OMOP 101: Introduction to the standard that’s changing healthcare research

Lecturer: Milou Brand

The global healthcare data ecosystem has various complexities, which include the disparate nature of database types, medical ontologies, and data collection methods, not to mention added layers of complexity due to privacy and security concerns. The OMOP Common Data Model (CDM) has emerged as a game-changer using standardization as a way to revolutionize the way real-world evidence (RWE) is generated and utilized.

OMOP stands for Observational Medical Outcomes Partnership and standardizes and structures the content of observational data to enable efficient federated and standardized analytics that can produce reliable evidence. The OMOP standard is managed by the organization OHDSI (Observational Health Data Sciences and Informatics) which is multi-stakeholder, interdisciplinary, open-science collaborative to bring out the value of health data through large-scale analytics.

This presentation will give and introduction to OMOP and will focus on why OMOP is at the forefront of discussions in the healthcare community and what you need to know to leverage its full potential. In more detail, we will:

• Explore the origins and development of the OMOP CDM, exploring its fundamental principles and structure, and explaining how OMOP standardizes healthcare data to facilitate seamless integration and analysis.
• Show how OMOP's approach enhances data interoperability and benefits multi-country and multi-institutional studies, such as federated research network model and its global scalability.
• Explore the methodologies and tools used in OMOP-driven analytics, which offer solutions for faster, more reliable multi-database studies.
• Focus on the collaborative network and community of the global OMOP/OHDSI initiative and the role of community-driven innovation.
• Discuss how OMOP relates to other standardization methods, such as how FHIR and OMOP complement each other to enhance data interoperability.

How to get started with OMOP and join the OHDSI community – learnings from Helsinki University Hospital

Lecturer: Eric Fey

Building on the OMOP 101 session, this presentation shifts from why OMOP to HOW to implement it. Organizations looking to adopt the OMOP Common Data Model (CDM) need to understand the technical, organizational, and collaborative steps involved.

Using Helsinki University Hospital (HUS) as a case study, this session provides a presentation of HUS roadmap to OMOP adoption—from data ingestion and ETL to quality control, the first federated real-world study to predictive models using advanced swarm learning.

From Theory to Practice:

• Preparation & Planning – Assess existing data infrastructure, define objectives, and engage stakeholders.
• Data harmonization in three steps:
  • Data discovery – Prepare raw data (Data analysts)
  • Concept mapping of data – identify OMOP concepts for the source data (Clinical experts)
  • ETL (Extract, Transform, Load) – Harmonize data into the OMOP CDM format (Data engineers)
• Data Quality & Validation – Implement verification and iterative testing to ensure reliability.
• Establishing analytical capabilities
  • Infrastructure & Tools – Leverage OHDSI’s open-source ecosystem, including Atlas and WhiteRabbit.
  • Training & Community Engagement – Build a multidisciplinary team and connect with OHDSI working groups and local country node.
  • Pilot Testing & Evaluation – Conduct initial studies, refine processes, and scale implementations.
  • Advanced OMOP: AI & Swarm Learning.

Helsinki University Hospital’s OMOP Journey:

HUS has successfully transformed its healthcare data into OMOP CDM in close collaboration with FinOMOP and the other university hospitals in Finland, enabling:

• Efficient data integration through version controlled national and international standards.
• Participation in federated studies to generate real-world evidence; EHDEN, DARWIN, OHDSI, …
• Advancing AI-driven research through swarm-learning – a form of federated machineleraning –   to collaboratively build decentralized predictive models.

The Value of the OHDSI Community. Joining OHDSI means access to:

• A global research network for large-scale studies.
• Open-source tools & best practices for OMOP adoption.
• Education, training, and mentorship to accelerate both research and implementation.

A Hands-On Case Study of a pan-European Cancer Research Project Using OMOP

Lecturer: Åslaug Helland

This session presents Oslo University Hospital's experience in a multi-center, federated research project as part of the DigiCore network. It will focus on how OMOP CDM was used to support a retrospective real-world evidence study on non-small cell lung cancer (mNSCLC) in collaboration with other leading cancer centers.

