Hallaj Shahin, Halfpenny William, Radgoudarzi Niloofar, Boland Michael V, Swaminathan Swarup S, Wang Sophia Y, Xu Benjamin Y, Amarasekera Dilru C, Stagg Brian, Chen Aiyin, Hribar Michelle, Thakoor Kaveri A, Goetz Kerry E, Myers Jonathan S, Lee Aaron Y, Christopher Mark A, Zangwill Linda M, Weinreb Robert N, Baxter Sally L
Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute.
Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA.
J Glaucoma. 2025 Aug 1;34(8):644-649. doi: 10.1097/IJG.0000000000002575. Epub 2025 Apr 8.
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 health care systems.
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.
SAP source data elements and Digital Imaging and Communications in Medicine (DICOM) standard from 2 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.
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. In addition, 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 (eg, visual field index, pupil diameter, perimeter format Kowa).
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.
在这项多机构合作的工作中，我们发现了医学术语中SAP数据元素存在的差距。我们向LOINC提出了新的概念，以增强SAP数据标准和大数据表示，并提高医疗保健系统之间的互操作性。
识别逻辑观察标识符名称和代码（LOINC）以及观察性医疗结果合作组织（OMOP）通用数据模型（CDM）中标准自动视野计（SAP）数据元素表示方面的差距，并针对这些差距提出解决方案。
从2种常用视野计设备中提取SAP源数据元素和医学数字成像与通信（DICOM）标准，并使用OMOP CDM雅典娜浏览器和使用LOINC浏览器的LOINC与标准化术语中的现有概念进行比较。按照健康级别7快速医疗保健互操作性资源使用的惯例确定并分类差距领域，并在OHDSI眼科护理和视觉研究工作组中进行反复讨论，旨在解决差距。在达成共识后开发新代码，并提议将其纳入LOINC。
在从视野计中提取的107个数据元素中，82%（n = 88）的SAP数据元素缺乏表示。在其余19个元素中，2.8%（n = 3）范围更广，1.9%（n = 2）范围更窄，13%（n = 14）具有等效表示。此外，与SAP相关的116个DICOM属性中只有2.6%（n = 3）在标准化术语中有表示。几个现有的相关代码定义不明确或错误（例如，视野指数、瞳孔直径、Kowa视野计格式）。
标准化医学术语中一些SAP数据元素缺乏表示，这阻碍了互操作性和数据共享。我们识别了差距并提出了添加到LOINC中的新概念，旨在改进SAP数据标准和互操作性。
