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.

中文翻译：

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医疗保健中的大数据分析-系统文献回顾和实际实施路线图

医疗保健信息管理系统（HIMSs）的出现继续在全球范围内产生大量的医疗保健数据，以满足患者护理以及合规性和法规要求。对这些大数据的分析为发现知识提供了无限的潜在结果。例如，医疗保健中的大数据分析（BDA）可以帮助确定疾病的原因，产生有效的诊断，通过提高医疗保健交付的效率以及治疗的有效性和可行性来增强QoS保证，生成准确的再入院预测，增强临床护理，并找出节省成本的机会。但是，任何领域的BDA实施通常都很复杂且资源密集，失败率很高，并且没有路线图或成功策略来指导从业人员。在本文中，我们根据系统的文献综述的结果，提出了一个全面的路线图，以从BDA在医疗保健（患者护理）领域中获取见解。我们首先确定医疗保健的大数据特征，然后在学术研究（尤其是NoSQL数据库）中审查BDA在医疗保健方面的应用。我们还确定了这些应用程序的局限性和挑战，并证明了NoSQL数据库应对这些挑战并进一步加强BDA医疗保健研究的潜力。然后，我们提出并描述一种用于医疗领域的最新BDA体系结构，称为Med-BDA，该体系结构解决了当前所有BDA挑战，并基于最新的zeta大数据范例。我们还提出了成功的策略，以确保Med-BDA的工作，并概述了BDA应用在医疗保健方面的主要好处。最后，我们将我们的工作与其他相关文献综述（十二个标志性特征）进行比较，以证明我们工作的新颖性和重要性。我们工作的上述贡献是独一无二的，并且清楚地为临床管理员，从业人员和专业人员提供了在其组织中成功实施BDA计划的路线图。