There are only four ingredients required to create Chaos.

The first is **Time**.

All processes and systems are time-dependent.

The second ingredient is a **Metric of Interest** (MoI).

That means a system performance metric that is important to all – such as a Safety or Quality or Cost; and usually all three.

The third ingredient is a feedback loop of a specific type – it is called a **Negative Feedback Loop**. The NFL is one that tends to adjust, correct and stabilise the behaviour of the system.

Negative feedback loops are very useful – but they have a drawback. They resist change and they reduce agility. The name is also a disadvantage – the word ‘negative feedback’ is often associated with criticism.

The fourth and final ingredient in our Recipe for Chaos is also a feedback loop but one of a different design – a **Positive Feedback Loop** (PFL)- one that amplifies variation and change.

Positive feedback loops are also very useful – they are required for agility – quick reactions to unexpected events. Fast reflexes.

The downside of a positive feedback loop is that increases instability.

The name is also confusing – ‘positive feedback’ is associated with encouragement and praise.

So in this context it is better to use the terms ‘stabilizing feedback’ and ‘destabilizing feedback’ loops.

When we mix these four ingredients in just the right amounts we get a system that may behave chaotically. That is surprising. It is counter-intuitive. It is also how the Universe works.

**For example:**

Suppose our Metric of Interest is the amount of time that patients spend in a Accident and Emergency Department. We know that the longer this time is the less happy they are and the higher the risk of avoidable harm – so it is a reasonable goal to reduce it.

Longer-than-possible waiting times have many root causes – it is a non-specific metric. That means there are many things that could be done to reduce waiting time and the most effective actions will vary from case-to-case, day-to-day and even minute-to-minute. There is no one-size-fits-all solution.

This implies that those best placed to correct the causes of the delays are the people who know the specific system well – because they work in it. Those who actually deliver urgent care. They are the stabilizing ingredient in our Recipe for Chaos.

The destabilizing ingredient is the *hit-the-arbitrary-target* policy feedback loop.

This policy typically involves:

(1) Setting a performance target that is desirable but impossible for the current design to achieve reliably;

(2) inspecting how close to the target we are; then

(3) using the real-time data to justify threats of dire consequences for failure.

Now we have a perfect Recipe for Chaos.

The higher the failure rate the more inspection, reports, meetings, exhortation, threats, interruptions, and interventions that are generated. Fear-fuelled management meddling. This behaviour consumes valuable time – so leaves less time to do the worthwhile work. Less time to devote to safety, flow, and quality. The queues build and the pressure increases and the system becomes even more sensitive to small changes. Delays multiply and errors are more likely and spawn more workload, more delays and more errors. Tempers become frayed and molehills become magnified into mountains. Irritations become arguments. And all of this makes the problem worse rather than better. Less stable. More variable. More chaotic.

It is actually possible to write a simple equation that captures this complex dynamic behaviour characteristic of real systems. And that was a **very** surprising finding when it was discovered in 1976 by a mathematician called Robert May.

This equation is called the logistic equation.

Here is the abstract of his seminal paper.

*Nature* **261**, 459-467 (10 June 1976)

**Simple mathematical models with very complicated dynamics**

*First-order difference equations arise in many contexts in the biological, economic and social sciences. Such equations, even though simple and deterministic, can exhibit a surprising array of dynamical behaviour, from stable points, to a bifurcating hierarchy of stable cycles, to apparently random fluctuations. There are consequently many fascinating problems, some concerned with delicate mathematical aspects of the fine structure of the trajectories, and some concerned with the practical implications and applications. This is an interpretive review of them.*

The fact that this chaotic behaviour is completely predictable and does **not** need any ‘random’ element was a big surprise. Chaotic is not the same as random. The observed chaos in the urgent healthcare care system is the result of the design of the system – or more specifically the current healthcare system management **policies**.

This has a number of profound implications – the most important of which is this:

*If the chaos we observe in our health care systems is the predictable and inevitable result of the management policies we ourselves have created and adopted – then eliminating the chaos will only require us to re-design these policies.*

In fact we only need to tweak one of the ingredients of the Recipe for Chaos – such as to reduce the strength of the destabilizing feedback loop. The gain. The volume control on the variation amplifier!

This is called the MM factor – otherwise known as ‘*Management Meddling*‘.

We need to keep all four ingredients though – because we need our system to have both agility and dynamic stability. It is the balance of ingredients that that is critical.

The flaw is not the Managers themselves – it is their learned behaviour – the Meddling. This is learned so it can be unlearned. We need to keep the Managers but “tweak” their role slightly. As they unlearn their old habits they move from being ‘Policy-Enforcers and Fire-Fighters’ to becoming ‘Policy-Engineers and Chaos-Calmers’. They focus on learning to understand the root causes of variation that come from outside the circle of influence of the non-Managers. They learn how to rationally and radically redesign system policies to achieve both agility and stability.

And doing that requires developing systemic-thinking and learning Improvement Science skills – because the causes of chaos are counter-intuitive. If it were intuitively-obvious we would have discovered the nature of chaos thousands of years ago. The fact that it was not discovered until 1976 demonstrates this fact.

It is our *homo sapiens* intuition that got us into this mess! The inherent flaws of the chimp-ware between our ears. Our current management policies are intuitively-obvious, collectively-agreed, rubber-stamped and wrong! They are part of the Recipe for Chaos.

And when we learn to re-design our system policies and upload the new system software then the chaos evaporates as if a magic wand had been waved.

And that comes as a **big** surprise!

What also comes as a big surprise is just how small the counter-intuitive policy design tweaks often are.

Safe, smooth, effective, efficient, and productive flow is restored. Calm confidence reigns. Safety, Flow, Quality and Productivity all increase – at the same time. The emotional storm clouds dissipate and the cultural sun shines again.

Everyone feels better. Everyone. Patients, managers, and non-managers.

This is Win-Win-Win improvement by design. Improvement Science.