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.
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August 2024
This paper explores the critical role of Interoperability (IOP) in the integration of Artificial Intelligence (AI) for clinical applications. As AI gains prominence in medical analytics, its application in clinical practice faces challenges due to the lack of standardization in the medical sector. IOP, the ability of systems to exchange information seamlessly, emerges as a fundamental solution.
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August 2024
This paper explores key success factors for the development and implementation of a Common Data Model (CDM) for Rare Diseases (RDs) focusing on the European context. Several challenges hinder RD care and research in diagnosis, treatment, and research, including data fragmentation, lack of standardisation, and Interoperability (IOP) issues within healthcare information systems. We identify key issues and recommendations for an RD-CDM, drawing on international guidelines and existing infrastructure, to address organisational, consensus, interoperability, usage, and secondary use challenges.
View Article and Find Full Text PDFThe Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema was released in 2022 and approved by ISO as a standard for sharing clinical and genomic information about an individual, including phenotypic descriptions, numerical measurements, genetic information, diagnoses, and treatments. A phenopacket can be used as an input file for software that supports phenotype-driven genomic diagnostics and for algorithms that facilitate patient classification and stratification for identifying new diseases and treatments. There has been a great need for a collection of phenopackets to test software pipelines and algorithms.
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