Ontologies to improve chronic disease management research and quality improvement studies - a conceptual framework.
There is a growing burden of chronic non-communicable disease (CNCD). Managing CNCDs requires use of multiple sources of health and social care data, and information about coordination and outcomes. Many people with CNCDs have multimorbidity. Problems with data quality exacerbate challenges in measuring quality and health outcomes especially where there is multimorbidity. We have developed an ontological toolkit to support research and quality improvement studies in CNCDs using heterogeneous data, with diabetes mellitus as an exemplar. International experts held a workshop meeting, with follow up discussions and consensus building exercise. We generated conceptual statements about problems with a CNCD that ontologies might support, and a generic reference model. There were varying degrees of consensus. We propose a set of tools, and a four step method: (1) Identification and specification of data sources; (2) Conceptualisation of semantic meaning; (3) How available routine data can be used as a measure of the process or outcome of care; (4) Formalisation and validation of the final ontology.
Clinical Informatics, Department of Health Care Management and Policy, University of Surrey, Guildford, UK.
Harshana
H
Liyanage
Siaw-Teng
ST
Liaw
Craig
C
Kuziemsky
Simon
S
de Lusignan
D064229:Biological Ontologies; D002908:Chronic Disease / Q000145:classification* / Q000453:epidemiology / Q000517:prevention & control; D057225:Data Mining / Q000379:methods* / Q000592:standards; D006302:Health Services Research / Q000379:methods* / Q000592:standards; D006801:Humans; D016347:Medical Records Systems, Computerized / Q000592:standards*; D009323:Natural Language Processing; D019984:Quality Indicators, Health Care; D012660:Semantics; D009626:Terminology as Topic; D006113:United Kingdom / Q000453:epidemiology