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Preventive healthcare has the potential to generate economic benefits and improve long-term health outcomes, but developing effective business models presents unique challenges. How can the economic benefits of investing in preventive healthcare be quantified? How to access and leverage the right data?  Will EHDS implementation be a game-changer for innovative companies?  What challenges do innovators face in designing reimbursement strategies for preventive digital health solutions? What examples of successful business models exist, and what lessons can be learned from their implementation? How to scale preventive digital health innovations effectively?

Despite the potential of digital health and innovative therapies to revolutionize healthcare, adoption and development remain complex challenges. What is the current state of the regulatory and reimbursement pathways for digital health and medicine? What to expect from the EHDS? What initiatives are underway to promote interoperability, data sharing and access to secondary use of health data? Have reimbursement processes like DiGA or PECAN proved their effectiveness in deploying digital health solutions? What are the key components of successful value proposition and revenue model for digital health innovations? How do investment strategies of VCs and big players influence the development and adoption of digital health technologies? How can further collaboration between healthcare providers, regulators, industry, and patients accelerate their adoption?

Augmented intelligence has the potential to enhance diagnostic decision-making in healthcare, but striking the right balance between human expertise and AI is crucial. How do clinicians work with AI systems in diagnostic decision-making, and what are the implications for patient care? What are the current training and education programmes available to equip clinicians with the skills and knowledge needed? What measures can be taken to ensure transparency and explainability of AI algorithms for both clinicians and patients? What ethical considerations arise when integrating AI into clinical practice? What lessons can be learned from successful implementations of AI in diagnostic decision-making?