加拿大安大略省初级保健电子病历自由文本数据的去识别化。
De-identification of primary care electronic medical records free-text data in Ontario, Canada.
机构信息
Institute for Clinical Evaluative Sciences G106, Toronto, Ontario, M4N 3M5, Canada.
出版信息
BMC Med Inform Decis Mak. 2010 Jun 18;10:35. doi: 10.1186/1472-6947-10-35.
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.
背景
电子病历（EMR）代表了一种潜在的丰富的健康信息来源，但 EMR 中的自由文本通常包含可识别信息。虽然已经开发了用于自由文本的去识别工具，但没有一个工具是针对整个初级保健 EMR 数据范围开发或测试的。
方法
我们使用了 deid 开源去识别软件，并对其进行了修改，使其适用于安大略省的初级保健 EMR 数据。我们在一个组实践的 1000 份自由文本记录的训练集中开发了修改后的程序，然后在来自 7 个不同城市的 5 个不同实践的 17 个不同医生的 700 份自由文本 EMR 记录的两个验证集中测试了它，以及 500 份来自与训练集所在组实践不同城市的组实践的自由文本记录。我们使用手动标记的自由文本记录来衡量修改后的工具的灵敏度/召回率、精度、特异性、准确性和 F 度量，以去除患者和医生的姓名、地点、地址、医疗记录、健康卡和电话号码。
结果
我们发现，修改后的培训计划的灵敏度为 88.3%，特异性为 91.4%，精度为 91.3%，准确性为 89.9%，F 度量为 0.90。验证集的灵敏度分别为 86.7%和 80.2%，特异性分别为 91.4%和 87.7%，精度分别为 91.1%和 87.4%，准确性分别为 89.0%和 83.8%，F 度量分别为 0.89 和 0.84。
结论
deid 程序可以进行修改，以合理准确地去除自由文本初级保健 EMR 记录中的可识别信息，同时保留临床内容。
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