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openEHR Is FAIR-Enabling by Design.

Francesca Frexia1, Cecilia Mascia1, Luca Lianas1

  • 1CRS4: Center for Advanced Studies, Research and Development in Sardinia, Italy.

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Summary
This summary is machine-generated.

The FAIR Principles enhance research discovery. This study shows openEHR specifications inherently support FAIR data principles, making them ideal for FAIR-compliant Clinical Data Repositories.

Keywords:
ArchetypesData IntegrationFAIR PrinciplesInformation ModelsRepresentation StandardsSemantic InteroperabilitySemanticsopenEHR

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Area of Science:

  • Health Informatics
  • Data Science
  • Digital Health

Background:

  • The FAIR Principles (Findable, Accessible, Interoperable, Reusable) are crucial for advancing research and knowledge integration.
  • Effective data management and exchange require accurate recording and contextual information in standardized, machine-readable formats.
  • openEHR specifications offer a framework for structured health data management.

Purpose of the Study:

  • To assess the alignment of openEHR specifications and its reference implementation with the 15 FAIR Principles.
  • To evaluate the potential of openEHR in facilitating FAIR data practices within clinical data repositories.

Main Methods:

  • Theoretical assessment of openEHR specifications against each of the 15 FAIR Principles.
  • Analysis of openEHR's computable semantics-oriented design for FAIR data enablement.

Main Results:

  • openEHR specifications demonstrate strong inherent support for all FAIR Principles.
  • The computable semantics design of openEHR is a key factor in its FAIR-enabling capabilities.
  • openEHR is well-suited for implementing FAIR-compliant Clinical Data Repositories (CDRs).

Conclusions:

  • openEHR specifications are fundamentally aligned with the FAIR Principles.
  • The openEHR approach provides a robust strategy for achieving FAIR data in clinical settings.
  • Implementing openEHR can significantly enhance the findability, accessibility, interoperability, and reusability of clinical data.