Predictive Analytics for Antibiotic Stewardship Programs

Abstract

Antibiotic stewardship programs frequently rely on retrospective audits, fixed clinical rules, and manual review of large numbers of prescriptions.  Although machine-learning models have been developed for resistance prediction and antibiotic selection, comparatively little attention has been given to forecasting which active prescriptions require immediate stewardship intervention. This study addresses that gap by proposing an uncertainty-aware predictive framework for prioritizing hospitalized patients according to the expected clinical and ecological value of antibiotic review. The framework is designed to combine longitudinal electronic health records, microbiological findings, local resistance patterns, treatment duration, organ-function indicators, and World Health Organization AccessWatch–Reserve categories. Rather than producing a simple appropriate-orinappropriate classification, the proposed system estimates distinct risks relating to ineffective therapy, unnecessary broad-spectrum exposure, delayed deescalation, excessive treatment duration, and avoidable intravenous administration. Temporal modelling is combined with calibrated risk estimation and interpretable patient-level explanations to support, rather than replace, infectious-disease specialists and clinical pharmacists.  The anticipated contribution is a clinically actionable prioritization mechanism that reduces low-value alert generation while directing limited stewardship resources toward prescriptions with the greatest potential benefit. The research establishes a foundation for evaluating predictive stewardship systems through discrimination, calibration, workload reduction, intervention yield, subgroup fairness, and patient-safety outcomes