Many AI medtech startups focus heavily on achieving FDA clearance, only to encounter reimbursement challenges afterwards. At what stage should reimbursement strategy enter a startup’s product and clinical development roadmap?
Your reimbursement strategy should start on day one. The industry standard has long been a linear sequence of building the product, securing FDA clearance, and then figuring out how to achieve reimbursement. That sequence made sense when medtech was mostly hardware, and clearance itself was the tough part. But it doesn’t hold up for AI-driven tools, where the clinical and economic questions payers ask are fundamentally different from the ones regulators ask.
Founders who treat reimbursement as a post-FDA clearance workstream are essentially designing their product twice: once for the FDA, and then again, at far greater cost and with far less runway, for private and commercial payers. The companies that move the needle are the ones that collapse those two design processes into one from the very beginning.
Avenda Health reportedly moved from FDA clearance to reimbursement considerably faster than the typical industry timeline. What did you approach differently, and which decisions had the greatest impact?
On average, it takes a new medical technology roughly six years and upwards of $100 million in capital to bridge the gap between clearance and Medicare reimbursement. This is true even for highly successful AI companies. At Avenda Health, we cleared this hurdle in just 18 months and under $10 million in capital.
There are a few key strategic decisions we made to achieve this with Unfold AI, our AI-driven prostate cancer mapping platform. First, we embedded our clinical validation and reimbursement strategy directly into our product architecture from the very beginning, and we pursued regulatory and billing pathways in parallel. We collaborated with the Centers for Medicare & Medicaid Services (CMS) and major national medical specialty societies early in the R&D phase. This was important because we needed to understand the exact clinical endpoints and health economic data payers required. With this, we were able to design our clinical trials specifically to generate that evidence, working with researchers at Stanford and UCLA on peer-reviewed clinical research to demonstrate meaningful improvements in physician capabilities and patient outcomes with Unfold AI. If your trial doesn’t prove economic and real-world value to a payer, you are building a product that clinicians will love, but healthcare systems can never afford to adopt. This backing is primarily what helped us secure a Category III CPT code from the American Medical Association (AMA) immediately followed by a national outpatient Medicare payment rate.
Second, while securing hospital outpatient reimbursement is critical, urology is highly outpatient-centric. We channeled much of our energy into regional office adoption and pushed to expand Unfold AI into regional Medicare Physician Fee Schedules (such as the West Coast and Mountain West). This gives private practices a direct, covered pathway to offer our technology.
We also launched an in-house Reimbursement Hub to work hand-in-hand with practices that were early adopters of Unfold AI. Having a dedicated reimbursement team working with providers quickly to resolve claims issues helps support growing private insurance coverage. We’ve been able to achieve high claim approval rates, which is the type of real data that builds confidence for prospective commercial payers.
How should AI medtech companies design clinical trials so that the evidence generated satisfies not only regulators but also CMS, medical societies, clinicians and payers?
The fundamental mistake is assuming that standard regulatory thresholds, such as safety and efficacy data, will satisfy payers, but unfortunately that alone isn’t enough to convince them. A trial built to hold up with every stakeholder, including regulators, CMS, medical societies, clinicians, and payers, needs several layers of evidence.
One of those is undeniable clinical ground truth. Regulators and clinicians must trust the algorithm’s baseline capability before they’ll trust anything it recommends downstream. For Unfold AI, we trained our models on the largest multi-modal dataset of its kind and validated with a robust ground truth from post-surgical pathology.
Clinical impact is another layer that many founders skip. You need to prove your technology has an actionable impact on clinical behavior and decision-making, meaning that when physicians see the output, their treatment decision changes in a way that’s traceable and defensible.
Developers must also demonstrate that the technology produces genuine cost savings for the broader healthcare industry. That means capturing downstream cost data such as fewer repeat procedures, shorter recovery times, or fewer complications requiring follow-up care.
What are the most common mistakes founders make when estimating the capital and time required between regulatory approval and meaningful commercial adoption?
The most overarching mistake is the belief that regulatory clearance is the finish line. Founders often raise just enough capital to reach clearance, leaving themselves with a short runway to navigate an unpredictable, multi-year battle for reimbursement. That gap can be a death sentence for venture-backed startups.
Another mistake is assuming SaaS metrics apply to medtech. Enterprise software startups are used to scaling rapidly to millions in annual recurring revenue, but clinical AI is completely inverted. It’s an expensive, slow climb to secure your first dollar in large part because of the regulatory and reimbursement hurdles. However, once a CPT code and national payment rate are set, scaling is nearly vertical because providers don’t pay out of pocket. If you plan your capital allocation like a traditional SaaS startup, you’ll often run out of cash before clearing the reimbursement bottleneck.
Lastly, many founders ignore the operational costs of billing and don’t budget for the heavy administrative lifting required to support early sales. Without dedicating capital to clinical adoption specialists, field training, and reimbursement hubs to fight payer denials, sales pipelines may stall.
As healthcare systems become increasingly interested in AI but remain cautious about measurable returns, what evidence should startups provide to demonstrate genuine clinical and economic value?
Startups have to show that their AI actually improves the capability of every practicing clinician. Health systems have grown skeptical of pilots that succeed in the hands of a few champion physicians and then quietly stall when rolled out more widely. So the evidence that actually moves a health system goes far beyond asking, “Does this work?” but, more specifically, “Does this work consistently, in the hands of an average clinician, on a Tuesday, with a full patient schedule?”
Beyond the clinical data itself, health systems increasingly want two other things before they’ll commit past a pilot. They want proof the tool integrates seamlessly into existing workflow without adding administrative burden. Additionally, they want a track record of billing and reimbursement success from peer institutions, because a chief financial officer isn’t going to greenlight a system-wide rollout based on clinical enthusiasm alone. Real-world reimbursement data from early adopter sites can often do more to convince the next health system than any additional clinical study will.
For AI medtech founders moving from pilot programmes towards sustainable revenue and P&L impact, what would your strategic playbook look like for the next three to five years?
A lot of health technology businesses raised money during a period when growth alone was the story investors wanted, but that era is over. Capital is now concentrating around companies that can show durable revenue tied to real reimbursement.
Our playbook comes down to three commitments, and each one makes the next easier to keep:
