Reinforcement Learning for Chronic Care Pathway Optimization: A Unified Framework across Three Clinical Goal Types

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Abstract Objective Chronic care requires sequential treatment under competing biomarker, safety, and cost constraints, yet clinical goal structures differ across diseases. We asked whether one physiology-informed reinforcement learning (RL) paradigm adapts to heterogeneous chronic-care goals without disease-specific policy architectures. Materials and Methods We formalized a Type A/B/C clinical goal taxonomy (target cure, stable cruise, cycle completion) as a Physiology-Informed Markov Decision Process registry for gout, chronic kidney disease (CKD), and PCOS-mediated fertility treatment—each with PK/PD transitions, discrete actions, safety zones, and guideline doctor baselines. Unified BC → PPO training (GAE λ =0.95) on 500 simulated trajectories per disease. Evaluation: paired seeds ( N =50 primary; N =500 bootstrap 95% CIs), 10-seed robustness, ablation, literature sUA calibration, and out-of-distribution stress. McNemar/Wilcoxon with Benjamini–Hochberg FDR. Results PCOS (Type C, primary): PPO 72.0% vs. doctor 54.0% at N =50 (+18 percentage points; FDR-significant); at N =500, PPO 69.8% [65.6, 73.8] vs. doctor 52.8% [48.8, 57.2]. Gout (Type A): PPO non-inferior—88.0% vs. 90.0% (McNemar p =1.0). CKD (Type B): doctor 32.0%, BC/PPO 38.0%. Offline CQL 92.0% on gout trajectories. PK recalibration RMSE 97.4 µ mol/L ( r =0.809). Conclusions Shared BC → PPO training generalizes across three goal types without cross-disease weight sharing. PCOS supports RL for bounded cycles; gout confirms guideline non-inferiority; CKD illustrates cruise-control difficulty. This framework offers a reproducible foundation for chronic pathway optimization pending prospective validation. Highlights Type A/B/C taxonomy unifies chronic-care RL across three clinical goal structures PCOS cycle completion +18pp vs doctor baseline (72% vs 54% at N =50) Cross-disease PIMDP: gout non-inferior, CKD cruise-control stress test BC → PPO generalizes without shared weights; open reproducible artifacts Graphical Abstract

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