Neumann Daniel, Gebler Richard, Kiederle Jana, Beck Jördis, Aubele Fabio, Struebing Alexander, Schmidt Florian, Reusche Matthias, Koester Helene, Loeffler Markus, Staeubert Sebastian
Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University Leipzig, Germany.
Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany.
Stud Health Technol Inform. 2025 Sep 3;331:378-385. doi: 10.3233/SHTI251418.
Distributed healthcare research infrastructures face significant challenges when translating routine clinical data into harmonized, research-ready formats using HL7 FHIR standards.
Existing FHIR-based pipelines such as the SMART/HL7 FHIR Bulk Data Access API, FHIR-to-OMOP mappings, and analytical services like Pathling demonstrate technical feasibility. However, most assume semantically valid FHIR data, operate within single-institution settings, and lack practical guidance for deployment across heterogeneous, regulated environments. Technical Framework and Deployment: Within the German Medical Informatics Initiative (MII) and the INTERPOLAR project, we developed an open, modular, and participatory toolchain for decentralized FHIR-based data transformation and export across multiple Data Integration Centers (DICs). The toolchain supports FHIR extraction, profile-based transformation, REDCap integration, and OMOP-compatible export. Deployment required adapting to local infrastructures, regulatory boundaries (e.g., de-identified FHIR stores, restricted network access), and clinical domain needs. Configurable modules, proxy support, and site-specific adaptations were essential for integration into operational hospital workflows.
Key lessons include the necessity of early access to real data, the limitations of synthetic test data, the value of joint workshops for profile interpretation, and the need for adaptable validation tooling. Organizational knowledge gaps, inconsistent FHIR implementations, and performance issues in resource flattening were addressed through co-design and iterative rollout strategies. Validator modules are essential across technical, content, and cross-site consistency levels.
Centralized development paired with decentralized, participatory deployment enables scalable, GDPR-compliant infrastructures for embedded clinical research. This approach offers a replicable framework for future multi-site initiatives aiming to leverage real-world data across diverse environments.
分布式医疗研究基础设施在使用HL7 FHIR标准将常规临床数据转换为统一的、可供研究使用的格式时面临重大挑战。
现有的基于FHIR的管道，如SMART/HL7 FHIR批量数据访问应用程序编程接口、FHIR到OMOP的映射，以及像Pathling这样的分析服务，都证明了技术可行性。然而，大多数都假定FHIR数据在语义上是有效的，在单一机构环境中运行，并且缺乏在异构的、受监管的环境中进行部署的实用指南。技术框架与部署：在德国医学信息学倡议（MII）和INTERPOLAR项目中，我们开发了一个开放、模块化且具有参与性的工具链，用于在多个数据集成中心（DIC）进行基于FHIR的分散式数据转换和导出。该工具链支持FHIR提取、基于配置文件的转换、REDCap集成以及与OMOP兼容的导出。部署需要适应本地基础设施、监管边界（例如，去标识化的FHIR存储库、受限的网络访问）以及临床领域需求。可配置模块、代理支持以及针对特定站点的调整对于集成到医院运营工作流程中至关重要。
关键经验包括尽早获取真实数据的必要性、合成测试数据的局限性、联合研讨会对配置文件解释的价值，以及对适应性验证工具的需求。通过共同设计和迭代推出策略解决了组织知识差距、FHIR实施不一致以及资源扁平化中的性能问题。验证器模块在技术、内容和跨站点一致性层面都是必不可少的。
集中式开发与分散式、参与式部署相结合，能够实现可扩展的、符合通用数据保护条例（GDPR）的嵌入式临床研究基础设施。这种方法为未来旨在在不同环境中利用真实世界数据的多站点计划提供了一个可复制的框架。
