Recent studies have highlighted significant challenges in risk management within Senegalese district hospitals, particularly regarding patient outcomes and resource allocation. A comprehensive time-series analysis was conducted using historical hospital data from three districts, focusing on patient admissions, bed turnover rates, and financial expenditure patterns over a five-year period. The model utilised an ARIMA (AutoRegressive Integrated Moving Average) approach to forecast future trends with robust standard errors provided. The forecasting model demonstrated an accuracy rate of 85% in predicting hospital resource needs, highlighting the need for proactive risk reduction strategies such as staffing adjustments and inventory management improvements. This study validates the utility of time-series forecasting models in enhancing district hospitals' operational efficiency by providing actionable insights into potential risks and enabling better resource allocation. Based on findings, we recommend implementing a continuous monitoring system to validate forecast accuracy and adjusting hospital protocols accordingly. Additionally, further research should explore the impact of these interventions on patient outcomes. Senegal, district hospitals, time-series forecasting, risk reduction, ARIMA model Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Mboup et al. (Thu,) studied this question.