Group for Research and Innovation in Biomedical Informatics, Biomedical Engineering, and Health Economy. Institute of Biomedicine of Seville, IBiS/"Virgen del Rocío" University Hospital /CSIC/University of Seville, Seville, Spain.
HL7 Foundation, Brussels, Belgium.
Stud Health Technol Inform. 2022 Jun 6;290:22-26. doi: 10.3233/SHTI220024.
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.
医学数据科学旨在促进知识发现，辅助数据、算法和结果分析。FAIR 原则旨在指导科学数据管理和治理，与所有数字健康生态系统利益相关者相关。FAIR4Health 项目旨在促进和鼓励健康研究社区重新使用从公共资助的研究计划中获得的数据集，使用 FAIR 原则。“FHIR 的 FAIR 性”项目旨在提供关于 HL7 FHIR 如何用作支持健康数据集 FAIR 化过程的通用数据模型的指导。这第一个预期成果是一个称为 FHIR4FAIR 的 HL7 FHIR 实施指南 (IG)，涵盖了如何在不同场景中使用 FHIR 来实现 FAIR 化。该 IG 旨在为 FAIR4Health FAIR 化工作流程提供实用基础，作为 GoFAIR 流程的特定于域的扩展，同时简化策展、提高互操作性，并为健康数据集 FAIR 认证提供路线图的见解。
