Public health surveillance systems play a crucial role in monitoring infectious diseases and other public health threats efficiently. A randomized field trial was conducted to evaluate the performance of public health surveillance systems across different regions of Ethiopia. Data were collected using standardised tools and analysed using logistic regression models to assess predictive accuracy. The analysis revealed a significant improvement in the detection rate of infectious diseases when compared to baseline data, with an increase of 20% in positive case notifications from the intervention group. This study provides robust evidence that targeted interventions can enhance the functionality and efficiency of public health surveillance systems in Ethiopia. Public health authorities should prioritise ongoing system improvements based on this research to better protect public health outcomes. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.