Public health surveillance systems are crucial for monitoring and responding to disease outbreaks efficiently in Kenya. However, their effectiveness can be enhanced through methodological improvements. A quasi-experimental design was employed to compare pre- and post-intervention performance metrics of the Kenyan public health surveillance systems. Data were collected from to and analysed using regression discontinuity designs (RDD) with robust standard errors for uncertainty quantification. The preliminary analysis suggests a significant improvement in data accuracy by 35% post-intervention, indicating enhanced system efficiency. This study demonstrates the utility of RDD in evaluating public health surveillance systems and highlights potential areas for further methodological refinement. Future research should explore scalability issues and long-term sustainability of these systems to ensure continuous effectiveness. public health, surveillance systems, regression discontinuity design, efficiency gains Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.