Health systems in sub-Saharan Africa face persistent challenges in delivering efficient care. Robust methodological frameworks for evaluating system-level efficiency improvements, particularly in decentralised community health centres, are underdeveloped. This short report details a novel quasi-experimental methodology designed to measure efficiency gains within a network of community health centres, focusing on its application in a West African context. We employed a difference-in-differences design, comparing intervention centres receiving a bundled systems-strengthening package to matched control centres. Efficiency was measured using a stochastic frontier analysis model: $\ln(Output_{it}) = \beta_0 + \beta\ln(Input_{it}) + v_{it} - u_{it}$, where $v_{it}$ is noise and $u_{it}$ represents inefficiency. Inference was based on bootstrapped standard errors to account for clustering. The methodological application demonstrated a measurable average efficiency gain of 18% in intervention centres. The bootstrapped 95% confidence interval for this effect ranged from 12% to 24%, indicating a statistically significant improvement. The analysis highlighted the critical role of supply chain integration within the efficiency bundle. The proposed quasi-experimental framework provides a rigorous and adaptable tool for quantifying the impact of health systems strengthening interventions on operational efficiency. Future evaluations of health system reforms should incorporate robust counterfactual designs and composite efficiency metrics to isolate intervention effects from secular trends. health systems research, efficiency measurement, quasi-experimental design, stochastic frontier analysis, primary health care This paper provides a novel methodological blueprint for isolating and quantifying efficiency gains from complex health systems interventions, moving beyond descriptive assessment.