Methodological Evaluation and Time-Series Forecasting of Clinical Outcomes in Rwandan District Hospital Systems: A Systematic Review

{ "background": "District hospitals are critical nodes in Rwanda's healthcare system, yet there is a lack of consolidated evidence on the methodological rigour of studies evaluating their clinical outcomes and the application of forecasting models to inform system planning.", "purpose and objectives": "This systematic review aims to critically appraise the methodologies used in evaluating clinical outcomes within Rwandan district hospital systems and to synthesise evidence on the application and performance of time-series forecasting models in this context.", "methodology": "A systematic search of multiple electronic databases was conducted following PRISMA guidelines. Eligible studies were those employing quantitative methods to assess clinical outcomes or utilising time-series forecasting (e.g., ARIMA, exponential smoothing) within the specified setting. Study quality was assessed using appropriate tools, such as the PROBAST for prediction model studies. A key forecasting model evaluated was the ARIMA(p,d,q) formulation: Xt = \μ + \φ1 Xt-1 + ... + \ Xt-p + \ + \θ1 \εt-1 + ... + \ \εt-q.", "findings": "Of the 27 included studies, only a minority (approximately 22%) explicitly reported using robust standard errors or confidence intervals to account for autocorrelation in time-series data. A dominant theme was the reliance on routine health management information system (HMIS) data, with forecasting models primarily applied to predict maternal and neonatal health service utilisation volumes.", "conclusion": "Methodological rigour in outcome evaluation and forecasting is variable, with frequent under-reporting of uncertainty measures and model validation procedures. However, applied forecasting demonstrates potential utility for operational planning.", "recommendations": "Future research should prioritise transparent reporting of model specifications, uncertainty quantification, and external validation. Investment in building analytical capacity for advanced time-series analysis within the health system is warranted.", "key words": "systematic review, health systems research, forecasting models, clinical outcomes, district