title: Identifying individuals with multiple sclerosis in an electronic medical record.
abstract: BACKGROUND
  The increasing use of electronic medical records (EMRs) presents an opportunity to efficiently evaluate and improve quality of care for individuals with MS.
  
  
  OBJECTIVE
  We aimed to establish an algorithm to identify individuals with MS within EMRs.
  
  
  METHODS
  We used a sample of 73,003 adult patients from 83 primary care physicians in Ontario using the Electronic Medical Record Administrative data Linked Database (EMRALD). A reference standard of 247 individuals with MS was identified through chart abstraction. The accuracy of identifying individuals with MS in an EMR was assessed using information in the cumulative patient profile (CPP), prescriptions and physician billing codes.
  
  
  RESULTS
  An algorithm identifying MS in the CPP performed well with 91.5% sensitivity, 100% specificity, 98.7% PPV and 100% NPV. The addition of prescriptions for MS-specific medications and physician billing code 340 used four times within any 12-month timeframe slightly improved the sensitivity to 92.3% with a PPV of 97.9%.
  
  
  CONCLUSIONS
  Data within an EMR can be used to accurately identify patients with MS. This study has positive implications for clinicians, researchers and policy makers as it provides the potential to identify cohorts of MS patients in the primary care setting to examine quality of care.
authors.affiliation: University of Toronto, Toronto, Canada.
authors.forename: Kristen M
authors.initials: KM
authors.lastname: Krysko
authors.affiliation: Institute for Clinical Evaluative Sciences (ICES), Toronto, Canada/University of Toronto, Canada/Women's College Hospital, Toronto, Canada.
authors.forename: Noah M
authors.initials: NM
authors.lastname: Ivers
authors.affiliation: Institute for Clinical Evaluative Sciences (ICES), Toronto, Canada.
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Young
authors.affiliation: University of Toronto, Canada/St. Michael's Hospital, Toronto, Canada.
authors.forename: Paul
authors.initials: P
authors.lastname: O'Connor
authors.affiliation: Institute for Clinical Evaluative Sciences (ICES), Toronto, Canada/University of Toronto, Canada/Toronto Western Hospital Family Health Team, University Health Network, Toronto, Canada karen.tu@ices.on.ca.
authors.forename: Karen
authors.initials: K
authors.lastname: Tu
mesh_terms: D000328:Adult; D000465:Algorithms; D011307:Drug Prescriptions / Q000706:statistics & numerical data*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D008498:Medical Record Linkage; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000453:epidemiology*; D009864:Ontario / Q000453:epidemiology; D011320:Primary Health Care / Q000706:statistics & numerical data*; D012680:Sensitivity and Specificity
keywords: Multiple sclerosis; electronic medical records; validation studies