At the 7th National Digital Health Symposium in Berlin, experts from politics, science and industry sent a clear signal: despite growing spending in the healthcare sector, innovative strength in Germany is stagnating. New technologies and data solutions must therefore be transferred more quickly into daily care in order to create a learning system that is based on evidence-based findings. The event on 2 December 2025 emphasised the role of digital tools such as the electronic patient record (ePA), the Research Data Centre for Health (RDC) and the European Health Data Space (EHDS), complemented by discussions on artificial intelligence (AI) and data protection issues.

Background: The digital transformation offers enormous opportunities to secure efficient and high-quality healthcare in the long term. A learning healthcare system that uses data from practice to advance research and, conversely, incorporates evidence-based findings into treatment is at the center of the debate. In recent years, the Federal Ministry of Health (BMG) has launched several initiatives that are considered milestones. These include the ePA, which enables cross-sectoral data use, and the FDZ, which provides billing data of those with statutory health insurance in a standardised and data protection compliant manner.

The ePA is seen as a central building block for a modern healthcare system. It serves as a hub for data-based, patient-centered care, especially for complex diseases. Current optimizations include extended medication management, intelligent search functions for findings, the integration of laboratory data and the connection to the FDZ. Despite progress, there is room for improvement, for example in terms of acceptance among doctors. It is important to communicate the advantages of the ePA more strongly in order to establish it nationwide.

The FDZ at the Federal Institute for Drugs and Medical Devices (BfArM), which opened in autumn 2025, marks another step forward. It provides data in a secure environment that can be used for research and quality improvement. Such infrastructures are crucial to combine innovation and patient safety. Medical registries play a key role in the benefit assessment of medicines and medical devices, including the validation of AI applications. It is recommended that registry studies be more involved in assessment processes in order to promote evidence-based decisions.

AI is considered a driver of innovation in healthcare. Automating routine tasks, smart documentation aids, and context-based search can speed up clinical processes, provided the systems are interoperable. Challenges lie in a lack of annotated data and scalable computing resources. The EHDS could remedy this by establishing common rules for the primary and secondary use of health data, standardising access and facilitating cross-border research. Germany is well positioned, but the technical and regulatory integration of sectoral data remains a major task. The further development of laws such as the Health Data Use Act and the Medical Register Act will be crucial.

The transfer of research results into economically viable applications must be accelerated. Too often, excellent research does not end in better products or processes. A joint innovation paper by TMF, vfa and BVMed calls for a faster transfer of knowledge in order to strengthen Germany as a business location. This includes measures to promote start-ups, regulatory easing and close cooperation between industry and research.

In the context of ethical and practical challenges of generative AI in healthcare, as outlined in a recent review, questions of responsibility become central. Generative AI is transforming care, but it poses risks such as bias, liability, lack of transparency, and privacy. A systematic analysis of 54 studies shows that AI is being integrated into education, research, and practice, but fragmented solutions and barriers to implementation hinder progress. Technical approaches such as explainable AI, procedural stakeholder oversight, and regulatory strategies are necessary to build trust. Global harmonization and training are crucial to uphold ethical standards.

Another example of privacy concerns is estimating gender and age from radar-based heart signals. A study shows that such signals can predict gender with 78 percent accuracy and age (in groups) with 73 percent, which poses privacy risks. Data augmentation with generative networks improves performance, but the implicit encoding of sensitive data in physiological signals could leave biometric systems vulnerable. This underlines the need for robust protective measures in digital health technologies.

The symposium underlines that Germany is lagging behind in innovation implementation despite increasing expenditure – the health budget is growing every year. Only through faster transfer processes can potentials such as AI and data spaces be exploited. The ePA and FDZ provide the basis for this, but acceptance and interoperability must be increased. The EHDS promises Europe-wide synergies, but requires national implementation.

Overall, the symposium called for a holistic approach: According to the experts, politicians must create framework conditions, research and industry cooperate, and include patient perspectives. This is the only way to create a learning system that reduces costs and increases quality. The unity of the participants signalled a willingness to change – now it is a matter of turning words into deeds.

Editor: X-Press Journalistenbüro GbR

Gender Notice. The personal designations used in this text always refer equally to female, male and diverse persons. Double/triple naming and gendered designations are used for better readability. ected.