Urban primary care networks in Rwanda face challenges in delivering consistent clinical outcomes due to varying local resources and expertise. A Bayesian hierarchical model was implemented to assess the variability in clinical outcomes within and between urban primary care networks in Rwanda. The model accounts for regional differences and provides robust estimates of treatment efficacy. The analysis revealed significant variation (e.g., a 15% difference) in clinical outcomes across different regions, necessitating tailored interventions to optimise network performance. Bayesian hierarchical models offer valuable insights into the variability of urban primary care networks and can guide targeted improvements for better patient care. Future studies should consider expanding the model’s scope to include additional variables influencing clinical outcomes and implement regional-specific interventions based on this study's findings. Bayesian Hierarchical Model, Urban Primary Care Networks, Rwanda, Clinical Outcomes Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.