Remote Patient Monitoring Technologies

Abstract

Remote patient monitoring (RPM) -- the use of digital technologies to collect physiological and symptom data from patients outside traditional healthcare settings -- has evolved from a niche telehealth application into a central pillar of chronic disease management, post-acute care, and pandemic response. The RPM technology landscape now spans implantable cardiac monitors, wearable biosensors, smartphone-connected peripherals, smart home sensors, and AI-driven predictive analytics platforms that synthesise multi-device data streams into clinically actionable alerts. We evaluated five RPM technology frameworks across 28,486 monitored patients at six European health systems (Rome, Paris, Madrid, Stockholm, Berlin, and Tallinn) from 2018 to 2022, covering heart failure, COPD, type 2 diabetes, hypertension, and post-surgical recovery. We compared: single-parameter telemonitoring, multi-parameter integrated platforms, wearable continuous monitoring, AI-driven predictive monitoring, and hybrid human-AI monitoring with nurse-led response hubs. Our RPM Technology Effectiveness Score (RPTES) integrates clinical outcome improvement, alert accuracy, patient engagement, cost-effectiveness, and scalability. Hybrid human-AI monitoring achieved the highest RPTES (0.928) by combining ML-driven alert prioritisation with nurse-led clinical response, reducing hospitalisations by 32% and emergency visits by 38% while maintaining alert fatigue at clinically manageable levels. AI-driven predictive monitoring (RPTES 0.912) detected clinical deterioration a median of 4.8 days before traditional symptom-triggered presentation.