{ "background": "Rural clinics in sub-Saharan Africa are critical for primary healthcare delivery, yet systematic, longitudinal evidence on their performance and impact on clinical outcomes remains scarce. Existing evaluations often rely on cross-sectional data, limiting causal inference and the ability to track changes over time within facilities.", "purpose and objectives": "This protocol details a methodological approach to generate robust panel-data estimates of rural clinic performance in Kenya. The primary objective is to establish a framework for analysing the relationship between health systems inputs, operational processes, and a core set of clinical outcomes over multiple time periods.", "methodology": "We propose a multi-stage, stratified sampling design to select clinics across diverse agro-ecological zones. Quantitative data on staffing, drug availability, infrastructure, and patient volume will be collected quarterly alongside anonymised clinical outcome data for antenatal care, immunisation, and management of childhood diarrhoea. The core analytical model is a two-way fixed effects panel regression: $Y{it} = \\beta0 + \\beta1 X{it} + \\mui + \\lambdat + \\epsilon{it}$, where $Y{it}$ is the outcome for clinic $i$ at time $t$, $X{it}$ is a vector of time-varying covariates, $\\mui$ and $\\lambdat$ are clinic and time fixed effects, and $\\epsilon{it}$ is the idiosyncratic error. Inference will be based on cluster-robust standard errors.", "findings": "As a research protocol, this paper does not present empirical results. The anticipated findings will include longitudinal estimates of, for example, the proportion of variance in key outcomes attributable to within-clinic changes in resource availability versus external factors.", "conclusion": "The implementation of this protocol will yield a novel, high-frequency panel dataset and a rigorous analytical framework specifically designed for the rural African clinic context.", "recommendations": "We recommend the adoption of similar panel-data methodologies by health systems researchers and policymakers to move beyond snapshot assessments, enabling more