Data-Driven Hemodialysis Demand Estimation: Comparing Patient Grouping Alternatives for Weekly EPO Requirements and Dialysis Hours

Abstract

This study develops a data-driven framework to anticipate hemodialysis (HD) workload and erythropoietin (EPO) requirements in Paraguay’s public healthcare system using routinely collected service records from a national nephrology center (2021–2024; n = 2,592 patients). The objective is to support operational planning of critical resources by combining patient grouping strategies with simple univariate forecasting approaches. After systematic data cleaning and harmonization, temporal consistency adjustments were applied using institutional year-to-year statistics to preserve the continuity of the demand series. We compared empirical stratification with unsupervised grouping methods (PAM, hierarchical clustering, and DBSCAN), and evaluated several forecasting rules (naïve, Holt, linear regression, and weighted moving average). Given the short annual history, a single-year holdout (2024) was used to consistently screen configurations. Under this evaluation protocol, empirical stratification coupled with a weighted moving average achieved the lowest weighted mean absolute errors: 10.79 patients, 126.47 HD hours/week, and 116,938 IU/week of EPO. The selected configuration was then used to construct uncertainty bands for 2025, projecting 6,444–8,115 HD hours/week and 4.43–5.95 million IU/week of EPO to inform resource planning.