ArticleScientific data2025
An ontology-based rare disease common data model harmonising international registries, FHIR, and Phenopackets.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Strengthening Rare Disease Data Readiness: a semantic approach to cross-registry interoperability.Scientific data · 2026Article
- A text mining and ontology-based approach using phenotypes to obtain relevant literature for rare diseases.iScience · 2026Article
- Development of the first myasthenia gravis patient registry in Latvia: a single-centre experience.Scientific reports · 2026Article
- Centering the marginalized: AI-driven strategies for advancing health equity in rare disease care.Patterns (New York, N.Y.) · 2026Review
- A Phenotypic Paradigm for Cerebral Palsy Genetics.medRxiv : the preprint server for health sciences · 2026Article
- GA4GH phenopacket-driven characterization of genotype-phenotype correlations in Mendelian disorders.American journal of human genetics · 2026Article
- Structuring integration for patient-centered care: a review-informed ontology-driven modular front-end framework for digital health innovation.Frontiers in digital health · 2026Article
- RareLink: scalable REDCap-based framework for rare disease interoperability linking international registries to FHIR and Phenopackets.NPJ genomic medicine · 2025Article
- Linking international registries to FHIR and Phenopackets with RareLink: a scalable REDCap-based framework for rare disease data interoperability.medRxiv : the preprint server for health sciences · 2025Article
- An ontology-based rare disease common data model harmonising international registries, FHIR, and Phenopackets.Scientific data · 2025Article
Corrections and comments
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Authors and funding
16 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.