Authors: Tanira Poddar
Abstract: The exponential growth of healthcare data, driven by electronic health records (EHRs), medical imaging, wearable sensors, genomic sequencing, and real-time monitoring systems, has resulted in unprecedented opportunities for transforming medical care. Big data frameworks provide the computational backbone to store, process, analyze, and visualize these massive, heterogeneous datasets. Their applications extend from early disease prediction and personalized medicine to hospital workflow optimization, fraud detection, and population health management. However, integrating big data solutions in healthcare presents challenges such as data privacy, fragmented systems, interoperability issues, and resource-intensive infrastructure requirements. This review comprehensively explores the evolution and impact of big data frameworks in healthcare IT, evaluating critical technologies, architectures, applications, and implementation strategies. It also highlights the barriers and future directions for leveraging big data to improve clinical practice, research, and administration. Insights are drawn from recent studies, practical use cases, and emerging trends in artificial intelligence, predictive analytics, and real-time decision support. The review ultimately provides a roadmap for stakeholders—clinicians, technologists, administrators, and researchers—to harness big data for better outcomes, operational efficiency, and patient-centric care.
DOI: http://doi.org/10.5281/zenodo.16982564
