M. Bender: What makes Synwisery special?
N. Erdmann: We have recognized that technology development and marketing must be conceived simultaneously to be successful. Often, these processes in companies are sequential, leading to delays and sometimes wrong decisions. Our approach integrates technological and market-specific knowledge from the outset. This allows us to achieve a clear, differentiating positioning with our clients and derive concrete action recommendations for elevating companies to the next level. Our partners value us as direct and honest sparring partners who accelerate the path to product-market fit and market success.
As a co-founder and managing partner of Synwisery, along with my colleague, physicist, and AI expert Dr. Stefan Braunewell, I bring extensive experience from hospital corporations, MedTech startups, academia, and consulting. This multidimensional perspective enables us to address the complex requirements and needs in health tech.
What, in your view, characterizes companies that have reached this “next level” as you put it?
Honestly, a focus on pure technology won’t impress any hospital chain. Yet, most manufacturers and investors in health tech still think this way.
First, there needs to be a clear focus on the actual added value of the solution. Then, it is crucial to stand out from the mass of providers and turn medical users and business decision-makers alike into enthusiastic customers. This involves, beyond traditional product marketing, a different kind of strategic marketing and sales, including a more emotional approach to target audiences. Ultimately, people buy from people. This has long been understood in the business-to-consumer sector.
Strong partnerships with healthcare providers or the industry are also crucial to ensure the successful integration of AI solutions into clinical workflows.
And: Nothing is more important in our view than the company’s leaders working together cohesively. Often, hidden project blockers are found here. Our strength lies in bringing about unity and successfully steering innovation processes with stringent roadmaps and transparent communication, while maintaining a pragmatic approach.
What challenges do hospitals face when implementing AI technologies? And what ethical concerns do you encounter?
Selecting 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 regarding their long-term value and integration capability into existing clinical workflows? Who decides on the use, who pays for the products, and what do IT and data protection say? Unfortunately, sales cycles in healthcare are very long.
The heterogeneous IT infrastructure of many hospitals often proves to be an obstacle to the widespread implementation of AI. In this context, standardization initiatives like HL7/FHIR play a key role by setting essential standards for data interoperability. This enables seamless integration and communication between different systems and institutions.
Ethically, data protection, liability issues, and the autonomy of medical decision-making come into focus. Handling sensitive patient data requires strict compliance with data protection regulations and transparent presentation of the function and performance limits of AI systems. This requires careful selection and preparation of training data, stringent product validation, and continuous monitoring and adaptation of AI systems.
What requirements does the use of AI place on hospital staff?
Using AI in a clinical environment requires technical understanding and data competency from the hospital staff to use the systems effectively. Continuous training and interdisciplinary collaboration with IT are also beneficial to ensure the integration of AI into daily clinical practice.
There is a lack of AI expertise in hospitals. Digital Technical Assistants and Digital Technical Specialists, for example, could provide relief.
In which areas do you see positive examples of the use of new technologies and AI in hospitals and medical technology?
Diagnostics is one of the leading fields for the use of AI in medicine, particularly in radiology, cardiology, and soon, pathology. AI enables faster and more accurate results, relieving medical staff of repetitive and simple tasks, thus allowing more time for complex cases. In surgery, the use of robotic surgical systems is increasing, enabling more precise and less invasive procedures. Apps and wearables for continuous patient data monitoring can detect and report early warning signals. In rural areas, remote monitoring for conditions like hypertension or heart failure can provide quicker emergency help.
The use of technology also leads to significant improvements in administration, logistics, and patient interaction. AI-supported speech recognition systems are increasingly used for time-consuming documentation. AI-supported logistics, such as automated transport systems for medications, samples, and medical devices, reduce waiting times. Digital registration systems and patient management platforms facilitate patient admission and scheduling.
How do you think AI will impact 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 doctors who need to be experienced in applying AI. I don’t see the primacy of human expertise and responsibility falling!
Personally, I would welcome AI as a naturally used second opinion to feel even safer as a patient. In the not-too-distant future, we will reach a point where patients will choose their healthcare facility based on how digitally equipped it is, from appointment booking to follow-up care. And our common desire is for medical staff to have more time for patients. AI can support this by creating more efficient workflows.
You also work for European companies. What is the difference in handling new technologies in healthcare between countries?
The adoption of new technologies varies greatly across Europe. This is largely due to different regulatory interpretations and health systems, but also cultural specifics. France has made significant progress in digitizing healthcare in recent years, particularly with the introduction of the “Health Data Hub,” providing simple, unified, transparent, and secure access to health data to support research and development. Countries like Denmark, Estonia, and Finland are already ahead due to their modern digital infrastructures and more open attitudes towards data-driven technologies. Sweden impresses with nationwide use of digital patient records and telemedicine services. In England, the National Health Service (NHS) is a pioneer in integrating AI and data-driven approaches. In Germany, we still see too little AI expertise in the certifying “notified bodies,” which can significantly extend regulatory processes and thus hinder the introduction of AI technologies.
There is also a heterogeneous investment climate in digital health: In Germany, funding volume fell from $44 million in the already investment shy Q4/23 to a meager $21.7 million in Q1/24. In contrast, England and France saw $143 million and $101 million, respectively, for health startups. I am concerned that Germany might fall further behind.
What is your personal outlook for the next five years regarding new technologies in healthcare 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 relieve medical workers from bureaucracy and inefficient processes. AI-supported operational excellence and more mature personalized/predictive medicine are, for me, the upcoming transformative trends.
Given the challenging financing conditions in Germany, it is crucial that companies focus on creating significant and demonstrable clinical and business value.
What it takes: An explicit AI strategy in hospitals and a smart prioritization of action fields so that the overused word “disruption” does not become an empty phrase, especially in healthcare. The success of AI and digital health solutions requires a deep understanding of clinical and operational as well as market conditions.
For manufacturers, it’s not just about bringing a product to market, but ensuring that it delivers validated and better results than without its use. And, of course, demonstrating what the specific ROI for hospitals will be. A comprehensive market-fit test is crucial to offering truly useful and highly sought-after technological solutions in the health market. That’s what Dr. Stefan Braunewell and I are committed to with Synwisery.
