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The gap between model or potential solutions and solutions that work in the real world – the translational gap – is arguably the greatest challenge we have in healthcare, and is something seen in both medical science and in digital health.
Translational Gap in Medical Science
The single most important lesson I learned from my many years as a bench scientist was how fragile most data are, whether presented by a colleague at lab meeting or (especially) if published by a leading academic in a high-profile journal. It was not uncommon to watch colleagues spend months or even years trying to build upon an exciting reported finding, only to eventually discover the underlying result was not reproducible.
This turns out to be a problem not only for other university researchers, but also for industry scientists who are trying to translate promising scientific findings into actual treatments for patients; obviously, if the underlying science doesn’t hold up, there isn’t anything to translate. Innovative analyses by John Ioannidis, now at Stanford, and more recently by scientists from Bayer and Amgen, have highlighted the surprisingly prevalence of this problem.
An invaluable function of industry (as I’ve argued) is pressure-testing academic results, and seeing if they are as robust and generalizable as the authors hope and expect. One reason early-stage VC funding in the life sciences is so hard to come by these days is that investors have learned through painful experience not to take hot published data at face value.
“An invaluable function of industry (as I’ve argued) is pressure-testing academic results, and seeing if they are as robust and generalizable as the authors hope and expect.” -David Shaywitz, M.D.The challenge of data reproducibility also represents an opportunity – if you can demonstrate your intriguing results are robust, and achievable by others, you’ve effectively differentiated yourself from a lot of the competition.
That’s the logic, anyway, behind a new effort called the Reproducibility Initiative, which aims to provide a mechanism for scientists to show their results are reproducible by paying a third party to replicate them (see this Reuters article by Sharon Begley, the science journalist who I suspect has done the most to focus attention on the need for better translation). A PLoS journal has committed to publishing such validation studies, and Nature journals that publish original studies have indicated (according to this excellent Carl Zimmer article in Slate) that they will link to subsequent validation publications.
I’m a bit skeptical that this initiative will actually catch on (and I also wonder what fraction of academic studies are even amenable to this approach); however, the very existence of this effort highlights the magnitude of the challenge we now face in bridging the gap between a self-sustaining academic enterprise that thrives on journal publications and a struggling medical products industry that requires a far higher (and underappreciated) standard of proof in order to be successful.
Translational Gap in Digital Health
The most significant gap that I’ve seen in digital health is between the seasoned clinicians and other healthcare providers who arguably have the best sense of the problems that need to be solved, and the enthusiastic but medically naive technologists and entrepreneurs eager to offer solutions.
Most of the digital health entrepreneurs I’ve met – even those who see themselves coming from the clinical side rather than the technology side – have remarkably little medical experience, something that comes across not only in the problems described and solutions envisioned, but also more generally in the failure of many entrepreneurs to grasp the gravitas of medicine, and to appreciate the range and depth of fears and concerns experienced by patients contending with serious or chronic conditions.
Meanwhile, the truly experienced physicians and providers immersed in patient care tend to be consumed with – that’s right – patient care, and often haven’t either the time or inclination to consider entrepreneurial solutions to the problems they encounter. They may have a comparatively full and nuanced appreciation of the problems to be solved, but may be disinclined to leave their established comfort zone and think about new, technologically-enabled solutions.
Ideally, this challenge should also represent an opportunity; I suspect experienced clinicians encounter specific problems every day that could be solved, perhaps in a generalizable and widely-applicable fashion, by a competent entrepreneur.
Failure to involve such seasoned physicians more fully in digital health will result in a lot more of what I’m seeing now – cutsie, tractionless health apps that might appeal to a young kid’s (or inexperienced tech VC’s) idea of what doctors do, rather than addressing a real pain point experienced by doctors and other medical providers. (See also this excellent recent post by Dr. Jay Chyung of the consultancy Recon Strategy about the need to ensure health data analytics efforts are focused on actual problems. [Disclosure: I’ve previously co-authored several EMR strategy articles with Recon Strategy partner Tory Wolff.])
Enterprises seeking to catalyze digital health are doing a great job drawing in young entrepreneurs, energized medical and graduate students, and junior med school faculty members – a tremendous achievement.
However, I suspect durable success in digital health will also depend upon the ability to access the insights of the experienced physicians who are less focused on disrupting medicine, and are consumed instead with the profound, worthy responsibility of delivering it.
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