title: Integrating electronic health record information to support integrated care: practical application of ontologies to improve the accuracy of diabetes disease registers.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: School of Public Health and Community Medicine, UNSW Medicine, Sydney, Australia; Centre for PHC & Equity, UNSW Medicine, Sydney, Australia; Academic General Practice Unit, South Western Sydney Local Health District, NSW, Australia. Electronic address: siaw@unsw.edu.au.
authors.forename: Siaw-Teng
authors.initials: ST
authors.lastname: Liaw
authors.affiliation: Centre for PHC & Equity, UNSW Medicine, Sydney, Australia.
authors.forename: Jane
authors.initials: J
authors.lastname: Taggart
authors.affiliation: Centre for PHC & Equity, UNSW Medicine, Sydney, Australia.
authors.forename: Hairong
authors.initials: H
authors.lastname: Yu
authors.affiliation: University of Surrey, Guildford, UK.
authors.forename: Simon
authors.initials: S
authors.lastname: de Lusignan
authors.affiliation: Telfer School of Management, University of Ottawa, Ottawa, Canada.
authors.forename: Craig
authors.initials: C
authors.lastname: Kuziemsky
authors.affiliation: School of Public Health and Community Medicine, UNSW Medicine, Sydney, Australia.
authors.forename: Andrew
authors.initials: A
authors.lastname: Hayen
mesh_terms: D000465:Algorithms; D001315:Australia; D064229:Biological Ontologies; D019033:Delivery of Health Care, Integrated / Q000379:methods*; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis*; D057286:Electronic Health Records / Q000145:classification*; D006801:Humans
keywords: Case finding; EHR; Integration; Knowledge engineering; Ontology; Patient register