District hospitals are critical nodes in Kenya's healthcare system, yet systematic evaluations of their technological adoption processes are scarce. Understanding the dynamics of health technology diffusion is essential for effective system strengthening and resource allocation. This study aims to develop and apply a novel panel-data econometric framework to estimate the adoption rates of essential medical technologies across district-level hospitals. The objective is to quantify temporal trends and identify systemic factors influencing adoption velocity. We constructed a balanced panel dataset from administrative records and conducted facility surveys. Adoption rates were estimated using a dynamic linear probability model: Adopt₈ₓ = + Adopt₈, ₓ-₁ + X₈ₓ + ᵢ + ₈ₓ, where ᵢ denotes hospital fixed effects and robust standard errors were clustered at the facility level. The mean annual adoption rate for core diagnostic technologies was estimated at 4. 7% (95% CI: 3. 2, 6. 1). Adoption exhibited significant positive spatial autocorrelation, with hospital procurement budget allocation being the most robust predictor (p<0. 01). The methodological framework provides a replicable tool for monitoring health system performance. Adoption rates for key technologies remain suboptimal, indicating systemic inertia within the hospital network. Policy should target budgetary mechanisms to accelerate diffusion. The estimation framework should be integrated into national health management information systems for routine performance benchmarking. health systems research, technology adoption, panel data, econometric modelling, hospital management, Kenya This paper provides a novel panel-data estimator specifically designed for tracking technology diffusion in resource-constrained health systems, yielding the first longitudinal, facility-level benchmarks for adoption rates in the country.
Wanjiku Mwangi (Wed,) studied this question.