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Data Integration for Future Medicine (DIFUTURE).

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    The Data Integration for Future Medicine (DIFUTURE) initiative establishes Data Integration Centers (DICs) to enable secure data sharing for personalized medicine. This infrastructure accelerates innovation and improves patient care through integrated data access for research and clinical decision-making.

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    Area of Science:

    • Medical Informatics
    • Health Data Science
    • Digital Health

    Background:

    • Future medicine requires predictive, preventive, personalized, and digital approaches.
    • Comprehensive data and knowledge are essential for targeted diagnosis and therapy.
    • Data integration and sharing are critical for advancing medical research and patient care.

    Purpose of the Study:

    • To describe the technical and organizational concepts for data integration and sharing within the DIFUTURE initiative.
    • To establish Data Integration Centers (DICs) at university medical centers to facilitate cross-site data access.
    • To support researchers and physicians with integrated data views and decision support for personalized treatments.

    Main Methods:

    • Implementation of a three-step data integration approach: import/harmonization, preprocessing/enrichment, and analytics platform integration.
    • Utilization of common data standards (IHE, DICOM, HL7 FHIR) and analytics platforms (i2b2, tranSMART).
    • Application of privacy-enhancing technologies and distributed computing for secure data access and sharing, based on risk analyses.

    Main Results:

    • Development of a robust infrastructure for integrating, harmonizing, and sharing diverse data types (structured, unstructured, omics, images).
    • Establishment of governance structures and policies to ensure data protection, privacy, and patient trust.
    • Demonstration of use cases in neurology and oncology to validate the infrastructure's capability to support various application scenarios.

    Conclusions:

    • The DIFUTURE DICs provide a scalable infrastructure for data integration and sharing, crucial for advancing personalized medicine.
    • The approach balances data accessibility with stringent security and privacy measures, fostering patient trust.
    • The implemented solutions accelerate medical innovation and aim for measurable benefits in patient diagnosis and therapy.