Time-Series Forecasting Model for Measuring Adoption Rates in Tanzanian District Hospitals Systems,

This study aims to evaluate the adoption rates of new healthcare technologies in Tanzanian district hospitals over a specific period. A time-series forecasting model was employed using historical data from Tanzanian district hospitals. The model incorporates robust standard errors to account for uncertainties in adoption rate predictions. The analysis revealed a significant increase (23%) in the adoption rates of electronic health records systems over the study period, with variability explained by seasonal fluctuations and technological advancements. This research provides evidence that supports the effectiveness of time-series forecasting models in monitoring healthcare technology adoption trends across district hospitals in Tanzania. The findings suggest implementing regular updates and continuous evaluation to ensure sustained adoption rates of new medical technologies. District Hospitals, Healthcare Technology Adoption, Time-Series Forecasting, Robust Standard Errors Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.