Smart Prescription Validation Using Artificial Intelligence

Abstract

Prescription errors remain one of the leading causes of preventable adverse drug events despite the widespread adoption of electronic health records and digital prescribing systems. Conventional prescription verification techniques primarily rely on predefined rules and are unable to interpret complex contextual relationships among medications, diagnoses, and patient characteristics. This study proposes a Hybrid Semantic Prescription Validation Framework (HSPVF) that integrates transformer-based clinical language understanding with graph-based pharmaceutical knowledge modeling for intelligent prescription assessment. The proposed framework performs semantic validation by jointly evaluating dosage appropriateness, therapeutic duplication, contraindications, drug interactions, and prescription completeness. Unlike conventional rule-based approaches, the model adapts to contextual clinical information while maintaining explainable validation decisions. Recent advances in clinical natural language processing and knowledge graph learning are incorporated to improve decision reliability. The proposed framework aims to enhance medication safety, reduce pharmacist workload, and strengthen clinical decision support in digital healthcare environments.