The People behind Healthcare Innovation: Interview with Ross Upton, founder and CEO of Ultromics

As part of my work, I have the privilege to speak with many inspiring innovators. However, the business community usually focuses more on companies, pitches, and valuations, and less on the innovator. I think it would be interesting to learn a bit more about the people behind healthcare innovation. Therefore, I’m sharing some of my conversations with innovators in a condensed format: six questions and six answers about their experience, their opinions, and their learnings.

The 12th conversation is with Ross Upton, founder and CEO of Ultromics.

Tobias: What’s your story? How did you become an innovator in healthcare?

Ross: My life journey started in Horsham, West Sussex (UK) where I grew up. I disliked school, I drove my teachers crazy due to my “unfulfilled potential” and remember going from achieving over 90 percent in a written law exam to not even turning up to other exams. Somehow, I managed to be accepted into the University of Manchester, where the school saga repeated itself, and I ended up dropping out. Shortly after this, I was involved in a high-speed car crash, which became a real turning point that subsequently prompted me to return to university.

I applied for a degree in Zoo Biology because I was quite strong scientifically and passionate about wildlife and conservation. I knew that if I could study something that interested me, then I’d have a chance of sticking at it. Luckily, this tactic prevailed and, after successfully obtaining my undergraduate degree, I completed three more: an MSc in Clinical Biochemistry, MSc in Clinical Cardiovascular Sciences, and a DPhil in cardiovascular medicine at the University of Oxford.

During my first masters, I was exposed to technology innovation in cardiovascular medicine. I met an inspiring guest lecturer (cardiologist) who supervised my research project. During this project, I worked with a startup company - Cardiocity - to help develop an iPad-like device that was capable of measuring an ECG using sophisticated computer algorithms and parts from a McLaren F1 car. This was back in 2011, so really quite innovative at the time and long before Apple came out with its equivalent. It was during this research project where my interests in cardiovascular medicine and computer sciences gained traction.

During my second masters, I specialized in cardiovascular imaging (specifically echocardiography) and undertook a research project at the University of Oxford. The project turned into my doctorate, during which I met my supervisor and co-founder Paul Leeson, Professor of Cardiovascular Medicine at the University of Oxford and Head of the Oxford Cardiovascular Clinical Research Facility. Paul and I wanted to focus on a specific problem for my DPhil project, where we could utilize both our clinical knowledge in echocardiography and the application of machine learning. Our research was spotted at a local conference by an investor who convinced us that “the quickest way to impact patients with your research is to spin it out as a company,” which heralded the birth of Ultromics.

The reason Paul and I are both passionate about echocardiography as a modality is because it’s widely used, very accessible, inexpensive, and without any dangers such as radiation. Clinicians can use the technology in any clinical setting, no matter how remote, and get real-time feedback of the patient’s heart structure and function. Echocardiograms are so widely used that they outnumber all other imaging modalities for cardiovascular disease combined.

For all of the positives, echocardiograms do have limitations, especially in terms of analysis and interpretation. Many of the quantitative measurements of heart function and resulting diagnosis can vary and this is heavily dependent on the clinician training and experience. This can lead to misdiagnosing one in five patients in the case of coronary heart disease—which is potentially disastrous for misdiagnosed patients, as they will either receive surgery unnecessarily or get sent home (when they should have gone for surgery), resulting in a heart attack. To counter this, we developed EchoGo, our first product under the Ultromics umbrella. It’s an AI-driven solution, to automate both the quantitative measurements and the diagnosis to improve patient outcomes, save clinicians’ time, and reduce the resultant cost burden on healthcare systems. Adopting this solution could save US healthcare systems billions annually.

Tobias: Where do you see the field of diagnostic imaging moving to in the next ten years?

Ross: AI is going to play a crucial role in the future of diagnostic imaging. Within the next ten years, we are going to witness an implementation phase of AI where hospital infrastructures, regulatory, and payment systems play catch-up with the rapid innovation in AI-based diagnostics. The three crucial elements where AI can have a massive impact in imaging is acquisition guidance, automation of quantification, and prediction of patient outcomes. With these elements, you can save time, increase consistency, improve patient outcomes, and save healthcare costs. In ten years, I envision AI software to perform the majority of the analysis/post-processing of scans. AI will be routine, and we’ll have many machine-learning algorithms active in clinical pilot testing. However, I believe AI will never replace doctors entirely, but rather augment specific tasks to optimize their performance.

Tobias: Looking more broadly, what are the biggest opportunities and obstacles you see for innovation in the healthcare environment?

Ross: The biggest opportunity for innovation in healthcare is utilizing AI to predict and improve patient outcomes. Currently, AI isn’t being leveraged to its full potential. In imagining diagnostics, for example, there are neat tools to help with image guidance or locate a region of interest. Still, we should be using AI to predict, and thus improve, patient outcomes by conducting extensive prospective longitudinal studies to demonstrate efficacy. That’s what we’re aiming to achieve with our AI platform, EchoGo—it will not only automate and analyze cardiovascular diagnosis but go further and predict outcomes.

Major obstacles for innovation are usually the payment system, reimbursement rules, and the speed of adoption. In the US, reimbursement isn’t structured in a way that’s complementary to innovative technologies aimed at improving patient outcomes—so healthcare systems are reticent to adopt the technology. This has led to a slow adoption rate and could lead to negative effects for future innovators seeking investment to bring their tech to market.

Tobias: When you look at the health system as a whole (providers, payers, regulators, doctors, patients) where do you see most/least openness for innovation?

Ross: The area which is the most open for innovation—particularly for regulators, payers, and providers—is tech geared towards time saving. This is because it is a tangible, provable benefit that is also reimbursable.

The reimbursement landscape is less responsive to innovative products. Medical devices are still reimbursed based on a clinician’s time and workload, and costs associated with improving patient outcomes have yet to be factored in.  

Tobias: What’s the most critical thing that policymakers could do to enable digital transformation of the health system(s)?

Ross: In the UK, we have a wonderfully linked system enabling tracking of patient outcomes. There are incredible innovation opportunities if we can get the appropriate systems in place that will allow rapid and responsible access to patient data.

In the US, linking up longitudinal data remains one of the biggest challenges for innovation in diagnostics. Patients can jump from system to system, making tracking longitudinal data very difficult. Systems such as the Care Everywhere network* are improving this, but there is still a long way to go.

Tobias: What do you know now that you wish you had known when you first started out as an innovator and entrepreneur?

Ross: I knew nothing about regulatory payment systems and investment strategies when spinning out the company from the University of Oxford. I also didn’t know how to build and run a company. If I had greater knowledge about how regulators and reimbursement worked, I would have started with a two-step product and clearance strategy (which we are currently doing). Whereas when we spun out, we thought we could put all our features/products into one 510(k) clearance, an idea that would’ve been courting disaster now!

For more information, see Ross Upton and Ultromics. 

* Since its inception in 2003, Care Everywhere has been focusing on the development of medical device data system software (MDDS) integrating point-of-care medical devices with hospital information systems.

Disclaimer: The views and opinions expressed in this article are solely those of the author and his guest contributor and do not reflect the views of McKinsey & Company.