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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, de Lusignan, S, Kuziemsky, C and Hayen, A (2014) Integrating electronic health record information to support integrated care: practical application of ontologies to improve the accuracy of diabetes disease registers. J Biomed Inform, 52. pp. 364-372.

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Abstract

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.

Item Type: Article
Subjects : Health Care Management
Divisions : Faculty of Arts and Social Sciences > Surrey Business School
Authors :
AuthorsEmailORCID
Liaw, STUNSPECIFIEDUNSPECIFIED
Taggart, JUNSPECIFIEDUNSPECIFIED
Yu, HUNSPECIFIEDUNSPECIFIED
de Lusignan, SUNSPECIFIEDUNSPECIFIED
Kuziemsky, CUNSPECIFIEDUNSPECIFIED
Hayen, AUNSPECIFIEDUNSPECIFIED
Date : December 2014
Identification Number : 10.1016/j.jbi.2014.07.016
Copyright Disclaimer : © 2014. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords : Case finding, EHR, Integration, Knowledge engineering, Ontology, Patient register, Algorithms, Australia, Biological Ontologies, Delivery of Health Care, Integrated, Diabetes Mellitus, Type 2, Electronic Health Records, Humans
Related URLs :
Additional Information : Full text not available from this repository
Depositing User : Symplectic Elements
Date Deposited : 09 Jun 2016 09:21
Last Modified : 26 Jul 2016 10:09
URI: http://epubs.surrey.ac.uk/id/eprint/810508

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