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April 1, 2026Computers1 citationsOpen Access

A Federated FHIR-Based Interoperability Framework for Multi-Site Heart Failure Monitoring: The RETENTION Project

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NVNikolaos VasileiouOGOlympia GiannakopoulouOMOurania Manta

Key Points

  • The aim is to develop a robust interoperability framework for effective heart failure monitoring across various clinic sites.
  • Developed a federated FHIR-based architecture for multi-site heart failure monitoring.
  • Created a semantic reference model with 444 clinical and contextual variables aligned with FHIR R4.
  • Implemented selective profiling, extending only the Patient resource while standardizing other variables.
  • Retained identifiable data locally while anonymizing datasets for aggregation into a Global Insights Cloud.
  • Deployed across six hospitals, supporting 390 patients and over 130,000 patient-days of monitoring.
  • Harmonised over 3.6 million remote device data points without schema conflicts.
  • Achieved large-scale semantic harmonisation and privacy-preserving data aggregation.

Abstract

Heart failure management increasingly relies on heterogeneous clinical and real-world data generated through remote monitoring technologies. However, transforming these multimodal data streams into actionable insights requires robust interoperability infrastructures. This study presents the RETENTION interoperability framework, a federated HL7 Fast Healthcare Interoperability Resources (FHIR)-based architecture designed to support multi-site heart failure monitoring across five independent clinical environments. A semantic reference model comprising 444 clinical and contextual variables was developed and aligned with FHIR R4 resources and internationally recognised terminology systems. The platform adopts a selective profiling strategy, extending only the Patient resource while standardising the remaining variables through example-driven Implementation Guide documentation. Identifiable data are retained locally within Clinical Site Backends, whereas anonymised datasets are periodically aggregated into a Global Insights Cloud to enable centralised analytics and controlled third-party interactions. The framework was deployed across six hospitals (with two Spanish hospitals sharing the same deployment), supporting 390 patients and over 130,000 patient-days of monitoring, with more than 3.6 million remote device data points harmonised without schema conflicts. The results demonstrate that large-scale semantic harmonisation and privacy-preserving aggregation can be achieved using a lightweight profiling approach, providing a scalable and reproducible interoperability model for multi-centre digital health research infrastructures.

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Cite This Study

Vasileiou et al. (2026) studied this question.

synapsesocial.com/papers/69cd7a915652765b073a7e29https://doi.org/10.3390/computers15040212
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