The TMF's Impulses for the 2025 Federal Election

Long-term Planning and Funding for Health Research: Health research requires long-term orientation and strategic planning. This is only possible if there is corresponding planning security. The TMF advocates for sustainable and long-term funding for health research to enable this.

Promotion of Early-Career Researchers: The TMF is committed to supporting early-career researchers in medical research. This includes creating attractive career paths and strengthening collaboration between universities, research institutions, and industry.

Data literacy as an essential part of digital health literacy must be systematically promoted and integrated into the curricula of training and study programs for health professionals.

Involving Patient Organizations in Research: The perspective of patient representatives must be routinely incorporated into research projects. On the one hand, their experiences are crucial for formulating research questions, and on the other, this participation increases involvement in studies and understanding of health data research and data provision.

Legal Framework for the Use of Health Data: The TMF supports the development of a comprehensive legal framework that enables the secure and ethical use of health data. The Health Data Utilization Act (GDNG) has opened the door to increased data use. Regulatory authorities and the executive branch must establish practical application processes and scenarios to improve its practical application and enable more research. The same legal framework that has been created for health data through the GDNG should also be applied to biosamples.

Promotion of Medical Registers and Data Set Integration: Medical registers are crucial for healthcare research and the development of new therapies. Their quality-controlled data collections offer significant potential for health data use, complementing and adding to routine data and study data. The TMF advocates for increased support and quality development for registers. This requires efficient science-based support structures, research funding for registers, and further development of the overview created in recent years of the heterogeneous landscape of existing registers. A simplification of data processing for publicly funded registers, similar to the GDNG, should be introduced under controlled conditions. Additionally, secure methods for data set integration (record linkage) must be enabled and created. This also requires a unique identifier in the field of health research so that data sets from different sources can be reliably linked.

Enabling Interoperability and Intersectoral Data Use: Data and samples benefit research and healthcare most when processed according to international standards. Interoperability must become mandatory across all sectors.

Cybersecurity: In recent decades, the focus has been primarily on data protection. Given the increasing digitalization in healthcare on the one hand and the rise in cyber-attacks on the other, information security and the resilience of software systems, as well as healthcare institutions and critical infrastructures, are crucial for supply security. However, unnecessary security measures or misinterpreted regulations should not hinder research and development. Therefore, the TMF calls for targeted competence building and the promotion of support services to secure our digital infrastructures, as well as regulatory requirements for the development and operation of academically created software and open-source software aligned with protection goals.

Strengthening Translation: Translation refers to the scientific-medical process of transferring findings from basic research into clinical evaluation and care. Many innovations in Germany fail in the "Death Valley of Translation" before they can be tested on humans or integrated into routine care. As a result, they do not reach the maturity required to make the transfer to industry appealing. The TMF calls on policymakers to promote biomedical research strategically along the translation process. This includes the development of necessary research infrastructures, particularly the establishment of translation centers in university medicine, with an appropriate accompanying structure. Additionally, funding is needed to make biosamples usable for translational research and (early) clinical studies.

Simplification of Regulatory Frameworks in the Translation Process: Innovations must be brought to clinical research and care more quickly. Those responsible for implementing complex regulations need more clarity and predictability. The TMF calls for better coordination of the various laws and regulations. Research privileges must be implemented in a way that ensures patient safety and research integrity while avoiding unnecessary delays.

Strengthening Transfer: Transfer refers to the economic-technological process of transferring knowledge or technologies between actors, specifically from research institutions to industry. To achieve this, collaboration must be strengthened. The TMF advocates for simplifying the regulatory framework for transfer and strengthening structures that support and accelerate the process.

Incentive Systems for Translation and Transfer: The evaluation of research institutions, as well as scientists, is primarily based on publications. Incentive and evaluation systems must be established that reward research institutions and, most importantly, scientists for their achievements in translation and transfer.

Further Development of the Innovation Fund of the Joint Federal Committee (G-BA): Financial support for new forms of care and healthcare research is well-invested when positively evaluated projects have a real chance of being integrated into regular care. Clear processes and criteria are needed to ensure that the integration into regular care becomes the norm and goes beyond recommendations.

Resource Efficiency, Cost-Effectiveness, and Supply Security through Repurposing: Repurposing refers to the reuse of already approved medications for new therapeutic indications by investigating their effectiveness in other disease areas. This can reduce development times and costs, providing patients with quicker or even any access to new treatment options, especially for rare or difficult-to-treat diseases. To fully leverage the potential of repurposing, funding programs for clinical trials to demonstrate safety and efficacy in indication expansions should be supported, and legal certainty for prescribing physicians should be established.

International Collaboration: The TMF supports international collaboration in health research and calls for active participation by Germany in European and international research initiatives, such as TEHDAS2.

EHDS: The European Health Data Space (EHDS) offers Europe and Germany a great opportunity to compete with Asia and North America in the use of health data. A prerequisite for national implementation is that Germany builds on existing expertise and addresses its “homework.” Germany must set standards to achieve a development lead. Furthermore, to implement the EHDS in Germany, a research data ecosystem should be built upon existing structures and processes. The structures, experiences, and expertise of the Medical Informatics Initiative (MII) should be taken into account in the implementation of EHDS.

Simplifying and Harmonizing Regulation: The development of AI systems in medicine is regulated by the European AI Regulation (EU AI Act), as well as medical device regulations (especially MDR/IVDR) and other laws. However, these regulations are sometimes difficult to interpret in practice. Additionally, they are not well coordinated, and it is not always clear which laws are relevant. This uncertainty and varied interpretation, compounded by federal structures, hinder research and development. Therefore, the TMF calls for the harmonization of regulatory requirements. This should be done with the help of use cases and implementation scenarios.

Promoting AI Development: On one hand, the development cycles of AI systems are becoming increasingly shorter, while on the other hand, these systems may need to be created and trained specifically for individual institutions. To accelerate the development, testing, and application of AI systems, adjustments to the regulations for self-built systems and performance evaluations of AI systems are necessary. Additionally, specialized AI laboratories should be established where AI systems can be developed and evaluated.