Abstract

The integration of cloud and edge computing in healthcare systems was revolutionizing the delivery of real-time, data-driven healthcare services. As healthcare systems increasingly rely on predictive analytics, real-time data processing, and personalized patient care, the combination of cloud and edge computing offers unprecedented opportunities to optimize performance, reduce latency, and enhance scalability. This chapter explores the synergies between cloud and edge computing for high-speed data analysis and response in predictive healthcare systems, emphasizing their potential to transform medical diagnostics, treatment planning, and patient monitoring. The chapter delves into key areas such as latency management, data privacy and security, resource optimization, and the integration of heterogeneous healthcare data. It highlights advanced techniques for reducing latency, ensuring data consistency, and managing dynamic healthcare workloads. By focusing on the challenges and solutions associated with cloud-edge integration, this chapter provides a comprehensive understanding of how these technologies can work in tandem to meet the growing demands of modern healthcare systems. The integration of these technologies was poised to significantly enhance patient care outcomes, streamline healthcare operations, and provide real-time, actionable insights that are critical for proactive healthcare management.

Introduction

The healthcare sector was undergoing a significant transformation driven by the increased adoption of digital technologies, aiming to improve patient outcomes, optimize resource utilization, and enhance healthcare delivery [1]. With the vast amounts of data generated by medical devices, sensors, wearables, and electronic health records (EHRs), traditional methods of data processing and storage have proven inadequate in meeting the needs of modern healthcare systems [2]. The complexity and volume of healthcare data require innovative solutions to manage, analyze, and interpret information efficiently and accurately [3]. In response to these challenges, cloud and edge computing technologies have emerged as pivotal enablers of predictive healthcare systems [4]. These technologies work synergistically to offer high-speed data analysis, reduce latency, and enable real-time decision-making at the point of care, facilitating more personalized and proactive healthcare delivery [5].

Cloud computing provides a scalable and flexible infrastructure that can accommodate large-scale data storage and processing, making it an ideal solution for healthcare systems managing vast amounts of patient data [6]. The cloud allows for centralized storage, where data from diverse sources can be aggregated, analyzed, and shared across healthcare providers [7]. On the other hand, edge computing complements cloud infrastructure by bringing computation and data storage closer to the data source [8]. This decentralization minimizes latency, ensuring real-time data processing and immediate decision-making at the point of care [9]. Edge devices, such as wearables or medical sensors, process data locally, reducing the need for continuous data transmission to the cloud and enabling faster responses [10]. The integration of cloud and edge computing creates a hybrid architecture that optimizes both the efficiency and speed of healthcare systems, ensuring a seamless flow of data between devices and centralized platforms [11].