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Distributed Edge-Cloud Healthcare Architecture: A Technical Overview

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  • Praveen Kumar Surabhi

    (Jawaharlal Nehru Technological University (JNTU), Hyderabad, India)

Abstract

Distributed Edge-Cloud Healthcare Architecture represents a transformative framework that strategically combines edge computing's localized processing capabilities with cloud computing's centralized analytical power to address critical challenges in modern healthcare delivery. This architecture creates a hybrid infrastructure spanning three essential layers: the edge layer with medical IoT devices and local servers performing time-sensitive computations near patients; the cloud layer providing robust computational resources for complex analytics and machine learning; and the communication layer ensuring secure connectivity between components. By processing data closer to its source while maintaining cloud-based analytical depth, this architecture significantly enhances healthcare delivery through reduced latency, optimized bandwidth usage, and strengthened data security. The framework enables breakthrough applications including real-time patient monitoring with immediate alert generation, enhanced telemedicine services with improved video quality, optimized hospital resource allocation through asset tracking, AI-powered diagnostic assistance at the point of care, and maintained functionality during network disruptions. These capabilities collectively address persistent healthcare challenges while creating unprecedented opportunities for proactive intervention, personalized treatment, and operational efficiency. The integration of edge and cloud capabilities within this architecture establishes a foundation for healthcare systems that are simultaneously more responsive, intelligent, resilient, and patient-centered.

Suggested Citation

  • Praveen Kumar Surabhi, 2025. "Distributed Edge-Cloud Healthcare Architecture: A Technical Overview," Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 7(4), pages 701-711, May.
  • Handle: RePEc:alk:jcsts0:v:7:y:2025:i:4:id:9619
    DOI: 10.32996/jcsts.2025.7.4.83
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