Investigative Ophthalmology & Visual Science Cover Image for Volume 64, Issue 8
June 2023
Volume 64, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2023
Advancing toward a common data model in ophthalmology: gap analysis of standard OMOP concepts for the general eye examination
Cindy Cai; William Halfpenny; Michael Boland; Harold Lehmann; Michelle Hribar; Kerry Goetz; Sally Baxter
Author Affiliations & Notes
  • Cindy Cai
    Johns Hopkins Medicine Wilmer Eye Institute, Baltimore, Maryland, United States
  • William Halfpenny
    Division of Biomedical Informatics, University of California San Diego Department of Medicine, La Jolla, California, United States
  • Michael Boland
    Massachusetts Eye and Ear, Boston, Massachusetts, United States
  • Harold Lehmann
    Department of Pediatrics, Johns Hopkins Medicine, Baltimore, Maryland, United States
  • Michelle Hribar
    Office of Data Science and Health Informatics, National Eye Institute, Bethesda, Maryland, United States
    Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University School of Medicine, Portland, Oregon, United States
  • Kerry Goetz
    Office of Data Science and Health Informatics, National Eye Institute, Bethesda, Maryland, United States
  • Sally Baxter
    Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California at San Diego Department of Ophthalmology at the Shiley Eye Institute, La Jolla, California, United States
  • Footnotes
    Commercial Relationships   Cindy Cai None; William Halfpenny None; Michael Boland None; Harold Lehmann None; Michelle Hribar None; Kerry Goetz None; Sally Baxter None
  • Footnotes
    Support  The study was supported by the National Institutes of Health (NIH, Bethesda, MD, USA) Grants DP5OD029610 (SLB), P30EY022589 (SLB), K23EY033440 (CXC), and unrestricted departmental grants from Research to Prevent Blindness (New York, NY, USA). Dr. Cai is the Jonathan and Marcia Javitt Rising Professor of Ophthalmology. The sponsors or funding organizations had no role in the design or conduct of this research.
Investigative Ophthalmology & Visual Science June 2023, Vol.64, 3067. doi:
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      Cindy Cai, William Halfpenny, Michael Boland, Harold Lehmann, Michelle Hribar, Kerry Goetz, Sally Baxter; Advancing toward a common data model in ophthalmology: gap analysis of standard OMOP concepts for the general eye examination. Invest. Ophthalmol. Vis. Sci. 2023;64(8):3067.

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Abstract

Purpose : A common data model (CDM) allows data from disparate sources to be integrated and harmonized to enable multi-institutional research. Aggregation of ophthalmology data has been limited by a lack of standardized representation. This study evaluated the degree of vocabulary coverage of the general eye exam in a widely used electronic health record (EHR) system using the Observational Outcomes Medical Partnership (OMOP) CDM.

Methods : Data elements including structured fields (e.g., anterior chamber) and pre-defined entry values (e.g., cell and flare) from the general eye exam in the Epic foundation system were analyzed. Source data elements were mapped, using the Observational Health Data Sciences and Informatics (OHDSI) tools Usagi and Athena, to OMOP standard concepts. The OMOP concept was given an HL7 concept-map equivalence designation. The OMOP concept was an equal match when it had the same meaning as the source concept, wider when it was missing information, narrower when it was overly specific, and unmatched when there was no match. Initial mappings were reviewed by two ophthalmologists with informatics training. Inter-grader agreement for equivalence designation was calculated using Cohen’s kappa. Agreement on the exact OMOP concept was calculated as a percentage of mapped concepts. Discrepancies were discussed and a final consensus mapping created.

Results : A total of 698 data elements, structured fields (N=210) and pre-defined entry values (N=488), were analyzed. The inter-grader kappa on the equivalence designation was 0.88 (standard error 0.03, p<0.001). There was a 96% agreement on the exact OMOP concept. In the final consensus mapping, 25% (N=177) of the concepts were considered equal, 50% (N=348) wider, 4% (N=25) narrower, and 21% (N=148) unmatched. Of the wider elements, 46% (N=160) were missing the laterality concept, 24% (N=85) had other missing concepts, and 30% (N=103) had both issues.

Conclusions : Most (75%) data elements in the general eye exam could not be represented precisely using the OMOP CDM. Our work suggests multiple ways to improve the coverage of important ophthalmology concepts in OMOP, including adding laterality to existing concepts.

This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.

 

Sample data elements mapped to OMOP concepts, with the corresponding HL7 concept-map equivalence designation, OMOP source vocabulary, and additional explanations.
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Sample data elements mapped to OMOP concepts, with the corresponding HL7 concept-map equivalence designation, OMOP source vocabulary, and additional explanations.

Sample data elements mapped to OMOP concepts, with the corresponding HL7 concept-map equivalence designation, OMOP source vocabulary, and additional explanations.

Sample data elements mapped to OMOP concepts, with the corresponding HL7 concept-map equivalence designation, OMOP source vocabulary, and additional explanations.
View OriginalDownload Slide

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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Copyright © 2025 Association for Research in Vision and Ophthalmology.
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