Big data analytics in healthcare. A systematic literature review and roadmap for practical implementation

Author Affiliation

Tariq Mahmood is Professor & Program Coordinator MS (CS) & MS (DS) Programs at Institute of Business Administration (IBA), Karachi

Faculty / School

School of Mathematics and Computer Science (SMCS)

Department

Department of Computer Science

Was this content written or created while at IBA?

Yes

Document Type

Article

Source Publication

IEEE/CAA Journal of Automatica Sinica

ISSN

2329-9266

Disciplines

Artificial Intelligence and Robotics | Computer Sciences | Databases and Information Systems | Engineering

Abstract

The advent of healthcare information management systems HIMSs continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global scale. Analysis of this big data allows for boundless potential outcomes for discovering knowledge. Big data analytics BDA in healthcare can, for instance, help determine causes of diseases, generate effective diagnoses, enhance QoS guarantees by increasing efficiency of the healthcare delivery and effectiveness and viability of treatments, generate accurate predictions of readmissions, enhance clinical care, and pinpoint opportunities for cost savings. However, BDA implementations in any domain are generally complicated and resource-intensive with a high failure rate and no roadmap or success strategies to guide the practitioners. In this paper, we present a comprehensive roadmap to derive insights from BDA in the healthcare patient care domain, based on the results of a systematic literature review. We initially determine big data characteristics for healthcare and then review BDA applications to healthcare in academic research focusing particularly on NoSQL databases. We also identify the limitations and challenges of these applications and justify the potential of NoSQL databases to address these challenges and further enhance BDA healthcare research. We then propose and describe a state-of-the-art BDA architecture called Med-BDA for healthcare domain which solves all current BDA challenges and is based on the latest zeta big data paradigm. We also present success strategies to ensure the working of Med-BDA along with outlining the major benefits of BDA applications to healthcare. Finally, we compare our work with other related literature reviews across twelve hallmark features to justify the novelty and importance of our work. The aforementioned contributions of our work are collectively unique and clearly present a roadmap for clinical administrators, practitioners and professionals to successfully implement BDA initiatives in their organizations.

Indexing Information

HJRS - W Category, Scopus, Web of Science - Science Citation Index Expanded (SCI)

Journal Quality Ranking

Impact Factor: 6.171

Publication Status

Published

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