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Gap Analysis of Standard Automated Perimetry Concept Representation in Medical Terminologies
Shahin Hallaj1,2, William Halfpenny1,2, Niloofar Radgoudarzi1,2
1Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute.
Standard Automated Perimetry (SAP) data elements have significant gaps in current medical terminologies, hindering data sharing. New concepts were proposed for LOINC to improve SAP data standards and healthcare interoperability.
Area of Science:
- Ophthalmology
- Medical Informatics
- Health Data Standards
Background:
- Standard Automated Perimetry (SAP) is crucial for diagnosing and monitoring visual field defects.
- Current medical terminologies like LOINC and OMOP CDM have limitations in representing SAP data elements.
- This deficiency impacts data interoperability and large-scale health data analysis.
Purpose of the Study:
- To identify and address gaps in the representation of Standard Automated Perimetry (SAP) data elements within the Logical Observation Identifiers Names and Codes (LOINC) and Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).
- To propose solutions for enhancing SAP data representation and improving interoperability across healthcare systems.
Main Methods:
- Extracted SAP data elements and DICOM attributes from two perimeter devices.
- Compared extracted elements against existing concepts in LOINC and OMOP CDM using respective browsers.
- Identified and classified data gaps, developing new LOINC concepts through consensus within the OHDSI Eye Care and Vision Research Workgroup.
Main Results:
- 82% of extracted SAP data elements lacked representation in standardized terminologies.
- Only 2.6% of related DICOM attributes were represented.
- Existing codes were found to be ambiguous or erroneous, necessitating new, standardized concepts aligned with DICOM.
Conclusions:
- Significant gaps exist in the representation of SAP data elements within current medical terminologies.
- Proposed new LOINC concepts aim to enhance SAP data standards and big data representation.
- Addressing these gaps is essential for improving data interoperability and facilitating research in ophthalmology.
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