01/10/2024
At the JP Morgan Healthcare conference this week in San Francisco our CEO and Founder, Mary Lou Jepsen, got lots of questions from the investment community regarding our business model. Here are her responses:
All I took the initiative to compile these questions from JP Morgan and share my responses below with everyone.
Free as in Speech, Not Free as in Beer
Our goal : A single device to treat, monitor and diagnose hundreds of diseases that can be made in the factories that make our phones and laptops. Open sourced. Trusted. Innovating at the speed of Moore’s Law not multi-decade speed and multi-billion dollar investment typical for medical innovation. While our R&D is free and open to all - we make money selling our hardware and services. Our focus is not on making a million dollars per treatment (as is increasingly becoming the norm for healthcare) but on treating millions of people quickly. We make it up on volume. Charge a smaller amount of profit per unit, use the same unit with differentiated software to treat many diseases and save millions of lives faster.
The old adage for open source went “Free as in speech, not free as in beer”. In our analogy we make the recipe for the beer freely available but still sell the beer for those who prefer not to make it themselves. Additionally we guarantee the quality of the beer through our manufacturing process. By making it at scale at the research and development stage (prior to FDA approval) we can offer the beer to enable concurrent, cost-effective trials without the burden of expensive product development costs for our customers.
Trial costs are also substantially reduced by sharing safety data across all trials. Our customers fund the trials and take ownership of the regulatory approvals, not us, and need fewer patients per trial (less cost) because of the shared safety data. We provide them with components, hardware, software, and quality management support, among other services.
Even patient groups could fund trials. How? They can persuade their preferred doctors to enroll say 20 patients (including themselves at no cost), purchase our hardware, and raise funds to hire someone to shepherd it through IRB processes and oversee the trial with the doctor. This approach can be remarkably cost-effective, and for certain diseases, just 20 patients may be sufficient for approval. The sharing of safety and adverse event data across trials - even for different diseases - reduces the number of required patients for each trial, significantly lowering trial costs initially and promising even greater reductions (10-100x) as safety data accumulates and becomes accessible to all.
Open source companies have generated billions of dollars while providing the trusted infrastructure for the internet, smart phones, web services, etc. We believe we can replicate this success in healthcare. By allowing everyone to scrutinize (and improve) the software, hardware designs, and data line by line, we can expedite progress and scale solutions across hundreds of diseases. As the volume of production increases, costs decrease exponentially—every 10x increase in production roughly corresponds to a 10x decrease in costs. Thus, a semiconductor-based platform with adaptable software for each disease and treatment could yield million-fold cost savings per treatment, spread across numerous diseases using the same hardware/software platform. Additionally, this platform introduces the potential for significant advancements in the treatment of common and rare diseases alike as shown already in our clinical feasibility results for treatment of diseases like Glioblastoma, Severe Depression and Large Vessel Occlusion Stroke.
Our approach is less risky than traditional business models for medical device development. It allows for numerous opportunities, utilizing the same hardware/software platform for treating multiple diseases and eliminating single points of failure that often thwart diagnostic or therapeutic development—either in the laboratory or during clinical trials. Many companies have faltered due to failed stage 3 trials, and countless others have perished prematurely because they couldn't amass the vast sums required for large-scale trials. Why not separate clinical trials from hardware/software platform development to reduce costs for a company and further reduce trial costs by extensively sharing adverse event and safety data across trials?
A silicon / software platform has the potential to disrupt healthcare, just as it has disrupted nearly every other industry over the past 30 years. An open-source platform can achieve this disruption both faster and cheaper. It's in reach to establish a multi-billion dollar business by selling the hardware systems and services, much like Red Hat has with its open-source business model. The concept of a healthcare R&D company focused on saving more lives more rapidly for less money should not be a novelty, yet regrettably, it is.
The journey from research to approval for medical devices often spans decades and frequently fails to achieve platform status, resulting in continued high costs upon regulatory approval for a narrow range of diseases.
Instead we are creating an open-source platform with the potential to treat all aggressive cancers. With a simple software change, we could treat any mental disease. Change the software once more for applications like stem cell stimulation, senescent cell rejuvenation, high fidelity blood flow detection including oxy/deoxy, pathogen deactivation, immune priming, and more - all achieved non-invasively at a unit cost similar to that of a smartphone at scale where each unit can treat thousands of people.
Currently, we are collaborating with other companies by providing them with our Early Access Systems for their clinical studies and trials. These systems called Open-Lifu 2.0 and Open-Motion 3.0 can be ordered here for R&D efforts. (link https://t.ly/b9fyg)
Please join us!
- Mary Lou Jepsen
Openwater was founded in 2016 to explore an idea: What if advancements in fields as diverse as consumer electronics, semiconductor device physics, and artificial intelligence could come together for a future where the profound act of curing disease becomes a reality for all, transcending borders and...
