Dear Ms. Erdmann,
With your management consultancy Synwisery, you work for start-ups and established companies in the development and successful marketing of innovative, complex products and services in healthcare and medical technology, often involving AI and software solutions.
What makes your company special?
We have recognized that technology development and marketing must be considered simultaneously in order to be successful. These processes often run sequentially in companies, which leads to delays and sometimes to wrong decisions. Synwisery’s approach integrates technological and market-specific knowledge right from the start. This enables us to work with our clients to achieve a clear, differentiating positioning and derive specific recommendations for action. These quickly take companies to the next level. The partners we work with value us as direct and honest sparring partners who accelerate the path to product-market fit and market success.
As co-founder and managing partner of Synwisery, together with my colleague, physicist and AI expert Dr. Stefan Braunewell, I bring extensive experience from hospital groups, medtech start-ups, science and consulting. This multidimensional perspective enables us to address the complex requirements and needs in the health tech sector.
In your opinion, what characterizes companies that have reached this “next level”?
To be honest, the focus on pure technology won’t knock the socks off any hospital chain. But that’s still the way most manufacturers and investors in the health tech sector think. First of all, there needs to be a clear focus on the actual added value of the solution. And then it is important to stand out from the crowd of providers and turn medical users and business decision-makers alike into enthusiastic customers. In addition to the usual product marketing, this involves what we consider to be a different type of strategic marketing and sales, including a more emotional approach to the target group. Because at the end of the day, people buy from people. This has long been internalized in the business-to-consumer business.
Strong partnerships with healthcare providers and industry are also crucial to ensure the successful integration of AI solutions into clinical workflows.
And: In our view, nothing is more important than those responsible in the company pulling together. This is often where hidden project blockers lie. Our strength lies in bringing about unity and successfully and pragmatically managing innovation processes through stringent roadmaps and transparent communication.
What challenges do hospitals face when implementing AI technologies? And what ethical concerns do you experience?
Even the selection of suitable AI solutions from a complex and dynamic market landscape requires experience, time and an appropriate budget. It also requires clear responsibilities: Who in the hospital can evaluate potential solutions in terms of their long-term added value and their ability to integrate into existing clinical workflows? Who decides on their use, who pays for the products, what do IT and data protection say? Unfortunately, sales cycles in the healthcare sector are very long.
The heterogeneous IT infrastructure of many hospitals often proves to be an obstacle to the widespread implementation of AI. Against this backdrop, standardization initiatives such as HL7/FHIR or MII play a key role by setting essential standards for data interoperability. This enables seamless integration and communication between different systems and institutions.
From an ethical perspective, data protection, liability issues and the autonomy of medical decision-making are coming into focus. The handling of sensitive patient data requires strict compliance with data protection regulations and a transparent presentation of the function and performance limits of AI systems. This requires not only careful selection and preparation of training data and strict product validation, but also continuous monitoring and adaptation of AI systems.
What are the requirements for hospital staff when using AI?
When using AI in a clinical environment, clinical staff need technical understanding and data expertise in order to be able to use the systems effectively. In addition, continuous further training and interdisciplinary collaboration with IT make sense in order to ensure the integration of AI into everyday clinical practice.
There is a lack of AI expertise in clinics. Digital technical assistants and digital technical assistants, for example, would provide a remedy. The VDE is now promoting their training. An important step!
In which areas do you see positive examples of the use of new technologies and AI in hospitals and medical technology in Germany?
Medical diagnostics is one of the leading fields for the use of AI. In particular, radiology, cardiology and, in the future, pathology. AI enables faster and more accurate results and relieves medical staff of repetitive and simple tasks, leaving them more time for complex cases, for example. In surgery, the use of robot-assisted surgical systems is increasing. These enable more precise and less invasive procedures. Apps and wearables for the continuous monitoring of patient data can detect and report warning signals at an early stage. In rural areas in particular, remote monitoring of high blood pressure or heart failure, for example, can mean faster help in emergencies.
The use of technology also leads to significant improvements in administration, logistics and patient interaction. For example, AI-supported speech recognition systems will increasingly be used for time-consuming documentation. AI support for logistics, such as automated transport systems for medicines, samples and medical devices, will reduce waiting times. Digital registration systems and patient management platforms facilitate patient admission and appointment scheduling.
How do you think AI will affect decision-making in patient care?
AI already supports and improves decision-making in patient care today, but the final decision must always remain in the hands of nurses and doctors who are necessarily experienced in the use of AI. I do not see the primacy of human expertise and responsibility falling!
Personally, I would like to see AI used as a second opinion as a matter of course, so that patients can feel even more secure. In the not-too-distant future, we will reach a point where patients will also make their choice of healthcare facility dependent on how digital it is, from booking appointments to aftercare. And we all want medical staff to be able to spend more time with patients. AI can also help with this by making workflows more efficient.
You also work for European companies. What is the difference in dealing with new technologies in the healthcare sector between the countries?
The handling of new technologies varies greatly across Europe. This is largely due to different regulatory interpretations and the respective healthcare systems, but also to cultural differences. France has made significant progress in the digitalization of the healthcare system in recent years. In particular, through the introduction of the “Health Data Hub” health platform with simple, standardized, transparent and secure access to health data to support research and development. Countries such as Denmark, Estonia and Finland are already further ahead due to their modern digital infrastructures and a more open attitude towards data-driven technologies. Sweden is impressing with its nationwide use of digital patient records and telemedicine services. In England, the National Health Service (NHS) is a pioneer in the integration of AI and data-driven approaches. In our view, we are still seeing too little AI expertise among the certifying “notified bodies” in Germany. This can significantly lengthen the regulatory processes and thus make the introduction of AI technologies more difficult.
In addition, there is a heterogeneous investment climate in digital health: in Germany, the financing volume fell from $44 million in Q4/23, which was already not so investment-friendly, to a meagre $21.7 million in Q1/24. In comparison, there was $143 million and $101 million for health start-ups in England and France respectively. I’m already worried that we’re falling further behind in Germany.
What is your personal outlook for the next 5 years with regard to new technologies in the healthcare sector in Germany?
My personal outlook for new technologies in healthcare is optimistic and characterized by significant developments that have the potential to fundamentally improve patient care and treatment and relieve medical workers of bureaucracy and inefficient processes. AI-powered operational excellence and more sophisticated personalized/predictive medicine are the transformative trends I see coming.
However, given the challenging financing conditions in Germany, it is crucial that companies focus on creating significant and demonstrable clinical and business value.
What is needed is an explicit AI strategy in the clinics and a smart prioritization of fields of action so that the inflationary use of the word “disruption” is not just an empty phrase, especially in the healthcare sector. The success of AI and digital healthcare solutions requires a deep understanding of clinical, operational and market conditions.
For manufacturers, it’s not just about launching a product on the market, but ensuring that there are validated and better results with it than without its use. And, of course, to show what the subsequent ROI will be for clinics. A comprehensive market fit assessment is crucial in order to offer truly useful and, at best, highly sought-after technological solutions in the healthcare market. This is what Dr. Stefan Braunewell and I have set out to do with Synwisery.
Learn more about: Synwisery and Natalie Erdmann
Learn more about: Michaela Bender
