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Implementing a Common Data Model in Ophthalmology: Mapping Structured Electronic Health Record Ophthalmic Examination

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Summary

The Observational Health Data Sciences and Informatics Observational Medical Outcomes Partnership (OMOP) common data model (CDM) has significant concept coverage gaps in ophthalmology examination data within Cerner electronic health records (EHRs). Addressing these gaps is crucial for enhancing ophthalmic research using standardized data.

Keywords:
Common data modelData standardsElectronic health recordOHDSIOMOP

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Area of Science:

  • Ophthalmology
  • Health Informatics
  • Electronic Health Records (EHRs)

Background:

  • Standardized data models are essential for multi-site observational research.
  • The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) aims to standardize health data.
  • Cerner Millennium is a widely used EHR system in clinical practice.

Purpose of the Study:

  • To identify and characterize concept coverage gaps in ophthalmology examination data elements.
  • To evaluate the alignment of Cerner EHR data with the OMOP CDM.
  • To inform improvements in the OMOP CDM for ophthalmic research.

Main Methods:

  • Extracted ophthalmology data elements from default and local Cerner EHR implementations.
  • Classified data elements into 8 subject categories.
  • Mapped data elements to the OMOP CDM, categorizing mappings as exact, wider, narrower, or unmatched.

Main Results:

  • Significant concept coverage gaps were found across all 8 ophthalmology categories in both EHR implementations.
  • The local Cerner module showed higher percentages of wider (54%) and unmatched (26%) mappings compared to the default module.
  • Specific categories like visual acuity, sensorimotor testing, and refraction exhibited substantial gaps, with over 80% of elements not having exact mappings.

Conclusions:

  • The OMOP CDM has considerable coverage gaps for ophthalmology examination data within Cerner EHRs.
  • Improvements in OMOP CDM granularity and concept standardization are needed for effective ophthalmic research.
  • Recommendations are provided to enhance data standards in ophthalmology.