An ontology-based rare disease common data model harmonising international registries, FHIR, and Phenopackets

An ontology-based rare disease common data model harmonising international registries, FHIR, and Phenopackets

Abstract Although rare diseases (RDs) affect over 260 million individuals worldwide, low data quality and scarcity challenge effective care and research. This work aims to harmonise the Common Data Set by European Rare Disease Registry Infrastructure, Health Level 7 Fast Healthcare Interoperability...

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Main Authors: Adam S. L. Graefe, Miriam R. Hübner, Filip Rehburg, Steffen Sander, Sophie A. I. Klopfenstein, Samer Alkarkoukly, Ana Grönke, Annic Weyersberg, Daniel Danis, Jana Zschüntzsch, Elisabeth F. Nyoungui, Susanna Wiegand, Peter Kühnen, Peter N. Robinson, Oya Beyan, Sylvia Thun
Format: Article
Language:English
Published: Nature Portfolio 2025-02-01
Series:Scientific Data
Online Access:https://doi.org/10.1038/s41597-025-04558-z
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Summary:Abstract Although rare diseases (RDs) affect over 260 million individuals worldwide, low data quality and scarcity challenge effective care and research. This work aims to harmonise the Common Data Set by European Rare Disease Registry Infrastructure, Health Level 7 Fast Healthcare Interoperability Base Resources, and the Global Alliance for Genomics and Health Phenopacket Schema into a novel rare disease common data model (RD-CDM), laying the foundation for developing international RD-CDMs aligned with these data standards. We developed a modular-based GitHub repository and documentation to account for flexibility, extensions and further development. Recommendations on the model’s cardinalities are given, inviting further refinement and international collaboration. An ontology-based approach was selected to find a common denominator between the semantic and syntactic data standards. Our RD-CDM version 2.0.0 comprises 78 data elements, extending the ERDRI-CDS by 62 elements with previous versions implemented in four German university hospitals capturing real world data for development and evaluation. We identified three categories for evaluation: Medical Data Granularity, Clinical Reasoning and Medical Relevance, and Interoperability and Harmonisation.
ISSN:2052-4463

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