Distributed Data Processing Frameworks for Large-Scale Healthcare Analytics

Abstract

The expansion of healthcare information generated by electronic health records, medical imaging systems, genome sequencing technologies, and wearable devices, alongside healthcare information systems, has been a challenge for data storage, management, and analysis. But traditional centralized computing methods can often fall short of efficiency when it comes to processing the vast amount, speed and complexity of health information. Distributed data processing frameworks have become a viable alternative to address the challenges of scalability, parallel processing, fault tolerance, and performance when dealing with large datasets and complex analytics tasks. The capabilities of these frameworks enable the extraction of insightful information from massive and complex healthcare data sets, enabling better clinical care, disease forecasting, personalised care plans, population health and operational optimization.
This research aims to discuss the architectures, features and applications of distributed data processing frameworks in the context of large-scale healthcare analytics. It delves into key distributed computing frameworks, such as cluster computing, distributed storage solutions, and parallel analytics tools, each of which plays a crucial role in managing and analyzing healthcare data. The study also analyzes the major applications such as clinical analytics, genomic research, epidemiological surveillance, and real-time patient monitoring. Furthermore, it highlights key challenges related to scalability, interoperability, security, privacy, and resource management in distributed healthcare settings. The results suggest that distributed data processing frameworks offer the computational infrastructure and basis for deriving valuable knowledge from large healthcare datasets, which has the potential to optimize healthcare delivery, research, and organizational processes. The study concludes that continued advancements in distributed computing technologies will play a crucial role in supporting the future evolution of data-driven healthcare systems.