AI-Driven Stewardship: Automating the Identification of Prescription Errors

Medication prescription errors remain one of the leading causes of preventable adverse drug events in healthcare systems worldwide. Conventional prescription verification methods mainly depend on manual review by pharmacists and physicians, which are often timeconsuming, inconsistent, and susceptible to human error, particularly in high-volume healthcare environments. Recent developments in artificial intelligence (AI), machine learning (ML), and
natural language processing (NLP) have enabled the development of automated systems capable of identifying prescription abnormalities with enhanced speed and accuracy. Despite considerable advancements in healthcare informatics, several limitations still exist in current AI-assisted prescription monitoring systems, including limited contextual understanding, inadequate real-time
implementation, poor explainability, and restricted generalizability across healthcare institutions. Therefore, the present study proposes an AI-driven stewardship framework for automating the identification
of prescription errors using machine learning and NLP-integrated
clinical decision support systems. The proposed framework performs
prescription preprocessing, contextual interpretation, anomaly
detection, and predictive classification to identify dosage errors, drug
interactions, contraindications, duplicate medications, and incomplete prescriptions. Comparative analysis indicates that the
proposed AI-assisted framework achieves an accuracy of 96.4%,
precision of 94.8%, recall of 95.6%, and F1-score of 95.2%,
outperforming conventional rule-based systems by approximately
12–18%. The findings demonstrate that AI-enabled prescription
stewardship systems can substantially reduce clinical workload,
improve patient safety, minimize medication-related risks, and
support evidence-based healthcare decision-making. The study
highlights the importance of intelligent prescription surveillance
systems for achieving safer and more sustainable healthcare delivery.