Hot take: some folks still believe that digital health and digital therapeutics can be reduced to simple SAAS models. Actually, that was kind of a tepid take. But the gripe still stands; you can’t equate digital health interventions to selling a license for a piece of project management software…but we still do! Any claim you make about the effectiveness of a digital health intervention will be scrutinized severely by the healthcare market, as healthcare is supposed to be built on evidence. A project management software claims to be useful? Great. Low stakes. A healthcare intervention claims to be useful? TIME FOR A BIG COMPLICATED ROI MODEL THAT NOBODY ON YOUR COMMERCIAL TEAM IS EQUIPPED TO BUILD! And then when you try to establish the ROI, you realize a bunch of data is missing. What populations are you targeting, and do you have demographic data of the folks using your tools? Do you have outcome data? If so, is it robust enough for inclusion in the model? How does the intervention work when combined with medication? Can you calculate a cost associated with a single point change on a validated scale? Do you know how much it costs to get someone into remission/recovery/reliable change territory? So many data points, so little time and expertise. And it’s not like it’s a single variable associated with each of those prior questions; it gets big, complex, and it also becomes very easy to make mistakes that can be torn apart by the market and your customers. But that’s where science comes in. Us scientists in the digital health field? I’ve never seen a more scrappy bunch. I’ve spent over 10 years as a scientist in the DTX industry, and have worn several hats; commercialisation, marketing, product, UX, clinical. That’s what the role demands of you! Over the years, I’ve learned that it’s my job to see how evidence informs all aspects of a business and create claims that stand up to scrutiny. If you’re working in the healthcare/health tech space, you need to understand that impact, value, outcomes and research are core to the language your market speaks. Otherwise, you’ll eventually be thrown in the bin when the next trend emerges. Science doesn’t slow things down, but rather…creates stickiness and longevity in the market by arming you and your teams with robust, scientifically validated claims. And there we go. That’s my hashtag. #StickyScience
Such a great take Dan Duffy, PhD! Not only do I agree with your points and those in the comments but another component of this is the fact that businesses and people are use to embracing SAAS and integrating it into their work but patients and providers aren't as used to this when it comes to integrating a digital therapeutic. It's not as easy as, here is the software, we've installed it on your device, now click around. Patients and providers have to grapple with questions like: who is going to pay for this, does this replace or supplement my existing treatment regimen, and how long am I expected to use this app among so many others. These are questions even the companies developing and disseminating these interventions still don't have clear answers for. Fortunately, as you and others have mentioned there is a scrappy group of solid researchers and behavioral scientists who have the tenacity and the toolkit to sort these challenges out.
Could not agree more - and we sure are scrappy! I have never seen people do more with less, while continuing to do high caliber work, than I have seen from the scientists I know in digital health. Which begs the question why we still have such a hard time convincing other folks of the value we bring to the table. I feel like we’re all preaching to the choir here on LinkedIn - but what can we do to get this message to the people who need to hear it?
Great post! A few of us have been talking about the use and misuse and recent letting go of industry scientists in digital health. A collection of our ideas was leveraged to create a playbook a few months ago using ChatGPT. Not exactly the playbook we had in mind, but was a fun experiment. https://www.linkedin.com/posts/lindsayayearst_aiexperiment-collaborativeinnovation-industryscience-activity-7199439968652386305-1pIN?utm_source=share&utm_medium=member_ios
Great points Dan, couldn’t agree more. Your argument clearly points out the need of a careful deployment into the service so that the value of intervention can be measured. This may seem obvious but sometimes customer expectations are to have a “quick deployment” and then realising later on that value cannot be established because the key metrics are not properly captured…
Brilliantly communicated, Dan! This is the kind of discourse we need around digital health interventions. Well said!
Digital Health │ Implementation Science │ Healthcare Innovation
2wI used PM software as an example, but only to illustrate the difference in stakes. Much love to my PM friends. Big Monday fan <3