Symbiosis International University, Pune, India.
Symbiosis Institute of Health Sciences, Pune, India.
Int J Med Inform. 2018 Jun;114:57-65. doi: 10.1016/j.ijmedinf.2018.03.013. Epub 2018 Mar 26.
The application of Big Data analytics in healthcare has immense potential for improving the quality of care, reducing waste and error, and reducing the cost of care.
This systematic review of literature aims to determine the scope of Big Data analytics in healthcare including its applications and challenges in its adoption in healthcare. It also intends to identify the strategies to overcome the challenges.
A systematic search of the articles was carried out on five major scientific databases: ScienceDirect, PubMed, Emerald, IEEE Xplore and Taylor & Francis. The articles on Big Data analytics in healthcare published in English language literature from January 2013 to January 2018 were considered.
Descriptive articles and usability studies of Big Data analytics in healthcare and medicine were selected.
Two reviewers independently extracted information on definitions of Big Data analytics; sources and applications of Big Data analytics in healthcare; challenges and strategies to overcome the challenges in healthcare.
A total of 58 articles were selected as per the inclusion criteria and analyzed. The analyses of these articles found that: (1) researchers lack consensus about the operational definition of Big Data in healthcare; (2) Big Data in healthcare comes from the internal sources within the hospitals or clinics as well external sources including government, laboratories, pharma companies, data aggregators, medical journals etc.; (3) natural language processing (NLP) is most widely used Big Data analytical technique for healthcare and most of the processing tools used for analytics are based on Hadoop; (4) Big Data analytics finds its application for clinical decision support; optimization of clinical operations and reduction of cost of care (5) major challenge in adoption of Big Data analytics is non-availability of evidence of its practical benefits in healthcare.
This review study unveils that there is a paucity of information on evidence of real-world use of Big Data analytics in healthcare. This is because, the usability studies have considered only qualitative approach which describes potential benefits but does not take into account the quantitative study. Also, majority of the studies were from developed countries which brings out the need for promotion of research on Healthcare Big Data analytics in developing countries.
大数据分析在医疗保健中的应用具有巨大的潜力，可以提高护理质量、减少浪费和错误，并降低护理成本。
本系统文献综述旨在确定大数据分析在医疗保健中的应用范围，包括其在医疗保健中的应用和采用面临的挑战。它还旨在确定克服这些挑战的策略。
对五个主要科学数据库（ScienceDirect、PubMed、Emerald、IEEE Xplore 和 Taylor & Francis）进行了系统的文章搜索。考虑了 2013 年 1 月至 2018 年 1 月期间以英语发表的关于医疗保健中大数据分析的文章。
选择了关于医疗保健中大数据分析的描述性文章和可用性研究。
两位审查员独立提取了有关大数据分析定义的信息；医疗保健中大数据的来源和应用；以及在医疗保健中克服挑战的策略。
根据纳入标准共选择了 58 篇文章进行分析。对这些文章的分析发现：（1）研究人员对医疗保健中大数据的操作定义缺乏共识；（2）医疗保健中的大数据来自医院或诊所的内部来源以及包括政府、实验室、制药公司、数据聚合商、医学期刊等在内的外部来源；（3）自然语言处理（NLP）是最广泛用于医疗保健的大数据分析技术，用于分析的大多数处理工具都基于 Hadoop；（4）大数据分析应用于临床决策支持；优化临床运营和降低护理成本；（5）采用大数据分析的主要挑战是缺乏其在医疗保健中的实际效益的证据。
本综述研究揭示了关于医疗保健中大数据分析实际应用的信息不足。这是因为，可用性研究仅采用了定性方法，描述了潜在的好处，但没有考虑到定量研究。此外，大多数研究来自发达国家，这就需要在发展中国家促进医疗保健大数据分析的研究。
