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What can digital health startups learn about commercialization from medtech and life science companies?


As the healthcare industry shifts from a fee-for-service model in favor of value-based care, technology entrepreneurs see an opportunity for digital health tools to support this transformation. But the lengthy sales cycle for healthcare, the reality that the people/organizations footing the bill for this new technology are not necessarily the same groups who would be using it, coupled with the need for buy-in from clinicians and/or payers as well as investors, and one soon appreciates just how complex the path to commercialization can be.

This meaty topic of how digital health tech developers navigate this path is the subject of a panel discussion at the MedCity INVEST Twin Cities conference in Minneapolis October 11 and sponsored by Baker Tilly Virchow Krause, LLP. Panelists include: David Gregory, Principal, Healthcare consulting leader with Baker Tilly; John Brownlee, CEO of Vidscript; and Anser Innovation CEO Lisa Lavin. Marcia Miller, Stone Arch president, will moderate.

David Gregory of Baker Tilly

For his part, Gregory believes that digital health companies could benefit from developing a better understanding of the lessons learned by medical device companies in bringing their products to market.

The panel discussion comes at an interesting time for the growing digital health industry. In the past few years, investor dollars have showered digital health startups with record amounts of funding. The U.S. Food and Drug Administration has adjusted the way it regulates some types of digital health technology. In 2015, the regulator said it would focus more on tools that are used in a clinical context rather than wellness apps. More recently, the FDA also launched a pre-certification program pilot program to streamline what can be a lengthy approval process.

On the other hand, for digital health products aimed at the direct-to-consumer market, the Federal Trade Commission has not shied away from fining companies over what it has described as misleading marketing claims. Examples include a teledermatology app that claimed it could help identify skin cancer earlier, a blood pressure monitoring app developer and a business that developed an app claiming brain training properties.

Focusing on digital health companies developing technology to be used in clinical settings, Gregory offered a few recommendations for what healthcare startups should consider.

“Ultimately, you should be prepared to explain the clinical utility for your product because if you can’t, it’s a dealbreaker,” Gregory observed.  Many companies have focused on improving the way Type 2 Diabetes is managed, for example, but unless they can develop a way to make the job of managing patients or a subset of patients with this condition significantly easier and cheaper, and/or significantly more effective compared with what other businesses offer and what is already available,  it won’t give clinicians or healthcare facilities much motivation to adopt the technology.

Clinical utility validation, generating data that can demonstrate the effectiveness of a digital health tool and its impact on clinical decision-making, is at least a starting point.

“It would be a mistake for the digital health sector, in general, to underappreciate the need for evidence,” Gregory said. He cited a checklist Baker Tilly has produced that’s designed to help companies map out a commercialization strategy.

Medical device and other life science companies have historically had a more rigorous approach to collecting clinical evidence. But digital health businesses face a new set of questions when they go down this pathway. Should they collect clinical evidence through randomized control trials? Meta-analyses? Prospective cohort studies, claims data analyses, or medical record reviews? The sample size of patients assessed in these studies needs to be large enough to be effective, in the neighborhood of 200 to 300 people. Who will carry out the study? A provider or another third party? Ideally, the analysis should measure long-term outcomes. But what inclusion/exclusion criteria and/or propensity matching is utilized to match cohorts?

Just as important is the issue of how companies prove that their digital health technology actually cuts down on medical expenses, be it on the part of the hospital or the insurer. How does the company’s service or product compare with the current standard of care? Budget impact models and incremental cost-effectiveness ratios could be used to quantify and analyze economic evidence for digital health.

In order to be effective, the studies to collect economic evidence need to include data from relevant patient populations. Digital health companies need to be prepared to show how they will safeguard patient claims data and clinical data before a healthcare organization will collaborate with them.

Even if the digital health company is eventually granted FDA clearance for their product, it may help them make their case for the technology to clinicians but it doesn’t guarantee adoption in and of itself.

“FDA clearance or approval is just a ticket to the dance,” Gregory quipped.

Several companies have recognized the need to treat their digital health tools just like drugs and devices under development. Among some examples are Omada Health in the diabetes management space, as well as Pear Therapeutics, which has developed digital tools supporting treatment for substance abuse, and Akili Interactive, which has focused on Attention Deficit Hyperactivity Disorder and Alzheimer’s disease, among other areas. These companies refer to their products as digital therapeutics and have made clinical validation a priority in product development.

As the digital health sector begins to mature, these and other companies are determined to play the long game of clinical and economic validation. Companies that succeed at doing this while scaling their businesses will hopefully offer a more predictable pathway for other digital health entrepreneurs.

Photo: Getty Images

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