Given our discussions on understanding systems, the characteristics of said systems and the gaps for improvement, the question that everyone invariably asks is how do we close that gap? Is there a better way to approach this kind of problem solving in novel situations?

It may surprise you but the way in which to approach these interventions is already a large part of the way in which we learn and develop as professionals in healthcare and sport and exercise performance. That is the application of the scientific method. But I will explain why we often do not use this method for its more simplistic but effective features. 

We must first go back and discuss a feature from blog one about complex systems. Complex systems exhibit what is termed emergent behaviours. That is the trends, actions or behaviours that are expressed emerge from a confluence of all of the factors that interact within a system to emerge in a particular output. In the last article we spoke about hamstring injuries. And the presentation of a hamstring strain is the emergent behaviour. That is what comes out of the system when all of the factors related to injury risk interact with each other (injury history, fatigue, poor strength, poor running mechanics). The part that has always been difficult to understand in such systems is the interactions between the factors and how this individual system will present them. For this reason, the most relevant thing to measure in complex systems is the emergent behaviour. Why I say this is that in the areas that we work, healthcare and sports performance, many professionals see the human systems as mechanical in nature. You will often hear people talk about the human body like a car needing a tune up or training building more horsepower. Now it is not entirely wrong to use such analogies, however mechanical systems have linear relationships meaning that you have a strong understanding of what effect a particular input will have on the output of a mechanical system. This is not the case in a complex system. And we all know this without realising it. 

If two athletes or patients are given the exact same training or rehabilitation program for the same sport or the same injury, we have all witnessed such vastly different responses from the exact same inputs. We are not mechanical systems, we are complex and thus the most valuable signal is that emergent one, not the input. 

That aside, it brings us back to the scientific method. The scientific method underpins applied sciences such as healthcare and sorts performance. And despite the misuse of science by those who claim to be using evidence based practice, the scientific method is one in which a behaviour is observed (emergent behaviour), a hypothesis is developed for a potential intervention to alter this observed behaviour and implemented in a controlled fashion and then measured. Once this is measured, inferences are drawn and the process starts again. Science is extremely iterative. And I say this because many people incorrectly see an observe a behaviour draw broad conclusions about what that means and then set elaborate interventions regarding the next input. If you observe the history of knowledge gain it happens in small steps before their may ultimately be a change in the thinking that is a significant adjustment. But it is these small movements of checking, rechecking and then inputting again that drives the needle of change. 

I see everyday that many practitioners say that they are applying a scientific method, but they are rather employing wholesale inferences from another persons or teams data and employing it verbatim without constantly checking and evaluating what is emerging with that individual person. 

Often I get asked about how do you close the gap in the areas that you have identified? And the answer is by taking small steps towards the target, checking and rechecking the output along the way. It may be a bastardized version but to me this is much more scientific than what people implement when they say they are acting using evidence based practice. To do this the idea is that you run safe to fail experiments, that is small inputs across time and measure the outcomes (emergent behavior). If your output is in the direction of your desired location, repeat this process, potentially amplifying some features of this input. If the outcome does not work, dampen this input or remove it. If the output step is small (i.e a safe to fail experiment), then you are likely to be able to correct the direction of travel relatively easily and with minimal harm done. 

So when closing the gap

  1. Measure the output features and develop a hypothesis for how to affect this.
  2. Develop a series of safe to fail experiments (inputs, treatments, training programs) and implement them
  3. Measure the emergent behaviour from the experiments and then amplify or dampen the use of these experiments based on what emerges.