I recently returned from participating in one of the most interesting recent conferences I’ve attended, a Keystone Symposium entitled “Digital Health: From Science To Application,” with a breadth reflected by the backgrounds of the three organizers: Geoff Ginsburg, Duke University professor and geneticist, Sue Siegel, GE’s Chief Innovation Officer and CEO of their (soon to be spun out) venture fund; and Eric Perakslis, a health data science leader with both industry and FDA experience who will be joining Duke University as a Rubenstein Fellow in February (Eric’s 2017 Tech Tonics interview is here). An excellent and articulate video capsule summary of the meeting, presented by Ginsburg and Siegel’s colleague Oliver Keown, can be found here.
Keystone meetings are, by design, off the record, in effort to stimulate the free exchange of pre-publication information among participants; however, I’m going to talk about one of the presentations, by UCSF professor Dr. Aenor Sawyer, with her explicit permission.
Aenor Sawyer’s mission – bringing healthtech innovation into the healthcare system — and vision (pragmatic, inclusionary, optimistic) have always captivated me (she was actually one of our very first Tech Tonics guests, when Lisa Suennen and I started in Jauary 2015); perhaps the consummate example of the reflective practitioner, Aenor trained as an orthopedic surgeon, and is on faculty at UCSF. She has led and participated in multiple efforts focused on cultivating healthtech innovation at UCSF, and was recently named Chief Health Innovation Officer of NASA’s Translational Institute for Space Health (an organization helmed by another captivating Tech Tonics guest, Dr. Dorit Donoviel); Sawyer’s new role enables her to remain at UCSF continuing her commitments as well.
Sawyer’s presentation covered a range of topics, but I want to focus on several highlights: the primacy of “frontline innovation,” and her advice to would-be health tech entrepreneurs.
In Sawyer’s view, successful innovation within healthcare system requires the early involvement of “frontline innovators” – who can be doctors, patients, other care providers, who are defined, in Sawyer’s view, by active engagement with a problem – problem solvers who are intensely frustrated by a specific aspect of the healthcare system that they touch, and are passionately interested in identifying a better approach.
Sawyer says frontline innovators bring unique value to developing successful solutions in that they understand the complexity of the problems, conceptualize solutions in context of the workflow (or identify where the workflow needs to be disrupted), and are dedicated to proving the effectiveness of solutions, as they will be using them for their patients. Frontline innovators are also eager to partner with external health tech developers or startups, and benefit from their complimentary skill sets in technology, design, product development, and commercialization.
Sawyer’s focus on frontline innovation turns out to reflect an important lesson of technology innovation (a point I also highlighted in a strikingly similar talk I presented on healthtech innovation within biopharma, and also wrote about here): implementation isn’t instantaneous, and it takes a long time to figure out how to use a powerful invention – this is the rule, not the exception.
In his essential treatise Learning By Doing, technologist (and now Boston University professor) James Bessennotes that “major new technologies typically go through long periods of sequential innovation.” Similarly, in the context of transportation innovation, Northwestern economist Robert Gordonwrites (in The Rise And Fall of American Growth), “Most of the benefits to individuals came not within a decade of the initial innovation, but over subsequent decades as subsidiary and complementary sub-inventions and incremental improvements became manifest.”
Finally, and perhaps most relevant, MIT innovation professor Eric von Hippel(who I’ve frequently cited – here, here, here, here) has emphasized the importance of “field discovery,” and the criticality of “lead users” — practitioners keen to apply a promising approach to a pressing problem with which they’re actively wrestling; Hippel’s approach has generated so much traction he’s even created a “Lead User Project Handbook” to teach some of the associated methodology – he generously makes it available for free download at his website, here.
Lead users, as Hippel and colleagues describe them, are “sophisticated product/service consumers who are facing and dealing with needs that are ahead of the bulk of the marketplace.” Lead users precede “early adopters,” who are among the first to adopt a new product or service; in contrast, lead users “are facing needs for products and services that don’t yet exist on the market” (emphasis in original).
