Tools – Page 2 – HCSE Blog

18/06/2016

The Capstan

A capstan is a simple machine for combining the effort of many people and enabling them to achieve more than any of them could do alone.

The word appears to have come into English from the Portuguese and Spanish sailors at around the time of the Crusades.

Each sailor works independently of the others. There is no requirement them to be equally strong because the capstan will combine their efforts. And the capstan also serves as a feedback loop because everyone can sense when someone else pushes harder or slackens off. It is an example of simple, efficient, effective, elegant design.

In the world of improvement we also need simple, efficient, effective and elegant ways to combine the efforts of many in achieving a common purpose. Such as raising the standards of excellence and weighing the anchors of resistance.

In health care improvement we have many simultaneous constraints and we have many stakeholders with specific perspectives and special expertise.

And if we are not careful they will tend to pull only in their preferred direction … like a multi-way tug-o-war. The result? No progress and exhausted protagonists.

There are those focused on improving productivity – Team Finance.

There are those focused on improving delivery – Team Operations.

There are those focused on improving safety – Team Governance.

And we are all tasked with improving quality – Team Everyone.

So we need a synergy machine that works like a capstan-of-old, and here is one design.

It has four poles and it always turns in a clockwise direction, so the direction of push is clear.

And when all the protagonists push in the same direction, they will get their own ‘win’ and also assist the others to make progress.

This is how the sails of success are hoisted to catch the wind of change; and how the anchors of anxiety are heaved free of the rocks of fear; and how the bureaucratic bilge is pumped overboard to lighten our load and improve our speed and agility.

And the more hands on the capstan the quicker we will achieve our common goal.

Collective excellence.

04/06/201606/01/2024

A Recipe for Chaos

There is an easy and quick-to-cook recipe for chaos.

All we have to do is to ensure that the maximum number of jobs that we can do in a given time is set equal to the average number of jobs that we are required to do in the same period of time.

Eh?

That does not make sense. Our intuition says that looks like the perfect recipe for a hyper-efficient, zero-waste, zero idle-time design which is what we want.

I know it does, but it isn’t. Our intuition is tricking us.

It is the recipe for chaos – and to prove it all we will have to do a real world experiment – because to prove it using maths is really difficult. So difficult in fact that the formula was not revealed until 1962 – by a mathematician called John Kingman while a postgraduate student at Pembroke College, Cambridge.

The empirical experiment is very easy to do – all we need is a single step process – and a stream of jobs to do.

And we could do it for real, or we can simulate it using an Excel spreadsheet – which is much quicker.

So we set up our spreadsheet to simulate a new job arriving every X minutes and each job taking X minutes to complete.

Our operator can only do one job at a time so if a job arrives and the operator is busy the job joins the back of a queue of jobs and waits.

When the operator finishes a job it takes the next one from the front of the queue, the one that has been waiting longest.

And if there is no queue the operator will wait until the next job arrives.

Simple.

And when we run simulation the we see that there is indeed no queue, no jobs waiting and the operator is always busy (i.e. 100% utilised). Perfection!

BUT ….

This is not a realistic scenario. In reality there is always some random variation. Not all jobs require the same length of time, and jobs do not arrive at precisely the right intervals.

No matter, our confident intuition tells us. It will average out. Swings-and-roundabouts. Give-and-take.

It doesn’t.

And if you do not believe me just build the simple Excel model outlined above, verify that it works, then add some random variation to the time it takes to do each job … and observe what happens to the average waiting time.

What you will discover is that as soon as we add even a small amount of random variation we get a queue, and waiting and idle resources as well!

But not a steady, stable, predictable queue … Oh No! … We get an unsteady, unstable and unpredictable queue … we get chaos.

Try it.

So what? How does this abstract ‘queue theory’ apply to the real world?

Well, suppose we have a single black box system called ‘a hospital’ – patients arrive and we work hard to diagnose and treat them. And so long as we have enough resource-time to do all the jobs we are OK. No unstable queues. No unpredictable waiting.

But time-costs-money and we have an annual cost improvement target (CIP) that we are required to meet so we need to ‘trim’ resource-time capacity to push up resource utilisation. And we will call that an ‘efficiency improvement’ which is good … yes?

It isn’t actually. I can just as easily push up my ‘utilisation’ by working slower, or doing stuff I do not need to, or by making mistakes that I have to check for and then correct. I can easily make myself busier and delude myself I am working harder.

And we are also a victim of our own success … the better we do our job … the longer people live and the more workload they put on the health and social care system.

So we have the perfect storm … the perfect recipe for chaos … slowly rising demand … slowly shrinking budgets … and an inefficient ‘business’ design.

And that in a nutshell is the reason the NHS is descending into chaos.

So what is the solution?

Reduce demand? Stop people getting sick? Or make them sicker so they die quicker?

Increase budgets? Where will the money come from? Beg? Borrow? Steal? Economic growth?

Improve the design? Now there’s a thought. But how? By using the same beliefs and behaviours that have created the current chaos?

Maybe we need to challenge some invalid beliefs and behaviours … and replace those that fail the Reality Test with some more effective ones.

29/05/2016

High Performing Design Teams

It is possible but unusual for significant improvement-by-design to be delivered by an individual.

It is much more likely to require a group of people – a design team.

And that is where efforts to improve often come to a grinding halt because, despite our good intentions, we are not always very good at collaborative improvement.

This is not a new problem so the solution must be elusive, yes?

Well, actually that is not the case. We all already know what to do, we all know the pieces of the productive team jigsaw … we just do not use all of them all of the time.

Fortunately, there is an easy way to get around this problem. A checklist.

Just like the ones that astronauts, pilots, and surgeons use.

And this week I discovered an excellent source of checklists for developing and sustaining high performance teams:

A Systematic Guide to High Performing Teams by Ken Thompson (ISBN 9-781522-871910) and here is a TEDx talk of Ken describing the ‘secrets’.

The ones that we all know.

17/04/2016

System of Profound Knowledge

This week I had the great pleasure of watching Dr Don Berwick sharing the story of his own ‘near religious experience‘ and his conversion to a belief that a Science of Improvement exists. In 1986, Don attended one of W.Edwards Deming’s famous 4-day workshops. It was an emotional roller coaster ride for Don! See here for a link to the whole video … it is worth watching all of it … the best bit is at the end.

Don outlines Deming’s System of Profound Knowledge (SoPK) and explores each part in turn. Here is a summary of SoPK from the Deming website.

W.Edwards Deming was a physicist and statistician by training and his deep understanding of variation and appreciation for a system flows from that. He was not trained as a biologist, psychologist or educationalist and those parts of the SoPK appear to have emerged later.

Here are the summaries of these parts – psychology first …

Neurobiologists and psychologists now know that we are the product of our experiences and our learning. What we think consciously is just the emergent tip of a much bigger cognitive iceberg. Most of what is happening is operating out of awareness. It is unconscious. Our outward behaviour is just a visible manifestation of deeply ingrained values and beliefs that we have learned – and reinforced over and over again. Our conscious thoughts are emergent effects.

So how do we learn? How do we accumulate these values and beliefs?

This is the summary of Deming’s Theory of Knowledge …

But to a biologist, neuroanatomist, neurophysiologist, doctor, system designer and improvement coach … this does not feel correct.

At the most fundamental biological level we do not learn by starting with a theory; we start with a sensory. The simplest element of the animal learning system – the nervous system – is called a reflex arc.

First, we have some form of sensor to gather data from the outside world. Eyes, ears, smell, taste, touch, temperature, pain and so on. Let us consider pain.

That signal is transmitted via a sensory nerve to the processor, the grey matter in this diagram, where it is filtered, modified, combined with other data, filtered again and a binary output generated. Act or Not.

If the decision is ‘Act’ then this signal is transmitted by a motor nerve to an effector, in this case a muscle, which results in an action. The muscle twitches or contracts and that modifies the outside world – we pull away from the source of pain. It is a harm avoidance design. Damage-limitation. Self-preservation.

