One of the most fundamental features of Darwinian evolution is its wastefulness. It is supremely good at shaping entities to fit their environment, whether they be organisations or organisms, but the journey to evolutionary fitness is profligate. Many, many more species of eukaryote than the 8.7 million believed to inhabit the earth today have evolved but are now extinct. Most of them were evolutionary dead-ends. Present-day species share common ancestors with these extinct species but are not descended from them. Interestingly, the fossil record reveals that this apparently spendthrift process is also fitful.
Rather than happening at a steady pace, biological evolution seems to happen in spurts, with long periods of slow change punctuated by intense bursts of extinction and speciation. These biological bursts are mirrored in the evolution of industries and a flurry of recent events in the life science business seem to suggest that such a shakeout is currently underway in the digital health area. As usual, let me stray into my research domain before I come back to the practical implications.
Ask 100 executives what they mean by digital health and you will get at least that many answers. Wikipedia, stealing from Paul Sonnier, gives the woolly and circular definition as ‘the convergence of digital and genomic revolutions with health, healthcare, living and society’. This makes it hard to imagine anything in the life science industry that could not be called digital health. My experience of dealing with pharma and medtech companies suggests that a better definition is a healthcare value proposition that is critically dependent on information technology. This definition fits all the examples I uncover in my work, from smart inhalers to clinical trials enabled by mobile technology and telehealth to smart pills. However, the semantic clarification of the term is not what this article is about.
What interests me is that digital health is showing a pattern that is familiar to biologists. We’ve seen an explosion of ‘species’ of value propositions made possible by IT. As well as the examples above, we see drug design enabled by bioinformatics, diagnostic imaging enabled by AI, blood sugar monitoring uploaded to the cloud and a thousand other ways that IT is enabling healthcare. What is striking is that they are so different in form and share only the essentialness of IT. None of these ways of creating healthcare value would have been possible without the revolution in IT capabilities. An evolutionary scientist like me looks at this and sees a mass speciation event, with close parallels in earth’s biological history.
I’ve written elsewhere about the difficulty of making evolutionary predications. The best we can do is to identify emergent properties – trying to extrapolate is a fool’s game. But an understanding of evolution can prepare our minds to look out for and understand the changes that are emerging. My work on the evolution of the life science industry, for example, tells us that we should look for an accompanying mass-extinction event, as business models die off, and we need to understand this in terms of digital health business models that will survive or become extinct according to how well they adapt to the Information Shift, one of six great selection pressures acting on our industry (see ’Selection Pressures’ PME February 2016). And that takes us back to the practical lessons we can learn from recent announcements.
Two successes and two failures provide interesting contrasts. The first good news is Flatiron’s acquisition by Roche, which reveals how using real-time big data is transforming oncology. The second positive is the success of Abbott’s Freestyle Libre, which is similarly changing blood glucose monitoring. On the downside, the research on the usage of health apps reveals that very few of the thousands available are actually being used to any significant extent. And this is echoed by Nokia’s announcement that it sees no future for its health division. What these and other events tell us is that digital health (that is, IT-enabled value propositions) are shaking out. A minority are thriving and a majority are heading towards extinction. Their different paths can be explained by the selection pressures created by the Information Shift. This shift favours models that use large-scale data bidirectionally to create customer-perceived value. Equally, models that don’t do this are disfavoured by the business environment.
What’s happening in digital health is therefore a great example of how evolutionary science can help leaders in the life science industry to improve the survivability of their own business model. In this case, Darwin’s brilliant idea is screaming at us about which digital health models will thrive while others, a majority, will become fossils in text books. One hopes that our industry’s leaders are listening.