The Role of Machine Learning in Remote Patient Monitoring and Telemedicine for Chronic Disease Management

Abstract

The present study explores the integration of machine learning (ML) approaches into remote patient monitoring (RPM) and telemedicine platforms, with a focus on chronic disease management. By analyzing multi-modal health data, including wearable sensor outputs, electronic health records, and teleconsultation logs, we developed predictive frameworks to improve early detection, continuous monitoring, and personalized interventions for patients with chronic illnesses. Results demonstrated that ML-enhanced monitoring significantly improved accuracy in detecting early signs of disease deterioration compared to conventional methods, with models achieving superior predictive performance in forecasting adverse events and hospital readmissions. Furthermore, clustering and classification algorithms facilitated patient stratification, allowing for more efficient allocation of healthcare resources. The integration of ML into telemedicine also demonstrated a reduction in patient wait times, enhanced continuity of care, and improved adherence to treatment protocols. The findings highlight that AI-driven RPM not only strengthens clinical decision-making but also enhances patient empowerment by providing actionable insights through user-friendly digital platforms. Collectively, the results emphasize that the use of machine learning in telemedicine has the potential to revolutionize chronic disease management by delivering proactive, data-driven, and patient-centered healthcare solutions.