AI-Enabled Medication Error Prevention Systems

Abstract

Medication errors remain a persistent patient-safety concern because prescribing, dispensing, administration, and monitoring decisions are distributed across complex clinical workflows. Conventional clinical decision-support systems primarily use fixed rules and frequently generate non-specific warnings, contributing to excessive alert burden and clinician override behaviour. This study identifies a research gap in the absence of context-aware systems capable of tracing medication risk across multiple stages of the medication-use pathway. A multimodal artificial intelligence framework is proposed to combine structured electronic health records, medication orders, laboratory trends, clinical notes, barcode events, and administration histories. The proposed system employs temporal representation learning, clinical language processing, medication knowledge graphs, and uncertainty-aware risk estimation to identify potentially harmful discrepancies before they reach the patient. Unlike single-stage prediction tools, the framework models the evolving relationship between patient condition, medication intent, operational workflow, and subsequent clinical observations. It also introduces severity-sensitive alert prioritisation and evidence-based explanations to reduce low-value notifications while retaining clinically important warnings. The study establishes a foundation for evaluating AIenabled medication-error prevention through predictive performance, alert usefulness, workflow compatibility, fairness, and prospective patient-safety outcomes