ArticleHealthcare informatics research2021
API Driven On-Demand Participant ID Pseudonymization in Heterogeneous Multi-Study Research.
Article in Healthcare informatics research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- LinkingLearning health systems · 2025Article
- Feasibility of regional center telehealth visits utilizing a rural research network in people with Parkinson's disease.Journal of clinical and translational science · 2024Article
- Feasibility of telemedicine research visits in people with Parkinson's disease residing in medically underserved areas.Journal of clinical and translational science · 2022Article
- 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).Frontiers in artificial intelligence · 2021Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
objectivesTo 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.
methodsFor 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.
resultsA 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.
conclusionsWe 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.
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