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Businesses exist to solve problems, right? Certainly, this is the heart of the classic entrepreneurial model: you become obsessed with a particular problem, and create a business to solve it. Example: eBay was created by Pierre Omidyar to solve a perceived problem with inefficient markets, and since its inception has generally focused on doing exactly this.
Most enterprises are not blessed by such a coherent focus, at least not for long. More often, organizations – including university research labs as well as for-profit businesses – have a point at which they realize that their challenge has changed, and the problem they thought there were going to solve has shifted or even completely disappeared. The team – often an impressive group of people representing a wide range of capabilities — is then left to figure out what to do.
While disbanding is always an option, it rarely seems to happen, at least volitionally. Businesses, projects, academic enterprises – all are obsessed with their own survival, which rapidly becomes the defining mission. As a result, the organization urgently tries to figure out a way to pivot, a way to apply established resources in a different, useful way as it searches for a purpose to justify its existence. Very often, the question becomes: what should we do – what problem should we solve?
This desperate struggle for survival isn’t necessarily a bad thing – in fact, the very urgency of this existential dilemma is arguably responsible for some of history’s most important advances, whether the crisis is faced by a small tech company that’s constantly shape-shifting as it tries to stay solvent or by a seventh-year PhD student whose first several projects crashed and burned and is desperate to find some way to graduate.
The constant need for businesses to adapt and survive rather than move towards a single fixed vision may help explain the David Brooks observation that continues to trouble me: most successful CEOs are not about self-actualization but good execution – they are diligent managers seeking to survive rather than inspirational leaders striving to fulfill a deep vision (it’s also why we’re so entranced with the rare visionary leaders like Branson, Jobs, and I’d add Howard Schultz), who offer so much more than simply responsible stewardship.
While biopharmaceutical companies may be thought of as mission oriented, they are almost certainly the poster children for adaptation.
Biotech start-ups are known to rapidly lurch in different directions in response to changing data – genomics platform companies become oncology products companies, for example.
“They often do not have a clear sense of what it is they should work on, or how they should do it.” – David Shaywitz
Perhaps less well appreciated is that big pharmas (many of which began as chemical companies) are beset by the same dilemma: at a fundamental level, they often do not have a clear sense of what it is they should work on, or how they should do it – therapeutic areas, disease areas, organ systems, severity of illness, location of patients – what to do?
The need for focus was perhaps best captured by a consulting project I once worked on: the goal was to construct, for a large pharma client, a rank list of all human ailments, from 1 to the end, based on some weighted integration of a large number of factors (number afflicted, burden of disease, generic presence, etc.). Presumably, this master list would help the client prioritize opportunities; I’m not sure whether it was actually useful.
In most businesses, of course, you identify a customer need (via market research, intuition, or some combination), then go out and design a product to address this; at some fairly coarse level, this is how biopharmas work: you create a “target product profile” (TPP) that supposedly represents where you think where a new product could be useful, and then you seek to deliver against this TPP. Problem is – funny story – biology is pretty damn complicated, and you can’t generally order up therapies along pre-determined lines. (Even if you succeed, forecasting turns out to be pretty damn fragile.)
The irony is that if you could actually design biopharma solutions as easily as this, I doubt that at least in the short-term there would be nearly as much obsessing about the nuances of these profiles – rather, we’d be out there developing outright cures for pancreatic cancer, Alzheimer’s Disease, brain tumors, etc., rather than debating how slight a change in progression-free-survival should be targeted by the next oncology drug.
Nevertheless, even with the complexity of biology, there are still a wide range of diseases (including, aspirationally, the ones above), and certainly aspects of diseases (such as bothersome symptoms, existing therapies that are unpleasant or difficult to take, or to remember to take), that industry can tackle effectively – if only there was a better sense of where to look.
Increasingly, open innovation is invoked as the Big Answer — we constantly hear about communities of solvers out there to work out our most pesky problems.
But that’s not our biggest problem right now: most companies have excellent solvers: the issue is figuring out the right questions. I suspect what’s needed most (as I’ve previously discussed) is more “field discovery” — the granular input of the patients and physicians who are trying to cope with some real-world aspect of an illness – challenges that in many cases medical product companies, or digital health companies, could effectively address, if only they realized these problems existed and could be adequately defined.
This challenge – which, as I recently discussed with Halle Tecco, founder of the non-profit SF incubator Rock Health, is also endemic is the digital health space – is fundamentally an issue of asymmetric information: the people who know what the problems are usually aren’t the people best equipped to develop and design the solutions (whether a drug, a device, or an algorithm or therapeutic approach), and as a result, you have solvers solving problems that often not germane (a huge problem in the technology area, as I’ve written, and also a problem in biopharma, where the bench scientists developing new treatments often have minimal understanding of the clinical entity they are targeting). You also wind up with frustrated patients and physicians who constantly encounter problems they are unable to solve.
The idea of bringing together stakeholders is a classic approach that was pioneered at Stanford to unite engineers, physicians, and business people to generate more useful devices, and more recently (as I’ve discussed here) has been used to work on health system problems as well. The question is whether a more general approach could be developed that might enable the more efficient surfacing of problems, and more effectively and efficiently bring the right solvers to bear to address it.
Critically, such an approach should be not just participatory but iterative and interactive, so that questions can be refined, and potential solutions discussed and prototyped.
Developing this sort of platform is an effort I expect to be working on with Halle and her Rock Health colleagues in the coming months; it’s a tough nut to crack, but I think if progress can be made here, there’s a huge opportunity for benefit, as right now there are thousands of solvers who could easily impact the right problem, and there are millions of stakeholders who could identify and help characterize specific health problems in need of a solution. Something’s gotta give.
David Shaywitz is an adjunct scholar at AEI.
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