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

PurposeEffective 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.ObjectiveIdentify mechanisms, including ontologies, to manage DQ in integrated CDM and whether improved DQ will better measure health outcomes.MethodsA realist review of English language studies (January 2001–March 2011) which addressed data quality, used ontology-based approaches and is relevant to CDM.ResultsWe 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.ConclusionDQ 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.

► 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.