dsOMOP: bridging OMOP CDM and DataSHIELD for secure federated analysis of standardized clinical data

Motivation Collaborative 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 pr...

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Autores: Sarrat González, David, Escribà Montagut, Xavier, Houghtaling, Jared, González, Juan R.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10459.1/468613
Acceso en línea:https://doi.org/10.1093/bioinformatics/btaf286
https://hdl.handle.net/10459.1/468613
http://hdl.handle.net/10459.1/468613
Access Level:acceso abierto
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spelling dsOMOP: bridging OMOP CDM and DataSHIELD for secure federated analysis of standardized clinical dataSarrat González, DavidEscribà Montagut, XavierHoughtaling, JaredGonzález, Juan R.Motivation Collaborative 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. Results We 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.This work was supported by the Spanish Ministry of Education, Innovation and Universities, the National Agency for Research, and the Fund for Regional Development (PID2021-122855OB-I00). We also acknowledge support from the grant CEX2023-0001290-S funded by MCIN/AEI/ 10.13039/501100011033, and support from the Generalitat de Catalunya through the CERCA Program and the Consolidated Group on HEALTH ANALYTICS (2021 SGR 01563). Additionally, this project has received funding from the Instituto de Salud Carlos III (ISCIII) through the project “PMP21/00090,” co-funded by the European Union’s Resilience and Recovery Facility. It has also been partially funded by the “Complementary Plan for Biotechnology Applied to Health,” coordinated by the Institut de Bioenginyeria de Catalunya (IBEC) within the framework of the Recovery, Transformation, and Resilience Plan (C17.I1) – Funded by the European Union—NextGenerationEU.Oxford University Press2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.1093/bioinformatics/btaf286https://hdl.handle.net/10459.1/468613http://hdl.handle.net/10459.1/468613reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2021-122855OB-I00Reproducció del document publicat a https://doi.org/10.1093/bioinformatics/btaf286Bioinformatics, 2025, vol. 41, núm. 6, btaf286cc-by (c) David Sarrat-González et al., 2025Attribution 4.0 Internationalinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:recercat.cat:10459.1/4686132026-05-29T05:05:01Z
dc.title.none.fl_str_mv dsOMOP: bridging OMOP CDM and DataSHIELD for secure federated analysis of standardized clinical data
title dsOMOP: bridging OMOP CDM and DataSHIELD for secure federated analysis of standardized clinical data
spellingShingle dsOMOP: bridging OMOP CDM and DataSHIELD for secure federated analysis of standardized clinical data
Sarrat González, David
title_short dsOMOP: bridging OMOP CDM and DataSHIELD for secure federated analysis of standardized clinical data
title_full dsOMOP: bridging OMOP CDM and DataSHIELD for secure federated analysis of standardized clinical data
title_fullStr dsOMOP: bridging OMOP CDM and DataSHIELD for secure federated analysis of standardized clinical data
title_full_unstemmed dsOMOP: bridging OMOP CDM and DataSHIELD for secure federated analysis of standardized clinical data
title_sort dsOMOP: bridging OMOP CDM and DataSHIELD for secure federated analysis of standardized clinical data
dc.creator.none.fl_str_mv Sarrat González, David
Escribà Montagut, Xavier
Houghtaling, Jared
González, Juan R.
author Sarrat González, David
author_facet Sarrat González, David
Escribà Montagut, Xavier
Houghtaling, Jared
González, Juan R.
author_role author
author2 Escribà Montagut, Xavier
Houghtaling, Jared
González, Juan R.
author2_role author
author
author
description Motivation Collaborative 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. Results We 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.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1093/bioinformatics/btaf286
https://hdl.handle.net/10459.1/468613
http://hdl.handle.net/10459.1/468613
url https://doi.org/10.1093/bioinformatics/btaf286
https://hdl.handle.net/10459.1/468613
http://hdl.handle.net/10459.1/468613
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2021-122855OB-I00
Reproducció del document publicat a https://doi.org/10.1093/bioinformatics/btaf286
Bioinformatics, 2025, vol. 41, núm. 6, btaf286
dc.rights.none.fl_str_mv cc-by (c) David Sarrat-González et al., 2025
Attribution 4.0 International
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv cc-by (c) David Sarrat-González et al., 2025
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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