Meystre Stéphane, Shen Shuying, Hofmann Deborah, Gundlapalli Adi
Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.
VA Salt Lake City Health Care System, Salt Lake City, Utah, USA.
Stud Health Technol Inform. 2014;205:778-82.
The adoption of Electronic Health Records is growing at a fast pace, and this growth results in very large quantities of patient clinical information becoming available in electronic format, with tremendous potentials, but also equally growing concern for patient confidentiality breaches. De-identification of patient information has been proposed as a solution to both facilitate secondary uses of clinical information, and protect patient information confidentiality. Automated approaches based on Natural Language Processing have been implemented and evaluated, allowing for much faster text de-identification than manual approaches. A U.S. Veterans Affairs clinical text de-identification project focused on investigating the current state of the art of automatic clinical text de-identification, on developing a best-of-breed de-identification application for clinical documents, and on evaluating its impact on subsequent text uses and the risk for re-identification. To evaluate this risk, we de-identified discharge summaries from 86 patients using our 'best-of-breed' text de-identification application with resynthesis of the identifiers detected. We then asked physicians working in the ward the patients were hospitalized in if they could recognize these patients when reading the de-identified documents. Each document was examined by at least one resident and one attending physician, and with 4.65% of the documents, physicians thought they recognized the patient because of specific clinical information, but after verification, none was correctly re-identified.
电子健康记录的采用正在迅速增长，这种增长使得大量患者临床信息以电子形式可得，具有巨大潜力，但同时对患者隐私泄露的担忧也在与日俱增。患者信息去识别化被提议作为一种解决方案，既能促进临床信息的二次利用，又能保护患者信息的保密性。基于自然语言处理的自动化方法已经得到实施和评估，与手动方法相比，能实现更快的文本去识别化。一个美国退伍军人事务部临床文本去识别化项目专注于研究自动临床文本去识别化的当前技术水平，开发用于临床文档的最佳去识别化应用程序，并评估其对后续文本使用的影响以及重新识别的风险。为了评估这种风险，我们使用我们的“最佳”文本去识别化应用程序对86名患者的出院小结进行去识别化处理，并重新合成检测到的标识符。然后，我们询问患者住院病房的医生，他们在阅读去识别化文档时是否能认出这些患者。每份文档至少由一名住院医生和一名主治医生进行检查，有4.65%的文档，医生认为他们因为特定的临床信息认出了患者，但经过核实，没有一人被正确重新识别。
