AI-Integrated Remote Patient Monitoring Framework for Next-Generation Virtual Care Management Systems

Abstract

The rapid expansion of Virtual Care Management Systems (VCMS) has created an urgent demand for intelligent, autonomous, and scalable Remote Patient Monitoring (RPM) solutions capable of processing continuous streams of physiological data. Traditional RPM architectures, which depend heavily on centralized cloud servers and rule-based analytics, often suffer from latency issues, limited scalability, and reduced effectiveness in providing timely clinical insights. To address these limitations, this study proposes an AI-Enhanced Remote Patient Monitoring Framework that integrates multimodal biomedical sensors, edge-level preprocessing, and cloud-coordinated deep learning workflows for real-time assessment of patient health states. The framework incorporates advanced artificial intelligence components—such as transformer-driven vital-sign forecasting networks, robust anomaly detection models, and federated learning mechanisms—to deliver secure, adaptive, and low-latency monitoring. Experimental results obtained from diverse RPM datasets reveal substantial improvements in event-detection accuracy, signal quality, and temporal prediction performance when compared with conventional machine learning approaches. Findings also show that the hybrid AI architecture reduces overall monitoring latency by up to 35% and increases early clinical alerting precision by 22%. Overall, this work provides a scalable, interoperable, and clinically dependable model for next-generation virtual care systems, supporting proactive and high-quality remote healthcare delivery.