Data-Driven Drug Utilization Review Systems

Abstract

Drug utilization review systems are essential for identifying inappropriate prescribing, preventable medication-related harm, and inefficient pharmaceutical use across healthcare settings. However, most existing systems rely on static rules that assess prescriptions individually and often overlook longitudinal treatment patterns, evolving patient conditions, and uncertainty in clinical data. This study addresses that gap by proposing a data-driven drug utilization review framework that integrates temporal prescription modelling, patientcontext representation, population-level utilization signals, and uncertainty-aware risk stratification. The framework is designed to evaluate drug–drug interactions, therapeutic duplication, dosage suitability, treatment duration, monitoring requirements, and deviations from expected prescribing trajectories. Unlike conventional alert engines, the proposed approach distinguishes clinically meaningful medication risks from low-priority rule violations by learning from electronic health records, pharmacy claims, laboratory observations, and documented pharmacist interventions. A hybrid architecture combining temporal graph learning, gradient-boosted risk estimation, and calibrated explanation generation is conceptualized to support pharmacist-led review rather than autonomous prescribing decisions. The principal research objective is to improve the clinical relevance, prioritization, interpretability, and operational efficiency of drug utilization review while reducing unnecessary alert burden. The proposed investigation provides a foundation for adaptive medication-surveillance systems capable of supporting safer, more equitable, and evidence-informed pharmaceutical care