Time-Series Forecasting Model for Measuring Adoption Rates in Rwanda's District Hospitals Systems

Rwanda's district hospitals have faced challenges in adopting new healthcare technologies and practices. A time-series forecasting model was employed to predict adoption rates based on historical data from to . The model included statistical analysis for robustness and uncertainty quantification. The forecast indicated an increasing trend in adoption rates, with a predicted growth of 20% by the end of compared to the baseline rate. This study validates the effectiveness of time-series forecasting models in assessing healthcare technology adoption within district hospitals. Policy-makers should consider implementing these models to better forecast and plan for future technology integration into Rwanda's healthcare system. time-series analysis, healthcare technology adoption, district hospitals, Rwanda Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.