Feasibility-Guided Fair Adaptive Offline Reinforcement Learning for Medicaid Care Management

Decision support for care coordination can benefit from offline RL, yet concerns about safety and equity limit deployment. We build on recent safety-aware (e.g., conformal) and fairness-aware learning to propose FG-FARL, which adjusts per-group feasibility thresholds before preference learning, targeting equitable selection (coverage) or equitable harm. Medicaid population health management programs coordinate services for members with complex needs (e.g., chronic conditions, behavioral health, social risks). Health plans and provider organizations employ community health workers, nurses, and social care teams to conduct outreach, assessments, and referrals. Each week, teams decide whom to contact, what type of outreach to attempt (e.g., phone call, home visit, coordination with a clinician), and when to follow up.