Emergency care units (ECUs) in Tanzania are critical for managing acute health conditions, yet their effectiveness varies widely across different settings. A Bayesian hierarchical linear regression model was applied to analyse data from multiple ECUs, accounting for both fixed effects (e.g., patient demographics) and random effects (e.g., differences between ECUs). The analysis revealed significant heterogeneity in clinical outcomes across different ECUs, with some units showing substantial improvement rates. This study demonstrated the utility of Bayesian hierarchical models for understanding variability in emergency care settings. Further research should consider longitudinal data and incorporate additional factors to enhance model accuracy and generalizability. Bayesian Hierarchical Model, Emergency Care Units, Clinical Outcomes, Tanzania Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.