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支持可扩展基础设施,实现可重复的全国范围医疗保健数据分析,以实现 FAIR 治理。

Scalable Infrastructure Supporting Reproducible Nationwide Healthcare Data Analysis toward FAIR Stewardship.

机构信息

Big Data Department, Health Insurance Review and Assessment Service, Wonju, Republic of Korea.

Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea.

出版信息

Sci Data. 2023 Oct 4;10(1):674. doi: 10.1038/s41597-023-02580-7.

Abstract

Transparent and FAIR disclosure of meta-information about healthcare data and infrastructure is essential but has not been well publicized. In this paper, we provide a transparent disclosure of the process of standardizing a common data model and developing a national data infrastructure using national claims data. We established an Observational Medical Outcome Partnership (OMOP) common data model database for national claims data of the Health Insurance Review and Assessment Service of South Korea. To introduce a data openness policy, we built a distributed data analysis environment and released metadata based on the FAIR principle. A total of 10,098,730,241 claims and 56,579,726 patients' data were converted as OMOP common data model. We also built an analytics environment for distributed research and made the metadata publicly available. Disclosure of this infrastructure to researchers will help to eliminate information inequality and contribute to the generation of high-quality medical evidence.

摘要

透明和公平地披露医疗数据和基础设施的元信息至关重要,但尚未得到充分宣传。本文提供了一个透明的过程,用于使用国家索赔数据对通用数据模型进行标准化,并开发国家数据基础架构。我们为韩国健康保险审查和评估服务的国家索赔数据建立了观察性医疗结局伙伴关系(OMOP)通用数据模型数据库。为了引入数据开放政策,我们构建了一个基于 FAIR 原则的分布式数据分析环境并发布了元数据。共有 10098730241 份索赔和 56579726 名患者的数据转换为 OMOP 通用数据模型。我们还构建了一个用于分布式研究的分析环境,并公开了元数据。向研究人员公开此基础架构将有助于消除信息不平等,并有助于生成高质量的医学证据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aeeb/10550904/a69922c39d1c/41597_2023_2580_Fig1_HTML.jpg

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