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

Research output: Contribution to journalArticlepeer-review

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 pseud-onymizes participant identifiers (PIDs) using a researcher-driven automated process that takes advantage of application-pro-gramming 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 pro-tected 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 pseudonymiza-tion by API request based on the provided PID and P-PID mappings. Conclusions: 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.

Original languageEnglish (US)
Pages (from-to)39-47
Number of pages9
JournalHealthcare Informatics Research
Volume27
Issue number1
DOIs
StatePublished - 2021

Keywords

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

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

Fingerprint

Dive into the research topics of 'Api driven on-demand participant id pseudonymization in heterogeneous multi-study research'. Together they form a unique fingerprint.

Cite this