Department Medical Information Systems, Heidelberg University Hospital, Germany.
GECKO Institute, Heilbronn University of Applied Sciences, Germany.
Stud Health Technol Inform. 2021 Sep 21;283:111-118. doi: 10.3233/SHTI210548.
Medical routine data has the potential to benefit research. However, transferring this data into a research context is difficult. For this reason Medical Data Integration Centers are being established in German university hospitals to consolidate data from primary information systems in a single location. But, small data-sets from one organization can be insufficient to answer a research question adequately. In order to obtain larger data-sets, attempts to merge and provide data-sets across institutional boundaries are made. Therefore, this paper proposes a possible process that can extract, merge, pseudonymize and provide distributed data-sets from several organizations conforming to privacy regulations. This process is executed according to the open standard BPMN 2.0, the underlying process data model is based on HL7 FHIR R4. The proposed solution is currently being deployed at eight university hospitals and one Trusted Third Party in the HiGHmed consortium.
医疗常规数据具有有益于研究的潜力。然而，将这些数据转化到研究环境中是很困难的。出于这个原因，德国大学医院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整合到一个位置。但是，来自一个组织的小数据集可能不足以充分回答研究问题。为了获得更大的数据集，人们试图合并和提供跨机构边界的数据。因此，本文提出了一种可能的过程，可以根据隐私法规从几个组织中提取、合并、假名化和提供分布式数据集。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。所提出的解决方案目前正在 HiGHmed 联盟的八所大学医院和一个可信第三方中部署。