Another example of this sensor-processor-effector design template is a knee-jerk reflex, so-named because if we tap the tendon just below the knee we can elicit a reflex contraction of the thigh muscle. It is actually part of a very complicated, dynamic, musculoskeletal stability cybernetic control system that allows us to stand, walk and run … with almost no conscious effort … and no conscious awareness of how we are doing it.

But we are not born able to walk. As youngsters we do not start with a theory of how to walk from which we formulate a plan. We see others do it and we attempt to emulate them. And we fail repeatedly. Waaaaaaah! But we learn.

Human learning starts with study. We then process the sensory data using our internal mental model – our rhetoric; we then decide on an action based on our ‘current theory’; and then we act – on the external world; and then we observe the effect. And if we sense a difference between our expectation and our experience then that triggers an ‘adjustment’ of our internal model – so next time we may do better because our rhetoric and the reality are more in sync.

The biological sequence is Study-Adjust-Plan-Do-Study-Adjust-Plan-Do and so on, until we have achieved our goal; or until we give up trying to learn.

So where does psychology come in?

Well, sometimes there is a bigger mismatch between our rhetoric and our reality. The world does not behave as we expect and predict. And if the mismatch is too great then we are left with feelings of confusion, disappointment, frustration and fear. (PS. That is our unconscious mind telling us that there is a big rhetoric-reality mismatch).

We can see the projection of this inner conflict on the face of a child trying to learn to walk. They screw up their faces in conscious effort, and they fall over, and they hurt themselves and they cry. But they do not want us to do it for them … they want to learn to do it for themselves. Clumsily at first but better with practice. They get up and try again … and again … learning on each iteration.

Study-Adjust-Plan-Do over and over again.

There is another way to avoid the continual disappointment, frustration and anxiety of learning. We can distort our sensation of external reality to better fit with our internal rhetoric. When we do that the inner conflict goes away.

We learn how to tamper with our sensory filters until what we perceive is what we believe. Inner calm is restored (while outer chaos remains or increases). We learn the psychological defense tactics of denial and blame. And we practice them until they are second-nature. Unconscious habitual reflexes. We build a reality-distortion-system (RDS) and it has a name – the Ladder of Inference.

And then one day, just by chance, somebody or something bypasses our RDS … and that is the experience that Don Berwick describes.

Don went to a 4-day workshop to hear the wisdom of W.Edwards Deming first hand … and he was forced by the reality he saw to adjust his inner model of the how the world works. His rhetoric. It was a stormy transition!

The last part of his story is the most revealing. It exposes that his unconscious mind got there first … and it was his conscious mind that needed to catch up.

Study-(Adjust)-Plan-Do … over-and-over again.

In Don’s presentation he suggests that Frederick W. Taylor is the architect of the failure of modern management. This is a commonly held belief, and everyone is equally entitled to an opinion, that is a definition of mutual respect.

But before forming an individual opinion on such a fundamental belief we should study the raw evidence. The words written by the person who wrote them not just the words written by those who filtered the reality through their own perceptual lenses. Which we all do.

19/03/2016

Type II Error

It was the time for Bob and Leslie’s regular Improvement Science coaching session.

<Leslie> Hi Bob, how are you today?

<Bob> I am getting over a winter cold but otherwise I am good. And you?

<Leslie> I am OK and I need to talk something through with you because I suspect you will be able to help.

<Bob> OK. What is the context?

<Leslie> Well, one of the projects that I am involved with is looking at the elderly unplanned admission stream which accounts for less than half of our unplanned admissions but more than half of our bed days.

<Bob> OK. So what were you looking to improve?

<Leslie> We want to reduce the average length of stay so that we free up beds to provide resilient space-capacity to ease the 4-hour A&E admission delay niggle.

<Bob> That sounds like a very reasonable strategy. So have you made any changes and measured any improvements?

<Leslie> We worked through the 6M Design® sequence. We studied the current system, diagnosed some time traps and bottlenecks, redesigned the ones we could influence, modified the system, and continued to measure to monitor the effect.

<Bob> And?

<Leslie> It feels better but the system behaviour charts do not show an improvement.

<Bob> Which charts, specifically?

<Leslie> The BaseLine XmR charts of average length of stay for each week of activity.

<Bob> And you locked the limits when you made the changes?

<Leslie> Yes. And there still were no red flags. So that means our changes have not had a significant effect. But it definitely feels better. Am I deluding myself?

<Bob> I do not believe so. Your subjective assessment is very likely to be accurate. Our Chimp OS 1.0 is very good at some things! I think the issue is with the tool you are using to measure the change.

<Leslie> The XmR chart? But I thought that was THE tool to use?

<Bob> Like all tools it is designed for a specific purpose. Are you familiar with the term Type II Error.

<Leslie> Doesn’t that come from research? I seem to remember that is the error we make when we have an under-powered study. When our sample size is too small to confidently detect the change in the mean that we are looking for.

<Bob> A perfect definition! The same error can happen when we are doing before and after studies too. And when it does, we see the pattern you have just described: the process feels better but we do not see any red flags on our BaseLine© chart.

<Leslie> But if our changes only have a small effect how can it feel better?

<Bob> Because some changes have cumulative effects and we omit to measure them.

<Leslie> OMG! That makes complete sense! For example, if my bank balance is stable my average income and average expenses are balanced over time. So if I make a small-but-sustained improvement to my expenses, like using lower cost generic label products, then I will see a cumulative benefit over time to the balance, but not the monthly expenses; because the noise swamps the signal on that chart!

<Bob> An excellent analogy!

<Leslie> So the XmR chart is not the tool for this job. And if this is the only tool we have then we risk making a Type II error. Is that correct?

<Bob> Yes. We do still use an XmR chart first though, because if there is a big enough and fast enough shift then the XmR chart will reveal it. If there is not then we do not give up just yet; we reach for our more sensitive shift detector tool.

<Leslie> Which is?

<Bob> I will leave you to ponder on that question. You are a trained designer now so it is time to put your designer hat on and first consider the purpose of this new tool, and then create the outline a fit-for-purpose design.

<Leslie> OK, I am on the case!

05/03/2016

Raising Awareness

The first step in the process of improvement is raising awareness, and this has to be done carefully.

Most of us spend most of our time in a mental state called blissful ignorance. We are happily unaware of the problems, and of their solutions.

Some of us spend some of our time in a different mental state called denial.

And we enter that from yet another mental state called painful awareness.

By raising awareness we are deliberately nudging ourselves, and others, out of our comfort zones.

But suddenly moving from blissful ignorance to painful awareness is not a comfortable transition. It feels like a shock. We feel confused. We feel vulnerable. We feel frightened. And we have a choice: freeze, flee or fight.

Freeze is shock. We feel paralysed by the mismatch between rhetoric and reality.

Flee is denial. We run away from a new and uncomfortable reality.

Fight is anger. Directed first at others (blame) and then at ourselves (guilt).

It is this anger-passion that we must learn to channel and focus as determination to listen, learn and then lead.

The picture is of a recent awareness-raising event; it happened this week.

The audience is a group of NHS staff from across the depth and breadth of a health and social care system.

On the screen is the ‘Save the NHS Game’. It is an interactive, dynamic flow simulation of a whole health care system; and its purpose is educational. It is designed to illustrate the complex and counter-intuitive flow behaviour of a system of interdependent parts: primary care, an acute hospital, intermediate care, residential care, and so on.

We all became aware of a lot of unfamiliar concepts in a short space of time!

We all learned that a flow system can flip from calm to chaotic very quickly.

We all learned that a small change in one part of a system of interdependent parts can have a big effect in another part – either harmful or beneficial and often both.

We all learned that there is often a long time-lag between the change and the effect.

We all learned that we cannot reverse the effect just by reversing the change.

And we all learned that this high sensitivity to small changes is the result of the design of our system; i.e. our design.

Learning all that in one go was a bit of a shock! Especially the part where we realised that we had, unintentionally, created near perfect conditions for chaos to emerge. Oh dear!

Denial felt like a very reasonable option; as did blame and guilt.

What emerged was a collective sense of determination. “Let’s Do It!” captured the mood.

The second step in the process of improvement is to show the door to the next phase of learning; the phase called ‘know how’.

This requires demonstrating that there is an another way out of the zone of painful awareness. An alternative to denial.

This is where how-to-diagnose-and-correct-the-design-flaws needs to be illustrated. A step-at-a-time.

And when that happens it feels like a light bulb has been switched on. What before was obscure and confusing suddenly becomes clear and understandable; and we say ‘Ah ha!’

So, if we deliberately raise awareness about a problem then, as leaders of change and improvement, we also have the responsibility to raise awareness about feasible solutions.

Because only then are we able to ask “Would we like to learn how to do this ourselves!”

And ‘Yes, please’ is what 68% of the people said after attending the awareness raising event. Only 15% said ‘No, thank you’ and only 17% abstained.

Raising awareness is the first step to improvement.
Choosing the path out of the pain towards knowledge is the second.
And taking the first step on that path is the third.

27/02/2016

The Cost of Chaos

This week I conducted an experiment – on myself.

I set myself the challenge of measuring the cost of chaos, and it was tougher than I anticipated it would be.

It is easy enough to grasp the concept that fire-fighting to maintain patient safety amidst the chaos of healthcare would cost more in terms of tears and time …

… but it is tricky to translate that concept into hard numbers; i.e. cash.

Chaos is an emergent property of a system. Safety, delivery, quality and cost are also emergent properties of a system. We can measure cost, our finance departments are very good at that. We can measure quality – we just ask “How did your experience match your expectation”. We can measure delivery – we have created a whole industry of access target monitoring. And we can measure safety by checking for things we do not want – near misses and never events.

But while we can feel the chaos we do not have an easy way to measure it. And it is hard to improve something that we cannot measure.

So the experiment was to see if I could create some chaos, then if I could calm it, and then if I could measure the cost of the two designs – the chaotic one and the calm one. The difference, I reasoned, would be the cost of the chaos.

And to do that I needed a typical chunk of a healthcare system: like an A&E department where the relationship between safety, flow, quality and productivity is rather important (and has been a hot topic for a long time).

But I could not experiment on a real A&E department … so I experimented on a simplified but realistic model of one. A simulation.

What I discovered came as a BIG surprise, or more accurately a sequence of big surprises!

First I discovered that it is rather easy to create a design that generates chaos and danger. All I needed to do was to assume I understood how the system worked and then use some averaged historical data to configure my model. I could do this on paper or I could use a spreadsheet to do the sums for me.
Then I discovered that I could calm the chaos by reactively adding lots of extra capacity in terms of time (i.e. more staff) and space (i.e. more cubicles). The downside of this approach was that my costs sky-rocketed; but at least I had restored safety and calm and I had eliminated the fire-fighting. Everyone was happy … except the people expected to foot the bill. The finance director, the commissioners, the government and the tax-payer.
Then I got a really big surprise! My safe-but-expensive design was horribly inefficient. All my expensive resources were now running at rather low utilisation. Was that the cost of the chaos I was seeing? But when I trimmed the capacity and costs the chaos and danger reappeared. So was I stuck between a rock and a hard place?
Then I got a really, really big surprise!! I hypothesised that the root cause might be the fact that the parts of my system were designed to work independently, and I was curious to see what happened when they worked interdependently. In synergy. And when I changed my design to work that way the chaos and danger did not reappear and the efficiency improved. A lot.
And the biggest surprise of all was how difficult this was to do in my head; and how easy it was to do when I used the theory, techniques and tools of Improvement-by-Design.

So if you are curious to learn more … I have written up the full account of the experiment with rationale, methods, results, conclusions and references and I have published it here.

20/02/2016

Anti-Chaos

Hypothesis: Chaotic behaviour of healthcare systems is inevitable without more resources.

This appears to be a rather widely held belief, but what is the evidence?

Can we disprove this hypothesis?

Chaos is a predictable, emergent behaviour of many systems, both natural and man made, a discovery that was made rather recently, in the 1970’s. Chaotic behaviour is not the same as random behaviour. The fundamental difference is that random implies independence, while chaos requires the opposite: chaotic systems have interdependent parts.

Chaotic behaviour is complex and counter-intuitive, which may explain why it took so long for the penny to drop.

Chaos is a complex behaviour and it is tempting to assume that complicated structures always lead to complex behaviour. But they do not. A mechanical clock is a complicated structure but its behaviour is intentionally very stable and highly predictable – that is the purpose of a clock. It is a fit-for-purpose design.

The healthcare system has many parts; it too is a complicated system; it has a complicated structure. It is often seen to demonstrate chaotic behaviour.

So we might propose that a complicated system like healthcare could also be stable and predictable. If it were designed to be.

But there is another critical factor to take into account.

A mechanical clock only has inanimate cogs and springs that only obey the Laws of Physics – and they are neither adaptable nor negotiable.

A healthcare system is different. It is a living structure. It has patients, providers and purchasers as essential components. And the rules of how people work together are both negotiable and adaptable.

So when we are thinking about a healthcare system we are thinking about a complex adaptive system or CAS.

And that changes everything!

The good news is that adaptive behaviour can be a very effective anti-chaos strategy, if it is applied wisely. The not-so-good news is that if it is not applied wisely then it can actually generate even more chaos.

Which brings us back to our hypothesis.

What if the chaos we are observing on out healthcare system is actually iatrogenic?

What if we are unintentionally and unconsciously generating it?

These questions require an answer because if we are unwittingly contributing to the chaos, with insight, understanding and wisdom we can intentionally calm it too.

These questions also challenge us to study our current way of thinking and working. And in that challenge we will need to demonstrate a behaviour called humility. An ability to acknowledge that there are gaps in our knowledge and our understanding. A willingness to learn.

This all sounds rather too plausible in theory. What about an example?

Let us consider the highest flow process in healthcare: the outpatient clinic stream.

The typical design is a three-step process called the New-Test-Review design. This sequential design is simpler because the steps are largely independent of each other. And this simplicity is attractive because it is easier to schedule so is less likely to be chaotic. The downsides are the queues and delays between the steps and the risk of getting lost in the system. So if we are worried that a patient may have a serious illness that requires prompt diagnosis and treatment (e.g. cancer), then this simpler design is actually a potentially unsafe design.

A one-stop clinic is a better design because the New-Test-Review steps are completed in one visit, and that is better for everyone. But, a one-stop clinic is a more challenging scheduling problem because all the steps are now interdependent, and that is fertile soil for chaos to emerge. And chaos is exactly what we often see.

Attending a chaotic one-stop clinic is frustrating experience for both patients and staff, and it is also less productive use of resources. So the chaos and cost appears to be price we are asked to pay for a quicker and safer design.

So is the one stop clinic chaos inevitable, or is it avoidable?

Simple observation of a one stop clinic shows that the chaos is associated with queues – which are visible as a waiting room full of patients and front-of-house staff working very hard to manage the queue and to signpost and soothe the disgruntled patients.

What if the one stop clinic queue and chaos is iatrogenic? What if it was avoidable without investing in more resources? Would the chaos evaporate? Would the quality improve? Could we have a safer, calmer, higher quality and more productive design?

Last week I shared evidence that proved the one-stop clinic chaos was iatrogenic – by showing it was avoidable.

A team of healthcare staff were shown how to diagnose the cause of the queue and were then able to remove that cause, and to deliver the same outcome without the queue and the associated chaos.

And the most surprising lesson that the team learned was that they achieved this improvement using the same resources as before; and that those resources also felt the benefit of the chaos evaporating. Their work was easier, calmer and more predictable.

The impossible-without-more-resources hypothesis had been disproved.

So, where else in our complicated and complex healthcare system might we apply anti-chaos?

Everywhere?

And for more about complexity science see Santa Fe Institute

13/02/2016

Melting the Queue

[Drrrrrrring]

<Leslie> Hi Bob, I hope I am not interrupting you. Do you have five minutes?

