title: Diabetics can be identified in an electronic medical record using laboratory tests and prescriptions.
abstract: OBJECTIVE
  With the increasing use of electronic medical records (EMRs) comes the potential to efficiently evaluate and improve quality of care. We set out to determine if diabetics could be accurately identified using structured data contained within an EMR.
  
  
  METHODS
  We used a 5% random sample of adult patients (969 patients) within a convenience sample of 17 primary care physicians using Practices Solutions EMR in Ontario. A reference standard of diabetes status was manually confirmed by reviewing each patient's record. Accuracy for identifying people with diabetes was assessed using various combinations of laboratory tests and prescriptions. EMR data was also compared with administrative data.
  
  
  RESULTS
  A rule of one elevated blood sugar or a prescription for an antidiabetic medication had a 83.1% sensitivity, 98.2% specificity, 80.0% positive predictive value (PPV) and 98.5% negative predictive value (NPV) compared with the reference standard of diabetes status.
  
  
  CONCLUSIONS
  We found that the use of structured data within an EMR could be used to identify patients with diabetes. Our results have positive implications for policy makers, researchers, and clinicians as they develop registries of diabetic patients to examine quality of care using EMR data.
authors.affiliation: Institute for Clinical Evaluative Sciences, Toronto, Ontario M4N 3M5, Canada. karen.tu@ices.on.ca
authors.forename: Karen
authors.initials: K
authors.lastname: Tu
authors.forename: Doug
authors.initials: D
authors.lastname: Manuel
authors.forename: Kelvin
authors.initials: K
authors.lastname: Lam
authors.forename: Doug
authors.initials: D
authors.lastname: Kavanagh
authors.forename: Tezeta F
authors.initials: TF
authors.lastname: Mitiku
authors.forename: Helen
authors.initials: H
authors.lastname: Guo
mesh_terms: D000328:Adult; D003920:Diabetes Mellitus / Q000175:diagnosis / Q000453:epidemiology*; D004282:Documentation / Q000592:standards*; D011307:Drug Prescriptions / Q000592:standards; D057286:Electronic Health Records / Q000592:standards* / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008297:Male; D009864:Ontario / Q000453:epidemiology; D005791:Patient Care / Q000379:methods; D011787:Quality of Health Care / Q000592:standards*; D012680:Sensitivity and Specificity