Introduction to DigiCore and DigiONE

The DigiCore network is a pan-European research initiative aimed at accelerating precision oncology research through harmonized, real-world data. The DigiONE pilot project established a federated research network to enable large-scale oncology studies while maintaining data privacy and regulatory compliance.

Study Overview: Survival Outcomes in mNSCLC by Metastasis Location

This specific study analyzed retrospective routine care data from OMOP databases at three centers, assessing overall survival (OS) differences based on metastasis patterns. The cohort included 1,294 mNSCLC patients diagnosed between 2018 and 2022, with OS estimates generated through a federated Kaplan-Meier analysis.

Experiences from Oslo University Hospital Implementing OMOP

Oslo University Hospital integrated multiple data sources into OMOP to support the study. This required aligning multiple data sources, overcoming technical and legal challenges, and refining data governance workflows to support international research collaboration.

Federated Analysis and Key Findings

The study used privacy-preserving federated analytics, applying Gaussian-noised survival times to reduce re-identification risks. The analysis revealed significant differences in survival outcomes based on metastasis location, with patients having multiple metastases (excluding the brain) showing the shortest median OS (5.88 months), while those with contralateral lung or pleura-only metastases had the longest OS (17.81 months).

Challenges and Solutions in a Multi-Center Network

The session will discuss local and network-wide challenges, including data standardization, validation across institutions, and regulatory compliance. Solutions implemented to bridge internal data integration efforts with collaborative research frameworks will be shared.

Outcomes and Lessons Learned

The federated research approach demonstrated the feasibility and value of OMOP-based multi-center oncology studies. The session will conclude with key takeaways from Oslo University Hospital’s experience, along with recommendations for institutions interested in OMOP-based federated research.

Panel Discussion: How can Sweden and the Nordics use federated analytics (OMOP) to unlock its health data and attract more studies?

Moderator: Susanna Flaherty
Panelists: Eric Fey, Gustav Klingstedt, Sofie Gustafsson, Åslaug Helland

This presenation and panel discussion explores how the Nordics can lead the next era of real-world evidence (RWE) generation by establishing a harmonized, federated data infrastructure, building on the successes of FinOMOP in Finland. Professor Kimmo Porkka will present how FinOMOP is developing a nationwide OMOP-based network across Finland’s university hospitals and health registries, enabling scalable, privacy-preserving real-world data analysis. By 2026, FinOMOP aims to provide an interoperable health data ecosystem that will support regulatory-grade evidence generation, precision medicine, and healthcare decision-making. Kimmo Porkka is a professor leading FinOMOP, specializing in health data infrastructure and Nordic collaboration for RWE generation.

The Nordic countries have unique strengths, including high-quality electronic health records (EHR), national health registries, and long-term patient follow-ups. By adopting a federated OMOP model, they could enable large-scale RWE studies while ensuring data privacy, sovereignty, and compliance with national and EU regulations. The session will discuss how Sweden and other Nordic countries can build on FinOMOP’s technical foundation, including steps for mapping their national registries and hospital data to OMOP, aligning governance frameworks, and establishing cross-border research collaborations.

A federated Nordic OMOP network could provide significant benefits for the life sciences sector by offering access to harmonized, high-quality real-world data. This would enable pharmaceutical and biotech companies to generate post-marketing regulatory evidence, support clinical trial optimization, and accelerate biomarker discovery. The integration of Nordic data could also enhance AI-driven healthcare research, improving predictive modeling and decision-support tools for personalized medicine.

Following his introduction, the panel will discuss how Sweden and other Nordic countries can build on FinOMOP’s success to leverage its high-quality health data and create a Nordic-wide federated research network. The discussion will explore lessons learned from existing federated networks, the steps Sweden should take to harmonize health data for secondary use, and which data sources should be prioritized for maximum impact. Can the Nordics be a global hub for pharmaceutical companies, biotech firms, AI-driven healthcare innovators, and clinical trial sponsors seeking access to high-quality, real-time real-world data.

This session concludes the four-part OMOP introduction at Vitalis, providing a forum for next steps, collaboration opportunities, and practical insights on Nordic OMOP adoption.