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.
Highlights
A Data Quality (DQ) framework is essential if we want to be able to assess data quality systematically, according to defined characteristics or dimensions [1, 2]
An important development of this kind was a systematic review of DQ frameworks from 1996 to 2013, published in 2016 by Kahn et al They focused on harmonizing data quality assessment terminology, and they augmented their analysis of the literature by workshops and interviews with health industry participants, to produce a comprehensive framework for the secondary use of Electronic Health Records (EHRs) data [2]
Through testing we found that revision was required, to describe the DQ framework characteristics that would address the problems the authors had experienced in working with a secondary use data warehouse
Summary
A Data Quality (DQ) framework is essential if we want to be able to assess data quality systematically, according to defined characteristics or dimensions [1, 2]. One issue is whether a framework has the flexibility to consider how DQ is assessed in the context of various potential primary and secondary uses. Across such contexts of use, definitions of DQ and its categories and subcategories are not always clear or agreed [2]. 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. 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. 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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