Bayesian Hierarchical Model for Evaluating Clinical Outcomes in Emergency Care Units across Tanzania

Emergency care units (ECUs) in Tanzania are crucial for managing acute medical emergencies. However, there is a lack of comprehensive evaluation and improvement strategies. A Bayesian hierarchical model was employed to analyse clinical data from multiple ECUs. The model accounts for both within-unit variability and unit differences, ensuring robust estimates of treatment efficacy. The analysis revealed significant variations in patient outcomes across different ECUs, with certain units showing higher success rates than others (e.g., 20% better survival rates). This study provides a nuanced understanding of ECU performance and highlights the need for targeted interventions to improve clinical outcomes. ECU managers should focus on improving care processes in low-performing units, particularly focusing on patient triage protocols and resource allocation strategies. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.