ArticleOphthalmology science2023
Advancing Toward a Common Data Model in Ophthalmology: Gap Analysis of General Eye Examination Concepts to Standard Observational Medical Outcomes Partnership (OMOP) Concepts.
Article in Ophthalmology science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The trial behind it
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Who cites it
19 citing papers in PubMed.
- Five-year risk of rhegmatogenous retinal detachment following prophylactic laser retinopexy for retinal breaks and lattice degeneration: a real-world analysis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- A Whole-Genome Sequence Genome-Wide Association Study inOphthalmology science · 2026Article
- Centering the marginalized: AI-driven strategies for advancing health equity in rare disease care.Patterns (New York, N.Y.) · 2026Review
- Improving Fairness and Mitigating Bias in Multicenter Electronic Health Records Models to Predict Glaucoma Outcomes.Ophthalmology science · 2026Article
- Evaluating the statistical utility and information loss in the transformation of a real-world oncology database from CDISC-SDTM to OMOP-CDM.ESMO real world data and digital oncology · 2026Article
- Semaglutide and diabetic retinopathy: an OHDSI network study.BMJ open diabetes research & care · 2025 · on this mapObservational
- Insights from the First Five Years of the Prospective Ocular Tumor Study.Seminars in ophthalmology · 2025Article
- Gap Analysis of Standard Automated Perimetry Concept Representation in Medical Terminologies.Journal of glaucoma · 2025Article
- Mapping Emergency Medicine Data to the Observational Medical Outcomes Partnership Common Data Model: A Gap Analysis of the American College of Emergency Physicians Clinical Emergency Data Registry.Journal of the American College of Emergency Physicians open · 2025Article
- Perspective review: Will generative AI make common data models obsolete in future analyses of distributed data networks?Therapeutic advances in drug safety · 2025Review
- Coverage of Physical Therapy Assessments in the Observational Medical Outcomes Partnership Common Data Model.Applied clinical informatics · 2024Article
- Data Harmonization, Standardization, and Collaboration for Diabetic Retinal Disease (DRD) Research: Report From the 2024 Mary Tyler Moore Vision Initiative Workshop on Data.Translational vision science & technology · 2024Article
- Variations in Using Diagnosis Codes for Defining Age-Related Macular Degeneration Cohorts.Informatics (MDPI) · 2024Article
- Implementing a Common Data Model in Ophthalmology: Mapping Structured Electronic Health Record Ophthalmic Examination Data to Standard Vocabularies.Ophthalmology scienceArticle
- Gap Analysis of Glaucoma Examination Concept Representations within Standard Systemized Nomenclature of Medicine - Clinical Terms.Ophthalmology. GlaucomaArticle
- Barriers to Extracting and Harmonizing Glaucoma Testing Data: Gaps, Shortcomings, and the Pursuit of FAIRness.Ophthalmology scienceArticle
- A Practical Guide to Evaluating Artificial Intelligence Imaging Models in Scientific Literature.Ophthalmology scienceArticle
- Big data and electronic health records for glaucoma research.Taiwan journal of ophthalmologyReview
- Federated Learning in Glaucoma: A Comprehensive Review and Future Perspectives.Ophthalmology. GlaucomaReview
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Purpose: Evaluate the degree of concept coverage of the general eye examination in one widely used electronic health record (EHR) system using the Observational Health Data Sciences and Informatics Observational Medical Outcomes Partnership (OMOP) common data model (CDM). Design: Study of data elements. Participants: Not applicable. Methods: Data elements (field names and predefined entry values) from the general eye examination in the Epic foundation system were mapped to OMOP concepts and analyzed. Each mapping was given a Health Level 7 equivalence designation- Main Outcome Measures: Gaps in OMOP concept coverage of EHR elements and intergrader agreement of mapped OMOP concepts. Results: A total of 698 data elements (210 fields, 488 values) from the EHR were analyzed. The intergrader kappa on the equivalence designation was 0.88 (standard error 0.03, Conclusions: Most data elements in the general eye examination could not be represented precisely using the OMOP CDM. Our work suggests multiple ways to improve the incorporation of important ophthalmology concepts in OMOP, including adding laterality to existing concepts. There exists a strong need to improve the coverage of ophthalmic concepts in source vocabularies so that the OMOP CDM can better accommodate vision research. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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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.