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Ontology for Data Quality and Chronic Disease Management: A Literature Review Publisher



Rahimi A1, 2 ; Liaw ST3, 4 ; Ray PK3 ; Taggart J5 ; Yu H5
Authors
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Authors Affiliations
  1. 1. School of Public Health and Community Medicine, University of New South Wales, Australia
  2. 2. Isfahan University of Medical Sciences, Iran
  3. 3. University of New South Wales, Australia
  4. 4. South Western Sydney Local Health District General Practice Unit, Australia
  5. 5. Centre for Primary Health Care and Equity, University of New South Wales, Australia

Source: Healthcare Informatics and Analytics: Emerging Issues and Trends Published:2014


Abstract

Improved Data Quality (DQ) can improve the quality of decisions and lead to better policy in health organizations. Ontologies can support automated tools to assess DQ. This chapter examines ontologybased approaches to conceptualization and specification of DQ based on fitness for purpose within the health context. English language studies that addressed DQ, fitness for purpose, ontology-based approaches, and implementations were included. The authors screened 315 papers; excluded 36 duplicates, 182 on abstract review, and 46 on full-text review; leaving 52 papers. These were appraised with a realist context-mechanism-impacts/outcomes template. The authors found a lack of consensus frameworks or definitions for DQ and comprehensive ontological approaches to DQ or fitness for purpose. The majority of papers described the processes of the development of DQ tools. Some assessed the impact of implementing ontology-based specifications for DQ. There were few evaluative studies of the performance of DQ assessment tools developed; none compared ontological with non-ontological approaches. © 2015 by IGI Global. All rights reserved.
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