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.