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.

The authors used DEID open source de-identification software and modified it for an Ontario context for use on primary care EMR data. They developed the modified program on a training set of 1,000 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 seven different practices in five 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. The authors 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.

The authors 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.

The DEID program can be modified to reasonably accurately de-identify free-text primary care EMR records while preserving clinical content.