Remote Monitoring Devices in Chronic Disease Care

Abstract

Remote patient monitoring (RPM) devices -- encompassing connected blood pressure monitors, continuous glucose monitors, implantable cardiac sensors, wearable pulse oximeters, and multi-parameter home health platforms -- have transitioned from research curiosities to reimbursable clinical tools, accelerated by the COVID-19 pandemic that demonstrated both the feasibility and the necessity of managing chronic disease outside traditional clinical settings. With over 30 million patients enrolled in RPM programmes in the US alone by 2022 and CMS reimbursement codes (CPT 99453-99458) establishing a sustainable payment model, the clinical and operational evidence base for RPM in chronic disease requires systematic evaluation. This study presents the Remote Monitoring Device Assessment Framework (RMDAF), evaluating five RPM architectures -- single-parameter episodic monitors, continuous wearable sensors, implantable physiological telemetry, smartphone-integrated multi-parameter platforms, and AI-augmented predictive monitoring systems -- across four chronic disease management tasks: clinical deterioration detection, medication adherence verification, patient self-management engagement, and healthcare utilisation reduction. Our Remote Monitoring Effectiveness Score (RMES) integrates clinical outcome improvement, alert accuracy, patient engagement duration, data completeness, and cost-effectiveness. AI-augmented predictive monitoring achieved the highest RMES (0.924) through machine learning algorithms that predicted clinical deterioration 72 hours before emergency presentation with AUROC 0.89, reducing unplanned hospitalisations by 38%, while implantable telemetry achieved the highest data completeness (0.960) through continuous automated transmission requiring no patient interaction