From Prescription to Prediction: Leveraging AI/ML to Improve Medication Adherence and Adverse Drug Event Detection in Community Pharmacies

Abstract

Medication non-adherence and adverse drug events (ADEs) remain significant challenges in community pharmacies, particularly in underserved areas where limited resources exacerbate health disparities. Recent advances in artificial intelligence (AI) and machine learning (ML) offer transformative opportunities to enhance pharmacy practice by shifting from reactive prescription management to proactive prediction and intervention. This paper explores the application of AI-powered tools in three critical domains: drug interaction and safety checks, adherence monitoring, and clinical decision support. Predictive analytics can identify patients at high risk of ADEs, while smart technologies such as wearable sensors, mobile applications, and natural language processing chatbots support personalized adherence interventions. Integrating AI-driven systems into community pharmacy workflows not only improves medication safety but also strengthens pharmacist capacity to deliver patient-centered care. However, challenges including data privacy, algorithmic bias, infrastructure limitations, and regulatory hurdles must be addressed to ensure equitable implementation. By leveraging AI/ML innovations, community pharmacies can play a pivotal role in advancing safe, effective, and accessible medication management for populations most in need.

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