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

Highlights ► The data quality (DQ) field is fragmented and ontological approaches not commonly used. ► DQ is a multidimensional construct, the most used dimensions being completeness, accuracy, correctness, consistency and timeliness. ► Included studies ( n = 61) reported tool development (80%), imp...

Full description

Saved in:
Bibliographic Details
Published in:International journal of medical informatics (Shannon, Ireland) Ireland), 2013-01, Vol.82 (1), p.10-24
Main Authors: Liaw, S.T, Rahimi, A, Ray, P, Taggart, J, Dennis, S, de Lusignan, S, Jalaludin, B, Yeo, A.E.T, Talaei-Khoei, A
Format: Article
Language:English
Subjects:
Chronic Disease
Data Collection
Data quality
Disease Management
Humans
Information Management
Information system
Internal Medicine
Medical Record Linkage
Ontology
Other
Realist
Research Design
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Highlights ► The data quality (DQ) field is fragmented and ontological approaches not commonly used. ► DQ is a multidimensional construct, the most used dimensions being completeness, accuracy, correctness, consistency and timeliness. ► Included studies ( n = 61) reported tool development (80%), implementation (23%); and descriptive evaluations (15%). ► Ontological approaches addressed semantic interoperability, decision support, flexibility of data management and linkage, and complexity of data models. ► We advocate ontologically rich methods to address DQ of routinely collected data.
ISSN:1386-5056
1872-8243
DOI:10.1016/j.ijmedinf.2012.10.001