The adoption of new health information systems by district hospitals in low-resource settings is critical for improving service delivery, yet robust methods for evaluating the causal impact of such interventions are often lacking. This study aimed to develop and apply a rigorous difference-in-differences (DiD) econometric framework to quantify the adoption rate of a new electronic health records system introduced in a sample of district hospitals. We conducted a quasi-experimental intervention study, collecting longitudinal administrative data from 42 district hospitals. The core statistical model is a two-way fixed effects DiD specification: $Y_{it} = \alpha + \beta (Treat_i \times Post_t) + \gamma_i + \delta_t + \epsilon_{it}$, where $Y_{it}$ is the adoption outcome. Inference is based on cluster-robust standard errors at the hospital level. The analysis indicates a statistically significant positive treatment effect. Hospitals receiving the intervention demonstrated a 24-percentage-point increase in system adoption metrics compared to control facilities (95% CI: 18 to 30). The methodological application confirms the DiD model as a valid and powerful tool for evaluating health technology adoption in real-world, non-randomised implementation contexts. Health policymakers should integrate quasi-experimental designs like DiD into routine monitoring and evaluation frameworks. Future system rollouts should be structured to facilitate such comparative analysis. difference-in-differences, health information systems, implementation science, quasi-experiment, health systems research, Ghana This paper provides a novel, replicable methodological blueprint for isolating the causal effect of health system interventions in settings where randomised controlled trials are not feasible.