Optimizing Remote Patient Monitoring Systems through Edge Artificial Intelligence and Wearable Biosensor Analytics for Chronic Disease Management
Keywords:
Remote Patient Monitoring, Edge Artificial Intelligence, Wearable Biosensors, Chronic Disease Management, Distributed Health Systems, Socio-Technical InfrastructuresAbstract
The convergence of wearable biosensor analytics and edge artificial intelligence represents a paradigm shift in healthcare delivery, particularly for chronic disease management. Traditional centralized remote patient monitoring systems face critical vulnerabilities, including network latency, escalating cloud storage expenditures, data privacy liabilities, and severe bandwidth constraints during high-frequency physiological data collection. This paper investigates the systematic optimization of distributed remote patient monitoring networks through the deployment of lightweight machine learning algorithms directly on edge devices. By shifting computing processes from centralized cloud infrastructures to localized network nodes near the patient, the system design achieves significant reductions in network overhead while ensuring real-time clinical intervention capabilities. We analyze the intricate architectural trade-offs inherent in edge-native healthcare systems, balancing localized computational resource constraints against the demand for high-accuracy diagnostic models. The manuscript addresses critical structural dimensions, including hardware-accelerated deep learning compilation, multi-modal sensor data fusion, energy-efficient operational scheduling, and robust local data governance frameworks that comply with stringent medical information privacy standards. Furthermore, we examine the socio-technical implications of decentralized clinical networks, exploring algorithmic fairness across diverse patient demographics, the integration of edge analytics into legacy hospital electronic health records, and the broader policy adjustments required for regulatory approval and reimbursement. Ultimately, this research demonstrates that optimized edge intelligence enhances system resilience, safeguards patient autonomy, and establishes a scalable framework for sustainable, proactive chronic disease intervention.
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