Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany.
Computational Biology & Simulation, Technical University Darmstadt, Darmstadt, Germany.
Stud Health Technol Inform. 2022 Aug 17;296:41-49. doi: 10.3233/SHTI220802.
The integration of routine medical care data into research endeavors promises great value. However, access to this extra-domain data is constrained by numerous technical and legal requirements. The German Medical Informatics Initiative (MII) - initiated by the Federal Ministry of Research and Education (BMBF) - is making progress in setting up Medical Data Integration Centers to consolidate data stored in clinical primary information systems. Unfortunately, for many research questions cross-organizational data sources are required, as one organization's data is insufficient, especially in rare disease research. A first step, for research projects exploring possible multi-centric study designs, is to perform a feasibility query, i.e., a cohort size calculation transcending organizational boundaries. Existing solutions for this problem, like the previously introduced feasibility process for the MII's HiGHmed consortium, perform well for most use cases. However, there exist use cases where neither centralized data repositories, nor Trusted Third Parties are acceptable for data aggregation. Based on open standards, such as BPMN 2.0 and HL7 FHIR R4, as well as the cryptographic techniques of secure Multi-Party Computation, we introduce a fully automated, decentral feasibility query process without any central component or Trusted Third Party. The open source implementation of the proposed solution is intended as a plugin process to the HiGHmed Data Sharing Framework. The process's concept and underlying algorithms can also be used independently.
将常规医疗保健数据集成到研究工作中具有很大的价值。然而，访问这些额外领域的数据受到众多技术和法律要求的限制。德国医学信息学倡议（MII）-由联邦研究与教育部（BMBF）发起-正在建立医疗数据集成中心方面取得进展，以整合存储在临床初级信息系统中的数据。不幸的是，对于许多研究问题，需要跨组织数据源，因为一个组织的数据是不够的，特别是在罕见病研究中。对于探索可能的多中心研究设计的研究项目，第一步是执行可行性查询，即跨越组织边界的队列大小计算。对于这个问题的现有解决方案，例如之前介绍的 MII 的 HiGHmed 联盟的可行性流程，对于大多数用例都表现良好。然而，存在一些用例，其中数据聚合既不能接受集中式数据存储库，也不能接受可信第三方。基于开放标准，如 BPMN 2.0 和 HL7 FHIR R4 以及安全多方计算的加密技术，我们引入了一个完全自动化的、去中心化的可行性查询过程，没有任何中央组件或可信第三方。所提出解决方案的开源实现旨在成为 HiGHmed 数据共享框架的插件过程。该过程的概念和基础算法也可以独立使用。
