Learning to Compress Time-to-Control: A Reinforcement Learning Framework for Chronic Disease Management

• URL: http://arxiv.org/abs/2605.09818v1
• Date: Sun, 10 May 2026 23:43:39 GMT
• Title: Learning to Compress Time-to-Control: A Reinforcement Learning Framework for Chronic Disease Management
• Abstract summary: We argue that chronic disease management is structurally a more tractable RL setting than the acute-care problems the field has primarily studied.<n>We present simulation results on synthetic state machines for hypertension and type 2 diabetes.
• Score: 4.772536418776007
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning (RL) in healthcare has had mixed results, with reward sparsity, unreliable off-policy evaluation, and deployment-simulation gap as recurring failure modes. We argue that chronic disease management is structurally a more tractable RL setting than the acute-care problems the field has primarily studied, but only if the problem is formalized to exploit chronic care's properties. We propose such a formalization. The agent's objective is to compress time-to-control (TTC) under a tiered reward calibrated to the CMS ACCESS Model. Two quantities from our companion preference-learning paper [Singh et al. 2026] enter as load-bearing structural elements: the execution intensity εbounds action availability under a constrained Markov Decision Process, and the clinician capability κweights offline-data transitions during RL training. Together they couple preference learning and RL into a two-loop architecture. We present simulation results on synthetic state machines for hypertension and type 2 diabetes. Capability-weighted offline RL outperforms uniform-weighted offline RL and the behavior policy by 15 percentage points on T2D TTC; the uniform-weighted formulation (the standard in existing healthcare RL) underperforms even the heterogeneous behavior policy. \Epsilon-aware policies generalize across deployment regimes while ε-naive policies do not.

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