<Bob> Hi Leslie. I have just finished what I was working on and a chat would be a very welcome break. Fire away.

<Leslie> I really just wanted to say how much I enjoyed the workshop this week, and so did all the delegates. They have been emailing me to say how much they learned and thanking me for organising it.

<Bob> Thank you Leslie. I really enjoyed it too … and I learned lots … I always do.

<Leslie> As you know I have been doing the ISP programme for some time, and I have come to believe that you could not surprise me any more … but you did! I never thought that we could make such a dramatic improvement in waiting times. The queue just melted away and I still cannot really believe it. Was it a trick?

<Bob> Ahhhh, the siren-call of the battle-hardened sceptic! It was no trick. What you all saw was real enough. There were no computers, statistics or smoke-and-mirrors used … just squared paper and a few coloured pens. You saw it with your own eyes; you drew the charts; you made the diagnosis; and you re-designed the policy. All I did was provide the context and a few nudges.

<Leslie> I know, and that is why I think seeing the before and after data would help me. The process felt so much better, but I know I will need to show the hard evidence to convince others, and to convince myself as well, to be brutally honest. I have the before data … do you have the after data?

<Bob> I do. And I was just plotting it as BaseLine charts to send to you. So you have pre-empted me. Here you are.

This is the waiting time run chart for the one stop clinic improvement exercise that you all did. The leftmost segment is the before, and the rightmost are the after … your two ‘new’ designs.

As you say, the queue and the waiting has melted away despite doing exactly the same work with exactly the same resources. Surprising and counter-intuitive but there is the evidence.

<Leslie> Wow! That fits exactly with how it felt. Quick and calm! But I seem to remember that the waiting room was empty, particularly in the case of the design that Team 1 created. How come the waiting is not closer to zero on the chart?

<Bob> You are correct. This is not just the time in the waiting room, it also includes the time needed to move between the rooms and the changeover time within the rooms. It is what I call the ‘tween-time.

<Leslie> OK, that makes sense now. And what also jumps out of the picture for me is the proof that we converted an unstable process into a stable one. The chaos was calmed. So what is the root cause of the difference between the two ‘after’ designs?

<Bob> The middle one, the slightly better of the two, is the one where all patients followed the newly designed process. The rightmost one was where we deliberately threw a spanner in the works by assuming an unpredictable case mix.

<Leslie> Which made very little difference! The new design was still much, much better than before.

<Bob> Yes. What you are seeing here is the footprint of resilient design. Do you believe it is possible now?

<Leslie> You bet I do!

31/01/2016

The Magic Black Box

It was the appointed time for Bob and Leslie’s regular coaching session as part of the improvement science practitioner programme.

<Leslie> Hi Bob, I am feeling rather despondent today so please excuse me in advance if you hear a lot of “Yes, but …” language.

<Bob> I am sorry to hear that Leslie. Do you want to talk about it?

<Leslie> Yes, please. The trigger for my gloom was being sent on a mandatory training workshop.

<Bob> OK. Training to do what?

<Leslie> Outpatient demand and capacity planning!

<Bob> But you know how to do that already, so what is the reason you were “sent”?

<Leslie> Well, I am no longer sure I know how to it. That is why I am feeling so blue. I went more out of curiosity and I came away utterly confused and with my confidence shattered.

<Bob> Oh dear! We had better start at the beginning. What was the purpose of the workshop?

<Leslie> To train everyone in how to use an Outpatient Demand and Capacity planning model, an Excel one that we were told to download along with the User Guide. I think it is part of a national push to improve waiting times for outpatients.

<Bob> OK. On the surface that sounds reasonable. You have designed and built your own Excel flow-models already; so where did the trouble start?

<Leslie> I will attempt to explain. This was a paragraph in the instructions. I felt OK with this because my Improvement Science training has given me a very good understanding of basic demand and capacity theory.

<Bob> OK. I am guessing that other delegates may have felt less comfortable with this. Was that the case?

<Leslie> The training workshops are targeted at Operational Managers and the ones I spoke to actually felt that they had a good grasp of the basics.

<Bob> OK. That is encouraging, but a warning bell is ringing for me. So where did the trouble start?

<Leslie> Well, before going to the workshop I decided to read the User Guide so that I had some idea of how this magic tool worked. This is where I started to wobble – this paragraph specifically …

<Bob> H’mm. What did you make of that?

<Leslie> It was complete gibberish to me and I felt like an idiot for not understanding it. I went to the workshop in a bit of a panic and hoped that all would become clear. It didn’t.

<Bob> Did the User Guide explain what ‘percentile’ means in this context, ideally with some visual charts to assist?

<Leslie> No and the use of ‘th’ and ‘%’ was really confusing too. After that I sort of went into a mental fog and none of the workshop made much sense. It was all about practising using the tool without any understanding of how it worked. Like a black magic box.

<Bob> OK. I can see why you were confused, and do not worry, you are not an idiot. It looks like the author of the User Guide has unwittingly used some very confusing and ambiguous terminology here. So can you talk me through what you have to do to use this magic box?

<Leslie> First we have to enter some of our historical data; the number of new referrals per week for a year; and the referral and appointment dates for all patients for the most recent three months.

<Bob> OK. That sounds very reasonable. A run chart of historical demand and the raw event data for a Vitals Chart® is where I would start the measurement phase too – so long as the data creates a valid 3 month reporting window.

<Leslie> Yes, I though so too … but that is not how the black box model seems to work. The weekly demand is used to draw an SPC chart, but the event data seems to disappear into the innards of the black box, and recommendations pop out of it.

<Bob> Ah ha! And let me guess the relationship between the term ‘percentile’ and the SPC chart of weekly new demand was not explained?

<Leslie> Spot on. What does percentile mean?

<Bob> It is statistics jargon. Remember that we have talked about the distribution of the data around the average on a BaseLine chart; and how we use the histogram feature of BaseLine to show it visually. Like this example.

<Leslie> Yes. I recognise that. This chart shows a stable system of demand with an average of around 150 new referrals per week and the variation distributed above and below the average in a symmetrical pattern, falling off to zero around the upper and lower process limits. I believe that you said that over 99% will fall within the limits.

<Bob> Good. The blue histogram on this chart is called a probability distribution function, to use the terminology of a statistician.

<Leslie> OK.

<Bob> So, what would happen if we created a Pareto chart of demand using the number of patients per week as the categories and ignoring the time aspect? We are allowed to do that if the behaviour is stable, as this chart suggests.

<Leslie> Give me a minute, I will need to do a rough sketch. Does this look right?

<Bob> Perfect! So if you now convert the Y-axis to a percentage scale so that 52 weeks is 100% then where does the average weekly demand of about 150 fall? Read up from the X-axis to the line then across to the Y-axis.

<Leslie> At about 26 weeks or 50% of 52 weeks. Ah ha! So that is what a percentile means! The 50th percentile is the average, the zeroth percentile is around the lower process limit and the 100th percentile is around the upper process limit!

<Bob> In this case the 50th percentile is the average, it is not always the case though. So where is the 85th percentile line?

<Leslie> Um, 52 times 0.85 is 44.2 which, reading across from the Y-axis then down to the X-axis gives a weekly demand of about 170 per week. That is about the same as the average plus one sigma according to the run chart.

<Bob> Excellent. The Pareto chart that you have drawn is called a cumulative probability distribution function … and that is usually what percentiles refer to. Comparative Statisticians love these but often omit to explain their rationale to non-statisticians!

<Leslie> Phew! So, now I can see that the 65th percentile is just above average demand, and 85th percentile is above that. But in the confusing paragraph how does that relate to the phrase “65% and 85% of the time”?

<Bob> It doesn’t. That is the really, really confusing part of that paragraph. I am not surprised that you looped out at that point!

<Leslie> OK. Let us leave that for another conversation. If I ignore that bit then does the rest of it make sense?

<Bob> Not yet alas. We need to dig a bit deeper. What would you say are the implications of this message?

