Intheris enables institutions to run studies together without centralizing patient records. This paradigm shift moves programs from fragmented collection to governed network execution, making study design and compliant delivery simpler in practice.
A unified platform for secure local data operations, federated collaboration, and analytics teams can act on.
ROOT is the control center for data owners. It governs local ingestion and data preparation, manages research invitations, and makes usage transparent before any execution starts. Institutions can approve or refuse each request and monitor clinical and financial quality in the same workspace.
FUSE orchestrates multi-site RWE programs at network scale. It supports retrospective and prospective cohorts, aggregated analytics, and federated model execution. A curated library of evidence-based study templates — spanning oncology, cardiology, critical care, and 15 other domains — accelerates protocol design from concept to multi-site execution. Regulatory submission packages with cryptographic fingerprints and tamper-evident audit chains are generated automatically.
AURA extends ROOT and FUSE for real-time operations. It adds clinical workflow and financial alerting, live monitoring, and operational recommendations for pathway management. It is intended for research and operations support only and is not a medical device.
ECHO generates realistic longitudinal synthetic patient trajectories by combining population models, pathway logic, and controlled noise. It continuously emits synthetic FHIR resources, HL7 streams, DICOM metadata, and unstructured clinical notes into ROOT. Teams use it to stress-test algorithms, validate multi-modal integrations, and explore edge cases without touching real patient records.
Intheris enables institutions to run studies together without centralizing patient records. This paradigm shift moves programs from fragmented collection to governed network execution, making study design and compliant delivery simpler in practice.
Intheris is an infrastructure partner: it does not claim ownership of clinical data and does not sell hospital information systems. This neutral position helps align providers, sponsors, and partners around shared rules and transparent execution.
The platform supports country-level hosting and governance requirements. This enables Swiss and European operating models where data control, compliance, and execution remain locally anchored.
Institutions keep decision rights on permissions, execution, and outputs. The model is built to support collaboration without forcing uncontrolled data transfers.
From pilot to multi-country execution, the architecture supports interoperable workflows across institutions. Teams can scale collaboration without rebuilding their local systems.
Institutional demand for governed real-world evidence execution is rising across Europe and beyond. Intheris is built to meet that demand with a compliant and operationally practical approach.
Intheris provides the architecture needed to run governed RWE programs at scale and finally unlocks access to the large, still underused pool of real-world data, while keeping local control and compliance. For research and operational support only, not a medical device.
Get it done with usStrengthen local operations and participate in sponsored research while keeping legal and technical control of patient data.
Access a trusted network of institutions for federated evidence programs without moving identifiable patient data between sites.
Run cross-institution analyses with privacy-preserving methods for policy, epidemiology, and translational research.
"Keep patient data sealed. Unlock evidence at network scale."
Enable · Federate · Discover
Intheris Health Ltd is a neutral infrastructure partner and does not claim ownership of patient data. Patient records stay inside each institution. The platform is built for clear governance, full traceability, and compliance with Swiss nFADP and GDPR. The network service can run in member countries to support sovereign operations.
Our platform builds on established research in federated learning and privacy-preserving analytics:
Rieke, N. et al. (2020). "The Future of Digital Health with Federated Learning."
npj Digital Medicine
McMahan, B. et al. (2017). "Communication-Efficient Learning of Deep Networks from Decentralized Data."
Proceedings of AISTATS 2017
Bonawitz, K. et al. (2017). "Practical Secure Aggregation for Privacy-Preserving Machine Learning."
Proceedings of ACM CCS 2017
Li, T. et al. (2020). "Federated Learning: Challenges, Methods, and Future Directions."
IEEE Signal Processing Magazine
Dwork, C. & Roth, A. (2014). "The Algorithmic Foundations of Differential Privacy."
Foundations and Trends in Theoretical Computer Science
Hersh, W. (2018). "Secondary Use of Electronic Health Records for Clinical Research."
Yearbook of Medical Informatics
Founder & CEO
Advisory Board Member, Physician
Advisory Board Member, Physician
