AI-Enhanced Regulatory Compliance in Pharmacies: A Predictive Analytics Approach

Fayazoddin Mohamad

Abstract: Regulatory compliance in the pharmaceutical sector is paramount to ensuring patient safety, data privacy, and adherence to legal standards. However, the complexity of evolving regulations coupled with high volumes of prescriptions, transactions, and patient data presents significant challenges for modern pharmacies. This paper proposes and evaluates an AI-Enhanced Regulatory Compliance (AERC) framework designed to automate compliance monitoring and improve operational efficiency. The framework leverages machine learning algorithms, predictive analytics, and secure system architectures to detect potential compliance breaches, optimize pharmacy workflows, and guide corrective actions in real time. Drawing on both simulated and real pharmacy datasets, the results highlight a notable reduction in compliance errors, increased adherence to guidelines, and enhanced accuracy in fraud detection. This study contributes a structured methodology for designing and implementing AI-driven compliance solutions, bridging the gap between innovative technology and stringent regulatory demands. Our findings underscore the potential of AI and predictive analytics to transform pharmacy compliance by minimizing risks, streamlining audits, and elevating patient-centered care.

Keywords: Pharmacy Management, Regulatory Compliance, Artificial Intelligence, Machine Learning, Predictive Analytics, Healthcare Informatics, HIPAA