EOSC-Life Report on data standards for observational and interventional studies, and interoperability between healthcare and research data

Canham, Steve; Ohmann, Christian; Boiten, Jan-Willem; Panagiotopoulou, Maria; Hughes, Nigel C.; David, Romain; Sánchez, Àlex; Maxwell, Lauren; Aerts, Jozef; Facile, Rhonda; Griffon, Nicolas; Saunders, Gary W.; Bochove, Kees van; Ewbank, Jonathan

doi:10.5281/zenodo.5810612

Cited by 2 publications

(1 citation statement)

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“…Although community-approved data standards exist that can help tackle the major issue of semantic and syntactic interoperability, in practice, the use of data standards remains low on a global scale and different standards are applied to different biomedical data types [e.g. Clinical Data Interchange Standards Consortium (CDISC) for clinical trial data, Observational Medical Outcomes Partnership (OMOP) for routinely collected healthcare data, Digital Imaging and Communications in Medicine (DICOM) for imaging data] [ 24 ]. Even when interoperability issues are overcome, data consistency and accuracy cannot be guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Sharing sensitive data in life sciences: an overview of centralized and federated approaches

Rujano,

Boiten,

Ohmann

et al. 2024

Briefings in Bioinformatics

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Biomedical data are generated and collected from various sources, including medical imaging, laboratory tests and genome sequencing. Sharing these data for research can help address unmet health needs, contribute to scientific breakthroughs, accelerate the development of more effective treatments and inform public health policy. Due to the potential sensitivity of such data, however, privacy concerns have led to policies that restrict data sharing. In addition, sharing sensitive data requires a secure and robust infrastructure with appropriate storage solutions. Here, we examine and compare the centralized and federated data sharing models through the prism of five large-scale and real-world use cases of strategic significance within the European data sharing landscape: the French Health Data Hub, the BBMRI-ERIC Colorectal Cancer Cohort, the federated European Genome-phenome Archive, the Observational Medical Outcomes Partnership/OHDSI network and the EBRAINS Medical Informatics Platform. Our analysis indicates that centralized models facilitate data linkage, harmonization and interoperability, while federated models facilitate scaling up and legal compliance, as the data typically reside on the data generator’s premises, allowing for better control of how data are shared. This comparative study thus offers guidance on the selection of the most appropriate sharing strategy for sensitive datasets and provides key insights for informed decision-making in data sharing efforts.

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Section: Introductionmentioning

confidence: 99%