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Optimizing Data Pipelines for Real-Time Healthcare Analytics in Distributed Systems: Architectural Strategies, Performance Trade-offs, and Emerging Paradigms

Year: 2025

Type: article

Abstract: The growing complexity and volume of healthcare data necessitate highly optimized real-time analytics systems capable of supporting clinical decision-making and operational efficiency. This study investigates architectural strategies for optimizing data pipelines in distributed healthcare analytics environments. It evaluates key performance metrics such as latency, throughput, scalability, reliability, and data consistency across multiple pipeline architectures, including Lambda, Kappa, and Micro-Batch (Spark). Using synthetic healthcare datasets and performance benchmarks, we highlight trade-offs between latency and operational costs, emphasizing the critical balance between system efficiency and clinical utility. Emerging paradigms such as edge computing, AI-driven optimization, and adaptive resource management are explored as pathways to enhance resilience and performance. The findings provide actionable insights for designing adaptive, secure, and cost-effective healthcare data pipelines capable of meeting stringent real-time demands.

Source: International Journal of Scientific and Management Research

DOI: 10.37502/ijsmr.2025.8708

Authors: Olasehinde Omolayo, Raphael Ugboko, Deborah Olamide Oyeyemi, Oluwafemi Oloruntoba, Samuel O. Fakunle

Institutions: Georgia State University, Clemson University, University of Delaware, Lamar University, University of East London

Language: English

Topic: Advanced Database Systems and Queries

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

OpenAlex ID: W4413200970

FWCI: 0.448

Cites:

Cited by: 6

Related to: 10