Table 1.

Potential challenges that can arise during and following implementation of federated health data networks, and enablers to help overcome them.

Categories	Challenges	Potential enablers
Cultural and organizational	• Resistance to transitioning from traditional centralized databases to FHDNs.	• Open addressing and overcoming of resistance at different levels of an organization. • Alignment along the value chain between data contributors and consumers to ensure incentives and expectations about responsible data use and ownership of results are aligned (36).
Technological	• Variability of IT infrastructure at different healthcare organizations. • Varying type and strength of security policies at different healthcare organizations.	• Integration with existing infrastructure and cybersecurity practices of healthcare organizations. • Asynchronous federated learning methods to overcome heterogeneity between computing resources at nodes and avoid bottlenecks in real-time training (37, 38).
Data standards	• Heterogenous and biased data. • Lack of harmonized standards which facilitate interoperability. This can be particularly challenging when processing data that has already been collected and structured according to different standards.	• Agreement between nodes on standards to curate and harmonize data, metadata concepts, structures and ontologies (11). • Resource prioritization for harmonization of legacy data
Legal and regulatory	• Unclear or unachievable requirements for documented compliance with legal and regulatory obligations. • Lack of clarity how the GDPR and the proposed Data Governance Act (DGA) impact FDHNs	• Agreement between partners on common, compliant governance structures of the health data and FHDNs. • Adhering to the GDPR or equivalent data protection legislation: data consent and revocation, transparency, security and privacy, and the DGA when it comes into effect.
Knowledge and competence	• Need to initiate, develop and maintain necessary competence to establish and operate FHDNs. • Insufficient education and training of researchers, clinicians and the general public about consent and personal health data.	• Advocating ease of use principles • Recognition of best practices. • Shared learnings across local and international networks. • Education and training of all stakeholders about consent. • Education and training of all stakeholders about FHDNs.
Ethical and social	• Lengthy and sometimes disjointed approval procedures with ethics committees and data protection officers, to allow others access to one's database. • Informed consent from patients. Determining preferences through dynamic consent technologies is possible within limited environments [e.g., within PHT (39)], however wide scale implementation of these has its own barriers (40).	• Standardized data access models to engender trust and maintain data protection. • Assurance that participation in a FHDN occurs within long-term ethical guiding principles.
Financial and political	• Limited clear and successful business, incentive, and reimbursement models. • Insufficient large-scale funding initiatives, such as Horizon Europe, supporting FHDNs.	• Learning from public and private initiatives for sharing of health data across borders such as 1+ Million Genomes (and beyond) (41), the European Health Data Space (17) the European Open Science Cloud (42) and GAIA-X (16).