DTx Spotlight: An Interview with Theo Ahadome

In this month's episode of DTx Spotlight I sat down with King of Partnerships and Digital Health Executive and Consultant, Theo Ahadome .

Theo started his career in biopharma before moving into management consulting in the digital health space. From there he had an extremely successful career selling into enterprise organisations in Digital Therapeutics and Decentralized Clinical Trials. He was the Former SVP of Commercialization at Twill by Dario (fka Happify) and former Chief Business Officer at Wellthy Therapeutics . Now, he is a Commercial Advisor/Consultant at GSD Healthcare and MDisrupt helping digital health companies commercialize their products effectively and responsibly.

In this interview, Theo and I discussed how to evaluate your commercial options and effectively scale commercial operations, in a way that matches your product profile and avoids the pit falls of various current DTx companies.

What is the biggest challenge digital health companies face in commercialising their solutions?

"The biggest challenge is often internal, not external. Many companies have a misplaced sense of the value that their product really delivers in healthcare. Selling without understanding the true value that your product provides in the context of your customers problems and challenges, leads to untenable approaches and misplaced expectations.

"These often fall into three categories:

1. Solutions seeking problems: this is cliched but still occurs. How many times have you heard of a solution that analyses data and delivers “real world insights.” Yet no one can tell you how exactly those insights solve an acute problem for any healthcare stakeholder.
2. Problems that are not really problems: “We enable physicians to practise at the top of their licence.” Ok, but I have never met a physician who only wants to see the most complex patients in their field in the limited amount of time they have to see patients.
3. Magic Pill Solutions: 80% reduction in pain; 100% improvement in drug adherence; 60% reduction in hospital readmission. These are numbers that real companies are pitching - and if they held up to scrutiny they would be incredible solutions. They often do not, however. 

"What does work instead, is consistently showing modest levels of value in areas of real challenge, in a clear and measurable way."

So how do you choose which customer and problems to focus on? We have seen companies try everything from traditional payers to direct-to-consumer models.

"Pear and Akili have obviously forced people to think outside the box on commercial channels and that is highly encouraging.

"The conventional options have been pharma, medical devices, payors, providers and employers. There are also companies deriving millions of dollars in recurring revenue by focusing on less traditional options like risk-bearing entities (managed care, accountable care organisations etc), long-term care facilities and even correctional facilities.

"In the end, it comes down to this -  for whom are you most acutely solving a problem and have strongest evidence of your ability to solve that problem? Because that is likely who will pay for the solution. In the case of Akili, today parents are the most appreciative stakeholder of the improvements they deliver for kids with ADHD in a safe manner; Payers unfortunately are not - certainly not without economic outcomes data. So it makes sense that they are seeing more traction in the over-the-counter market. Now, is that sustainable long-term from a customer-acquisition cost (CAC) perspective? Maybe. But it buys time to get the evidence that other types of customers might need to pay for the solution."

"Build the product, generate some evidence, gain regulatory approvals, hire a commercial team. This sequential model is broken and just does not work in emerging fields of innovation like digital health and digital therapeutics"

How should companies think about human capital and having the right commercial team to scale?

"Build the product, generate some evidence, gain regulatory approvals, hire a commercial team. This sequential model is broken and just does not work in emerging fields of innovation like digital health and digital therapeutics. In tech we talk about product-market fit. In digital health there is evidence-customer fit. Very early on you need a commercial lead that can work alongside product, clinical and scientific functions to assess channel options, develop and implement a go-to-market strategy that is backed by an evidence generation plan for different customer segments.

"This of course pre-supposes that you even have clinical and scientific functions - and not just “advisors” to put on a pitch deck. I often say to founders, yes hire me, or an Anish Shindore , Dean Gray etc (commercial people), but before or alongside us, you need to have an Acacia Parks PhD, MBA , a Paul Wicks , a Lydia Roos, PhD and other real scientific and clinical leaders on your team, and together give us the mandate to deliver sales and sales forecasts rooted in the reality of where the evidence is today and can be tomorrow."

What about digital health platforms? What are their commercial prospects and do these same principles apply to them?

"Digital health platforms seek to solve the problem, often for pharma, of the need to rapidly and cost-effectively create highly customised digital applications. The challenge for these platforms is that pharma then has to use the apps they create to deliver value for their drugs, patients and physicians. Hence the mere ability to create an application is a pretty weak value driver and the wrong place to focus. Moreover, the barrier to entry is pretty low and again if the value is creating solutions, then the evidence bar is low as well. As such, we are seeing many companies entering this space, or suddenly pivot to enter this space with the same product that was once a point solution. However, the best platforms are  those that are able to build solutions with the end goal in mind, have legitimate data on their ability to impact those goals, and get better with every successful (or unsuccessful) deployment."

It is not quite the New Year, but what is your prediction for digital health in 2024?

"2023 was the year of generative AI. There are various ongoing pilots applying LLM to healthcare problems. I think we will see some breakthrough applications come to the forefront and I am excited about what those may be."

If you'd be interested in contributing your thoughts to the DTx Spotlight series please get in touch.