Data Overload And The “VNR”
Sawyer is captivated by the potential of technology and data to improve healthcare, entranced by the promise, but notes that in reality, there are “oceans of underutilized fragmented data, with much redundancy, instead of actionable insights.” This information overload tends to overwhelm everyone, she notes, contributing to provider burnout, and leaving patients frustrated with what to do with a lot of disconnected data; not surprisingly, she points out, nearly half of wearables wind up in the drawer by six months.
Sawyer coined a concept I really liked, the “VNR” (a play on the “INR” ratio used to modulate warfarin dosing), which stands for “Value to Nuisance Ratio.” Essentially, a VNR > 1 is good, it means a device or approach is likely to be used; a value < 1 is bad, likely a technology that seemed clever or helpful in theory but in practice turned out to be more trouble than it was worth.
In her role at UCSF, Sawyer encounters many startup entrepreneurs, who she jokes are so plentiful in the hospital corridors that they “may be the new drug reps.” (I suspect this may be less of an issue at hospitals outside the Bay Area, though I’d love to be mistaken). In her discussions with these startups, Sawyer’s observed several common problems, areas of disconnect between what the entrepreneurs think they’re offering and what the healthcare system needs.
In Sawyer’s view, many startups are soliciting pilots when in fact they really should be getting frontline innovator input on their proposed solution, and thinking a lot more about evidence generation; she notes she’s spoken with over 300 startups in the last several years, and the vast majority (over 80%) really aren’t ready for a meaningful pilot, and need to refine their proposed offering first.
From Mega Data To Matta Data
Sawyer also emphasizes the importance of going “from mega data to ‘matta’ data” – data that matters. The ultimate goal of data generation and analysis in healthcare, she points out, is to lead to well-informed actions.
Solution builders, Sawyer says, would do well to:
The point about evidence seems especially salient in light of a recent paper by Ioannidis and colleagues noting that, as TechCrunch succinctly summarized, “Most of the highest-valued startups in healthcare have not published any significant scientific literature.”
As Ioannidis et al write, “[I]n contrast to the tech sector, in healthcare published peer‐reviewed research is essential to ensure a minimum threshold of transparency, accountability, and credibility for the underlying work in the scientific community.”
Sawyer’s message isn’t only for startups. Healthcare organizations, for their part, says Sawyer, need to support internal and external innovation and the bridge between them. She argues they need to provide mechanisms and funding for frontline innovators and need to improve how the healthcare system engages with external entrepreneurs, providing more open and efficient partnering models for collaboration, validation, and even co-development.
Taking a step back here, you can see how difficult innovation within healthcare systems is, leaving would-be innovators with two unpalatable choices, at least in the extreme: innovate entirely outside of the existing system, and compete with entrenched incumbents who are experts at working the established processes; or develop a solution for use by incumbents, which involves integrating with byzantine systems and stultifying (if familiar) workflows.
You can also see why “full stack” approaches focusing on outcomes offer so much appeal, offering a potential way to break free; you can largely work outside the existing system, but connect with it in what might be a value-add fashion (assuming you can find someone willing to pay for the value…).
As I understand it, this is what Virta does (reminder: I’m a non-diabetic, paying customer, not an investor). They basically offer to manage the type two diabetes care for patients on the program – their licensed physicians adjust doses, track labs, advise patients, and remain in contact with the patient’s primary care physicians. In some ways, this might be described as type two diabetology-as-a-service. If you believe Virta generates good outcomes, you can see how it might make sense in a value-based care world, but also how it might run into difficulties in fee-for-service health settings, where there is enormous pressure on physicians to keep as many referrals as possible within the system. (If you ever want to depress yourself, Google “referral leakage,” and you’ll discover there’s a whole industry devoted to reducing this “problem.”)
Innovation within the healthcare system can be exceptionally challenging, as UCSF professor Aenor Sawyer helpfully summarizes. Tech entrepreneurs should work closely with front-line innovators to ensure their company is focused on a relevant problem and is developing an implementable solution; innovators must also recognize that robust evidence of impact (not just clicks or eyeballs) is essential for adoption by most established players.