title: Investigating concordance in diabetes diagnosis between primary care charts (electronic medical records) and health administrative data: a retrospective cohort study.
abstract: BACKGROUND
  Electronic medical records contain valuable clinical information not readily available elsewhere. Accordingly, they hold important potential for contributing to and enhancing chronic disease registries with the goal of improving chronic disease management; however a standard for diagnoses of conditions such as diabetes remains to be developed. The purpose of this study was to establish a validated electronic medical record definition for diabetes.
  
  
  METHODS
  We constructed a retrospective cohort using health administrative data from the Institute for Clinical Evaluative Sciences Ontario Diabetes Database linked with electronic medical records from the Deliver Primary Healthcare Information Project using data from 1 April 2006-31 March 2008 (N = 19,443). We systematically examined eight definitions for diabetes diagnosis, both established and proposed.
  
  
  RESULTS
  The definition that identified the highest number of patients with diabetes (N = 2,180) while limiting to those with the highest probability of having diabetes was: individuals with ≥2 abnormal plasma glucose tests, or diabetes on the problem list, or insulin prescription, or ≥2 oral anti-diabetic agents, or HbA1c ≥6.5%. Compared to the Ontario Diabetes Database, this definition identified 13% more patients while maintaining good sensitivity (75%) and specificity (98%).
  
  
  CONCLUSIONS
  This study establishes the feasibility of developing an electronic medical record standard definition of diabetes and validates an algorithm for use in this context. While the algorithm may need to be tailored to fit available data in different electronic medical records, it contributes to the establishment of validated disease registries with the goal of enhancing research, and enabling quality improvement in clinical care and patient self-management.
authors.affiliation: Centre for Studies in Family Medicine, Department of Family Medicine, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada. sharris1@uwo.ca
authors.forename: Stewart B
authors.initials: SB
authors.lastname: Harris
authors.forename: Richard H
authors.initials: RH
authors.lastname: Glazier
authors.forename: Jordan W
authors.initials: JW
authors.lastname: Tompkins
authors.forename: Andrew S
authors.initials: AS
authors.lastname: Wilton
authors.forename: Vijaya
authors.initials: V
authors.lastname: Chevendra
authors.forename: Moira A
authors.initials: MA
authors.lastname: Stewart
authors.forename: Amardeep
authors.initials: A
authors.lastname: Thind
mesh_terms: D000328:Adult; D015331:Cohort Studies; D016208:Databases, Factual / Q000592:standards / Q000706:statistics & numerical data*; D003920:Diabetes Mellitus / Q000175:diagnosis* / Q000503:physiopathology / Q000628:therapy; D057286:Electronic Health Records / Q000191:economics / Q000592:standards*; D005260:Female; D006801:Humans; D008297:Male; D008487:Medical History Taking; D008498:Medical Record Linkage / Q000379:methods; D009864:Ontario; D010043:Outcome and Process Assessment, Health Care / Q000379:methods; D010820:Physicians / Q000592:standards; D010818:Practice Patterns, Physicians' / Q000592:standards; D011320:Primary Health Care / Q000706:statistics & numerical data*; D012042:Registries; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018571:Sentinel Surveillance