Federated Health Data Platforms: Transforming Clinical Silos into Economic Assets. Business Models for European Digital Health Research Networks

The convergence of healthcare interoperability standards and emerging data regulations presents an unexplored opportunity to reimagine clinical research networks through both technical and economic lenses. This paper proposes a theoretical framework examining how federated learning architectures, when integrated with OHDSI’s OMOP Common Data Model [2, 3] and HL7-FHIR [1] standards, could potentially enable sustainable business models within the European Health Data Space (EHDS) regulatory context [4]. Our investigation addresses three interconnected objectives. First, we explore how existing technical infrastructure investments in OHDSI networks and FHIR implementations might be repurposed for federated architectures, potentially reducing adoption barriers. Second, we design theoretical business models that balance economic sustainability with privacy preservation requirements mandated by EHDS. Third, we analyze the fundamental trade-offs between technical complexity, privacy guarantees, and economic viability in distributed healthcare networks. The paper deliberately positions itself at the intersection of technical feasibility and economic sustainability, acknowledging that while federated learning technologies have demonstrated promise in research settings [5, 13, 15], their translation into economically viable platforms remains largely theoretical. We aim to contribute to the discourse on how technical standards might serve as economic enablers rather than mere compliance requirements.