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Volume 8 Issue 1
Jan.  2021

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Sohail Imran, Tariq Mahmood, Ahsan Morshed and Timos Sellis, "Big Data Analytics in Healthcare — A Systematic Literature Review and Roadmap for Practical Implementation," IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 1-22, Jan. 2021. doi: 10.1109/JAS.2020.1003384
Citation: Sohail Imran, Tariq Mahmood, Ahsan Morshed and Timos Sellis, "Big Data Analytics in Healthcare — A Systematic Literature Review and Roadmap for Practical Implementation," IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 1-22, Jan. 2021. doi: 10.1109/JAS.2020.1003384

Big Data Analytics in Healthcare — A Systematic Literature Review and Roadmap for Practical Implementation

doi: 10.1109/JAS.2020.1003384
Funds:  This work was supported by two research grants provided by the Karachi Institute of Economics and Technology (KIET) and the Big Data Analytics Laboratory at the Insitute of Business Administration (IBA-Karachi)
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  • 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.

     

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  • 1 https://neo4j.com
    2 http://www.hl7.org/implement/standards/fhir/)
    3 A group of graduate students participated in this activity over a period of 3 months. For the sake of brevity, the details are outside the scope of this paper.
    4 To the best of our knowledge, this list is complete as of June 2020.
    5 A detailed discussion of the nine compared papers is outside the scope of this work; we invite the reader to go through these papers for more required information.
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    • The most thorough systematic literature review on big data analytics applications to healthcare
    • Focus on healthcare applications for NoSQL databases and Apache Hadoop ecosystem
    • Proposes the first-ever Zeta architecture called Med-BDA for big healthcare data analytics
    • Med-BDA has the potential to solve ALL current limitations for big healthcare data analytics
    • We present business strategies to successfully implement Med-BDA in any clinical organization

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