<Leslie> Well. I know that if our flow-capacity is less than our average demand then we will guarantee to create an unstable queue and chaos. That is the Flaw of Averages trap.

<Bob> OK. The creator of this tool seems to know that.

<Leslie> And my outpatient manager colleagues are always complaining that they do not have enough slots to book into, so I conclude that our current flow-capacity is just above the 50th percentile.

<Bob> A reasonable hypothesis.

<Leslie> So to calm the chaos the message is saying I will need to increase my flow capacity up to the 85th percentile of demand which is from about 150 slots per week to 170 slots per week. An increase of 7% which implies a 7% increase in costs.

<Bob> Good. I am pleased that you did not fall into the intuitive trap that a increase from the 50th to the 85th percentile implies a 35/50 or 70% increase! Your estimate of 7% is a reasonable one.

<Leslie> Well it may be theoretically reasonable but it is not practically possible. We are exhorted to reduce costs by at least that amount.

<Bob> So we have a finance versus governance bun-fight with the operational managers caught in the middle: FOG. That is not the end of the litany of woes … is there anything about Did Not Attends in the model?

<Leslie> Yes indeed! We are required to enter the percentage of DNAs and what we do with them. Do we discharge them or re-book them.

<Bob> OK. Pragmatic reality is always much more interesting than academic rhetoric and this aspect of the real system rather complicates things, at least for a comparative statistician. This is where the smoke and mirrors will appear and they will be hidden inside the black magic box. To solve this conundrum we need to understand the relationship between demand, capacity, variation and yield … and it is rather counter-intuitive. So, how would you approach this problem?

<Leslie> I would use the 6M Design® framework and I would start with a map and not with a model; least of all a magic black box one that I did not design, build and verify myself.

<Bob> And how do you know that will work any better?

<Leslie> Because at the One Day ISP Workshop I saw it work with my own eyes. The queues, waits and chaos just evaporated. And it cost nothing. We already had more than enough “capacity”.

<Bob> Indeed you did. So shall we do this one as an ISP-2 project?

<Leslie> An excellent suggestion. I already feel my confidence flowing back and I am looking forward to this new challenge. Thank you again Bob.

16/01/2016

Hot and Cold

Last week Bob and Leslie were exploring the data analysis trap called a two-points-in-time comparison: as illustrated by the headline “This winter has not been as bad as last … which proves that our winter action plan has worked.”

Actually it doesn’t.

But just saying that is not very helpful. We need to explain the reason why this conclusion is invalid and therefore potentially dangerous.

So here is the continuation of Bob and Leslie’s conversation.

<Bob> Hi Leslie, have you been reflecting on the two-points-in-time challenge?

<Leslie> Yes indeed, and you were correct, I did know the answer … I just didn’t know I knew if you get my drift.

<Bob> Yes, I do. So, are you willing to share your story?

<Leslie> OK, but before I do that I would like to share what happened when I described what we talked about to some colleagues. They sort of got the idea but got lost in the unfamiliar language of ‘variance’ and I realized that I needed an example to illustrate.

<Bob> Excellent … what example did you choose?

<Leslie> The UK weather – or more specifically the temperature. My reasons for choosing this were many: first it is something that everyone can relate to; secondly it has strong seasonal cycle; and thirdly because the data is readily available on the Internet.

<Bob> OK, so what specific question were you trying to answer and what data did you use?

<Leslie> The question was “Are our winters getting warmer?” and my interest in that is because many people assume that the colder the winter the more people suffer from respiratory illness and the more that go to hospital … contributing to the winter A&E and hospital pressures. The data that I used was the maximum monthly temperature from 1960 to the present recorded at our closest weather station.

<Bob> OK, and what did you do with that data?

<Leslie> Well, what I did not do was to compare this winter with last winter and draw my conclusion from that! What I did first was just to plot-the-dots … I created a time-series chart … using the BaseLine© software.

And it shows what I expected to see, a strong, regular, 12-month cycle, with peaks in the summer and troughs in the winter.

<Bob> Can you explain what the green and red lines are and why some dots are red?

<Leslie> Sure. The green line is the average for all the data. The red lines are called the upper and lower process limits. They are calculated from the data and what they say is “if the variation in this data is random then we will expect more than 99% of the points to fall between these two red lines“.

<Bob> So, we have 55 years of monthly data which is nearly 700 points which means we would expect fewer than seven to fall outside these lines … and we clearly have many more than that. For example, the winter of 1962-63 and the summer of 1976 look exceptional – a run of three consecutive dots outside the red lines. So can we conclude the variation we are seeing is not random?

<Leslie> Yes, and there is more evidence to support that conclusion. First is the reality check … I do not remember either of those exceptionally cold or hot years personally, so I asked Dr Google.

This picture from January 1963 shows copper telephone lines that are so weighed down with ice, and for so long, that they have stretched down to the ground. In this era of mobile phones we forget this was what telecommunication was like!

And just look at the young Michal Fish in the Summer of ’76! Did people really wear clothes like that?

And there is more evidence on the chart. The red dots that you mentioned are indicators that BaseLine© has detected other non-random patterns.

So the large number of red dots confirms our Mark I Eyeball conclusion … that there are signals mixed up with the noise.

<Bob> Actually, I do remember the Summer of ’76 – it was the year I did my O Levels! And your signals-in-the-noise phrase reminds me of SETI – the search for extra-terrestrial intelligence! I really enjoyed the 1997 film of Carl Sagan’s book Contact with Jodi Foster playing the role of the determined scientist who ends up taking a faster-than-light trip through space in a machine designed by ET and built by humans. And especially the line about 10 minutes from the end when those-in-high-places who had discounted her story as “unbelievable” realized they may have made an error … the line ‘Yes, that is interesting isn’t it’.

<Leslie> Ha ha! Yes. I enjoyed that film too. It had lots of great characters – her glory seeking boss; the hyper-suspicious head of national security who militarized the project; the charismatic anti-hero; the ranting radical who blew up the first alien machine; and John Hurt as her guardian angel. I must watch it again.

Anyway, back to the story. The problem we have here is that this type of time-series chart is not designed to extract the overwhelming cyclical, annual pattern so that we can search for any weaker signals … such as a smaller change in winter temperature over a longer period of time.

<Bob>Yes, that is indeed the problem with these statistical process control charts. SPC charts were designed over 60 years ago for process quality assurance in manufacturing not as a diagnostic tool in a complex adaptive system such a healthcare. So how did you solve the problem?

<Leslie> I realized that it was the regularity of the cyclical pattern that was the key. I realized that I could use that to separate out the annual cycle and to expose the weaker signals. I did that using the rational grouping feature of BaseLine© with the month-of-the-year as the group.

Now I realize why the designers of the software put this feature in! With just one mouse click the story jumped out of the screen!

<Bob> OK. So can you explain what we are looking at here?

<Leslie> Sure. This chart shows the same data as before except that I asked BaseLine© first to group the data by month and then to create a mini-chart for each month-group independently. Each group has its own average and process limits. So if we look at the pattern of the averages, the green lines, we can clearly see the annual cycle. What is very obvious now is that the process limits for each sub-group are much narrower, and that there are now very few red points … other than in the groups that are coloured red anyway … a niggle that the designers need to nail in my opinion!

<Bob> I will pass on your improvement suggestion! So are you saying that the regular annual cycle has accounted for the majority of the signal in the previous chart and that now we have extracted that signal we can look for weaker signals by looking for red flags in each monthly group?

<Leslie> Exactly so. And the groups I am most interested in are the November to March ones. So, next I filtered out the November data and plotted it as a separate chart; and I then used another cool feature of BaseLine© called limit locking.

What that means is that I have used the November maximum temperature data for the first 30 years to get the baseline average and natural process limits … and we can see that there are no red flags in that section, no obvious signals. Then I locked these limits at 1990 and this tells BaseLine© to compare the subsequent 25 years of data against these projected limits. That exposed a lot of signal flags, and we can clearly see that most of the points in the later section are above the projected average from the earlier one. This confirms that there has been a significant increase in November maximum temperature over this 55 year period.

