In this study, conducted by Basu et al., the aim was to determine whether reinforcement learning guided decision-making could outperform standard care management practices in regulating optimal treatment plans for patients with complex needs. They specifically focus on the state-action-reward-state-action (SARSA) algorithm within reinforcement learning as it is particularly well-suited for clinical decision support due to its on-policy learning approach. To compare these two systems, a mixed-methods study was conducted, using data from Medicaid beneficiaries in care management programs. They first trained and reinforced each model, then they evaluated performance based on clinical impact metrics, assessed fairness across demographic subgroups, and conducted counterfactual causal inference analyses to estimate reductions in acute care events. Furthermore, they performed qualitative chart reviews where the models differed. They found that in counterfactual analysis, SARSA-guided care management reduced acute care events by 12 percentage points compared to the status quo approach and showed improved fairness across different demographic groups. Furthermore, in qualitative reviews, the SARSA model detected and recommended interventions for complex medical-social interactions that the traditional model could not conduct. Overall, they found that the SARSA-guided care management shows potential to reduce acute care use compared to standard practice. It can improve complex decision-making when assessing multiple social factors, while maintaining safety in health care across all demographics.