Ludwig Melanie, Schneider Katharina, Heß Steffen, Broich Karl
Forschungsdatenzentrum, Bundesinstitut für Arzneimittel und Medizinprodukte (BfArM), Kurt-Georg-Kiesinger Allee 3, 53175, Bonn, Deutschland.
Bundesinstitut für Arzneimittel und Medizinprodukte (BfArM), Kurt-Georg-Kiesinger-Allee 3, 53175, Bonn, Deutschland.
Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2024 Feb;67(2):131-138. doi: 10.1007/s00103-023-03831-z. Epub 2024 Jan 12.
The analysis of real-world data (RWD) has become increasingly important in health research in recent years. With the BfArM Health Data Lab (HDL), which is currently being set up, researchers will in future be able to gain access to routine data from the statutory health insurance of around 74 million people in Germany. Data from electronic patient records can also be made available for research prospectively. In doing so, the Health Data Lab guarantees the highest data protection and IT security standards. The digital application process, the provision of data in secure processing environments as well as the features supporting the analyses such as catalogues of coding systems, a point-and-click analysis tool and predefined standard analyses increase user-friendliness for researchers. The use of the extensive health data accessible at HDL will open a wide range of future possibilities for improving the health system and the quality of care. This article begins by highlighting the advantages of the HDL and outlining the opportunities that the RWD offers for research in healthcare and for the population. The structure and central aspects of the HDL are explained afterwards. An outlook on the opportunities of linking different data is given. What the application and data usage processes at the HDL will look like is illustrated using the example of fictitious possibilities for analysing long COVID based on the routine data available at the HDL in the future.
近年来,真实世界数据(RWD)分析在健康研究中变得越来越重要。随着目前正在建立的德国联邦药品和医疗器械研究所健康数据实验室(HDL),研究人员未来将能够获取来自德国约7400万人法定医疗保险的常规数据。电子病历数据也可前瞻性地用于研究。在此过程中,健康数据实验室保证了最高的数据保护和信息技术安全标准。数字申请流程、在安全处理环境中提供数据以及支持分析的功能,如编码系统目录、点击式分析工具和预定义的标准分析,提高了对研究人员的用户友好性。利用HDL可获取的大量健康数据将为改善卫生系统和护理质量开辟广泛的未来可能性。本文首先强调了HDL的优势,并概述了RWD为医疗保健研究和人群提供的机会。之后解释了HDL的结构和核心方面。给出了链接不同数据机会的展望。以未来基于HDL现有常规数据分析长期新冠的虚拟可能性为例,说明了HDL的应用和数据使用流程。