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IF 3.2 Q1 OPHTHALMOLOGY

Ophthalmology science Pub Date : 2024-11-28 DOI:10.1016/j.xops.2024.100666

Justin C. Quon MD , Christopher P. Long MD , William Halfpenny MBBS, MEng , Amy Chuang MS , Cindy X. Cai MD, MS , Sally L. Baxter MD, MSc , Vamsi Daketi MS , Amanda Schmitz BS , Neil Bahroos MS , Benjamin Y. Xu MD, PhD , Brian C. Toy MD

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引用次数: 0

摘要

目的通过观察性健康数据科学和信息学观察性医疗结果合作组织（OMOP）通用数据模型（CDM），确定 Cerner Millennium 电子健康记录（EHR）实施中眼科检查数据元素的概念覆盖差距，并描述其特征：设计：分析电子病历中的数据元素：不适用：从默认的 Cerner Model Experience EHR 和本地实施的 Cerner Millennium EHR 中提取眼科检查数据元素，将其归类为 8 个主题类别之一，并映射到 OMOP CDM 中语义最接近的标准概念。如果数据元素和 OMOP 概念代表了等同的信息，则将映射归类为精确映射；如果 OMOP 概念缺少概念粒度，则将映射归类为较宽映射；如果 OMOP 概念引入了多余的信息，则将映射归类为较窄映射；如果没有标准概念能充分代表数据元素，则将映射归类为不匹配映射。描述性统计和定性分析用于描述每个主题类别的概念覆盖情况：主要结果指标：OMOP CDM 对 8 个眼科学科类别数据元素的概念覆盖差距：默认和本地 Cerner 模块中分别有 409 和 947 个眼科数据元素。在默认 Cerner 模块的 409 个映射中，25%（n = 102）是精确映射，53%（n = 217）是较宽映射，3%（n = 11）是较窄映射，19%（n = 79）是不匹配映射。在本地 Cerner 模块中，18%（n = 173）的映射是精确的，54%（n = 514）的映射较宽，1%（n = 10）的映射较窄，26%（n = 250）的映射不匹配。本地 Cerner 模块中视力、感觉运动测试和屈光类别的覆盖差距最大，每个类别中分别有 95%、95% 和 81% 的数据元素的映射不精确。在两个电子病历实施方案中，概念覆盖差距横跨所有 8 个类别：结论：OMOP CDM 在眼科检查的所有领域都存在相当大的覆盖差距，应加以解决，以提高 OMOP CDM 在眼科研究中的有效性。我们确定了可从提高 OMOP CDM 的细化程度中获益的特定主题类别，并就促进标准概念的一致性提出了建议，目的是提高眼科数据标准：专有或商业信息披露见本文末尾的脚注和披露。

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

Objective

To identify and characterize concept coverage gaps of ophthalmology examination data elements within the Cerner Millennium electronic health record (EHR) implementations by the Observational Health Data Sciences and Informatics Observational Medical Outcomes Partnership (OMOP) common data model (CDM).

Design

Analysis of data elements in EHRs.

Subjects

Not applicable.

Methods

Source eye examination data elements from the default Cerner Model Experience EHR and a local implementation of the Cerner Millennium EHR were extracted, classified into one of 8 subject categories, and mapped to the semantically closest standard concept in the OMOP CDM. Mappings were categorized as exact, if the data element and OMOP concept represented equivalent information, wider, if the OMOP concept was missing conceptual granularity, narrower, if the OMOP concept introduced excess information, and unmatched, if no standard concept adequately represented the data element. Descriptive statistics and qualitative analysis were used to describe the concept coverage for each subject category.

Main Outcome Measures

Concept coverage gaps in 8 ophthalmology subject categories of data elements by the OMOP CDM.

Results

There were 409 and 947 ophthalmology data elements in the default and local Cerner modules, respectively. Of the 409 mappings in the default Cerner module, 25% (n = 102) were exact, 53% (n = 217) were wider, 3% (n = 11) were narrower, and 19% (n = 79) were unmatched. In the local Cerner module, 18% (n = 173) of mappings were exact, 54% (n = 514) were wider, 1% (n = 10) were narrower, and 26% (n = 250) were unmatched. The largest coverage gaps were seen in the local Cerner module under the visual acuity, sensorimotor testing, and refraction categories, with 95%, 95%, and 81% of data elements in each respective category having mappings that were not exact. Concept coverage gaps spanned all 8 categories in both EHR implementations.

Conclusions

Considerable coverage gaps by the OMOP CDM exist in all areas of the ophthalmology examination, which should be addressed to improve the OMOP CDM’s effectiveness in ophthalmic research. We identify specific subject categories that may benefit from increased granularity in the OMOP CDM and provide suggestions for facilitating consistency of standard concepts, with the goal of improving data standards in ophthalmology.