Shahin Hallaj, William Halfpenny, Niloofar Radgoudarzi, Michael V Boland, Swarup S Swaminathan, Sophia Y Wang, Benjamin Y Xu, Dilru C Amarasekera, Brian Stagg, Aiyin Chen, Michelle Hribar, Kaveri A Thakoor, Kerry E Goetz, Jonathan S Myers, Aaron Y Lee, Mark A Christopher, Linda M Zangwill, Robert N Weinreb, Sally L Baxter
{"title":"Gap Analysis of Standard Automated Perimetry Concept Representation in Medical Terminologies.","authors":"Shahin Hallaj, William Halfpenny, Niloofar Radgoudarzi, Michael V Boland, Swarup S Swaminathan, Sophia Y Wang, Benjamin Y Xu, Dilru C Amarasekera, Brian Stagg, Aiyin Chen, Michelle Hribar, Kaveri A Thakoor, Kerry E Goetz, Jonathan S Myers, Aaron Y Lee, Mark A Christopher, Linda M Zangwill, Robert N Weinreb, Sally L Baxter","doi":"10.1097/IJG.0000000000002575","DOIUrl":null,"url":null,"abstract":"<p><strong>Precis: </strong>In this multi-institutional effort, we identified gaps in SAP data elements within medical terminologies. We proposed new concepts to LOINC to enhance SAP data standards and big data representation and improve interoperability across healthcare systems.</p><p><strong>Purpose: </strong>To identify gaps in the representation of Standard Automated Perimetry (SAP) data elements in Logical Observation Identifiers Names and Codes (LOINC) and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and propose solutions for those gaps.</p><p><strong>Methods: </strong>SAP source data elements and Digital Imaging and Communications in Medicine (DICOM) standard from two commonly used perimeter devices were extracted and compared against existing concepts in standardized terminologies using the OMOP CDM Athena browser and LOINC using the LOINC browser. Gap areas were identified and classified following conventions used by Health Level 7 Fast Healthcare Interoperability Resources and discussed within the OHDSI Eye Care and Vision Research Workgroup in iterative rounds aiming to address gaps. New codes were developed upon reaching a consensus and proposed for inclusion in LOINC.</p><p><strong>Results: </strong>Among 107 data elements extracted from the perimeters, 82%(n=88) of SAP data elements lacked representation. Of the 19 remaining elements, 2.8%(n=3) were wider, 1.9%(n=2) were narrower, and 13%(n=14) had equivalent representation. Additionally, only 2.6%(n=3) of the 116 DICOM attributes related to SAP had representation in standardized terminologies. Several existing relevant codes were defined ambiguously or erroneously (e.g., visual field index, pupil diameter, perimeter format Kowa). New codes closely aligning with DICOM standard for SAP were proposed for addition to LOINC.</p><p><strong>Conclusion: </strong>There is a lack of representation of some SAP data elements in standardized medical terminologies, hampering interoperability and data-sharing. We identified gaps and proposed new concepts for addition to LOINC, aiming to improve SAP data standards and interoperability.</p>","PeriodicalId":15938,"journal":{"name":"Journal of Glaucoma","volume":" ","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Glaucoma","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/IJG.0000000000002575","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Precis: In this multi-institutional effort, we identified gaps in SAP data elements within medical terminologies. We proposed new concepts to LOINC to enhance SAP data standards and big data representation and improve interoperability across healthcare systems.
Purpose: To identify gaps in the representation of Standard Automated Perimetry (SAP) data elements in Logical Observation Identifiers Names and Codes (LOINC) and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and propose solutions for those gaps.
Methods: SAP source data elements and Digital Imaging and Communications in Medicine (DICOM) standard from two commonly used perimeter devices were extracted and compared against existing concepts in standardized terminologies using the OMOP CDM Athena browser and LOINC using the LOINC browser. Gap areas were identified and classified following conventions used by Health Level 7 Fast Healthcare Interoperability Resources and discussed within the OHDSI Eye Care and Vision Research Workgroup in iterative rounds aiming to address gaps. New codes were developed upon reaching a consensus and proposed for inclusion in LOINC.
Results: Among 107 data elements extracted from the perimeters, 82%(n=88) of SAP data elements lacked representation. Of the 19 remaining elements, 2.8%(n=3) were wider, 1.9%(n=2) were narrower, and 13%(n=14) had equivalent representation. Additionally, only 2.6%(n=3) of the 116 DICOM attributes related to SAP had representation in standardized terminologies. Several existing relevant codes were defined ambiguously or erroneously (e.g., visual field index, pupil diameter, perimeter format Kowa). New codes closely aligning with DICOM standard for SAP were proposed for addition to LOINC.
Conclusion: There is a lack of representation of some SAP data elements in standardized medical terminologies, hampering interoperability and data-sharing. We identified gaps and proposed new concepts for addition to LOINC, aiming to improve SAP data standards and interoperability.
期刊介绍：
The Journal of Glaucoma is a peer reviewed journal addressing the spectrum of issues affecting definition, diagnosis, and management of glaucoma and providing a forum for lively and stimulating discussion of clinical, scientific, and socioeconomic factors affecting care of glaucoma patients.
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