Healthc Inform Res.  2021 Jan;27(1):39-47. 10.4258/hir.2021.27.1.39.

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

• Syed S
• Syed M
• Syeda HB
• Garza M
• Bennett W
• Bona J
• Begum S
• Baghal A
• Zozus M
• Prior F

• KMID: 2512623
• DOI: http://doi.org/10.4258/hir.2021.27.1.39

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.

Keyword

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

Figure

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  Figure 1 ARIES, POSDA, and AR-CDR setup at the University of Arkansas for Medical Sciences. De-identified research data in ARIES can be linked back to fully identified AR-CDR data using (1) P-PID to PID mappings maintained by AR-CDR and (2) de-identified P-PID to P-PID mappings from POSDA. ARIES: Arkansas Image Enterprise Systems, POSDA: Perl Open-Source Digital Imaging and Communications in Medicine Archive, AR-CDR: Arkansas Clinical Data Repository, PID: participant identifiers, P-PID: pseudonyms of participant identifiers, EHR: electronic health record, PHI: protected health information.

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  Figure 2 Pipeline for receiving heterogeneous-longitudinal data, pseudonymization of PIDs for NICR and diagnostic imaging data, POSDA P-PIDs and PHI de-identification, and transformation into ARIES database for secondary data use. The pseudonymization algorithm is hosted in AR-CDR. The details of pseudonymization using AR-CDR data for both NICR pseudonymization (NICR-P) and radiologic image pseudonymization (RIP) requests is shown in Figure 3. The “Pseudonymization Layer” represents O-CAPP’s framework to receive PIDs, execute the pseudonymization algorithm, and return P-PIDs. The process is presented in detail in Figure 4. The blue dotted line represents de-identified research data in ARIES that can be linked back to fully identified AR-CDR data using the mappings maintained in AR-CDR and POSDA. PID: participant identifiers, P-PID: pseudonyms of participant identifiers, AR-CDR: Arkansas Clinical Data Repository, POSDA: Perl Open-Source Digital Imaging and Communications in Medicine Archive, NICR: non-imaging clinical research, ARIES: Arkansas Image Enterprise Systems, PHI: protected health information, O-CAPP: participant identifier pseudonymization, PACS: picture archiving and communication system, DICOM: Digital Imaging and Communications in Medicine.

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  Figure 3 Flow chart of the O-CAPP pseudonymization process based on the type of incoming research data: NICR vs. radiologic imaging data. Paths ① and ② represent the steps involved in receiving pseudonymization requests, pseudonymizing PIDs using AR-CDR data, and returning the P-PIDs for NICR and radiologic imaging data, respectively. O-CAPP: participant identifier pseudonymization, NICR: non-imaging clinical research, PID: participant identifiers, P-PID: pseudonyms of participant identifiers, AR-CDR: Arkansas Clinical Data Repository, API: application-programming interface.

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  Figure 4 O-CAPP’s framework for receiving pseudonymization requests and the process to pseudonymize PIDs. Presentation Layer authenticates requestors and submits the pseudonymization request to Database Pseudonymization Layer for P-PID generation. Presentation Layer returns P-PIDs to requestor. O-CAPP: participant identifier pseudonymization, PID: participant identifiers, P-PID: pseudonyms of participant identifiers, NICR: non-imaging clinical research, AR-CDR: Arkansas Clinical Data Repository, UAMS: University of Arkansas for Medical Sciences, API: application-programming interface.

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