{ "background": "Rigorous evaluation of clinical outcomes in low-resource primary healthcare systems is methodologically challenging, particularly in remote rural settings where randomised controlled trials are often impractical. There is a critical evidence gap regarding the causal impact of service delivery models on patient health in such contexts.", "purpose and objectives": "This case study aimed to demonstrate the application of a quasi-experimental design to estimate the effect of a redesigned community-based service delivery model on key clinical outcomes within a rural primary healthcare system.", "methodology": "We employed a difference-in-differences design, leveraging the phased rollout of the service model across clinics. The primary analysis used a linear regression model: $Y{it} = \\beta0 + \\beta1 (Treatment{it}) + \\beta2 (Postt) + \\beta3 (Treatment{it} \\times Postt) + \\epsilon{it}$, where $Y_{it}$ is the clinical outcome for clinic $i$ at time $t$. Inference was based on cluster-robust standard errors at the clinic level.", "findings": "The intervention was associated with a statistically significant improvement in the composite management score for antenatal care, with an estimated increase of 15.2 percentage points (95% CI: 8.7, 21.7). No significant effect was detected on postnatal care indicators within the study period.", "conclusion": "The quasi-experimental approach provided a viable method for causal inference in a complex operational setting, revealing that the service model had a selective, positive impact on specific clinical pathways.", "recommendations": "Programme planners should consider phased implementation to facilitate robust evaluation. Future evaluations should incorporate longer follow-up periods to capture lagged effects on outcomes like postnatal care.", "key words": "quasi-experimental design, primary healthcare, clinical outcomes, difference-in-differences, health systems evaluation, rural health", "contribution statement": "This study provides a novel methodological blueprint for conducting rigorous