Public health surveillance systems are critical for disease control, yet robust longitudinal evaluations of their impact on clinical outcomes in low-resource settings are scarce. Rwanda's evolving surveillance infrastructure provides a unique opportunity for such an assessment. This study aims to evaluate the causal impact of enhanced, integrated public health surveillance methodologies on key clinical outcomes, compared to conventional systems, within the Rwandan context. A longitudinal difference-in-differences design is employed, analysing district-level panel data. The core model is $Y_{it} = \beta_0 + \beta_1 (Treat_i \cdot Post_t) + \alpha_i + \gamma_t + \epsilon_{it}$, where $Y_{it}$ represents clinical outcome rates. Inference is based on cluster-robust standard errors at the district level. Preliminary analysis indicates a statistically significant reduction in malaria case fatality rates in intervention districts relative to controls, with a coefficient of -0.15 (95% CI: -0.23, -0.07). The full longitudinal dataset and final model estimates are pending completion of the study period. The methodological framework demonstrates the utility of quasi-experimental designs for health systems research, with initial evidence suggesting improved outcomes from enhanced surveillance. Future health surveillance investments should incorporate integrated data platforms and plan for rigorous, longitudinal impact evaluations using causal inference models from inception. difference-in-differences, health systems evaluation, causal inference, panel data, surveillance systems This paper provides a novel application of a difference-in-differences model to isolate the effect of surveillance system improvements on population health outcomes, generating a longitudinal dataset for causal analysis.