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iPatient in Medical Information Systems and Future of Internet of Health

机译:IPatient在医疗信息系统和健康互联网上的未来

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The results of Study "iHealthCare Optimization", provided by Dell EMC External Research and Academic Alliances, are presented. Big Data analytics of Medical information system qMS records was implemented using cluster analysis in Python. Software for cluster analysis was created by Andrey Mazelis (Vladivostok State University of Economics and Service). There are two directions of cluster analysis: Series treatment (number of investigation procedures for each patient) and Series time (waiting time for investigation procedures for each patient). Two models of patients management (Model A and Model B) were found, that can be used for better planning of care management. Models approach provides the new capability to implement Health Care Standard in mode aaS, using feedback after Big Data analytics. Around 80-90% of patients with Essential hypertension can get treatment in Day Hospital without hospitalization.
机译:介绍了戴尔EMC外部研究和学术联盟提供的“iHealthCare优化”的结果。使用Python中的集群分析实现了医疗信息系统QMS记录的大数据分析。集群分析软件是由Andrey Mazelis(符拉迪沃斯州立经济学和服务大学)创建的。聚类分析有两个方向:序列处理(每个患者的调查程序数量)和串联时间(每位患者的调查程序等待时间)。发现了两种患者管理(模型A和型号B),可用于更好地规划护理管理。模型方法提供了在Mode AAS中实现医疗保健标准的新功能,在大数据分析后使用反馈。大约80-90%的必需高血压患者可以在日内医院治疗,没有住院。

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