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The evaluation of real-world data (RWD) enables insights to be gained from a wide range of patient data collected in routine clinical practice. In addition, multicenter analyses represent a broad and representative patient population and have the potential to capture the actual treatment situation. As a basis for this, the definition of datasets and an infrastructure for data exchange is necessary. Data integration centers (DIC) have already been established at (university) hospitals throughout Germany in order to extract RWD for scientific analyses from the various source systems and integrate them into research-compatible data infrastructures. The project described here aims to demonstrate the added value of this data integration using a case of application in ophthalmology, defining a core dataset as an ophthalmology extension module and establishing a cross-site data exchange infrastructure. As a first step, the treatment success of eye diseases treated with intravitreal injection (IVI) should be improved. To achieve this goal a dashboard for clinical data is provided that clearly visualizes the merged data. Furthermore, algorithms will be developed to identify new imaging biomarkers that can be used for treatment monitoring and predict treatment outcomes., Competing Interests: Einhaltung ethischer Richtlinien Interessenkonflikt L. Holtrup, J. Varghese, A.K. Schuster, B. Poschkamp, C. Hampf, D. Waltemath, L. Wahle, M. Lipprandt, D.A. Merle, P. Berens, O. Kohlbacher, V. Kakkassery, M. Bartos, F. Ziemssen, T. Wendt, K. Hoffmann und N. Eter geben an, dass kein Interessenkonflikt besteht.Für diesen Beitrag wurden von den Autor/-innen keine Studien an Menschen oder Tieren durchgeführt. Für die aufgeführten Studien gelten die jeweils dort angegebenen ethischen Richtlinien., (© 2024. The Author(s).)

Health data are extremely important in today's data-driven world. Through automation, healthcare processes can be optimized, and clinical decisions can be supported. For any reuse of data, the quality, validity, and trustworthiness of data are essential, and it is the only way to guarantee that data can be reused sensibly. Specific requirements for the description and coding of reusable data are defined in the FAIR guiding principles for data stewardship. Various national research associations and infrastructure projects in the German healthcare sector have already clearly positioned themselves on the FAIR principles: both the infrastructures of the Medical Informatics Initiative and the University Medicine Network operate explicitly on the basis of the FAIR principles, as do the National Research Data Infrastructure for Personal Health Data and the German Center for Diabetes Research.To ensure that a resource complies with the FAIR principles, the degree of FAIRness should first be determined (so-called FAIR assessment), followed by the prioritization for improvement steps (so-called FAIRification). Since 2016, a set of tools and guidelines have been developed for both steps, based on the different, domain-specific interpretations of the FAIR principles.Neighboring European countries have also invested in the development of a national framework for semantic interoperability in the context of the FAIR (Findable, Accessible, Interoperable, Reusable) principles. Concepts for comprehensive data enrichment were developed to simplify data analysis, for example, in the European Health Data Space or via the Observational Health Data Sciences and Informatics network. With the support of the European Open Science Cloud, among others, structured FAIRification measures have already been taken for German health datasets., (© 2024. The Author(s).)