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API Driven On-Demand Participant ID Pseudonymization in Heterogeneous Multi-Study Research
Shorabuddin Syed,Mahanazuddin Syed,Hafsa Bareen Syeda,Maryam Garza,William Bennett,Jonathan Bona,Salma Begum,Ahmad Baghal,Meredith Zozus,Fred Prior 대한의료정보학회 2021 Healthcare Informatics Research Vol.27 No.1
Objectives: To facilitate clinical and translational research, imaging and non-imaging clinical data from multiple disparatesystems must be aggregated for analysis. Study participant records from various sources are linked together and to patient recordswhen possible to address research questions while ensuring patient privacy. This paper presents a novel tool that pseudonymizesparticipant identifiers (PIDs) using a researcher-driven automated process that takes advantage of application-programminginterface (API) and the Perl Open-Source Digital Imaging and Communications in Medicine Archive (POSDA) tofurther de-identify PIDs. The tool, on-demand cohort and API participant identifier pseudonymization (O-CAPP), employsa pseudonymization method based on the type of incoming research data. Methods: For images, pseudonymization of PIDsis done using API calls that receive PIDs present in Digital Imaging and Communications in Medicine (DICOM) headersand 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 protectedhealth information is further de-identified using POSDA. Results: A sample of 250 PIDs pseudonymized by O-CAPPwere selected and successfully validated. Of those, 125 PIDs that were pseudonymized by the nightly automated process werevalidated by multiple clinical trial investigators (CTIs). For the other 125, CTIs validated radiologic image pseudonymizationby API request based on the provided PID and P-PID mappings. Conclusions: We developed a novel approach of an ondemandpseudonymization process that will aide researchers in obtaining a comprehensive and holistic view of study participantdata without compromising patient privacy.
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