ArticleBioinformatics (Oxford, England)2025
dsOMOP: bridging OMOP CDM and DataSHIELD for secure federated analysis of standardized clinical data.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The trial behind it
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Who cites it
4 citing papers in PubMed.
- Artificial Intelligence in Rare Diseases: Workflow-Integrated Precision Kidney Care.Clinics and practice · 2026Review
- Extending the Observational Medical Outcomes Partnership (OMOP) Common Data Model for Critical Care Medicine: A Framework for Standardizing Complex ICU Data Using the Society of Critical Care Medicine's Critical Care Data Dictionary (C2D2).Critical care medicine · 2026Article
- Federated analytics for non-communicable disease surveillance in the European health data space: a scoping review and conceptual framework.Frontiers in public health · 2026Article
- The P4COPD Study: Rationale, Goals and Study Design - Prediction, Prevention, Personalized and Precision Management of COPD in Young Adults.Open respiratory archivesArticle
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Authors and funding
4 authors.
Funding
Abstract
motivationCollaborative clinical research projects face several challenges related to data sharing. The disparity between data standards and strict privacy regulations become more relevant as the number of involved institutions increases. To address these challenges, the scientific community has progressively adopted common data models like the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) for multicenter data standardization and implemented federated data analysis platforms like DataSHIELD to perform remote analyses without transferring individual-level data between centers, thus mitigating disclosure risks. However, there is no native implementation that automatically combines both solutions, revealing the need for a tool that enables interoperability between these systems.
resultsWe present dsOMOP, a collection of DataSHIELD packages that facilitates automated extraction and transformation of OMOP CDM data into DataSHIELD-compatible datasets, enabling disclosure-controlled federated analyses of standardized clinical data. dsOMOP allows research institutions to provide access to their data for collaborative projects in a format that is interoperable with the project's available data, thus facilitating the analysis of large-scale, multicenter clinical data. It incorporates OMOP data directly into the DataSHIELD workflow, where all analyses occur entirely in a federated environment subject to rigorous disclosure controls, ensuring that only aggregated, non-disclosive results are ever returned to analysts. AVAILABILITY AND IMPLEMENTATION: The general information page for the dsOMOP environment is available at https://isglobal-brge.github.io/dsOMOP, where the most recent installation instructions and usage guides for all dsOMOP packages and their extensions can be found in the "Packages" section.The dsOMOP package and its complementary tools are fully available under the MIT license on GitHub: dsOMOP (https://github.com/isglobal-brge/dsOMOP), dsOMOPClient (https://github.com/isglobal-brge/dsOMOPClient), dsOMOPHelper (https://github.com/isglobal-brge/dsOMOPHelper), and dsOMOP.oracle (https://github.com/isglobal-brge/dsOMOP.oracle).Usage vignettes for the client-side packages are available at the websites of dsOMOPClient (https://isglobal-brge.github.io/dsOMOPClient) and dsOMOPHelper (https://isglobal-brge.github.io/dsOMOPHelper). A permanent archival snapshot of the exact code used in this manuscript is deposited at Figshare: https://doi.org/10.6084/m9.figshare.28607186.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.