<Bob> Excellent! You have answered part of your question. So what about December onwards?

<Leslie> I was on a roll now! I also noticed from my second chart that the December, January and February groups looked rather similar so I filtered that data out and plotted them as a separate chart.

MaxTempDecJanFeb1960-2015_Grouped These were indeed almost identical so I lumped them together as a ‘winter’ group and compared the earlier half with the later half using another BaseLine© feature called segmentation.

This showed that the more recent winter months have a higher maximum temperature … on average. The difference is just over one degree Celsius. But it also shows that that the month-to-month and year-to-year variation still dominates the picture.

<Bob> Which implies?

<Leslie> That, with data like this, a two-points-in-time comparison is meaningless. If we do that we are just sampling random noise and there is no useful information in noise. Nothing that we can learn from. Nothing that we can justify a decision with. This is the reason the ‘this year was better than last year’ statement is meaningless at best; and dangerous at worst. Dangerous because if we draw an invalid conclusion, then it can lead us to make an unwise decision, then decide a counter-productive action, and then deliver an unintended outcome.

By doing invalid two-point comparisons we can too easily make the problem worse … not better.

<Bob> Yes. This is what W. Edwards Deming, an early guru of improvement science, referred to as ‘tampering‘. He was a student of Walter A. Shewhart who recognized this problem in manufacturing and, in 1924, invented the first control chart to highlight it, and so prevent it. My grandmother used the term meddling to describe this same behavior … and I now use that term as one of the eight sources of variation. Well done Leslie!

09/01/2016

The Two-Points-In-Time Comparison Trap

[Bzzzzzz] Bob’s phone vibrated to remind him it was time for the regular ISP remote coaching session with Leslie. He flipped the lid of his laptop just as Leslie joined the virtual meeting.

<Leslie> Hi Bob, and Happy New Year!

<Bob> Hello Leslie and I wish you well in 2016 too. So, what shall we talk about today?

<Leslie> Well, given the time of year I suppose it should be the Winter Crisis. The regularly repeating annual winter crisis. The one that feels more like the perpetual winter crisis.

<Bob> OK. What specifically would you like to explore?

<Leslie> Specifically? The habit of comparing of this year with last year to answer the burning question “Are we doing better, the same or worse?” Especially given the enormous effort and political attention that has been focused on the hot potato of A&E 4-hour performance.

<Bob> Aaaaah! That old chestnut! Two-Points-In-Time comparison.

<Leslie> Yes. I seem to recall you usually add the word ‘meaningless’ to that phrase.

<Bob> H’mm. Yes. It can certainly become that, but there is a perfectly good reason why we do this.

<Leslie> Indeed, it is because we see seasonal cycles in the data so we only want to compare the same parts of the seasonal cycle with each other. The apples and oranges thing.

<Bob> Yes, that is part of it. So what do you feel is the problem?

<Leslie> It feels like a lottery! It feels like whether we appear to be better or worse is just the outcome of a random toss.

<Bob> Ah! So we are back to the question “Is the variation I am looking at signal or noise?”

<Leslie> Yes, exactly.

<Bob> And we need a scientifically robust way to answer it. One that we can all trust.

<Leslie> Yes.

<Bob> So how do you decide that now in your improvement work? How do you do it when you have data that does not show a seasonal cycle?

<Leslie> I plot-the-dots and use an XmR chart to alert me to the presence of the signals I am interested in – especially a change of the mean.

<Bob> Good. So why can we not use that approach here?

<Leslie> Because the seasonal cycle is usually a big signal and it can swamp the smaller change I am looking for.

<Bob> Exactly so. Which is why we have to abandon the XmR chart and fall back the two points in time comparison?

<Leslie> That is what I see. That is the argument I am presented with and I have no answer.

<Bob> OK. It is important to appreciate that the XmR chart was not designed for doing this. It was designed for monitoring the output quality of a stable and capable process. It was designed to look for early warning signs; small but significant signals that suggest future problems. The purpose is to alert us so that we can identify the root causes, correct them and the avoid a future problem.

<Leslie> So we are using the wrong tool for the job. I sort of knew that. But surely there must be a better way than a two-points-in-time comparison!

<Bob> There is, but first we need to understand why a TPIT is a poor design.

<Leslie> Excellent. I’m all ears.

<Bob> A two point comparison is looking at the difference between two values, and that difference can be positive, zero or negative. In fact, it is very unlikely to be zero because noise is always present.

<Leslie> OK.

<Bob> Now, both of the values we are comparing are single samples from two bigger pools of data. It is the difference between the pools that we are interested in but we only have single samples of each one … so they are not measurements … they are estimates.

<Leslie> So, when we do a TPIT comparison we are looking at the difference between two samples that come from two pools that have inherent variation and may or may not actually be different.

<Bob> Well put. We give that inherent variation a name … we call it variance … and we can quantify it.

<Leslie> So if we do many TPIT comparisons then they will show variation as well … for two reasons; first because the pools we are sampling have inherent variation; and second just from the process of sampling itself. It was the first lesson in the ISP-1 course.

<Bob> Well done! So the question is: “How does the variance of the TPIT sample compare with the variance of the pools that the samples are taken from?”

<Leslie> My intuition tells me that it will be less because we are subtracting.

<Bob> Your intuition is half-right. The effect of the variation caused by the signal will be less … that is the rationale for the TPIT after all … but the same does not hold for the noise.

<Leslie> So the noise variation in the TPIT is the same?

<Bob> No. It is increased.

<Leslie> What! But that would imply that when we do this we are less likely to be able to detect a change because a small shift in signal will be swamped by the increase in the noise!

<Bob> Precisely. And the degree that the variance increases by is mathematically predictable … it is increased by a factor of two.

<Leslie> So as we usually present variation as the square root of the variance, to get it into the same units as the metric, then that will be increased by the square root of two … 1.414

<Bob> Yes.

<Leslie> I need to put this counter-intuitive theory to the test!

<Bob> Excellent. Accept nothing on faith. Always test assumptions. And how will you do that?

<Leslie> I will use Excel to generate a big series of normally distributed random numbers; then I will calculate a series of TPIT differences using a fixed time interval; then I will calculate the means and variations of the two sets of data; and then I will compare them.

<Bob> Excellent. Let us reconvene in ten minutes when you have done that.

10 minutes later …

<Leslie> Hi Bob, OK I am ready and I would like to present the results as charts. Is that OK?

<Bob> Perfect!

<Leslie> Here is the first one. I used our A&E performance data to give me some context. We know that on Mondays we have an average of 210 arrivals with an approximately normal distribution and a standard deviation of 44; so I used these values to generate the random numbers. Here is the simulated Monday Arrivals chart for two years.

<Bob> OK. It looks stable as we would expect and I see that you have plotted the sigma levels which look to be just under 50 wide.

<Leslie> Yes, it shows that my simulation is working. So next is the chart of the comparison of arrivals for each Monday in Year 2 compared with the corresponding week in Year 1.

<Bob> Oooookaaaaay. What have we here? Another stable chart with a mean of about zero. That is what we would expect given that there has not been a change in the average from Year 1 to Year 2. And the variation has increased … sigma looks to be just over 60.

<Leslie> Yes! Just as the theory predicted. And this is not a spurious answer. I ran the simulation dozens of times and the effect is consistent! So, I am forced by reality to accept the conclusion that when we do two-point-in-time comparisons to eliminate a cyclical signal we will reduce the sensitivity of our test and make it harder to detect other signals.

<Bob> Good work Leslie! Now that you have demonstrated this to yourself using a carefully designed and conducted simulation experiment, you will be better able to explain it to others.

<Leslie> So how do we avoid this problem?

<Bob> An excellent question and one that I will ask you to ponder on until our next chat. You know the answer to this … you just need to bring it to conscious awareness.

21/11/2015

Whip or WIP?

