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Cited by 170Electronic Health Records SystemsMedical Coding and Health InformationData Quality and Management

The DOI matches its Crossref record; 122 of the 128 references it cites were found in a registry or an index.

Towards an ontology for data quality in integrated chronic disease management: A realist review of the literature

Liaw, S.T., Rahimi, A., Ray, P., Taggart, J., Dennis, S., de Lusignan, S., Jalaludin, B., Yeo, A.E.T., Talaei-Khoei, A.·2013·International Journal of Medical Informatics
DOI 10.1016/j.ijmedinf.2012.10.001
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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 litt…

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Research works printed but not linked: 3

  1. Liaw, 2011, Health reform: is current electronic information fit for purpose?, Emerg. Med. Australasia, September
  2. Wand, 1996, Anchoring data quality dimensions in ontological foundations, CACM, 36, 10
  3. Han, 2008, An overview of data quality research, Comput. Sci., 35

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Metadata

Authors
Liaw, S. T., Rahimi, A., Ray, P., Taggart, J., Dennis, S., de Lusignan, S., Jalaludin, B., Yeo, A. E. T., & Talaei-Khoei, A.
Affiliations
  • UNSW Sydney (AU)
  • Isfahan University of Medical Sciences (IR)
  • University of Surrey (GB)
  • Ingham Institute (AU)
Corresponding author
Liaw, S.T.
ORCID
Venue
International Journal of Medical Informatics
Publisher
Elsevier BV
Published
2013-01
Volume
82
Issue
1
Pages
10-24
ISSN
1386-5056 (ISSN-L)
DOI
10.1016/j.ijmedinf.2012.10.001
OpenAlex
W2031500001
Cited by
170
References
128
Citations by year
2026: 4 · 2025: 11 · 2024: 17 · 2023: 15 · 2022: 8 · 2021: 9 · 2020: 12 · 2019: 17 · 2018: 11 · 2017: 16 · 2016: 14 · 2015: 16 · 2014: 13 · 2013: 7
FWCI
1.94
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • International Journal of Medical Informatics (publishedVersion)
  • PubMed (publishedVersion)
  • Surrey Research Insight Open Access (The University of Surrey) (submittedVersion)
  • UTS ePRESS (University of Technology Sydney) (submittedVersion)
Topics
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
  • Physical Sciences › Computer Science › Information Systems › Data Quality and Management
Keywords
data quality, ontology (information science), chronic disease management, semantic interoperability, administrative data, population health, health information systems, data quality dimensions, decision support, health outcome measurement
MeSH terms
Chronic Disease, Data Collection, Humans, Medical Record Linkage, Research Design, Information Management, Disease Management

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