J Biomed Inform - Integrating electronic health record information to support integrated care: practical application of ontologies to improve the accuracy of diabetes disease registers.

Tópicos

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Resumo

CKGROUND: 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.

Resumo Limpo

ckground inform electron health record ehr promot use clinic decis support patient regist measur improv integr qualiti care translat research ehrderiv data product creator need logic integr patient data inform knowledg divers sourc contextsobject examin accuraci ontolog multiattribut approach creat type diabet mellitus tdm regist support integr caremethod guid australian best practic guidelin tdm diagnosi manag ontolog conceptu contextu valid clinician specifi formal implement algorithm standard domain ontolog snome ctau accuraci implement measur dataset vari size patient integr dataset patient result crosscheck sensit specif calcul confid intervalsresult increment integr reason visit rfv medic rx patholog algorithm identifi near tdm case increment integr four dataset improv accuraci control sampl size data incomplet duplic manual valid confirm accuraci algorithmconclus integr multipl data element within ehr use ontologybas casefind algorithm can improv accuraci diagnosi compens suboptim data qualiti henc creat dataset fitforpurpos clinic pragmat applic ontolog ehr data improv integr data potenti better use data improv qualiti care

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