title: De-identification of primary care electronic medical records free-text data in Ontario, Canada.
abstract: BACKGROUND
  Electronic medical records (EMRs) represent a potentially rich source of health information for research but the free-text in EMRs often contains identifying information. While de-identification tools have been developed for free-text, none have been developed or tested for the full range of primary care EMR data
  
  
  METHODS
  We used deid open source de-identification software and modified it for an Ontario context for use on primary care EMR data. We developed the modified program on a training set of 1000 free-text records from one group practice and then tested it on two validation sets from a random sample of 700 free-text EMR records from 17 different physicians from 7 different practices in 5 different cities and 500 free-text records from a group practice that was in a different city than the group practice that was used for the training set. We measured the sensitivity/recall, precision, specificity, accuracy and F-measure of the modified tool against manually tagged free-text records to remove patient and physician names, locations, addresses, medical record, health card and telephone numbers.
  
  
  RESULTS
  We found that the modified training program performed with a sensitivity of 88.3%, specificity of 91.4%, precision of 91.3%, accuracy of 89.9% and F-measure of 0.90. The validations sets had sensitivities of 86.7% and 80.2%, specificities of 91.4% and 87.7%, precisions of 91.1% and 87.4%, accuracies of 89.0% and 83.8% and F-measures of 0.89 and 0.84 for the first and second validation sets respectively.
  
  
  CONCLUSIONS
  The deid program can be modified to reasonably accurately de-identify free-text primary care EMR records while preserving clinical content.
authors.affiliation: Institute for Clinical Evaluative Sciences G106, Toronto, Ontario, M4N 3M5, Canada. karen.tu@ices.on.ca
authors.forename: Karen
authors.initials: K
authors.lastname: Tu
authors.forename: Julie
authors.initials: J
authors.lastname: Klein-Geltink
authors.forename: Tezeta F
authors.initials: TF
authors.lastname: Mitiku
authors.forename: Chiriac
authors.initials: C
authors.lastname: Mihai
authors.forename: Joel
authors.initials: J
authors.lastname: Martin
mesh_terms: D003219:Confidentiality; D057286:Electronic Health Records; D006123:Group Practice; D006801:Humans; D008498:Medical Record Linkage / Q000379:methods / Q000592:standards*; D009864:Ontario; D010355:Patient Identification Systems; D010363:Pattern Recognition, Automated; D011320:Primary Health Care / Q000458:organization & administration*; D012637:Security Measures; D012984:Software