AI for health: New innovation processes and financing channels

Artificial Intelligence (AI) can improve medicine and care. Commercially viable business models help to bring AI innovations to the breadth of the healthcare system. However, the financing and approval of AI applications poses major challenges for medical technology companies, many of which are medium-sized. A recent white paper from Plattform Lernende Systeme discusses new business models and innovation processes that take into account the specifics of AI. In order for patients to benefit from the opportunities offered by this key technology, new instruments for cost coverage and advice centers for small and medium-sized enterprises are needed, it says.

AI systems can support doctors in the prevention, diagnosis and treatment of diseases by intelligently linking health data; AI-based assistants relieve caregivers and enable people to live self-determined lives into old age. But on the way from research to healthcare, AI innovations in Germany have to overcome many hurdles. Especially for small and medium-sized enterprises (SMEs), which shape the German medical technology industry, as well as startups, financing and bringing AI medical products to market in healthcare is not easy, emphasize the authors of the white paper "AI Business Models for Health." In addition, they say, there is a lack of AI expertise, the necessary training data and trust in the innovations among physicians and patients. The authors call for greater consideration to be given to the special features of AI, such as the potential modifiability of its functionality, in the certification of medical devices prior to their market launch, which is necessary throughout Europe, as well as in the assumption of costs by health insurers and liability.

New ways of covering costs

Above all, an important building block for financing medical technology is the prospect of having the costs covered by statutory health insurers. "Reimbursement by health insurers is the bottleneck that AI innovations have to pass on their way to patients. Hospitals will use a device with AI functions that assists in the evaluation of CT images or in the treatment of intensive care patients primarily if they can bill the treatment with an AI function via the statutory health insurance funds. As long as reimbursement is not clarified, it is a big risk for manufacturers to prefinance the high costs of developing the product," says Karsten Hiltawsky, head of Corporate Technology & Innovation at Dräger and co-leader of the Health Care, Medical Technology, Care working group of Plattform Lernende Systeme. For the cost of treatment with an AI medical device to be reimbursed, the manufacturer must provide proof of benefit. Here, the white paper points to an AI-specific innovation problem: The actual benefit can often only be proven in the long term, it says, because the quality of an AI system's outcome depends heavily on the training data available - which is usually only available in sufficiently large quantities during the course of a new AI medical device's operation. "What would help us is a time-limited reimbursement until the final proof of benefit, along the lines of the fast-track process for digital health applications," Hiltawsky said.

Data availability is key to successful AI business models in medicine and care. Collecting patient data in compliance with data protection regulations accounts for a large part of the high initial outlay in Germany, the paper says. The authors suggest reimbursement of these expenses and specialized funding programs to improve international competitiveness.

"In care and rehabilitation, business models with AI are still in their infancy - although it is precisely here that patients could benefit enormously from solutions such as intelligent leg orthoses or AI systems for fall prevention. In addition to unclear financing, the difficulties in obtaining the necessary data for training AI systems are a major hurdle for healthcare companies and make them shy away from investing in AI," says Susanne Boll, professor of media informatics at the University of Oldenburg and co-leader of the Business Model Innovations working group of Plattform Lernende Systeme.

Revenue models for AI innovations

Medical technology AI innovations also make novel revenue models possible. "The secondary healthcare market also offers alternative revenue streams for manufacturers of AI solutions, and also has great potential for healthcare delivery, from prevention to intervention to aftercare," Boll said. In particular, if the use of AI enables an entirely new service offering, such as medical decision support, the authors recommend offering the AI function as "software-as-a-service" - a type of rental model in which hospitals and practices pay per use of the AI function instead of purchasing a license once.

About the white paper

The white paper Business models for AI in healthcare was written by experts from the Health Care, Medical Technology, Care working group and the Business Model Innovations working group of Plattform Lernende Systeme.

Further information:

Linda Treugut / Birgit Obermeier
Press and Public Relations

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