Reinforcement Learning to Prevent Acute Care Events Among Medicaid Populations: Mixed Methods Study.

backgroundMultidisciplinary care management teams must rapidly prioritize interventions for patients with complex medical and social needs. Current approaches rely on individual training, judgment, and experience, missing opportunities to learn from longitudinal trajectories and prevent adverse outcomes through recommender systems.

objectiveThis study aims to evaluate whether a reinforcement learning approach could outperform standard care management practices in recommending optimal interventions for patients with complex needs.

methodsUsing data from 3175 Medicaid beneficiaries in care management programs across 2 states from 2023 to 2024, we compared alternative approaches for recommending "next best step" interventions: the standard experience-based approach (status quo) and a state-action-reward-state-action (SARSA) reinforcement learning model. We evaluated performance using clinical impact metrics, conducted counterfactual causal inference analyses to estimate reductions in acute care events, assessed fairness across demographic subgroups, and performed qualitative chart reviews where the models differed.

resultsIn counterfactual analyses, SARSA-guided care management reduced acute care events by 12 percentage points (95% CI 2.2-21.8 percentage points, a 20.7% relative reduction; P=.02) compared to the status quo approach, with a number needed to treat of 8.3 (95% CI 4.6-45.2) to prevent 1 acute event. The approach showed improved fairness across demographic groups, including gender (3.8% vs 5.3% disparity in acute event rates, reduction 1.5%, 95% CI 0.3%-2.7%) and race and ethnicity (5.6% vs 8.9% disparity, reduction 3.3%, 95% CI 1.1%-5.5%). In qualitative reviews, the SARSA model detected and recommended interventions for specific medical-social interactions, such as respiratory issues associated with poor housing quality or food insecurity in individuals with diabetes.

conclusionsSARSA-guided care management shows potential to reduce acute care use compared to standard practice. The approach demonstrates how reinforcement learning can improve complex decision-making in situations where patients face concurrent clinical and social factors while maintaining safety and fairness across demographic subgroups.