{ "background": "The adoption of formal health systems by community health centres in sub-Saharan Africa is critical for improving service delivery and health outcomes. However, robust empirical evidence on the drivers and rates of adoption remains scarce, particularly for longitudinal evaluations in resource-constrained settings.", "purpose and objectives": "This case study aimed to evaluate the adoption rates of a standardised health management information system (HMIS) across a network of community health centres, and to identify the facility-level factors associated with successful implementation.", "methodology": "A quasi-experimental difference-in-differences design was employed, analysing longitudinal administrative data from a panel of centres that phased in the HMIS. The primary analysis estimated the intent-to-treat effect using a linear probability model: $Adoption{it} = \\beta0 + \\beta1 (Postt \\times Treati) + \\gamma X{it} + \\alphai + \\deltat + \\epsilon{it}$, where $\\alphai$ and $\\delta_t$ are facility and time fixed effects. Inference was based on cluster-robust standard errors.", "findings": "The intervention was associated with a 34-percentage-point increase in the probability of full HMIS adoption (95% CI: 24 to 44). The presence of a dedicated data officer and reliable electricity supply were the most significant facility-level predictors of successful adoption, whereas staff turnover showed a strong negative association.", "conclusion": "The phased rollout of the HMIS led to a substantial and statistically significant increase in adoption rates. The findings underscore that technological interventions require concurrent investments in human resources and infrastructure to achieve sustainable integration.", "recommendations": "Policymakers should mandate the creation of dedicated data officer positions within centre staffing structures. Programme design must incorporate resilience strategies to mitigate the negative effects of staff attrition. Future system rollouts should prioritise centres with stable infrastructure or pair the intervention with targeted infrastructural support.", "key words": "health systems, adoption, quasi-experimental