This blog was written by Adam Rosenberg, CEO of Rodin Therapeutics, as part of the From The Trenches feature of LifeSciVC.
In 2015 I went to both the JPMorgan and CES conferences, back to back, and attended numerous can’t-miss dinners, receptions and parties. It was also hard to miss the contrast between the consumer healthcare technology landscape in Las Vegas – focusing on splashy displays of wearable devices and such – with the deal-focused biopharma landscape in San Francisco.
Much has already been written on the 2018 version of both events, and the potential overlap of digital health and drug development. For an excellent overview, see David Shaywitz (here).
Progress is certainly evident over the last three years, as highlighted in my previous digital health posts, focused on digital health in clinical trials (here), and the convergence of consumer devices and clinical applications (here). Tremendous potential remains, in areas such as patient monitoring, disease management and clinical trial optimization. Successful interventions will integrate various modalities and inputs; hardware, software, therapeutics, telemedicine, diagnostics, genomics and electronic medical records.
So the vision remains powerful. The reality is messy, however. Exciting press releases do not predict a sustainable, long-term business; especially in a field moving as quickly as digital health.
What does this mean to a digital health startup or investor? Or a biopharma company seeking to leverage new technologies in discovery, development or patient engagement? Further, when does digitial health become just another routine healthcare tool?
Before joining Rodin, I had the opportunity to lead a digital health landscape evaluation for Brightstar (since acquired by Softbank), the leading supply chain/solutions provider in the mobile industry. I also served on the advisory board of two startups that were ultimately acquired.
All three companies shared successful outcomes, at least measured by the exit, and provided a window to better understand some of the challenges and opportunities in the sector. I will try to distill some takeaways here, for new entrants to the field, or others trying to evaluate potential opportunities. Some of this may come off as MBA 101, and there is clear overlap amongst the themes. Yet it is striking how many digital health participants fail to follow these basic principles.
Know Your Customer
Grand plans to “lower cost and improve outcomes” – the compelling but also now oversold twin mantras of digital health – require coordination, buy-in and/or budget from multiple players. This may look great on a PowerPoint, but is difficult to implement.
Even for a large multinational company, stitching together contemporaneous buy-in from various stakeholders is an exceptionally high bar. And adopting a ‘Field of Dreams’ approach in digital health is a recipe for capital misallocation.
At JPMorgan this year I met Leah Sparks, CEO/founder of Wildflower Health (and fellow Hatteras Ventures portfolio company). Wildflower (here) is a good example of focusing on a specific healthcare pain point, for a specific customer segment. When pregnant with her first child, Leah recognized that the healthcare system failed to meet the needs of moms and families. Leah and Wildflower built an enterprise mobile solution for new and expectant parents, now adopted by dozens of health plans, covering many millions of patients.
It is critical to know your customer, and ideally require buy-in from only a single healthcare value chain participant to reach scale.
If we look to digital health today as a stand-alone cure, we are asking too much. If instead we look to enable discrete improvements in areas such as compliance or workflow, we are more likely to see a meaningful ROI, and potential to scale.
Adoption remains the big hurdle in digital health. For more on this, see another excellent David Shaywitz piece, highlighting the need for implementation over invention (here). Digital health is stuck in a world of pilots – entrepreneurs and investors need to appreciate this, and in most cases, increase their timelines by many factors.
A good example here is CAP, where I served as an advisor. CAP was a digital dental company founded by friends from Brontes (another digital dental company, acquired by 3M). The team developed an integrated solution for small dental labs – specifically targeted to their unmet commercial needs, in a fragmented field. After a few initial years refining their software and services offering, and listening carefully to their target customer base, CAP ramped quickly and achieved considerable market share in a short time period. CAP raised virtually no outside capital, and was acquired by Henry Schein, on attractive terms.
Startups want rapid, immediate uptake – this is understandable, because they are typically in a cash-flow negative position. Even larger companies need to see ROI in a meaningful timeframe to justify budget and resource commitments. Timelines and solutions need to be titrated to the realities of the fragmented healthcare market, however, and rational expectations are key to drive strategies for capital formation, budget and solution definition.
The Business Model Matters
It is fashionable to suggest that the power of digital health is so huge, and the trendlines towards value-based healthcare so strong, that there should be opportunities for novel, risk-sharing business models beyond a SAAS/subscription approach. If a solutions provider can help lower the cost of covering a certain patient population, the thinking goes, a payer should be willing to share the cost savings.
A top venture investor told me at JPMorgan that if she sees a pitch deck that relies on selling to accountable care organizations (ACOs) solely because of their economic model, however, she stops reading. This investor confirmed my hunch that everyone still makes the same argument; the ROI is clear on paper, so of course we’ll sell it.
But if everyone sells similar solutions to the same ACOs, pushing for creative business models? Well, good luck.
A corollary is that healthcare sector experience makes a huge difference in predicting a new venture’s success. Technology timelines and business models do not generally map simply to healthcare. Ideally, the drive and impatience of the classic technology entrepreneur can be matched with the learned caution of an experienced healthcare entrepreneur. If both phenotypes reside within the same individual, great. Otherwise, solve for gaps when team-building.
Where’s the Data?
Digital health participants often point to a single case study; reducing unnecessary hospital readmissions to minimize penalties and hospital-borne infections (for example, providing discharged heart failure patients with an integrated device kit). It makes good sense – key cardiac vitals are measurable.
A 2016 study suggested, however, that telemonitoring did not reduce 180-day heart failure readmissions (here). Does this suggest that all interventions will fail in this area? Of course not. It does reinforce that while an intervention may make good conceptual sense, however, it does not guarantee translational or market success.
Focus on Quantifiable and Defensible Metrics, with Limited Behavioral Change Required for Adoption
Behavioral modification is difficult, even for digital natives. You may have a phone in your hand or pocket during all waking hours, and on your bedtable at night, but you’re still a human being, with a digital rhythm – checking out the same apps, listening to the same music, etc.
To drive lasting change, the data need to have impact, the outcomes need to be measurable, and the need for behavioral modification to generate such data and outcomes must be limited.
I served on the advisory board of Gecko Health, ultimately acquired by Teva Pharmaceuticals. Gecko is a good example here; digitizing a previously analog process by tracking how often a patient utilizes a metered dose inhaler. Automated data collection matters here; parents can better monitor their children’s compliance, and tracking enables deeper understanding of episodic triggers. Yet the technology is simple: a wireless cap with a long-lasting battery. Further, while it is up to the patient, parent or case manager to leverage the data, there is no behavioral modification required to generate the data.
So Why Bother?
Healthcare incumbents are inundated with promises of technology that will save them buckets of money and change the world. They are understandably wary.
It is easy to be cynical; no one has all the answers in digital health. Just like development of a novel chemical entity, however, patience will be rewarded. Targeted, customer-centric solutions will eventually scale. It has been said that it generally takes 20+ years to translate new biological insights into drugs. Hopefully the timelines are shorter in digital health, but substitute the challenges of validation, scale and integration (digital health) for the challenges of safety and efficacy (drug development), and the parallel has some merit. Failed drug trials can be driven by many factors, and serve as learnings for future efforts. Likewise, just because an early heart failure study failed to show measurable improvements does not suggest that remote patient monitoring is doomed.
Digital health is still in its infancy, but at some point will be an integral component of healthcare, rather than a separate field. It may take ten years, or fifty. Until then, my advice to new entrants is to pick your application and target market carefully, plan realistic budget and go-to-market strategies, and don’t try to be all things to all people.