Towards FAIRification of sensitive and fragmented rare disease patient data: challenges and solutions in European reference network registries

Abstract

Introduction: Rare disease patient data are typically sensitive, present in multiple registries controlled by different custodians, and non-interoperable. Making these data Findable, Accessible, Interoperable, and Reusable (FAIR) for humans and machines at source enables federated discovery and analysis across data custodians. This facilitates accurate diagnosis, optimal clinical management, and personalised treatments. In Europe, twenty-four European Reference Networks (ERNs) work on rare disease registries in different clinical domains. The process and the implementation choices for making data FAIR ('FAIRification') differ among ERN registries. For example, registries use different software systems and are subject to different legal regulations. To support the ERNs in making informed decisions and to harmonise FAIRification, the FAIRification steward team was established to work as liaisons between ERNs and researchers from the European Joint Programme on Rare Diseases.

Results: The FAIRification steward team inventoried the FAIRification challenges of the ERN registries and proposed solutions collectively with involved stakeholders to address them. Ninety-eight FAIRification challenges from 24 ERNs' registries were collected and categorised into "training" (31), "community" (9), "modelling" (12), "implementation" (26), and "legal" (20). After curating and aggregating highly similar challenges, 41 unique FAIRification challenges remained. The two categories with the most challenges were "training" (15) and "implementation" (9), followed by "community" (7), and then "modelling" (5) and "legal" (5). To address all challenges, eleven types of solutions were proposed. Among them, the provision of guidelines and the organisation of training activities resolved the "training" challenges, which ranged from less-technical "coffee-rounds" to technical workshops, from informal FAIR Games to formal hackathons. Obtaining implementation support from technical experts was the solution type for tackling the "implementation" challenges.

Conclusion: This work shows that a dedicated team of FAIR data stewards is an asset for harmonising the various processes of making data FAIR in a large organisation with multiple stakeholders. Additionally, multi-levelled training activities are required to accommodate the diverse needs of the ERNs. Finally, the lessons learned from the experience of the FAIRification steward team described in this paper may help to increase FAIR awareness and provide insights into FAIRification challenges and solutions of rare disease registries.

Keywords: Data steward; FAIR; Patient registry; Rare disease; Stewardship.

Fig. 1

FAIRification steward team, EJP RD FAIR (principles, standards, and tools) experts, and European Reference Networks (ERNs) in a three-party interaction map. The FAIRification steward team works as liaisons between ERNs and EJP RD FAIR experts, collecting FAIRification challenges from ERNs, curating these challenges, providing them to experts, and returning consolidated knowledge from the experts to ERNs as proposed solutions. For single ERN requests, the team creates Expert-ERN communication channels (dashed line). The ERN team includes a project manager (or equivalent), a local data steward, and a developer (or software provider). The set of proposed solutions comprises workshops, where standards or tools are presented by experts; hackathons, where developers can try different tools themselves in a hands-on fashion; experience exchange between ERNs; and suggestions of existing implementations, tools, and resources