"background": "The integration of digital health technologies (DHTs) into community health centre systems is a critical component of health systems strengthening in sub-Saharan Africa. However, robust methodological frameworks for quantifying and analysing the longitudinal adoption of these technologies are lacking, hindering evidence-based policy. ", "purpose and objectives": "This case study aims to methodologically evaluate the systems of community health centres by developing and applying a panel-data estimation model to measure DHT adoption rates over a 24-year period. The objective is to identify key drivers and temporal patterns of adoption. ", "methodology": "We constructed a novel, longitudinal dataset from administrative records and conducted a methodological evaluation using a two-way fixed effects panel model. The core specification was Adoption{it = \ + \ Xit + \ + \ +, where \ and \ₜ represent entity and time fixed effects. Inference was based on robust standard errors clustered at the sub-county level. ", "findings": "The analysis revealed a non-linear trajectory of adoption, characterised by rapid initial uptake followed by a significant plateau in recent years. A one-standard-deviation increase in clinical staff digital literacy was associated with a 15. 2 percentage point increase in the probability of DHT adoption (95% CI: 11. 8, 18. 6). Infrastructure constraints emerged as a persistent barrier. ", "conclusion": "The methodological approach provides a rigorous framework for analysing health technology adoption, demonstrating that adoption dynamics are complex and influenced by both human capital and infrastructural factors. The plateau effect indicates systemic limitations requiring targeted intervention. ", "recommendations": "Policy should prioritise sustained investment in digital infrastructure alongside continuous training programmes. Future research should employ similar panel methods to evaluate the causal impact of DHT adoption on specific health outcomes. ", "key words": "digital health, panel data, health systems, technology adoption, fixed effects model, sub-Saharan Africa", "contribution statement": "
Ochieng et al. (Tue,) studied this question.
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