Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/161812
Authors: 
Rahimi, Alireza
Liaw, Siaw-teng
Ray, Pradeep
Taggart, Jane
Yu, Hairong
Year of Publication: 
2014
Citation: 
[Journal:] Decision Analytics [ISSN:] 2193-8636 [Volume:] 1 [Year:] 2014 [Issue:] 1 [Pages:] 1-31
Abstract: 
This systematic review examined the current state of conceptualization and specification of data quality and the role of ontology based approaches to develop data quality based on "fitness for purpose" within the health context. A literature review was conducted of all English language studies, from January 2000-March 2013, which addressed data/information quality, fitness for purpose of data, used and implemented ontology-based approaches. Included papers were critically appraised with a "context-mechanism-impacts/outcomes" overlay. We screened 315 papers, excluded 36 duplicates, 182 on abstract review and 46 on full-text review; leaving 52 papers for critical appraisal. Six papers conceptualized data quality within the "fitness for purpose" definition. While most agree with a multidimensional definition of DQ, there is little consensus on a conceptual framework. We found no reports of systematic and comprehensive ontological approaches to DQ based on fitness for purpose or use. However, 16 papers used ontology-specified implementations in DQ improvement, with most of them focusing on some dimensions of DQ such as completeness, accuracy, correctness, consistency and timeliness. The majority of papers described the processes of the development of DQ in various information systems. There were few evaluative studies, including any comparing ontological with non-ontological approaches, on the assessment of clinical data quality and the performance of the application.
Subjects: 
Data quality
Fitness for purpose
Data model
Ontology development methodology
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/2.0/
Document Type: 
Article
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