Big data analytics in healthcare &#x2212 A systematic literature review and roadmap for practical implementation

• Published in: IEEE/CAA journal of automatica sinica Vol. 8; no. 1; pp. 1 - 22
• Main Authors: Imran, Sohail, Mahmood, Tariq, Morshed, Ahsan, Sellis, Timos
• Format: Journal Article
• Language: English
• Published: Chinese Association of Automation (CAA) 01-01-2021
• Subjects: Bibliographies; Big Data; Business; Computer architecture; Google; Medical services; Systematics
• ISSN: 2329-9266
• DOI: 10.1109/JAS.2020.1003384

The advent of healthcare information management systems &#x0028 HIMSs &#x0029 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 &#x0028 BDA &#x0029 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 &#x0028 patient care &#x0029 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.