Internet of Medical Things for Smart Medication Monitoring

Abstract

Medication non-adherence remains one of the leading causes of preventable hospitalizations, treatment failures, and rising healthcare expenditures despite continuous advances in digital health technologies. The Internet of Medical Things (IoMT) has created opportunities to monitor medication intake through interconnected wearable devices, smart dispensers, physiological sensors, and cloud-based healthcare platforms. However, most existing IoMT solutions focus on event logging rather than continuously interpreting patient behavior and physiological responses in real time. This study identifies this limitation and proposes a behavior-aware smart medication monitoring framework integrating multimodal IoMT sensing with adaptive machine learning for personalized adherence assessment. The framework aims to distinguish intentional, accidental, and clinically significant deviations in medication routines while minimizing false alerts. A hybrid edge-cloud intelligence mechanism is designed to support timely clinical intervention with reduced latency. The proposed architecture is intended to improve monitoring reliability, patient engagement, and decision support in digital healthcare environments.