In the era of Digital health, data is the lifeblood of innovation. However, health data is often fragmented, inconsistent, and difficult to share across institutions or borders. OMOP is the Observational Medical Outcomes Partnership Common Data Model, a transformative approach that makes health data interoperable, standardise and ready for meaningful use. For us, OMOP is not just a model but a strategic enabler driving the project’s mission to unlock the potential of decentralised healthcare data.

What is OMOP?

OMOP is a common data model (CDM) developed under the Observational Health Data Sciences and Informatics (OHDSI) initiative. It standardises the structure and content of health data from diverse source, including electronic health records, hospital systems, and insurance claims, into a unified format. This allows researchers and healthcare professionals to query and analyse data in a consistent, reproducible way across different institutions and countries.

Instead of adapting research tools to every data format, OMOP allows data to be mapped to a common language, both structurally and semantically. This means that tools built on top of OMOP, like AI models, dashboards, and cohort selectors—can work across any dataset that has been transformed into the model.

Why is OMOP important?

Health data is notoriously difficult to use at scale due to differences in coding systems (ICD, SNOMED, LOINC), formats, and governance policies. OMOP addresses these barriers by creating:

• Semantic consistency, through standard vocabularies and ontologies.
• Structural uniformity, using a shared table structure across domains like conditions, drugs, measurements, and observations.
• Reproducibility, enabling studies to be replicated across datasets and countries.
• Scalability, allowing rapid deployment of tools and methods across new sources.

In essence, OMOP turns local, siloed datasets into components of a global learning health system.

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Building a future-proof Health Data ecosystem

For us, OMOP is a cornerstone in our mission to build a secure, decentralised platform for secondary use of health data. Within the framework of the UNICO I+D CLOUD programme, we are developing solutions that enable data to be used for research, clinical innovation, and public health, without compromising patient privacy or control.

By adopting the OMOP Common Data Model, we are ensuring that health data from participating hospitals and institutions is interoperable, comparable, and ready for advanced analytics, including AI and machine learning. This standardisation allows us to connect previously incompatible datasets and draw meaningful insights to improve patient care and support scientific research.

Use cases within the project include:

• Implement a standardised ETL process for clinical data, transforming data into the OMOP-CDM model to ensure seamless data extraction and interoperability across centres.
• Structuring free-text clinical notes (e.g. HIV patient records) into OMOP format.
• Enabling dashboard visualisation of chronic disease management across hospitals.
• Supporting AI-driven pattern recognition and drug repurposing studies.

OMOP also helps us align with broader EU initiatives like the European Health Data Space (EHDS), where cross-border data exchange and standardisation are fundamental principles.

The impact beyond technology

Standardising data using OMOP is not merely a technical achievement, it represents a shift in how we think about trust, collaboration, and equity in digital health. By enabling safe, scalable, and standards-based use of real-world data, OMOP empowers stakeholders to work together for better, evidence-based healthcare.

For Spain, and for Europe, our use of OMOP is a powerful step forward ensuring that health data serves not only the system, but the patient.