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

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.

Feasibility of telemedicine research visits in people with Parkinson’s disease residing in medically underserved areas

“…All ARIES data are de-identified using an integrated utility 49 to facilitate use by the study team and potential reuse by other researchers. Data for a given patient can be added using a secure record linkage mechanism connecting ARIES data instances with data pulled from the AR-CDR.…”

“…To support optimal data integration for analysis, we combined all study data into a single collection using the Arkansas Research Image Enterprise System (ARIES) [46,47]. ARIES supports integration of multimedia data, including sound files, and extracts from both the REDCap database and the UAMS Arkansas Research Clinical Data Repository (AR-CDR) [48,49]. All ARIES data are de-identified using an integrated utility [49] to facilitate use by the study team and potential reuse by other researchers.…”

“…ARIES supports integration of multimedia data, including sound files, and extracts from both the REDCap database and the UAMS Arkansas Research Clinical Data Repository (AR-CDR) [48,49]. All ARIES data are de-identified using an integrated utility [49] to facilitate use by the study team and potential reuse by other researchers. Data for a given patient can be added using a secure record linkage mechanism connecting ARIES data instances with data pulled from the AR-CDR.…”

High-performance pseudo-anonymization of virtual power plant data on a CPU cluster

“…That is, to establish the required degree of trust in real persons who join the VPP, a certain level of security and privacy must be provided. Two common methods for this objective are anonymization and pseudo-anonymization [13]. The former is the changing of personal information so that the individual information about personal or material relationships can no longer be assigned to a certain person or determinable natural person or only with an unreasonably great expense of time, costs, and effort.…”

Semantic Integration of Multi-Modal Data and Derived Neuroimaging Results Using the Platform for Imaging in Precision Medicine (PRISM) in the Arkansas Imaging Enterprise System (ARIES)

“…(4) These imaging data are deposited into the ARIES instance of the Perl Open Source DICOM Archive (POSDA) (Bennett et al, 2018 ), a data curation tool that tracks the time and date of image data receipt, manages unique data identifiers, and performs image verification and de-identification with change history. (5) Pseudonymous subject identifiers are generated to maintain secure linkages across the imaging and non-imaging data from AR-CDR using the On-Demand Cohort and API Subject Identifier Pseudonymization (O-CASP) (Syed et al, 2020 ) to ensure the anonymity of research subjects. (6) All de-identified data (research assessments, clinical information, demographics, and imaging) are collected into the ARIES data repository, which consists of a MongoDB NoSQL database (Banker, 2011 ; Han et al, 2011 ) as the default storage location for all non-image data, and a semantic database (triple-store) (Rohloff et al, 2007 ) to manage data integration and provide semantic representations of metadata and key components of study data.…”