Henley-Smith Sandra, Boyle Douglas, Gray Kathleen
University of Melbourne, AU.
EGEMS (Wash DC). 2019 Aug 2;7(1):38. doi: 10.5334/egems.298.
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.
After review of literature to identify relevant research, an existing data quality framework was refined and expanded to encompass the contextual requirements not present.
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.
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.
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.
信息技术领域以及最近医疗保健领域的数据质量框架自诞生以来已经有了很大的发展。在评估数据二次使用的质量时,这些框架中尚未充分解决的一个领域是数据预期用途的背景。
在查阅文献以确定相关研究之后,对现有的数据质量框架进行了完善和扩展,以涵盖所缺少的背景要求。
结果是一个两级框架,用于满足维护数据内在价值的需求,以及表明数据是否能够为特定感兴趣领域的答案或问题提供依据的需求。
数据质量框架一直都是一维的,要求这些框架的实施者根据框架的设计功能来调整数据使用的要求。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的数据质量管理自然环境,系统地解决了现有框架的缺点。
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情境以及如何开发二级数据质量框架,从而确保在整个健康数据质量评估过程中质量和情境都能得到维护和处理。