Bayesian Hierarchical Model Evaluation of Clinical Outcomes in Rwanda's District Hospitals Systems,

This study focuses on evaluating clinical outcomes in Rwanda's district hospitals systems from to , providing a comprehensive assessment of healthcare delivery. Bayesian hierarchical models were used to analyse data from district hospitals, incorporating regional variability and patient characteristics. A key component was the inclusion of robust standard errors to account for uncertainty in model estimation. The analysis revealed significant variance (p < 0.05) in clinical outcomes across different districts, with some hospitals achieving a 20% higher success rate in treatment outcomes compared to others. Bayesian hierarchical models successfully captured the complex interplay of system and patient factors affecting clinical outcomes in Rwanda's district hospitals. Policy recommendations include targeted interventions for underperforming districts, focusing on improving access to essential medicines and training healthcare providers. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.