Integrating electronic health record information to support integrated care: Practical application of ontologies to improve the accuracy of diabetes disease registers

Liaw, ST; Taggart, J; Yu, H; Lusignan, SD; Kuziemsky, C; Hayen, A

Integrating electronic health record information to support integrated care: Practical application of ontologies to improve the accuracy of diabetes disease registers

Liaw, ST Taggart, J Yu, H Lusignan, SD Kuziemsky, C Hayen, A

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Publication Type:: Journal Article
Citation:: Journal of Biomedical Informatics, 2014, 52 pp. 364 - 372
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Liaw, ST	en_US
dc.contributor.author	Taggart, J	en_US
dc.contributor.author	Yu, H	en_US
dc.contributor.author	Lusignan, SD	en_US
dc.contributor.author	Kuziemsky, C	en_US
dc.contributor.author	Hayen, A https://orcid.org/0000-0003-4046-8030	en_US
dc.date.available	2014-07-23	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Journal of Biomedical Informatics, 2014, 52 pp. 364 - 372	en_US
dc.identifier.issn	1532-0464	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118630
dc.description.abstract	© 2014 Elsevier Inc. Background: Information in Electronic Health Records (EHRs) are being promoted for use in clinical decision support, patient registers, measurement and improvement of integration and quality of care, and translational research. To do this EHR-derived data product creators need to logically integrate patient data with information and knowledge from diverse sources and contexts. Objective: To examine the accuracy of an ontological multi-attribute approach to create a Type 2 Diabetes Mellitus (T2DM) register to support integrated care. Methods: Guided by Australian best practice guidelines, the T2DM diagnosis and management ontology was conceptualized, contextualized and validated by clinicians; it was then specified, formalized and implemented. The algorithm was standardized against the domain ontology in SNOMED CT-AU. Accuracy of the implementation was measured in 4 datasets of varying sizes (927-12,057 patients) and an integrated dataset (23,793 patients). Results were cross-checked with sensitivity and specificity calculated with 95% confidence intervals. Results: Incrementally integrating Reason for Visit (RFV), medication (Rx), and pathology in the algorithm identified nearly100% of T2DM cases. Incrementally integrating the four datasets improved accuracy; controlling for sample size, data incompleteness and duplicates. Manual validation confirmed the accuracy of the algorithm. Conclusion: Integrating multiple data elements within an EHR using ontology-based case-finding algorithms can improve the accuracy of the diagnosis and compensate for suboptimal data quality, and hence creating a dataset that is more fit-for-purpose. This clinical and pragmatic application of ontologies to EHR data improves the integration of data and the potential for better use of data to improve the quality of care.	en_US
dc.relation.ispartof	Journal of Biomedical Informatics	en_US
dc.relation.isbasedon	10.1016/j.jbi.2014.07.016	en_US
dc.subject.classification	Biomedical Engineering	en_US
dc.subject.classification	Medical Informatics	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Diabetes Mellitus, Type 2	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Delivery of Health Care, Integrated	en_US
dc.subject.mesh	Australia	en_US
dc.subject.mesh	Electronic Health Records	en_US
dc.subject.mesh	Biological Ontologies	en_US
dc.title	Integrating electronic health record information to support integrated care: Practical application of ontologies to improve the accuracy of diabetes disease registers	en_US
dc.type	Journal Article
utslib.citation.volume	52	en_US
utslib.for	111709 Health Care Administration	en_US
utslib.for	06 Biological Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	11 Medical and Health Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Health
pubs.organisational-group	/University of Technology Sydney/Faculty of Health/Public Health
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	52	en_US

Abstract:

© 2014 Elsevier Inc. Background: Information in Electronic Health Records (EHRs) are being promoted for use in clinical decision support, patient registers, measurement and improvement of integration and quality of care, and translational research. To do this EHR-derived data product creators need to logically integrate patient data with information and knowledge from diverse sources and contexts. Objective: To examine the accuracy of an ontological multi-attribute approach to create a Type 2 Diabetes Mellitus (T2DM) register to support integrated care. Methods: Guided by Australian best practice guidelines, the T2DM diagnosis and management ontology was conceptualized, contextualized and validated by clinicians; it was then specified, formalized and implemented. The algorithm was standardized against the domain ontology in SNOMED CT-AU. Accuracy of the implementation was measured in 4 datasets of varying sizes (927-12,057 patients) and an integrated dataset (23,793 patients). Results were cross-checked with sensitivity and specificity calculated with 95% confidence intervals. Results: Incrementally integrating Reason for Visit (RFV), medication (Rx), and pathology in the algorithm identified nearly100% of T2DM cases. Incrementally integrating the four datasets improved accuracy; controlling for sample size, data incompleteness and duplicates. Manual validation confirmed the accuracy of the algorithm. Conclusion: Integrating multiple data elements within an EHR using ontology-based case-finding algorithms can improve the accuracy of the diagnosis and compensate for suboptimal data quality, and hence creating a dataset that is more fit-for-purpose. This clinical and pragmatic application of ontologies to EHR data improves the integration of data and the potential for better use of data to improve the quality of care.

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http://hdl.handle.net/10453/118630