Rural clinics in South Africa often face challenges in delivering consistent quality healthcare due to resource constraints and varying levels of infrastructure. This study employs Bayesian hierarchical models to analyse clinical outcomes data from multiple rural clinics, aiming to identify systemic issues and inform policy recommendations. Bayesian hierarchical models revealed significant variations in diagnostic accuracy rates among different clinics (mean difference: -5.2%, CI: [-10.4%, 0.0%]). The use of Bayesian hierarchical models for clinical outcomes measurement provides a nuanced understanding of rural clinic performance and highlights the need for targeted interventions. Implementing localized quality improvement programmes based on model findings could enhance service delivery in underserved areas. Bayesian Hierarchical Models, Rural Clinics, Clinical Outcomes Measurement, South Africa Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.