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Values, challenges and future directions of big data analytics in healthcare: A systematic review

机译:医疗保健大数据分析的价值观,挑战和未来方向:系统评价

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The emergence of powerful software has created conditions and approaches for large datasets to be collected and analyzed which has led to informed decision-making towards tackling health issues. The objective of this study is to systematically review 804 scholarly publications related to big data analytics in health in order to identify the organizational and social values along with associated challenges. Key principles of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology were followed for conducting systematic reviews. Following a research path, we present the values, challenges and future directions of the scientific area using indicative examples from relevant published articles. The study reveals that one of the main values created is the development of analytical techniques which provides personalized health services to users and supports human decision-making using automated algorithms, challenging the power issues in the doctor-patient relationship and creating new working conditions. A main challenge to data analytics is data management and security when processing large volumes of sensitive, personal health data. Future research is directed towards the development of systems that will standardize and secure the process of extracting private healthcare datasets from relevant organizations. Our systematic literature review aims to provide to governments and health policy-makers a better understanding of how the development of a data driven strategy can improve public health and the functioning of healthcare organizations but also how can create challenges that need to be addressed in the near future to avoid societal malfunctions.
机译:强大的软件的出现已经为要收集和分析的大型数据集创造了条件和方法,这导致了对解决健康问题的知情决策。本研究的目的是系统地审查与健康中的大数据分析有关的804个学术出版物,以便识别组织和社会价值以及相关的挑战。进行系统评价和荟萃分析(PRISMA)方法的首选报告项目的关键原则进行系统评价。在研究路径之后,我们使用来自相关公布文章的指示性示例展示了科学领域的价值观,挑战和未来方向。该研究表明,创建的主要价值观是开发分析技术,为用户提供个性化的健康服务,并支持使用自动化算法来解决人力决策,挑战医生关系中的权力问题并创造新的工作条件。在处理大量敏感的个人健康数据时,对数据分析的主要挑战是数据管理和安全性。未来的研究是针对系统的发展,以标准化和确保从相关组织提取私人医疗保健数据集的过程。我们的系统文献综述旨在为各国政府和健康政策制定者提供更好地了解数据驱动策略的发展如何改善公共卫生和医疗组织的运作,而​​且还如何创造在附近所需的挑战未来,以避免社会故障。

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