Reinforcement Learning over Patient Trajectories for Clinical Reasoning in EHR Foundation Models

Abstract:Electronic health record (EHR) foundation models trained on longitudinal patient trajectories have demonstrated strong performance across diverse clinical prediction tasks. However, their clinical reasoning capabilities remain constrained by next-token prediction on limited and incomplete EHR data. To address this, we propose a reinforcement learning (RL) fine-tuning framework that treats EHR foundation models as generative policies over patient trajectories. We formulate common clinical prediction problems (e.g., hospital readmission) as event-conditioned, time-windowed reasoning tasks. We then design time-aware, rollout-sensitive rewards to account for finite rollout lengths and temporally inconclusive outcomes. We find that RL fine-tuning consistently improves over pre-trained backbones and strong baselines. Notably, it enables smaller models to surpass larger pre-trained models in data-limited regimes and induces positive transfer across tasks. Further analysis shows that RL fine-tuned models generate trajectories with stronger structural and semantic alignment to ground truth and greater downstream utility.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)

arXiv:2609.12277 [cs.LG]

(or arXiv:2609.12277v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.12277 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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