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

<p>Abstract</p> <p>Background</p> <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> <p>Methods</p> <p>We used <it>deid </it>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> <p>Results</p> <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> <p>Conclusion</p> <p>The <it>deid </it>program can be modified to reasonably accurately de-identify free-text primary care EMR records while preserving clinical content.</p

Examining community and consumer food environments for children: An urban-suburban-rural comparison in Southwestern Ontario

The aim of this study is to evaluate how retail food environments for children in the City of London and Middlesex County, Ontario, Canada, vary according to level of urbanicity and level of socioeconomic distress. Urbanicity in this study is defined as a neighbourhood\u27s designation as urban, suburban, or rural. We assessed community food environments (e.g., the type, location, and accessibility of food outlets) using 800m and 1600m network buffers (school zones) around all public and private elementary schools, and we calculated and compared density of junk food opportunities (JFO) (e.g., fast food and full-service restaurants, grocery stores, and convenience stores) within each school zone in urban, suburban and rural settings. The study also assessed consumer food environments (e.g., the price, promotion, placement, and availability of healthy options and nutrition information) through restaurant children\u27s menu audits using the Children\u27s Menu Assessment tool. Results suggest JFO density is greater around elementary schools in areas with higher levels of socioeconomic distress and urbanicity, while urbanicity is also associated with greater use of branded marketing and inclusion of an unhealthy dessert on children\u27s menus