OcrV1, Main, Exploration, bibRecord, 000607

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

Identifieur interne : 000607 ( Main/Exploration ); précédent : 000606; suivant : 000608

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

Auteurs : Karen Tu [Canada] ; Julie Klein-Geltink [Canada] ; Tezeta F. Mitiku [Canada] ; Chiriac Mihai [Canada] ; Joel Martin [Canada]

Source :

BMC Medical Informatics and Decision Making [ 1472-6947 ] ; 2010.

RBID : PMC:2907300

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.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2907300

DOI: 10.1186/1472-6947-10-35
PubMed: 20565894
PubMed Central: 2907300

Affiliations:

Le document en format XML

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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>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</p>
</sec>
<sec><title>Methods</title>
<p>We used <italic>deid </italic>
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.</p>
</sec>
<sec><title>Results</title>
<p>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.</p>
</sec>
<sec><title>Conclusion</title>
<p>The <italic>deid </italic>
program can be modified to reasonably accurately de-identify free-text primary care EMR records while preserving clinical content.</p>
</sec>
</div>
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De-identification of primary care electronic medical records free-text data in Ontario, Canada

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

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