Int J Med Inform - Validating an ontology-based algorithm to identify patients with type 2 diabetes mellitus in electronic health records.


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CKGROUND: Improving healthcare for people with chronic conditions requires clinical information systems that support integrated care and information exchange, emphasizing a semantic approach to support multiple and disparate Electronic Health Records (EHRs). Using a literature review, the Australian National Guidelines for Type 2 Diabetes Mellitus (T2DM), SNOMED-CT-AU and input from health professionals, we developed a Diabetes Mellitus Ontology (DMO) to diagnose and manage patients with diabetes. This paper describes the manual validation of the DMO-based approach using real world EHR data from a general practice (n=908 active patients) participating in the electronic Practice Based Research Network (ePBRN).METHOD: The DMO-based algorithm to query, using Semantic Protocol and RDF Query Language (SPARQL), the structured fields in the ePBRN data repository were iteratively tested and refined. The accuracy of the final DMO-based algorithm was validated with a manual audit of the general practice EHR. Contingency tables were prepared and Sensitivity and Specificity (accuracy) of the algorithm to diagnose T2DM measured, using the T2DM cases found by manual EHR audit as the gold standard. Accuracy was determined with three attributes - reason for visit (RFV), medication (Rx) and pathology (path) - singly and in combination.RESULTS: The Sensitivity and Specificity of the algorithm were 100% and 99.88% with RFV; 96.55% and 98.97% with Rx; and 15.6% and 98.92% with Path. This suggests that Rx and Path data were not as complete or correct as the RFV for this general practice, which kept its RFV information complete and current for diabetes. However, the completeness is good enough for this purpose as confirmed by the very small relative deterioration of the accuracy (Sensitivity and Specificity of 97.67% and 99.18%) when calculated for the combination of RFV, Rx and Path. The manual EHR audit suggested that the accuracy of the algorithm was influenced by data quality such as incorrect data due to mistaken units of measurement and unavailable data due to non-documentation or documented in the wrong place or progress notes, problems with data extraction, encryption and data management errors.CONCLUSION: This DMO-based algorithm is sufficiently accurate to support a semantic approach, using the RFV, Rx and Path to define patients with T2DM from EHR data. However, the accuracy can be compromised by incomplete or incorrect data. The extent of compromise requires further study, using ontology-based and other approaches.

Resumo Limpo

ckground improv healthcar peopl chronic condit requir clinic inform system support integr care inform exchang emphas semant approach support multipl dispar electron health record ehr use literatur review australian nation guidelin type diabet mellitus tdm snomedctau input health profession develop diabet mellitus ontolog dmo diagnos manag patient diabet paper describ manual valid dmobas approach use real world ehr data general practic n activ patient particip electron practic base research network epbrnmethod dmobas algorithm queri use semant protocol rdf queri languag sparql structur field epbrn data repositori iter test refin accuraci final dmobas algorithm valid manual audit general practic ehr conting tabl prepar sensit specif accuraci algorithm diagnos tdm measur use tdm case found manual ehr audit gold standard accuraci determin three attribut reason visit rfv medic rx patholog path singl combinationresult sensit specif algorithm rfv rx path suggest rx path data complet correct rfv general practic kept rfv inform complet current diabet howev complet good enough purpos confirm small relat deterior accuraci sensit specif calcul combin rfv rx path manual ehr audit suggest accuraci algorithm influenc data qualiti incorrect data due mistaken unit measur unavail data due nondocument document wrong place progress note problem data extract encrypt data manag errorsconclus dmobas algorithm suffici accur support semant approach use rfv rx path defin patient tdm ehr data howev accuraci can compromis incomplet incorrect data extent compromis requir studi use ontologybas approach

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