ArticleOrphanet journal of rare diseases2024
How to customize common data models for rare diseases: an OMOP-based implementation and lessons learned.
Article in Orphanet journal of rare diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pharmacoepidemiologic Research Based on Common Data Models: Systematic Review and Bibliometric Analysis.JMIR medical informatics · 2025Pooled it
- Centering the marginalized: AI-driven strategies for advancing health equity in rare disease care.Patterns (New York, N.Y.) · 2026Review
- Development of a risk prediction model for second primary thyroid cancer in female breast cancer patients based on machine learning algorithms.Scientific reports · 2026Article
- FAIR data gaps and collaboration willingness among hemoglobinopathy research centers.Scientific data · 2026Article
- Experiences With Integrating Medical Terminologies Into User Interfaces for a Decision Support System for Primary Care: Conceptual and Development Study.JMIR medical informatics · 2026Article
- Harmonizing patient health data into the OMOP common data model: a case study using tuberculosis data from Douala General Hospital, Cameroon.Frontiers in digital health · 2026Article
- Integrating Artificial Intelligence with Global Genomic Resources: A Narrative Review of Implications for Precision Medicine.Journal of multidisciplinary healthcare · 2026Review
- Enhancing medical care for aniridia through a public health lens: a scoping review of organizational models, patient management, and quality of life assessment.Frontiers in public health · 2026Article
- Enhancing diagnostic precision for rare diseases using case-based reasoning.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- RareLink: scalable REDCap-based framework for rare disease interoperability linking international registries to FHIR and Phenopackets.NPJ genomic medicine · 2025Article
- Semiautomatic mapping of a national drug terminology to standardised OMOP drug concepts using publicly available supplementary information.BMC medical research methodology · 2025Article
- Evaluating the Prototype of a Clinical Decision Support System in Primary Care: Qualitative Study.JMIR formative research · 2025Article
- Breaking Digital Health Barriers Through a Large Language Model-Based Tool for Automated Observational Medical Outcomes Partnership Mapping: Development and Validation Study.Journal of medical Internet research · 2025Article
- An ontology-based rare disease common data model harmonising international registries, FHIR, and Phenopackets.Scientific data · 2025Article
- From Data Integration to Precision Medicine: A Value-Based Healthcare Approach for Sarcoma Care.Journal of clinical medicine · 2024Review
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23 authors.
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Abstract
backgroundGiven the geographical sparsity of Rare Diseases (RDs), assembling a cohort is often a challenging task. Common data models (CDM) can harmonize disparate sources of data that can be the basis of decision support systems and artificial intelligence-based studies, leading to new insights in the field. This work is sought to support the design of large-scale multi-center studies for rare diseases.
methodsIn an interdisciplinary group, we derived a list of elements of RDs in three medical domains (endocrinology, gastroenterology, and pneumonology) according to specialist knowledge and clinical guidelines in an iterative process. We then defined a RDs data structure that matched all our data elements and built Extract, Transform, Load (ETL) processes to transfer the structure to a joint CDM. To ensure interoperability of our developed CDM and its subsequent usage for further RDs domains, we ultimately mapped it to Observational Medical Outcomes Partnership (OMOP) CDM. We then included a fourth domain, hematology, as a proof-of-concept and mapped an acute myeloid leukemia (AML) dataset to the developed CDM.
resultsWe have developed an OMOP-based rare diseases common data model (RD-CDM) using data elements from the three domains (endocrinology, gastroenterology, and pneumonology) and tested the CDM using data from the hematology domain. The total study cohort included 61,697 patients. After aligning our modules with those of Medical Informatics Initiative (MII) Core Dataset (CDS) modules, we leveraged its ETL process. This facilitated the seamless transfer of demographic information, diagnoses, procedures, laboratory results, and medication modules from our RD-CDM to the OMOP. For the phenotypes and genotypes, we developed a second ETL process. We finally derived lessons learned for customizing our RD-CDM for different RDs. DISCUSSION: This work can serve as a blueprint for other domains as its modularized structure could be extended towards novel data types. An interdisciplinary group of stakeholders that are actively supporting the project's progress is necessary to reach a comprehensive CDM.
conclusionThe customized data structure related to our RD-CDM can be used to perform multi-center studies to test data-driven hypotheses on a larger scale and take advantage of the analytical tools offered by the OHDSI community.
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