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API Driven On-Demand Participant ID Pseudonymization in Heterogeneous Multi-Study Research

Healthcare Informatics Research 2021년 27권 1호 p.39 ~ 47
Syed Shorabuddin, Syed Mahanazuddin, Syeda Hafsa Bareen, Garza Maryam, Bennett William, Bona Jonathan, Begum Salma, Baghal Ahmad, Zozus Meredith, Prior Fred,
소속 상세정보
 ( Syed Shorabuddin ) - University of Arkansas for Medical Sciences Department of Biomedical Informatics
 ( Syed Mahanazuddin ) - University of Arkansas for Medical Sciences Department of Biomedical Informatics
 ( Syeda Hafsa Bareen ) - University of Arkansas for Medical Sciences Department of Biomedical Informatics
 ( Garza Maryam ) - University of Arkansas for Medical Sciences Department of Biomedical Informatics
 ( Bennett William ) - University of Arkansas for Medical Sciences Department of Biomedical Informatics
 ( Bona Jonathan ) - University of Arkansas for Medical Sciences Department of Biomedical Informatics
 ( Begum Salma ) - University of Arkansas for Medical Sciences Department of Information Technology
 ( Baghal Ahmad ) - University of Arkansas for Medical Sciences Department of Biomedical Informatics
 ( Zozus Meredith ) - University of Texas Health Science Center at San Antonio Department of Population Health Sciences
 ( Prior Fred ) - University of Arkansas for Medical Sciences Department of Biomedical Informatics

Abstract


Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: We developed a novel approach of an ondemand pseudonymization process that will aide researchers in obtaining a comprehensive and holistic view of study participant data without compromising patient privacy.

키워드

Data Management; De-identification; Multimedia; PACS; Semantic Web

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