API Driven On-Demand Participant ID Pseudonymization in Heterogeneous Multi-Study Research.
API Driven On-Demand Participant ID Pseudonymization in Heterogeneous Multi-Study Research.
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DOI：
10.4258/hir.2021.27.1.39
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发表时间：
2021-01
影响因子：
2.9
通讯作者：
Prior F
中科院分区：
文献类型：
--
作者：
Syed S;Syed M;Syeda HB;Garza M;Bennett W;Bona J;Begum S;Baghal A;Zozus M;Prior F
为了促进临床和转化研究，来自多个不同系统的成像和非成像临床数据必须被聚合以进行分析。将来自不同来源的研究参与者记录链接在一起，并在可能的情况下与患者记录链接，以解决研究问题，同时确保患者隐私。本文提出了一种新的工具，使用研究人员驱动的自动化过程，利用应用程序编程接口（API）和Perl开源数字成像和通信医学档案（POSDA），以进一步去识别参与者标识符（PID）。该工具，按需队列和API参与者标识符匿名化（O-CAPP），采用基于传入的研究数据的类型的匿名化方法。对于图像，使用API调用完成PID的数字化，该API调用接收医学数字成像和通信（DICOM）报头中存在的PID并返回数字化的标识符。对于非影像学临床研究数据，研究主要研究者（PI）提供的PID使用夜间自动化过程进行数据化。使用POSDA进一步去识别化的PID（P-PID）沿着其他受保护的健康信息。选择了250个由O-CAPP系统生成的PID样本，并成功地进行了验证。其中，125个通过夜间自动化过程进行匿名化的PID由多个临床试验研究者（CTI）进行了验证。对于其他125例，CTI根据所提供的PID和P-PID映射，通过API请求确认放射学图像自动化。我们开发了一种按需匿名化过程的新方法，该方法将帮助研究人员在不损害患者隐私的情况下获得研究参与者数据的全面和整体视图。
To facilitate clinical and translational research, imaging and non-imaging clinical data from multiple disparate systems must be aggregated for analysis. Study participant records from various sources are linked together and to patient records when possible to address research questions while ensuring patient privacy. This paper presents a novel tool that pseudonymizes participant identifiers (PIDs) using a researcher-driven automated process that takes advantage of application-programming interface (API) and the Perl Open-Source Digital Imaging and Communications in Medicine Archive (POSDA) to further de-identify PIDs. The tool, on-demand cohort and API participant identifier pseudonymization (O-CAPP), employs a pseudonymization method based on the type of incoming research data. For images, pseudonymization of PIDs is done using API calls that receive PIDs present in Digital Imaging and Communications in Medicine (DICOM) headers and returns the pseudonymized identifiers. For non-imaging clinical research data, PIDs provided by study principal investigators (PIs) are pseudonymized using a nightly automated process. The pseudonymized PIDs (P-PIDs) along with other protected health information is further de-identified using POSDA. A sample of 250 PIDs pseudonymized by O-CAPP were selected and successfully validated. Of those, 125 PIDs that were pseudonymized by the nightly automated process were validated by multiple clinical trial investigators (CTIs). For the other 125, CTIs validated radiologic image pseudonymization by API request based on the provided PID and P-PID mappings. We developed a novel approach of an on-demand pseudonymization process that will aide researchers in obtaining a comprehensive and holistic view of study participant data without compromising patient privacy.
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影响因子：
46.9
作者：
Smith, Barry;Ashburner, Michael;Lewis, Suzanna
通讯作者：
Lewis, Suzanna
影响因子：
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作者：
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通讯作者：
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DOI：
10.1016/j.ijmedinf.2018.09.007
发表时间：
2018-11-01
影响因子：
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作者：
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通讯作者：
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通讯作者：
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影响因子：
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作者：
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