The Intelligent Continuum: AI’s Impact on Remote Health Monitoring

Purpose: This research investigates Artificial Intelligence (AI) technology for Remote Patient Monitoring (RPM) systems, specifically focusing on the continuous monitoring of chronic diseases. The research addresses the crucial issue of prompt patient care through the implementation of intelligent automated systems. Methodology: The research combines deep learning models with federated learning frameworks to support ongoing health data tracking from wearable devices. Real-time physiological signal processing is achieved through decentralized data processing, ensuring patient privacy. The research examined how AI-based RPM systems perform in comparison to standard monitoring systems, focusing on diagnostic precision, system flexibility, and response times. Findings: The research findings demonstrate that AI outperforms standard RPM systems in detecting anomalies early, enhancing patient compliance, and reducing hospital admission rates. The AI models achieved both high sensitivity and specificity levels while keeping patient data secure. Real-time analytics enabled immediate interventions, resulting in improved clinical outcomes and more efficient healthcare operations. Unique contribution to theory, practice, and policy: The research establishes a novel approach to RPM by combining deep learning with federated learning to create a scalable healthcare solution that protects patient privacy. The research expands theoretical knowledge by demonstrating the application of decentralized AI in clinical monitoring. The research presents an actionable model that healthcare providers can use to deliver individualized care for patients with chronic diseases. The research presents a policy framework to enhance health equity and digital transformation through AI-enabled RPM, which recommends investments in ethical and interoperable and inclusive health technologies.