Enhancing Telemedicine Services Through Real-Time Patient Monitoring and Predictive Analytics Using Edge Computing

Abstract

Telemedicine has emerged as a powerful healthcare delivery method, enabling remote consultations and real-time interventions. However, conventional telehealth systems that rely on centralized cloud infrastructures often face high latency, bandwidth overload, and privacy risks. Edge computing addresses these limitations by processing datacloser to where it is generated, allowing for real-time patient monitoring and predictive analytics with improved responsiveness and reduced transmission of sensitive information. This paper explores the integration of edge computing into telemedicine systems to enhance care quality, responsiveness, and privacy. We present a review of original research published before 2020 that laid the foundation for this shift and propose a conceptual architecture for edge-enhanced telehealth systems. Our findings highlight that combining edge processing with intelligent analytics significantly enhances telemedicine performance and supports proactive healthcare delivery.