Department Medical Information Systems, Heidelberg University Hospital, Germany.
GECKO Institute, Heilbronn University of Applied Sciences, Germany.
Stud Health Technol Inform. 2021 May 24;278:134-141. doi: 10.3233/SHTI210061.
Medical routine data promises to add value for research. However, the transfer of this data into a research context is difficult. Therefore, Medical Data Integration Centers are being set up to merge data from primary information systems in a central repository. But, data from one organization is rarely sufficient to answer a research question. The data must be merged beyond institutional boundaries. In order to use this data in a specific research project, a researcher must have the possibility to query available cohort sizes across institutions. A possible solution for this requirement is presented in this paper, using a process for fully automated and distributed feasibility queries (i.e. cohort size estimations). This process is executed according to the open standard BPMN 2.0, the underlying process data model is based on HL7 FHIR R4 resources. The proposed solution is currently being deployed at eight university hospitals and one trusted third party across Germany.
医疗常规数据有望为研究增加价值。然而，将这些数据转化为研究背景是困难的。因此，正在建立医疗数据集成中心，以便将来自主要信息系统的数据合并到中央存储库中。但是，一个组织的数据很少足以回答一个研究问题。数据必须超越机构界限进行合并。为了在特定的研究项目中使用这些数据，研究人员必须能够查询跨机构的可用队列大小。本文提出了一种可能的解决方案，使用一种用于完全自动化和分布式可行性查询（即队列大小估计）的过程。该过程是根据开放标准 BPMN 2.0 执行的，基础过程数据模型基于 HL7 FHIR R4 资源。该解决方案目前正在德国的八所大学医院和一个可信的第三方部署。
