At the II National Congress on Innovation for the Advancement of Health Data Management and Evaluation (INNODATA 2025), held on 24–25 September 2025 in Seville (Spain), the Computational Health Informatics Group from the Virgen del Rocio University Hospital, as part of the Andalusian Health Service (Servicio Andaluz de Salud—SAS), presented their work on advancing federated integration of ICU data in Europe.
The audience—composed of healthcare professionals, clinical researchers, biomedical informatics experts, data engineers, governance managers, and institutional stakeholders—gathered to hear the presentation titled “ETL Guide for the Federated Integration of ICU Data in Europe”, delivered by Maria Parra Rodriguez-Armijo, Celia Alvarez Romero, Maria Gonzalez Lopez, Alberto Garcia-Agundez Blanco, Silvia Rodriguez Mejias, and Carlos Luis Parra-Calderón.
The talk began by setting the stage with the motivation behind the work: the pressing need for federated infrastructures that comply with FAIR principles in order to enable the secondary use of ICU data and facilitate the deployment of Artificial Intelligence in critical care. The talk also outlined key regulatory hurdles, including GDPR compliance, governance responsibilities, and the challenge of building trust in data sharing, alongside persistent technical barriers such as data fragmentation, limited semantic interoperability, and the absence of common ETL (Extract, Transform, Load) pipelines.
The session then introduced the ETL pipeline structure of INDICATE, which operates within a federated architecture, ensuring that data remain under local governance. The pipeline is organised into three essential layers:
- Extraction through HL7 FHIR resources.
- Transformation and storage in OMOP-CDM for analytics and AI model development.
- FAIR metadata to support data discovery, interoperability, and reuse.
At the heart of the presentation was the ETL Guide (INDICATE Data Provider Handbook), conceived as a living document designed to support data providers in navigating the complexities of ICU data integration. The Handbook encompasses:
- A dual approach using both OMOP-CDM and HL7 FHIR.
- Adoption of semantic and syntactic standards, including SNOMED CT, LOINC, ATC, ICD-10, and DICOM.
- Practical guidance on ETL processes, de-identification, and GDPR compliance.
- Data quality assessment frameworks such as OHDSI DQD (Data Quality Dashboard from the Observational Health Data Sciences and Informatics (OHDSI) community) and validation metrics.
- A publication and maintenance strategy to ensure the document evolves continuously with emerging needs.
The closing remarks emphasized the Handbook’s pivotal role as a bridge between local ICU data and the INDICATE federated infrastructure, guaranteeing standardisation, GDPR compliance, interoperability, and scalability. By underpinning the six INDICATE clinical use cases, the Handbook contributes directly to the European Health Data Space (EHDS), aligns with the FAIR principles, and lays the groundwork for AI-readiness in critical care.
Abbreviations
- ETL (Extract, Transform, Load)
- Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM)
- OHDSI DQD = the Data Quality Dashboard from the Observational Health Data Sciences and Informatics (OHDSI) community.
- HL7 FHIR (Fast Healthcare Interoperability Resources)
- SNOMED CT: Systematized Nomenclature of Medicine – Clinical Terms
- LOINC: Logical Observation Identifiers Names and Codes
- ATC: Anatomical Therapeutic Chemical Classification System
- ICD-10: International Classification of Diseases (Tenth Revision)
- DICOM: Digital Imaging and Communications in Medicine
