Building Bridges for Federated Learning in Healthcare: Review on Approaches for Common Data Model Development




von Gerich, Hanna; Chomutare, Taridzo; Peltonen, Laura-Maria

Gillian Strudwick, Nicholas R. Hardiker, Glynda Rees, Robyn Cook, Young Ji Lee

International Congress on Nursing Informatics

PublisherIOS Press

2024

 Studies in Health Technology and Informatics

Innovation in Applied Nursing Informatics

315

711

712

978-1-64368-527-4

0926-9630

1879-8365

DOIhttps://doi.org/10.3233/SHTI240292

https://doi.org/10.3233/shti240292

https://research.utu.fi/converis/portal/detail/Publication/515530794



Common data models provide a standardized way to represent data used in federated learning tasks. The aim of this review was to explore the development and use of common data models to harmonize electronic health record data in health research. The data search yielded 724 records, of which 19 were included for this study. None of the research focused on nursing specific topics. All studies either utilized the Observational Medical Outcomes Partnership (OMOP) common data model, or developed a model partly based on the OMOP. A roadmap to guide research for the development of common data models for federated learning are warranted.


This work is supported by Nordic Innovation.


Last updated on 19/02/2026 01:03:04 PM