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Open Access Research article

De-identification of primary care electronic medical records free-text data in Ontario, Canada

Karen Tu123*, Julie Klein-Geltink1, Tezeta F Mitiku1, Chiriac Mihai1 and Joel Martin4

Author Affiliations

1 Institute for Clinical Evaluative Sciences (ICES) G106, 2075 Bayview Avenue, Toronto, Ontario, M4N 3M5, Canada

2 Department of Family and Community Medicine-University of Toronto, 263 McCaul Street, 5th Floor Toronto, Ontario, M5T 1W7, Canada

3 Toronto Western Hospital Family Health Team-University Health Network, 399 Bathurst Street, Toronto, Ontario, M5T 2S8, Canada

4 Institute for Information Technology, National Research Council, 1200 Montreal Road, Ottawa, Ontario, K1A 0R6, Canada

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BMC Medical Informatics and Decision Making 2010, 10:35  doi:10.1186/1472-6947-10-35

Published: 18 June 2010

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.

Conclusion

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