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Are big data analytics helpful in caring for multimorbid patients in general practice? - A scoping review

机译:大数据分析是否在一般实践中有助于照顾多病患者? -范围审查

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The treatment of multimorbid patients is one crucial task in general practice as multimorbidity is highly prevalent in this setting. However, there is little evidence how to treat these patients and consequently there are but a few guidelines that focus primarily on multimorbidity. Big data analytics are defined as a method that obtains results for high volume data with high variety generated at high velocity. Yet, the explanatory power of these results is not completely understood. Nevertheless, addressing multimorbidity as a complex condition might be a promising field for big data analytics. The aim of this scoping review was to evaluate whether applying big data analytics on patient data does already contribute to the treatment of multimorbid patients in general practice. In January 2018, a review searching the databases PubMed, The Cochrane Library, and Web of Science, using defined search terms for “big data analytics” and “multimorbidity”, supplemented by a search of grey literature with Google Scholar, was conducted. Studies were not filtered by type of study, publication year or language. Validity of studies was evaluated independently by two researchers. In total, 2392 records were identified for screening. After title and abstract screening, six articles were included in the full-text analysis. Of those articles, one reported on a model generated with big data techniques to help caring for one group of multimorbid patients. The other five articles dealt with the analysis of multimorbidity clusters. No article defined big data analytics explicitly. Although the usage of the phrase “Big Data” is growing rapidly, there is nearly no practical use case for big data analysis techniques in the treatment of multimorbidity in general practice yet. Furthermore, in publications addressing big data analytics, the term is rarely defined. However, possible models and algorithms to address multimorbidity in the future are already published.
机译:多发病患者的治疗是一般实践中的一项关键任务,因为在这种情况下多发病非常普遍。但是,几乎没有证据表明如何治疗这些患者,因此,仅有一些主要针对多发病的指南。大数据分析被定义为一种以高速生成大量高多样性数据的结果的方法。但是,这些结果的解释能力尚未完全理解。然而,将多发病率作为复杂的条件解决可能是大数据分析的一个有前途的领域。范围界定审查的目的是评估在大范围实践中,对患者数据应用大数据分析是否确实已经对多病患者的治疗做出了贡献。 2018年1月,进行了一项评论,使用“大数据分析”和“多发病”的定义搜索词搜索PubMed,Cochrane图书馆和Web of Science数据库,并辅以Google Scholar搜索灰色文献。没有按研究类型,出版年份或语言过滤研究。研究的有效性由两名研究人员独立评估。总共确定了2392条记录用于筛选。经过标题和摘要筛选后,全文分析中包括六篇文章。在这些文章中,有一篇报道了使用大数据技术生成的模型,以帮助照顾一组多病患者。其他五篇文章涉及多发病率集群的分析。没有文章明确定义大数据分析。尽管“大数据”一词的使用正在迅速增长,但是在一般实践中,大数据分析技术在治疗多发病方面几乎没有实际的使用案例。此外,在涉及大数据分析的出版物中,很少定义该术语。但是,未来解决多发病的可能模型和算法已经发布。

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