The NHS appears to be suffering from some form of obsessive-compulsive disorder.

OCD sufferers feel extreme anxiety in certain situations. Their feelings drive their behaviour which is to reduce the perceived cause of their feelings. It is a self-sustaining system because their perception is distorted and their actions are largely ineffective. So their anxiety is chronic.

Perfectionists demonstrate a degree of obsessive-compulsive behaviour too.

In the NHS the triggers are called ‘targets’ and usually take the form of failure metrics linked to arbitrary performance specifications.

The anxiety is the fear of failure and its unpleasant consequences: the name-shame-blame-game.

So a veritable industry has grown around ways to mitigate the fear. A very expensive and only partially effective industry.

Data is collected, cleaned, manipulated and uploaded to the Mothership (aka NHS England). There it is further manipulated, massaged and aggregated. Then the accumulated numbers are posted on-line, every month for anyone with a web-browser to scrutinise and anyone with an Excel spreadsheet to analyse.

An ocean of measurements is boiled and distilled into a few drops of highly concentrated and sanitized data and, in the process, most of the useful information is filtered out, deleted or distorted.

For example …

One of the failure metrics that sends a shiver of angst through a Chief Operating Officer (COO) is the failure to deliver the first definitive treatment for any patient within 18 weeks of referral from a generalist to a specialist.

The infamous and feared 18-week target.

Service providers, such as hospitals, are actually fined by their Clinical Commissioning Groups (CCGs) for failing to deliver-on-time. Yes, you heard that right … one NHS organisation financially penalises another NHS organisation for failing to deliver a result over which they have only partial control.

Service providers do not control how many patients are referred, or a myriad of other reasons that delay referred patients from attending appointments, tests and treatments. But the service providers are still accountable for the outcome of the whole process.

This ‘Perform-or-Pay-The-Price Policy‘ creates the perfect recipe for a lot of unhappiness for everyone … which is exactly what we hear and what we see.

So what distilled wisdom does the Mothership share? Here is a snapshot …

Q1: How useful is this table of numbers in helping us to diagnose the root causes of long waits, and how does it help us to decide what to change in our design to deliver a shorter waiting time and more productive system?

A1: It is almost completely useless (in this format).

So what actually happens is that the focus of management attention is drawn to the part just before the speed camera takes the snapshot … the bit between 14 and 18 weeks.

Inside that narrow time-window we see a veritable frenzy of target-failure-avoiding behaviour.

Clinical priority is side-lined and management priority takes over. This is a management emergency! After all, fines-for-failure are only going to make the already bad financial situation even worse!

The outcome of this fire-fighting is that the bigger picture is ignored. The focus is on the ‘whip’ … and avoiding it … because it hurts!

Message from the Mothership: “Until morale improves the beatings will continue”.

The good news is that the undigestible data liquor does harbour some very useful insights. All we need to do is to present it in a more palatable format … as pictures of system behaviour over time.

We need to use the data to calculate the work-in-progress (=WIP).

And then we need to plot the WIP in time-order so we can see how the whole system is behaving over time … how it is changing and evolving. It is a dynamic living thing, it has vitality.

So here is the WIP chart using the distilled wisdom from the Mothership.

And this picture does not require a highly trained data analyst or statistician to interpret it for us … a Mark I eyeball linked to 1.3 kg of wetware running ChimpOS 1.0 is enough … and if you are reading this then you must already have that hardware and software.

Two patterns are obvious:

1) A cyclical pattern that appears to have an annual frequency, a seasonal pattern. The WIP is higher in the summer than in the winter. Eh? What is causing that?

2) After an initial rapid fall in 2008 the average level was steady for 4 years … and then after March 2012 it started to rise. Eh? What is causing is that?

The purpose of a WIP chart is to stimulate questions such as:

Q1: What happened in March 2012 that might have triggered this change in system behaviour?

Q2: What other effects could this trigger have caused and is there evidence for them?

A1: In March 2012 the Health and Social Care Act 2012 became Law. In the summer of 2012 the shiny new and untested Clinical Commissioning Groups (CCGs) were authorised to take over the reins from the exiting Primary care Trusts (PCTs) and Strategic Health Authorities (SHAs). The vast £80bn annual pot of tax-payer cash was now in the hands of well-intended GPs who believed that they could do a better commissioning job than non-clinicians. The accountability for outcomes had been deftly delegated to the doctors. And many of the new CCG managers were the same ones who had collected their redundancy checks when the old system was shut down. Now that sounds like a plausible system-wide change! A massive political experiment was underway and the NHS was the guinea-pig.

A2: Another NHS failure metric is the A&E 4-hour wait target which, worringly, also shows a deterioration that appears to have started just after July 2010, i.e. just after the new Government was elected into power. Maybe that had something to do with it? Maybe it would have happened whichever party won at the polls.

A plausible temporal association does not constitute proof – and we cannot conclude a political move to a CCG-led NHS has caused the observed behaviour. Retrospective analysis alone is not able to establish the cause.

It could just as easily be that something else caused these behaviours. And it is important to remember that there are usually many causal factors combining together to create the observed effect.

And unraveling that Gordian Knot is the work of analysts, statisticians, economists, historians, academics, politicians and anyone else with an opinion.

We have a more pressing problem. We have a deteriorating NHS that needs urgent resuscitation!

So what can we do?

One thing we can do immediately is to make better use of our data by presenting it in ways that are easier to interpret … such as a work in progress chart.

Doing that will trigger different conversions; ones spiced with more curiosity and laced with less cynicism.

We can add more context to our data to give it life and meaning. We can season it with patient and staff stories to give it emotional impact.

And we can deepen our understanding of what causes lead to what effects.

And with that deeper understanding we can begin to make wiser decisions that will lead to more effective actions and better outcomes.

This is all possible. It is called Improvement Science.

And as we speak there is an experiment running … a free offer to doctors-in-training to learn the foundations of improvement science in healthcare (FISH).

In just two weeks 186 have taken up that offer and 13 have completed the course!

And this vanguard of curious and courageous innovators have discovered a whole new world of opportunity that they were completely unaware of before. But not anymore!

So let us ease off applying the whip and ease in the application of WIP.

PostScript

Here is a short video describing how to create, animate and interpret a form of diagnostic Vitals Chart® using the raw data published by NHS England. This is a training exercise from the Improvement Science Practitioner (level 2) course.

How to create an 18 weeks animated Bucket Brigade Chart (BBC)

12/09/2015

A Case of Chronic A&E Pain: Part 1

The blog last week seems to have caused a bit of a stir … so this week we will continue on the same theme.

I’m Dr Bob and I am a hospital doctor: I help to improve the health of poorly hospitals.

And I do that using the Science of Improvement – which is the same as all sciences, there is a method to it.

Over the next few weeks I will outline, in broad terms, how this is done in practice.

And I will use the example of a hospital presenting with pain in their A&E department. We will call it St.Elsewhere’s ® Hospital … a fictional name for a real patient.

It is a while since I learned science at school … so I thought a bit of a self-refresher would be in order … just to check that nothing fundamental has changed.

This is what I found on page 2 of a current GCSE chemistry textbook.

Note carefully that the process starts with observations; hypotheses come after that; then predictions and finally designing experiments to test them.

The scientific process starts with study.

Which is reassuring because when helping a poorly patient or a poorly hospital that is exactly where we start.

So, first we need to know the symptoms; only then can we start to suggest some hypotheses for what might be causing those symptoms – a differential diagnosis; and then we look for more specific and objective symptoms and signs of those hypothetical causes.

<Dr Bob> What is the presenting symptom?

<StE> “Pain in the A&E Department … or more specifically the pain is being felt by the Executive Department who attribute the source to the A&E Department. Their pain is that of 4-hour target failure.“

<Dr Bob> Are there any other associated symptoms?

<StE> “Yes, a whole constellation. Complaints from patients and relatives; low staff morale, high staff turnover, high staff sickness, difficulty recruiting new staff, and escalating locum and agency costs. The list is endless.”

