Feasibility of cross-vendor linkage of ophthalmic images with electronic health record data: an analysis from the IRIS Registry®.

IF 2.5 Q2 HEALTH CARE SCIENCES & SERVICES JAMIA Open Pub Date : 2024-01-25 eCollection Date: 2024-04-01 DOI:10.1093/jamiaopen/ooae005
Michael Mbagwu, Zhongdi Chu, Durga Borkar, Alex Koshta, Nisarg Shah, Aracelis Torres, Hylton Kalvaria, Flora Lum, Theodore Leng
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Abstract

Purpose: To link compliant, universal Digital Imaging and Communications in Medicine (DICOM) ophthalmic imaging data at the individual patient level with the American Academy of Ophthalmology IRIS® Registry (Intelligent Research in Sight).

Design: A retrospective study using de-identified EHR registry data.

Subjects participants controls: IRIS Registry records.

Materials and methods: DICOM files of several imaging modalities were acquired from two large retina ophthalmology practices. Metadata tags were extracted and harmonized to facilitate linkage to the IRIS Registry using a proprietary, heuristic patient-matching algorithm, adhering to HITRUST guidelines. Linked patients and images were assessed by image type and clinical diagnosis. Reasons for failed linkage were assessed by examining patients' records.

Main outcome measures: Success rate of linking clinicoimaging and EHR data at the patient level.

Results: A total of 2 287 839 DICOM files from 54 896 unique patients were available. Of these, 1 937 864 images from 46 196 unique patients were successfully linked to existing patients in the registry. After removing records with abnormal patient names and invalid birthdates, the success linkage rate was 93.3% for images. 88.2% of all patients at the participating practices were linked to at least one image.

Conclusions and relevance: Using identifiers from DICOM metadata, we created an automated pipeline to connect longitudinal real-world clinical data comprehensively and accurately to various imaging modalities from multiple manufacturers at the patient and visit levels. The process has produced an enriched and multimodal IRIS Registry, bridging the gap between basic research and clinical care by enabling future applications in artificial intelligence algorithmic development requiring large linked clinicoimaging datasets.

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眼科图像与电子健康记录数据跨供应商链接的可行性:IRIS Registry® 分析。
目的:将符合要求的、通用的医学数字成像和通信(DICOM)眼科成像数据在患者个人层面与美国眼科学会 IRIS® 注册表(视力智能研究)连接起来:设计:一项使用去标识化电子病历登记数据的回顾性研究:材料与方法:从两家大型视网膜眼科诊所获取了多种成像模式的 DICOM 文件。根据 HITRUST 指南,使用专有的启发式患者匹配算法提取并统一元数据标签,以便与 IRIS 注册表建立链接。通过图像类型和临床诊断对链接的患者和图像进行评估。通过检查患者的病历来评估连接失败的原因:主要结果测量指标:在患者层面连接临床影像和电子病历数据的成功率:共有来自 54 896 名患者的 2 287 839 份 DICOM 文件。其中,46 196 名患者的 1 937 864 张图像与登记册中的现有患者成功建立了链接。在删除了病人姓名异常和出生日期无效的记录后,图像的成功链接率为 93.3%。参与实践的所有患者中有 88.2% 至少与一幅图像建立了链接:利用 DICOM 元数据中的标识符,我们创建了一个自动管道,可在患者和就诊级别将纵向真实世界临床数据全面、准确地与来自多个制造商的各种成像模式连接起来。这一过程产生了一个丰富的多模态 IRIS 注册表,通过在需要大型链接临床成像数据集的人工智能算法开发中的未来应用,在基础研究和临床护理之间架起了一座桥梁。
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来源期刊
JAMIA Open
JAMIA Open Medicine-Health Informatics
CiteScore
4.10
自引率
4.80%
发文量
102
审稿时长
16 weeks
期刊最新文献
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