In Phillipa Malmgren’s excellent book Signals she points to the weak signals in the economy and politics that point to the future. In so doing, she’s making a general point that in any complex adaptive system (for example the economy, society or the life science industry), it is not mathematical extrapolation that points to the future but the aggregate of many small incidents that reveal the emerging properties of that system. In my scanning of industry news this month, there have been some really good examples of signals that portend the future. As usual, allow me to talk a little bit about the science before I come back to the practical implications of this.  

The life science industry is not complicated. Many readers will widen their eyes at such a statement, but it’s true. Complicated systems have a large number of very different parts but they are all connected in relatively simple ways. The archetypal example of a complicated system is an airliner. The important and reassuring thing about complicated systems like airliners is that if you make one particular input you usually get the same response every time. If I push the throttle, the engines produce more thrust. If I pull back the stick, the aeroplane climbs. The same is not true of complex systems, such as the rainforest or the life science industry. The relationship between an input, such as the regulatory change or using pesticides, and what happens next is unpredictable. This is because the different parts of the system are connected in many different ways and feedback loops can work in both directions. So, although it may sound like a semantic game, this is an important point for industry leaders to understand. The life science industry is not complicated like an airliner, it is complex like a rainforest.

The relationship between an input, such as a regulatory change, and what happens next is unpredictable.

The implications of this important difference are very profound. Generally speaking, we can predict and model the behaviours of complicated systems. If we could not, I would be much less happy about going to Heathrow. The same is not true of complex systems. Ecosystems like the rainforest, weather systems like Atlantic storms and economic systems like the life science industry can only be forecast, in the mathematical sense, to a very limited extent. When we deforest an area, put carbon dioxide into the atmosphere or change an HTA methodology, we can predict the consequences with limited confidence and then only in the short term. This sounds like a counsel of despair and indeed it is for traditional strategic planners who rely on forecasting alone. But there is a positive side to the idea that we work in a complex adaptive system. An important characteristic of such systems is that they have emergent properties, which are the result of the interactions of the component parts. As the need suggests, these system characteristics emerge gradually.

This means that, with care and attention, we can see important changes emerging long before they become obvious to the less attentive observer. I have written elsewhere about one such property of the life science market called the value shift. This is a fundamental change in how the value of medical technology is defined and who defines it. In recent news, payers in Manchester in the UK are considering how they might pay for drugs based on efficacy, itself an evolution of risk-sharing arrangements. At the same time, the NHS is studying how not to pay for over-the-counter medicines such as paracetamol or even, unbelievably, sun cream. These news stories and others like them, while of relatively little consequence on their own, are signals of the emerging value shift. They tell us that, in the future, health systems funded by governments and other payers will seek value in three ways. Expensive, innovative technologies will be paid for by results; cheap, generic technologies will be bought on price and many medicines will not be paid for at all out of the payers’ pockets. Instead, the patients will pay for much of their own medicine. I should stress that, although these examples are recent, the evidence is that the emerging value shift is very strong and very extensive.

Complexity has very important implications for executives in the life science industry. While we cannot predict the future, we can with care see it before our competitors do. What we see may be an opportunity or a threat but it need not be a surprise and so we can plan for it. All that is necessary is a shift in culture, and to some extent resources, away from a business intelligence system dominated by numbers and extrapolation towards one which scans the environment widely and then synthesises many different kinds of information. You can adapt to the future but only if you see the signals first.