Implementing a Common Data Model in Ophthalmology: Mapping Structured Electronic Health Record Ophthalmic Examination Data to Standard Vocabularies - Justin C. Quon, MD; Christopher P. Long, MD; William Halfpenny, MBBS, MEng; Amy Chuang, MS; Cindy X. Cai, MD, MS; Sally L. Baxter, MD, MSc; Vamsi Daketi, MS; Amanda Schmitz, BS; Neil Bahroos, MS; Benjamin Y. Xu, MD, PhD; Brian C. Toy, MD (Ophthalmology Science)
Objective: To identify and characterize concept coverage gaps of ophthalmology examination data elements within the Cerner Millennium electronic health record (EHR) implementations by the Observational Health Data Sciences and Informatics Observational Medical Outcomes Partnership (OMOP) common data model (CDM). Design: Analysis of data elements in EHRs. Subjects: Not applicable. Methods: Source eye examination data elements from the default Cerner Model Experience EHR and a local implementation of the Cerner Millennium EHR were extracted, classified into one of 8 subject categories, and mapped to the semantically closest standard concept in the OMOP CDM. Mappings were categorized as exact, if the data element and OMOP concept represented equivalent information, wider, if the OMOP concept was missing conceptual granularity, narrower, if the OMOP concept introduced excess information, and unmatched, if no standard concept adequately represented the data element. Descriptive statistics and qualitative analysis were used to describe the concept coverage for each subject category. Main Outcome Measures: Concept coverage gaps in 8 ophthalmology subject categories of data elements by the OMOP CDM. Results: There were 409 and 947 ophthalmology data elements in the default and local Cerner modules, respectively. Of the 409 mappings in the default Cerner module, 25% (n = 102) were exact, 53% (n = 217) were wider, 3% (n = 11) were narrower, and 19% (n = 79) were unmatched. In the local Cerner module, 18% (n = 173) of mappings were exact, 54% (n = 514) were wider, 1% (n = 10) were narrower, and 26% (n = 250) were unmatched. The largest coverage gaps were seen in the local Cerner module under the visual acuity, sensorimotor testing, and refraction categories, with 95%, 95%, and 81% of data elements in each respective category having mappings that were not exact. Concept coverage gaps spanned all 8 categories in both EHR implementations. Conclusions: Considerable coverage gaps by the OMOP CDM exist in all areas of the ophthalmology examination, which should be addressed to improve the OMOP CDM’s effectiveness in ophthalmic research. We identify specific subject categories that may benefit from increased granularity in the OMOP CDM and provide suggestions for facilitating consistency of standard concepts, with the goal of improving data standards in ophthalmology. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.