The adoption of robust health information systems in district hospitals is critical for improving service delivery and health outcomes. However, rigorous, quantitative evaluations of system adoption in low-resource settings are scarce, limiting evidence-based policy. This study aimed to develop and apply a quasi-experimental difference-in-differences (DiD) model to quantify the causal effect of a national digital health system intervention on adoption rates within Nigerian district hospitals. We employed a longitudinal DiD design, analysing panel data from a representative sample of treatment and control hospitals. The core statistical model was specified as Y₈ₓ = ₀ + ₁ (Treatᵢ Postₜ) + ᵢ + ₜ + ₈ₓ, where Y₈ₓ is the adoption score. Inference was based on cluster-robust standard errors at the hospital level. The intervention significantly increased the composite adoption score by 18. 7 percentage points (95% CI: 12. 4, 25. 0; p<0. 001). The most substantial improvement was observed in data completeness and timeliness of reporting modules. The applied DiD methodology provides a robust framework for evaluating health system adoption, confirming a strong positive causal impact of the digital intervention in the study context. Policy makers should scale the intervention nationally, prioritising modules with the highest adoption returns. Future evaluations should incorporate this DiD model to strengthen causal inference in health systems research. health information systems, difference-in-differences, quasi-experimental design, programme evaluation, digital health, health systems strengthening This paper provides a novel application of the DiD model to health system adoption metrics, generating the first causally identified estimates for a major digital health rollout in the region.
Adeyemi et al. (Thu,) studied this question.
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