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Towards an Ontology for Data Quality in Integrated Chronic Disease Management: A Realist Review of the Literature Publisher Pubmed



Liaw ST1, 2, 3 ; Rahimi A1, 4, 5 ; Ray P1, 4 ; Taggart J2 ; Dennis S2 ; De Lusignan S6 ; Jalaludin B1, 7 ; Yeo AET8 ; Talaeikhoei A4
Authors
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Authors Affiliations
  1. 1. University of NSW School of Public Health and Community Medicine, Sydney, Australia
  2. 2. University of NSW Centre for Primary Health Care and Equity, Sydney, Australia
  3. 3. General Practice Unit, South West Sydney Local Health District, Australia
  4. 4. Asia Pacific ubiquitous Healthcare research Centre (APuHC), University of NSW, Sydney, Australia
  5. 5. Isfahan University of Medical Sciences, Faculty of Management, Medical Information Sciences, Iran
  6. 6. Department of Health Care Management and Policy, University of Surrey, Guildford, United Kingdom
  7. 7. Population Health Unit, South West Sydney Local Health District, Australia
  8. 8. Ingham Institute of Applied Medical Research, Australia

Source: International Journal of Medical Informatics Published:2013


Abstract

Purpose: Effective use of routine data to support integrated chronic disease management (CDM) and population health is dependent on underlying data quality (DQ) and, for cross system use of data, semantic interoperability. An ontological approach to DQ is a potential solution but research in this area is limited and fragmented. Objective: Identify mechanisms, including ontologies, to manage DQ in integrated CDM and whether improved DQ will better measure health outcomes. Methods: A realist review of English language studies (January 2001-March 2011) which addressed data quality, used ontology-based approaches and is relevant to CDM. Results: We screened 245 papers, excluded 26 duplicates, 135 on abstract review and 31 on full-text review; leaving 61 papers for critical appraisal. Of the 33 papers that examined ontologies in chronic disease management, 13 defined data quality and 15 used ontologies for DQ. Most saw DQ as a multidimensional construct, the most used dimensions being completeness, accuracy, correctness, consistency and timeliness. The majority of studies reported tool design and development (80%), implementation (23%), and descriptive evaluations (15%). Ontological approaches were used to address semantic interoperability, decision support, flexibility of information management and integration/linkage, and complexity of information models. Conclusion: DQ lacks a consensus conceptual framework and definition. DQ and ontological research is relatively immature with little rigorous evaluation studies published. Ontology-based applications could support automated processes to address DQ and semantic interoperability in repositories of routinely collected data to deliver integrated CDM. We advocate moving to ontology-based design of information systems to enable more reliable use of routine data to measure health mechanisms and impacts. © 2012 Elsevier Ireland Ltd.
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