Forecasting for Social Welfare: Time Series for Optimizing the Equitable Distribution of High-Cost Medicines - Picón-Sossa, Donovan-Steven; Suarez Barón, Marco-Javier; González-Sanabria, Juan-Sebastián (Revista Científica, 2025)
Access to high-cost medication represents one of the most complex and critical challenges in managing health systems, especially in regions with economic and structural limitations. To mitigate said impact, this study proposes the use of forecasting models, integrating time series to anticipate demand and promote a more efficient, equitable, and fair distribution. To this effect, a monthly dataset is used which includes a four-year record of hospital consumption, which has been processed and enriched with public cost information in order to identify the medications with the highest economic impact. The MLOps methodology is employed for the creation, automation, and validation of three models: Prophet, XGBoost, and SARIMAX. Each of these models is optimized through hyperparameter search and evaluated using cross-validation and metrics such as MAPE, RMSE, and MAE. The results show that Prophet stands out in relative accuracy by capturing smooth trends and seasonalities, that XGBoost fits better in absolute units when facing abrupt variations, and that SARIMAX robustly models recurring cycles. These strengths allow adapting the approach according to planning objectives, e.g., optimizing percentage accuracy or reducing volume-related error.