TY - JOUR AU - Sinaci, A Anil AU - Gencturk, Mert AU - Teoman, Huseyin Alper AU - Laleci Erturkmen, Gokce Banu AU - Alvarez-Romero, Celia AU - Martinez-Garcia, Alicia AU - poblado - plou, Beatriz AU - Carmona-Pírez, Jonás AU - Löbe, Matthias AU - Parra-Calderon, Carlos Luis PY - 2023 DA - 2023/3/8 TI -一种数据转换方法，用于创建可查找、可访问、可互操作和可重用的健康数据:软件设计、开发和评估研究JO - J Med Internet Res SP - e42822 VL - 25 KW - Health Level 7快速医疗互操作性资源KW - HL7 FHIR KW -可查找、可访问、可互操作和可重用原则KW - FAIR原则KW -健康数据共享KW -健康数据转换KW -二次使用AB -背景:由于若干技术、伦理和监管问题，共享健康数据具有挑战性。可查找、可访问、可互操作和可重用(FAIR)指导原则已经概念化，以实现数据互操作性。许多研究提供了实施指南、评估指标和软件，以获得符合fair标准的数据，特别是健康数据集。健康级别7 (HL7)快速医疗保健互操作性资源(FHIR)是一种健康数据内容建模和交换标准。目的:我们的目标是设计一种新的方法，根据FAIR原则提取、转换和加载现有的健康数据集到HL7 FHIR存储库中，开发一个数据管理工具来实施该方法，并对来自两个不同但互补的机构的健康数据集进行评估。我们的目标是通过标准化提高对现有卫生数据集公平原则的遵守程度，并通过消除相关的技术障碍促进卫生数据共享。方法:我们的方法自动处理给定FHIR端点的功能，并在根据FHIR配置文件定义强制的规则配置映射时指导用户。代码系统映射可以配置为术语翻译，通过自动使用FHIR资源。自动检查创建的FHIR资源的有效性，不允许不合法的资源持久化。 At each stage of our data transformation methodology, we used particular FHIR-based techniques so that the resulting data set could be evaluated as FAIR. We performed a data-centric evaluation of our methodology on health data sets from 2 different institutions. Results: Through an intuitive graphical user interface, users are prompted to configure the mappings into FHIR resource types with respect to the restrictions of selected profiles. Once the mappings are developed, our approach can syntactically and semantically transform existing health data sets into HL7 FHIR without loss of data utility according to our privacy-concerned criteria. In addition to the mapped resource types, behind the scenes, we create additional FHIR resources to satisfy several FAIR criteria. According to the data maturity indicators and evaluation methods of the FAIR Data Maturity Model, we achieved the maximum level (level 5) for being Findable, Accessible, and Interoperable and level 3 for being Reusable. Conclusions: We developed and extensively evaluated our data transformation approach to unlock the value of existing health data residing in disparate data silos to make them available for sharing according to the FAIR principles. We showed that our method can successfully transform existing health data sets into HL7 FHIR without loss of data utility, and the result is FAIR in terms of the FAIR Data Maturity Model. We support institutional migration to HL7 FHIR, which not only leads to FAIR data sharing but also eases the integration with different research networks. SN - 1438-8871 UR - //www.mybigtv.com/2023/1/e42822 UR - https://doi.org/10.2196/42822 UR - http://www.ncbi.nlm.nih.gov/pubmed/36884270 DO - 10.2196/42822 ID - info:doi/10.2196/42822 ER -