Currently accepted at: JMIR Medical Informatics
Date Submitted: Dec 16, 2025
Date Accepted: Sep 29, 2026
This paper has been accepted and is currently in production.
It will appear shortly on 10.2196/89685
The final accepted version (not copyedited yet) is in this tab.
From Pilots to Services: Admission Mechanisms for Scalable Federated Health Data Collaboration - Design and Proof-of-Concept Study
ABSTRACT
Background:
Healthcare research relies increasingly on secure, multi-partner collaboration across genomic, clinical, behavioural, and social datasets. Yet regulatory, ethical, and operational constraints often prevent institutions from pooling raw data. Existing infrastructures—Trusted Research Environments (TREs), federated learning pilots, and integration platforms—remain fragmented and frequently lack runtime safeguards, interoperable standards, or equitable governance models [1]. Meanwhile, regulatory initiatives are becoming more supportive of distributed and privacy-preserving solutions, underscoring the need for scalable federated approaches.
Objective:
This paper reviews current solutions for collaborative health research and introduces Open Health, a federated computing framework and proof-of-concept (PoC) demonstrator designed to enable secure, interoperable, and person-centred collaboration while maintaining privacy and data sovereignty.
Methods:
We undertook: (1) a comparative review of collaborative platforms, assessing maturity, capabilities, and governance; (2) development of the Open Health framework, adapted from Federated Computing [2]; and (3) implementation of a PoC demonstrator and roadmap to operationalise future use cases.
Results:
Open Health builds on four pillars—distributed datasets, federated analytics-to-data services, standards-based APIs, and governance-as-code—coordinated through a federated orchestrator. The PoC validated feasibility by executing analytics and federated learning at source, exchanging anonymised outputs via APIs, and enforcing governance at runtime. A co-creation roadmap links requirements, design, testing, and evaluation, embedding fairness, transparency, and usability.
Conclusions:
Open Health demonstrates that Federated Computing can be technically feasible, policy aligned, and person centred. It provides a blueprint for learning health systems that preserve privacy, strengthen trust, and support multi-partner research across organisational and jurisdictional boundaries.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.