Real-Time Monitoring of Patient Adherence Using AI

Abstract

Patient compliance with prescribed therapies remains a problem in healthcare practice, especially in chronic disease management. Non-adherence up to the point where it may result in suboptimal response to the treatment, more hospitalizations and higher expenditures on health care. The latest advances in Artificial Intelligence (AI) and wearable technologies have created real-time adherence monitoring systems that work proactively to track patient behavior and medication intake. This paper presents a detailed architecture of an AI-based adherence monitoring framework that involves the deployment of wearable sensors, secure transmission protocols, edge processing nodes, and cloud-based analytics engines. The proposed system can capture important health metrics like heart rates, motions, medication ingestion, and sleep patterns through smart bands and IoT devices. Data is encrypted, sent to the cloud, and analyzed by machine learning modes (CNN, LSTM, and XGBoost) that identify non-adherence in real time based on behavioral patterns. Feedback is received from the patients and clinicians through mobile alerts, dashboards, and EMR/EHR integration. Experimental tests with real-world data show out-performance in predictive accuracy (up to 97.7%) and increased adherence rates (6.1%–32.7%) to a traditional approach. This research demonstrates the promise of AI in transforming adherence monitoring from a passive process to a dynamic, intelligent and patient-focused mode. The system is scalable, secure, and ready for real-time deployment with promising implications for chronic disease management and personalized care.