- World models / [Medical and surgical world models](https://mlsanity.com/wm/medical-and-surgical-world-models/)

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [SGLang: Efficient Execution of Structured Language Model Programs](https://mlsanity.com/paper/2312.07104/) (2023)
- [DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models](https://mlsanity.com/paper/2512.02556/) (2025)
- [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (2020)
- [HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs](https://mlsanity.com/paper/2412.18925/) (2024)
- [BEHRT: Transformer for Electronic Health Records](https://mlsanity.com/paper/1907.09538/) (2019)
- [AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments](https://mlsanity.com/paper/2405.07960/) (2024)
- [R2GenGPT: Radiology Report Generation with Frozen LLMs](https://mlsanity.com/paper/2309.09812/) (2023)
- [PromptMRG: Diagnosis-Driven Prompts for Medical Report Generation](https://mlsanity.com/paper/2308.12604/) (2023)
- [PatientSim: A Persona-Driven Simulator for Realistic Doctor-Patient Interactions](https://mlsanity.com/paper/2505.17818/) (2025)
- [CP-Env: Evaluating Large Language Models on Clinical Pathways in a Controllable Hospital Environment](https://mlsanity.com/paper/2512.10206/) (2025)
- [MedCEG: Reinforcing Verifiable Medical Reasoning with Critical Evidence Graph](https://mlsanity.com/paper/2512.13510/) (2025)
- [AutoMedic: An Automated Evaluation Framework for Clinical Conversational Agents with Medical Dataset Grounding](https://mlsanity.com/paper/2512.10195/) (2025)

## Built on by (most cited first, 19 of 19)

- [ChronoMedicalWorld: A Medical World Model for Learning Patient Trajectories from Longitudinal Care Data](https://mlsanity.com/paper/2605.21963/) (2026)
- [Medical world models: representing medical states, modelling clinical dynamics and guiding intervention policies](https://mlsanity.com/paper/2606.16721/) (2026)
- [DreamReg: Belief-Driven World Model for 2D-3D Ultrasound Registration](https://mlsanity.com/paper/2606.18825/) (2026)
- [Medical world models in healthcare: foundations, applications, and challenges for trustworthy clinical translation](https://mlsanity.com/paper/2607.25242/) (2026)
- [ResidencyRL: Reinforcement Learning in Simulated Clinical Environments](https://mlsanity.com/paper/2608.07418/) (2026)
- [DRIFT: Direct-Recursive Intervention-Conditioned Forecasting of ICU Physiological Trajectories](https://mlsanity.com/paper/2607.25864/) (2026)
- [Intervention-Aware Clinical World Model for Post-Op Outcome Forecasting in Cardiology](https://mlsanity.com/paper/2608.13518/) (2026)
- [From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction](https://mlsanity.com/paper/2605.16927/) (2026)
- [Generated Context versus Governed State: Functional Conditions for Accountable Longitudinal Clinical Reasoning](https://mlsanity.com/paper/2608.14804/) (2026)
- [Intervention Granularity Matters: Coherent Treatment Bundles in Counterfactual Simulation with Clinical World Models](https://mlsanity.com/paper/2609.21906/) (2026)
- [Design-Ignoring versus Design-Respecting World Models for Epidemiology](https://mlsanity.com/paper/2609.30679/) (2026)
- [What Next-Event Accuracy Cannot See: Closed-Loop Evaluation of Emergency Department Trajectory Simulators](https://mlsanity.com/paper/2609.31635/) (2026)
- [Admissible Diffusion for Multimodal Interventional Trajectories](https://mlsanity.com/paper/2609.34433/) (2026)
- [Can LLMs Value the Right Evidence? Evidence-Value Misalignment in Dynamic Medical Diagnosis](https://mlsanity.com/paper/2609.35627/) (2026)
- [EHR2Trace: Auditable EHR Data Infrastructure for Patient World Models and Clinical Agents](https://mlsanity.com/paper/2609.38193/) (2026)
- [Understanding Trajectory Heterogeneity in Federated World Model Learning](https://mlsanity.com/paper/2610.02957/) (2026)
- [Repeated-Measure Leakage, Distribution Shift, and Reliability under Partial Observation in Patient World Models](https://mlsanity.com/paper/2610.04778/) (2026)
- [BeliefGraph-JEPA: Structured Latent World Models for Action-Conditioned Time Series](https://mlsanity.com/paper/2610.05409/) (2026)
- [PatientWM: Towards Generalizable Clinical World Models via Unified Patient States](https://mlsanity.com/paper/2610.17059/) (2026)

## Abstract (arXiv)

World models offer a principled framework for simulating future states under interventions, but realizing such models in complex, high-stakes domains like medicine remains challenging. Recent large language models (LLMs) have achieved strong performance on static medical reasoning tasks, raising the question of whether they can function as dynamic medical world models capable of simulating disease progression and treatment outcomes over time. In this work, we show that LLMs only incorporating medical knowledge struggle to maintain consistent patient states under sequential interventions, leading to error accumulation in long-horizon clinical simulation. To address this limitation, we introduce EHRWorld, a patient-centric medical world model trained under a causal sequential paradigm, together with EHRWorld-110K, a large-scale longitudinal clinical dataset derived from real-world electronic health records. Extensive evaluations demonstrate that EHRWorld significantly outperforms naive LLM-based baselines, achieving more stable long-horizon simulation, improved modeling of clinically sensitive events, and favorable reasoning efficiency, highlighting the necessity of training on causally grounded, temporally evolving clinical data for reliable and robust medical world modeling.