Medical data science aims to facilitate knowledge discovery assisting humans and machines in analyzing algorithms, tasks, and results.

The FAIR (Findable, Accessible, Interoperable, Reusable) data principles aim to guide scientific data management and stewardship, and are relevant to all stakeholders in the digital health ecosystem. The FAIR principles were formally published in 2016, and promoted by the European Commission Research and Innovation as part of the European open science strategy.

The FAIR4Health project aims to facilitate and encourage the health research community to FAIRify, that is, to augment, share and reuse datasets derived from publicly-funded research initiatives, demonstrating the FAIR strategy’s potential impact has on health outcomes and health research.

Taking the FAIR4Health project as a starting point, in MedInfo 2019 conference, MIE 2020 conference, and EU-China 2020 conference, the ‘FHIR for FAIR’ workshops reflected how the HL7 FHIR standard could support the FAIRification process in the case of health datasets, providing input to policy, standards, and research. These workshops concluded that using the HL7 FHIR standard, complemented with FAIR principles, to share personal health data internally in the institution or with other external ones,, is very relevant for the researchers, allowing the leverage reuse of health data for research and innovation.

So, the ‘FAIRness for FHIR’ project (https://confluence.hl7.org/pages/viewpage.action?pageId=91991234) was created, with the short-term aim of developing a new HL7 FHIR Implementation Guide called FHIR4FAIR to be presented in HL7 September Ballot. In addition, the chosen methodology will be tested prototypically on different scenarios and use cases. Since January 2021, the ‘FAIRness for FHIR’ project participates in the HL7 FHIR connect-a-thons. This work serves to provide practical underpinnings for the FAIR4Health FAIRification workflow, which is a domain-specific extension of the GoFAIR process.