Understanding the Butterfly Effect in Contextualising Health Outcomes: How Minor Changes Can Cause Major Impact

I’d like to talk to you about contextualising health indicators.

So just as a refresher; most of you will know the butterfly effect describes a phenomenon in chaos theory where a minor change in circumstance can cause a large change in outcome.

The concept is attributed to Edward Lorenz, a mathematician and meteorologist who used the metaphor to describe his research findings related to chaos theory and weather prediction. A perhaps rather nerdy fact; Lorenz originally used the metaphor ‘a seagull causing a storm’ but was persuaded to make it more poetic with the use of butterfly and tornado.

So his concept was that a small change can sometimes make a much bigger seemingly unconnected change happen. I think this is especially true in health.

I’m sure you know that health systems around the world are under increasing strain due to ageing and growing populations. Doctors often don’t have the time to question the patient on minor changes that might have occurred to their routine and or their living environment over the preceding few days or weeks. However let’s imagine they were able to do so, what would the right questions be? And perhaps more importantly would the patients know or be able to provide the answers?

Understanding a patients living environment and their activities of daily living is the critical starting point.  I don’t just mean understanding the general movement around the house, it’s understanding the impact of that movement.

How stressful was a sitting to standing transition at the end of the day? Why did moving from point A to point B today take significantly longer than a week ago? And indeed, why where they so exhausted at bedtime?These give us the problem statements.

Then we look for the minor changes that could be the trigger for these outcomes.

Could it be disease or pain related? Could the weather or air quality be impacting them? Could they have had a new carer? Could we be seeing the early signs of infection or illness?

Cause and effect is never simple but once you understand that you can give a probability weight to each of these things and the impact they have on the patient. When there is a strong probability of one of these things showing as a trigger it can be tracked, correlated and explained.

Really interestingly you can then see if you’re getting similar results across cohorts of patients and therefore refine the new patient parameters based on what we’ve seen before.

The human body is like a very complex machine and like any machine it will respond fairly predictably to external and internal forces around it. If you understand the triggers you can intervene earlier before something minor becomes something major.

If you augment the clinical teams contextual understanding of the patient and give them a window into the often unique small changes that could cause their patient to decline, it could significantly enhance the care that can be given.