FAIRness for FHIR: Towards Making Health Datasets FAIR Using HL7 FHIR.
Stud Health Technol Inform
; 290: 22-26, 2022 Jun 06.
Article
en En
| MEDLINE
| ID: mdl-35672963
ABSTRACT
Medical data science aims to facilitate knowledge discovery assisting in data, algorithms, and results analysis. The FAIR principles aim to guide scientific data management and stewardship, and are relevant to all digital health ecosystem stakeholders. The FAIR4Health project aims to facilitate and encourage the health research community to reuse datasets derived from publicly funded research initiatives using the FAIR principles. The 'FAIRness for FHIR' project aims to provide guidance on how HL7 FHIR could be utilized as a common data model to support the health datasets FAIRification process. This first expected result is an HL7 FHIR Implementation Guide (IG) called FHIR4FAIR, covering how FHIR can be used to cover FAIRification in different scenarios. This IG aims to provide practical underpinnings for the FAIR4Health FAIRification workflow as a domain-specific extension of the GoFAIR process, while simplifying curation, advancing interoperability, and providing insights into a roadmap for health datasets FAIR certification.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Registros Electrónicos de Salud
/
Estándar HL7
Tipo de estudio:
Guideline
/
Prognostic_studies
Idioma:
En
Revista:
Stud Health Technol Inform
Asunto de la revista:
INFORMATICA MEDICA
/
PESQUISA EM SERVICOS DE SAUDE
Año:
2022
Tipo del documento:
Article
País de afiliación:
España