Optimizing healthcare big data performance through regional computing

Area of Science:

• Digital Health
• Health Informatics
• Data Science

Background:

• The healthcare sector is undergoing a digital transformation with technologies like the Internet of Medical Things (IOMT), Electronic Health Records (EHR), and wearable devices.
• This digital shift generates vast amounts of Healthcare Big Data (HBD), necessitating efficient analysis for improved patient outcomes and care delivery.
• Conventional cloud-based processing faces latency and network congestion issues with large, time-sensitive HBD, hindering real-time applications.

Purpose of the Study:

• To propose a Regional Computing (RC) paradigm to manage Healthcare Big Data (HBD).
• To mitigate the latency and network congestion challenges associated with centralized cloud processing of HBD.
• To enable timely, real-time data analysis for enhanced healthcare decision-making.

Main Methods:

• The study proposes a Regional Computing (RC) framework.
• This framework utilizes strategically positioned regional servers for localized data collection, processing, and storage.
• The RC approach aims to reduce reliance on centralized cloud infrastructure, particularly during peak loads.

Main Results:

• The RC paradigm effectively reduces latency in Healthcare Big Data (HBD) processing.
• Regionalized data management facilitates real-time analysis at the local level.
• The framework alleviates constraints imposed by traditional cloud computing for time-sensitive medical data.

Conclusions:

• Regional Computing (RC) offers a viable solution for managing Healthcare Big Data (HBD) challenges.
• This approach enhances the ability of healthcare providers to leverage real-time data for personalized and optimized patient care.
• RC empowers data-driven decision-making, leading to improved diagnostics, monitoring, and surgical interventions.