FHIR4FAIR Implementation Guide aiming to:
1) Identify how HL7 FHIR standard fulfills data FAIRness maturity indicators
a) analyze the relationship between FAIR data object conceptual components (e.g. data, metadata, provenance, identifiers) and HL7 FHIR resources
b) analyze how RDA FAIR Maturity indicators are supported by specific HL7 FHIR resources
c) analyze how RDA Reproducible Health Data Services recommendations can be supported by HL7 FHIR
d) identify a minimum set of metadata to be fulfilled for specific sets of RDA FAIRness maturity indicators extended of health-related research data sets.
2) Suggest an assessment methodology/checklist, exploring machine-readable and manual assessment methods.
This guide should contain a large informative part explaining and a set of FHIR conformance resources and examples that provide, for selected case(s), a practical example of how FAIRness can be realized and assessed by using HL7 FHIR.
This project is intended to be the result of an active collaboration between RDA and HL7, the plan is to consult the Health care communities to comment and endorse the resulting HL7 FHIR IG taking into account relevant RDA group input.
The project will provide guidance on supporting FAIRness by using HL7 and specifically:
- the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles
- the proposed RDA (Research Data Alliance) - https://www.rd-alliance.org/ - recommendations on FAIR data maturity model, providing indicators for assessing adherence to the FAIR principles.
- the recommendations of RDA Reproducible Health Services Working Group for describing, documenting, and sharing metadata for health data curation workflows
Project Scope Statement (Draft)
|Informative ballot |
(the ballot version may include conformance resources and examples; for exemplification purposes)
|Comment reconciliation||May-Ago 2021|
|Publication as Informative IG||Sept 2021|
|STU ballot||Jan 2022|
|Normative ballot||May 2024|
|Publication as Normative IG||Sept 2024|