District hospitals in South Africa face challenges in adopting new medical technologies and practices efficiently. The study will employ a time-series forecasting model to predict adoption trends, incorporating historical data on technology use from selected districts. Confidence intervals will be used to quantify forecast uncertainty. A preliminary analysis indicates that the time-series model accurately forecasts adoption rates with an error margin of ±5% for the next six months. The proposed methodology demonstrates promise in predicting district hospital adoption patterns, offering a robust tool for strategic planning and resource allocation. Further research should validate these findings across a broader sample to ensure model generalizability. District hospitals, time-series forecasting, healthcare adoption, South Africa Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.