Forecasting Adoption Rates in Uganda's District Hospitals Using Time-Series Models: A Methodological Evaluation

Uganda's district hospitals face challenges in adopting new medical technologies, affecting patient care and operational efficiency. A systematic literature review was conducted to identify suitable time-series models. The study used historical data from five randomly selected district hospitals over two years. The Holt-Winters Exponential Smoothing model predicted an average adoption rate increase of 15% in the next year, with uncertainty bounds indicating a 95% confidence interval of ±3% Time-series models can effectively forecast adoption rates for new medical technologies in Ugandan district hospitals. Implementing these forecasting tools could inform policy-makers and hospital administrators on resource allocation and training needs. Holt-Winters Exponential Smoothing, time series analysis, medical technology adoption, Ugandan district hospitals Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.