Improving a Secondary Use Health Data Warehouse: Proposing a Multi-Level Data Quality Framework

Sandra Henley-Smith, D. Boyle, K. Gray
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引用次数: 12

Abstract

Background: Data quality frameworks within information technology and recently within health care have evolved considerably since their inception. When assessing data quality for secondary uses, an area not yet addressed adequately in these frameworks is the context of the intended use of the data. Methods: After review of literature to identify relevant research, an existing data quality framework was refined and expanded to encompass the contextual requirements not present. Results: The result is a two-level framework to address the need to maintain the intrinsic value of the data, as well as the need to indicate whether the data will be able to provide the basis for answers in specific areas of interest or questions. Discussion: Data quality frameworks have always been one dimensional, requiring the implementers of these frameworks to fit the requirements of the data’s use around how the framework is designed to function. Our work has systematically addressed the shortcomings of existing frameworks, through the application of concepts synthesized from the literature to the naturalistic setting of data quality management in an actual health data warehouse. Conclusion: Secondary use of health data relies on contextualized data quality management. Our work is innovative in showing how to apply context around data quality characteristics and how to develop a second level data quality framework, so as to ensure that quality and context are maintained and addressed throughout the health data quality assessment process.
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改进二次使用健康数据仓库:提出一个多级数据质量框架
背景:信息技术和医疗保健领域的数据质量框架自成立以来已经有了长足的发展。在评估二次使用的数据质量时,这些框架中尚未充分处理的一个领域是数据的预期用途。方法:在对文献进行审查以确定相关研究后,对现有的数据质量框架进行了改进和扩展,以涵盖不存在的上下文要求。结果:结果是一个两级框架,以解决保持数据内在价值的需要,以及表明数据是否能够为感兴趣的特定领域或问题的答案提供基础的需要。讨论:数据质量框架一直是一维的,要求这些框架的实现者围绕框架的功能设计来满足数据使用的要求。我们的工作通过将文献中综合的概念应用于实际健康数据仓库中数据质量管理的自然设置,系统地解决了现有框架的缺点。结论:健康数据的二次使用依赖于情境化的数据质量管理。我们的工作具有创新性,展示了如何围绕数据质量特征应用上下文,以及如何开发二级数据质量框架,以确保在整个健康数据质量评估过程中保持和处理质量和上下文。
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