Predictive Analytics and Real-Time Data Integration for Resilient Prescribing: An AI-Powered Approach to Mitigate Drug Shortages and Control Costs

Abstract

Outpatient prescribing is increasingly disrupted by medication shortages that raise costs, delay therapy, and erode

adherence. This paper proposes a supply chain–aware approach that blends shortage prediction, evidence-based

substitution, and Real-Time Benefit Tools (RTBT) embedded in e-prescribing workflows. Using a mixed-methods

design, we modeled quarterly shortage alerts and compared a predictive model against heuristic baselines; we also

evaluated RTBT’s impact on patient out-of-pocket expenses and adherence under shortage-aware substitution. Results

indicate the predictive model improved precision/recall over heuristics, RTBT reduced median out-of-pocket costs,

and substitution during shortages increased adherence compared with no substitution. We argue that coupling

predictive signals with cost transparency and safe alternatives enables resilient, equitable outpatient medication

access.