Advancing Toward a Common Data Model in Ophthalmology

Objective or PurposeEvaluate the degree of concept coverage of the general eye exam in one widely used electronic health record (EHR) system using the Observational Health Data Sciences and Informatics (OHDSI) Observational Outcomes Medical Partnership (OMOP) common data model (CDM).DesignStudy of data elements Subjects, Participants, and/or Controls: Not applicableMethods, Intervention, or TestingData elements (field names and pre-defined entry values) from the general eye exam in the Epic foundation system were mapped to OMOP concepts and analyzed. Each mapping was given a Health Level 7 (HL7®) equivalence designation–equal when the OMOP concept had the same meaning as the source EHR 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 graders. Inter-grader agreement for equivalence designation was calculated using Cohen’s kappa. Agreement on the mapped OMOP concept was calculated as a percentage of total mappable concepts. Discrepancies were discussed and a final consensus created. Quantitative analysis was performed on wider and unmatched concepts.Main Outcome MeasuresGaps in OMOP concept coverage of EHR elements, and inter-grader agreement of mapped OMOP concepts.ResultsA total of 698 data elements (210 fields, 488 values) from the EHR 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 mapped OMOP concept. In the final consensus mapping, 25% (1% fields, 31% values) of the EHR to OMOP concept mappings were considered equal, 50% (27% fields, 60% values) wider, 4% (8% fields, 2% values) narrower, and 21% (52% fields, 8% values) unmatched. Of the wider mapped elements, 46% were missing the laterality specification, 24% had other missing attributes, and 30% had both issues. Wider and unmatched EHR elements could be found in all areas of the general eye exam.ConclusionsMost data elements in the general eye exam 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.