Siaw-Teng Liaw1, Jane Taggart2, Hairong Yu2, Simon de Lusignan3, Craig Kuziemsky4, Andrew Hayen5. 1. 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. 2. Centre for PHC & Equity, UNSW Medicine, Sydney, Australia. 3. University of Surrey, Guildford, UK. 4. Telfer School of Management, University of Ottawa, Ottawa, Canada. 5. School of Public Health and Community Medicine, UNSW Medicine, Sydney, Australia.
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.
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.
Authors: Jacqueline C Kirby; Peter Speltz; Luke V Rasmussen; Melissa Basford; Omri Gottesman; Peggy L Peissig; Jennifer A Pacheco; Gerard Tromp; Jyotishman Pathak; David S Carrell; Stephen B Ellis; Todd Lingren; Will K Thompson; Guergana Savova; Jonathan Haines; Dan M Roden; Paul A Harris; Joshua C Denny Journal: J Am Med Inform Assoc Date: 2016-03-28 Impact factor: 4.497
Authors: Christopher M Pearce; Adam McLeod; Jon Patrick; Douglas Boyle; Marianne Shearer; Paula Eustace; Mary Catherine Pearce Journal: JMIR Res Protoc Date: 2016-12-20
Authors: Nicoletta Musacchio; Annalisa Giancaterini; Giacomo Guaita; Alessandro Ozzello; Maria A Pellegrini; Paola Ponzani; Giuseppina T Russo; Rita Zilich; Alberto de Micheli Journal: J Med Internet Res Date: 2020-06-22 Impact factor: 5.428