@文章{信息:doi/10.2196/18579，作者=“Ozaydin, Bunyamin and Zengul, Ferhat and Oner, Nurettin and Feldman, Sue S”，标题=“医疗保健研究与分析数据基础架构解决方案:面向医疗服务研究的数据仓库”，期刊=“J Med Internet Res”，年=“2020”，月=“6”，日=“4”，卷=“22”，号=“6”，页=“e18579”，关键词=“医疗服务研究;数据仓库;迭代过程模型;系统分析与设计;数据集成"，摘要="背景:卫生服务研究人员花费大量时间对来自多个公共或私有数据源的原始数据进行集成、清理、解释和聚合。通常，每个研究人员(或他们团队中的某个人)在他们自己的项目中重复这种努力，面对同样的挑战，经历前人发现的同样的陷阱。目的:本文描述了一个创建数据仓库的设计过程，其中包括卫生服务研究中最常用的数据库。方法:该设计基于概念性迭代过程模型框架，该框架利用社会技术系统理论方法，包括对现有数据源的后续更新和新数据源的添加的能力。我们将介绍理论和框架，然后解释如何使用它们来告知本研究的方法。结果:描述了迭代过程模型在医疗保健研究与分析数据基础设施解决方案(HRADIS)问题识别和解决方案设计研究过程中的应用。 Each phase of the iterative model produced end products to inform the implementation of HRADIS. The analysis phase produced the problem statement and requirements documents. The projection phase produced a list of tasks and goals for the ideal system. Finally, the synthesis phase provided the process for a plan to implement HRADIS. HRADIS structures and integrates data dictionaries provided by the data sources, allowing the creation of dimensions and measures for a multidimensional business intelligence system. We discuss how HRADIS is complemented with a set of data mining, analytics, and visualization tools to enable researchers to more efficiently apply multiple methods to a given research project. HRADIS also includes a built-in security and account management framework for data governance purposes to ensure customized authorization depending on user roles and parts of the data the roles are authorized to access. Conclusions: To address existing inefficiencies during the obtaining, extracting, preprocessing, cleansing, and filtering stages of data processing in health services research, we envision HRADIS as a full-service data warehouse integrating frequently used data sources, processes, and methods along with a variety of data analytics and visualization tools. This paper presents the application of the iterative process model to build such a solution. It also includes a discussion on several prominent issues, lessons learned, reflections and recommendations, and future considerations, as this model was applied. ", issn="1438-8871", doi="10.2196/18579", url="//www.mybigtv.com/2020/6/e18579", url="https://doi.org/10.2196/18579", url="http://www.ncbi.nlm.nih.gov/pubmed/32496199" }