Smith Mark, Lix Lisa M, Azimaee Mahmoud, Enns Jennifer E, Orr Justine, Hong Say, Roos Leslie L
Manitoba Centre for Health Policy, Department of Community Health Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
Department of Community Health Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
J Am Med Inform Assoc. 2018 Mar 1;25(3):224-229. doi: 10.1093/jamia/ocx078.
The growth of administrative data repositories worldwide has spurred the development and application of data quality frameworks to ensure that research analyses based on these data can be used to draw meaningful conclusions. However, the research literature on administrative data quality is sparse, and there is little consensus regarding which dimensions of data quality should be measured. Here we present the core dimensions of the data quality framework developed at the Manitoba Centre for Health Policy, a world leader in the use of administrative data for research purposes, and provide examples and context for the application of these dimensions to conducting data quality evaluations. In sharing this framework, our ultimate aim is to promote best practices in rigorous data quality assessment among users of administrative data for research.
全球范围内行政数据存储库的增长推动了数据质量框架的开发与应用,以确保基于这些数据的研究分析能够用于得出有意义的结论。然而,关于行政数据质量的研究文献稀少,对于应衡量数据质量的哪些维度,几乎没有共识。在此,我们介绍了曼尼托巴卫生政策中心开发的数据质量框架的核心维度,该中心在将行政数据用于研究目的方面处于世界领先地位,并提供了将这些维度应用于进行数据质量评估的示例和背景。在分享这个框架时,我们的最终目标是在行政数据研究用户中推广严格数据质量评估的最佳实践。