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.
