{ "background": "Rwanda's rural health clinic systems are critical for delivering primary care, yet robust methodological frameworks for evaluating their clinical outcomes are underdeveloped. Existing approaches often lack the statistical rigour to handle the hierarchical, multi-source data characteristic of these settings.", "purpose and objectives": "This systematic review aims to critically appraise the application of Bayesian hierarchical modelling (BHM) for evaluating clinical outcomes within Rwanda's rural clinic systems, assessing its methodological advantages, implementation challenges, and evidence of impact.", "methodology": "A systematic search of multiple electronic databases was conducted following PRISMA guidelines. Studies were included if they employed a BHM to analyse clinical outcome data from rural Rwandan health facilities. Data were extracted on model specification, data sources, and inference methods. The core model form was $y{ij} \\sim \\text{Bernoulli}(p{ij}), \\; \\text{logit}(p{ij}) = \\alpha + \\alpha{j[i]} + \\beta X{ij}$, where $\\alpha{j} \\sim N(0, \\sigma{\\alpha}^2)$ represents clinic-level random effects.", "findings": "The review identified a limited but growing corpus of literature. A prominent theme was the model's utility in quantifying clinic-level variation while accounting for sparse data, with one key study reporting a 95% credible interval for the clinic standard deviation $\\sigma{\\alpha}$ of [0.4, 1.2] on the log-odds scale, indicating substantial heterogeneity in performance.", "conclusion": "BHM provides a statistically coherent framework for analysing nested clinical data from these systems, offering advantages in uncertainty quantification and borrowing strength across units. However, its adoption remains nascent, constrained by technical capacity and data quality.", "recommendations": "Future research should prioritise the development of open-source, context-adapted BHM templates and invest in local analytical capacity building. National health management information systems should be designed to capture the hierarchical data