Bockhacker Markus, Martens Peter, von Münchow Clara, Löser Sarah, Günther Rosita, Kuhlen Ralf, Kannt Olaf, Ortleb Sebastian
Helios Kliniken, Friedrichstraße 136, Berlin, 10114, Germany, 49 305213210.
Helios IT Service GmbH, Berlin, Germany.
JMIR Med Inform. 2025 Sep 12;13:e69853. doi: 10.2196/69853.
Increasing adoption of electronic health records (EHRs) enables research on real-world data. In Germany, this has been limited to university hospitals, and data from acute care hospitals below the university level are lacking. To address this, we used established design patterns to build a data platform that aggregates and standardizes pseudonymized EHR data with patients' consent.
We report on the design and implementation of the research platform, as well as patient participation and lessons learned during the scaling of the platform, to incorporate real-world data (with participant consent) from 77 hospitals into a unified data lake.
Due to variations in EHR adoption, IT infrastructure, software vendors, interface availability, and regulatory requirements, we used an agile development cycle that involves constant, incremental standardization of data. We implemented a layered lambda infrastructure built on Apache Hadoop. Decentralized connectors ensured data minimization and pseudonymization.
We successfully scaled our data model both vertically and horizontally in 77 hospitals. However, we encountered issues during the scaling of real-time data pipelines and IHE (Integrating the Healthcare Enterprise) interfaces. During the first 2 years, patients were asked to consent to secondary data use 1,475,244 times during inpatient admission. We registered 1,023,633 broad instances of consent (consent rate 70.2%).
Patients are generally willing to provide consent for secondary use of their data, but obtaining consent requires considerable effort. Building a research data platform is not an end goal, but rather a necessary step in collecting and standardizing longitudinal data to enable research on real-world data. Through the combination of agile development, phased rollouts, and very high levels of automation, we have been able to achieve fast turnaround times for incorporating user feedback and are constantly improving data quality and standardization.
电子健康记录（EHR）的采用率不断提高，使得对真实世界数据的研究成为可能。在德国，这一应用仅限于大学医院，缺乏来自大学以下急性护理医院的数据。为了解决这一问题，我们利用既定的设计模式构建了一个数据平台，该平台在患者同意的情况下聚合并标准化假名化的EHR数据。
我们报告研究平台的设计与实施，以及在平台扩展过程中的患者参与情况和经验教训，以便将来自77家医院的真实世界数据（经参与者同意）纳入统一的数据湖。
由于EHR采用情况、IT基础设施、软件供应商、接口可用性和监管要求存在差异，我们采用了敏捷开发周期，其中涉及数据的持续增量标准化。我们实施了基于Apache Hadoop构建的分层lambda基础设施。分散式连接器确保了数据最小化和假名化。
我们成功地在77家医院对数据模型进行了纵向和横向扩展。然而，我们在实时数据管道和整合医疗卫生企业（IHE）接口的扩展过程中遇到了问题。在最初的两年里，患者在住院期间被要求同意二次使用数据1475244次。我们记录了1023633次广泛的同意实例（同意率70.2%）。
患者通常愿意同意二次使用其数据，但获得同意需要付出相当大的努力。构建研究数据平台不是最终目标，而是收集和标准化纵向数据以实现对真实世界数据进行研究的必要步骤。通过敏捷开发、分阶段推出和高度自动化的结合，我们能够快速响应以纳入用户反馈，并不断提高数据质量和标准化程度。
