An Ontology-Based Rare Disease Common Data Model for international registry use, HL7 FHIR, and GA4GH Phenopackets (v2.0.0.dev0)

Please see our GitHub repository here: https://github.com/BIH-CEI/rd-cdm/ Please see our RD CDM documentation here: https://rd-cdm.readthedocs.io/en/latest/index.html/ Attention: The RD CDM paper is currently under review (version 2.0.0.dev0). As soon as the paper is accepted, we will publish v2.0.0. For more information please see our ChangeLog: https://rd-cdm.readthedocs.io/en/latest/changelog.htmlWe introduce our RD CDM v2.0.0— a common data model specifically designed for rare diseases. This RD CDM simplifies the capture, storage, and exchange of complex clinical data, enabling researchers and healthcare providers to work with harmonized datasets across different institutions and countries. The RD CDM is based on the ERDRI-CDS, a common data set developed by the European Rare Disease Research Infrastructure (ERDRI) to support the collection of harmonized data for rare disease research. By extending the ERDRI-CDS with additional concepts and relationships, based on HL7 FHIR v4.0.1 and the GA4GH Phenopacket Schema v2.0, the RD CDM provides a comprehensive model for capturing detailed clinical information alongisde precise genetic data on rare diseases.Background:<br>Rare diseases (RDs), though individually rare, collectively impact over 260 million people worldwide, with over 17 million affected in Europe. These conditions, defined by their low prevalence of fewer than 5 in 10,000 individuals, are often genetically driven, with over 70% of cases suspected to have a genetic cause. Despite significant advances in medical research, RD patients still face lengthy diagnostic delays, often due to a lack of awareness in general healthcare settings and the rarity of RD-specific knowledge among clinicians. Misdiagnosis and underrepresentation in routine care further compound the challenges, leaving many patients without timely and accurate diagnoses.<br>Interoperability plays a critical role in addressing these challenges, ensuring the seamless exchange and interpretation of medical data through the use of internationally agreed standards. In the field of rare diseases, where data is often scarce and scattered, the importance of structured, standardized, and reusable medical records cannot be overstated. Interoperable data formats allow for more efficient research, better care coordination, and a clearer understanding of complex clinical cases. However, existing medical systems often fail to support the depth of phenotypic and genotypic data required for rare disease research and treatment, making interoperability a crucial enabler for improving outcomes in RD care.