<Dr Bob> How long have these symptoms been present?

<StE> “As long as we can remember.”

<Dr Bob> Are the symptoms staying the same, getting worse or getting better?

<StE> “Getting worse. It is worse in the winter and each winter is worse than the last.”

<Dr Bob> And what have you tried to relieve the pain?

<StE> “We have tried everything and anything – business process re-engineering, balanced scorecards, Lean, Six Sigma, True North, Blue Oceans, Golden Hours, Perfect Weeks, Quality Champions, performance management, pleading, podcasts, huddles, cuddles, sticks, carrots, blogs and even begging. You name it we’ve tried it! The current recommended treatment is to create a swarm of specialist short-stay assessment units – medical, surgical, trauma, elderly, frail elderly just to name a few.”

<Dr Bob> And how effective have these been?

<StE> “Well some seemed to have limited and temporary success but nothing very spectacular or sustained … and the complexity and cost of our processes just seem to go up and up with each new initiative. It is no surprise that everyone is change weary and cynical.”

The pattern of symptoms is that of a chronic (longstanding) illness that has seasonal variation, which is getting worse over time and the usual remedies are not working.

And it is obvious that we do not have a clear diagnosis; or know if our unclear diagnosis is incorrect; or know if we are actually dealing with an incurable disease.

So first we need to focus on establishing the diagnosis.

And Dr Bob is already drawing up a list of likely candidates … with carveoutosis at the top.

<Dr Bob> Do you have any data on the 4-hour target pain? Do you measure it?

<StE> “We are awash with data! I can send the quarterly breach performance data for the last ten years!”

<Dr Bob> Excellent, that will be useful as it should confirm that this is a chronic and worsening problem but it does not help establish a diagnosis. What we need is more recent, daily data. Just the last six months should be enough. Do you have that?

<StE> “Yes, that is how we calculate the quarterly average that we are performance managed on. Here is the spreadsheet. We are ‘required’ to have fewer than 5% 4-hour breaches on average. Or else.”

This is where Dr Bob needs some diagnostic tools. He needs to see the pain scores presented as picture … so he can see the pattern over time … because it is a very effective way to generate plausible causal hypotheses.

Dr Bob can do this on paper, or with an Excel spreadsheet, or use a tool specifically designed for the job. He selects his trusted visualisation tool : BaseLine©.

<Dr Bob> This is your A&E pain data plotted as a time-series chart. At first glance it looks very chaotic … that is shown by the wide and flat histogram. Is that how it feels?

<StE> “That is exactly how it feels … earlier in the year it was unremitting pain and now we have a constant background ache with sharp, severe, unpredictable stabbing pains on top. I’m not sure what is worse!

<Dr Bob> We will need to dig a bit deeper to find the root cause of this chronic pain … we need to identify the diagnosis or diagnoses … and your daily pain data should offer us some clues.

So I have plotted your data in a different way … grouping by day of the week … and this shows there is a weekly pattern to your pain. It looks worse on Mondays and least bad on Fridays. Is that your experience?

<StE> “Yes, the beginning of the week is definitely worse … because it is like a perfect storm … more people referred by their GPs on Mondays and the hospital is already full with the weekend backlog of delayed discharges so there are rarely beds to admit new patients into until late in the day. So they wait in A&E.

Dr Bob’s differential diagnosis is firming up … he still suspects acute-on-chronic carveoutosis as the primary cause but he now has identified an additional complication … Forrester’s Syndrome.

And Dr Bob suspects an unmentioned problem … that the patient has been traumatised by a blunt datamower!

So that is the evidence we will look for next … here

05/09/2015

The Catastrophe is Coming

This week an interesting report was published by Monitor – about some possible reasons for the A&E debacle that England experienced in the winter of 2014.

Summary At A Glance

“91% of trusts did not meet the A&E 4-hour maximum waiting time standard last winter – this was the worst performance in 10 years”.

So it seems a bit odd that the very detailed econometric analysis and the testing of “Ten Hypotheses” did not look at the pattern of change over the previous 10 years … it just compared Oct-Dec 2014 with the same period for 2013! And the conclusion: “Hospitals were fuller in 2014“. H’mm.

The data needed to look back 10 years is readily available on the various NHS England websites … so here it is plotted as simple time-series charts. These are called system behaviour charts or SBCs. Our trusted analysis tools will be a Mark I Eyeball connected to the 1.3 kg of wetware between our ears that runs ChimpOS 1.0 … and we will look back 11 years to 2004.

First we have the A&E Arrivals chart … about 3.4 million arrivals per quarter. The annual cycle is obvious … higher in the summer and falling in the winter. And when we compare the first five years with the last six years there has been a small increase of about 5% and that seems to associate with a change of political direction in 2010.

So over 11 years the average A&E demand has gone up … a bit … but only by about 5%.

In stark contrast the A&E arrivals that are admitted to hospital has risen relentlessly over the same 11 year period by about 50% … that is about 5% per annum … ten times the increase in arrivals … and with no obvious step in 2010. We can see the annual cycle too. It is a like a ratchet. Click click click.

But that does not make sense. Where are these extra admissions going to? We can only conclude that over 11 years we have progressively added more places to admit A&E patients into. More space-capacity to store admitted patients … so we can stop the 4-hour clock perhaps? More emergency assessment units perhaps? Places to wait with the clock turned off perhaps? The charts imply that our threshold for emergency admission has been falling: Admission has become increasingly the ‘easier option’ for whatever reason. So why is this happening? Do more patients need to be admitted?

In a recent empirical study we asked elderly patients about their experience of the emergency process … and we asked them just after they had been discharged … when it was still fresh in their memories. A worrying pattern emerged. Many said that they had been admitted despite them saying they did not want to be. In other words they did not willingly consent to admission … they were coerced.

This is anecdotal data so, by implication, it is wholly worthless … yes? Perhaps from a statistical perspective but not from an emotional one. It is a red petticoat being waved that should not be ignored. Blissful ignorance comes from ignoring anecdotal stuff like this. Emotionally uncomfortable anecdotal stories. Ignore the early warning signs and suffer the potentially catastrophic consequences.

And here is the corresponding A&E 4-hour Target Failure chart. Up to 2010 the imposed target was 98% success (i.e. 2% acceptable failure) and, after bit of “encouragement” in 2004-5, this was actually achieved in some of the summer months (when the A&E demand was highest remember).

But with a change of political direction in 2010 the “hated” 4-hour target was diluted down to 95% … so a 5% failure rate was now ‘acceptable’ politically, operationally … and clinically.

So it is no huge surprise that this is what was achieved … for a while at least.

In the period 2010-13 the primary care trusts (PCTs) were dissolved and replaced by clinical commissioning groups (CCGs) … the doctors were handed the ignition keys to the juggernaut that was already heading towards the cliff.

The charts suggest that the seeds were already well sown by 2010 for an evolving catastrophe that peaked last year; and the changes in 2010 and 2013 may have just pressed the accelerator pedal a bit harder. And if the trend continues it will be even worse this coming winter. Worse for patients and worse for staff and worse for commissioners and worse for politicians. Lose lose lose lose.

So to summarise the data from the NHS England’s own website:

1. A&E arrivals have gone up 5% over 11 years.
2. Admissions from A&E have gone up 50% over 11 years.
3. Since lowering the threshold for acceptable A&E performance from 98% to 95% the system has become unstable and “fallen off the cliff” … but remember, a temporal association does not prove causation.

So what has triggered the developing catastrophe?

Well, it is important to appreciate that when a patient is admitted to hospital it represents an increase in workload for every part of the system that supports the flow through the hospital … not just the beds. Beds represent space-capacity. They are just where patients are stored. We are talking about flow-capacity; and that means people, consumables, equipment, data and cash.

So if we increase emergency admissions by 50% then, if nothing else changes, we will need to increase the flow-capacity by 50% and the space-capacity to store the work-in-progress by 50% too. This is called Little’s Law. It is a mathematically proven Law of Flow Physics. It is not negotiable.

So have we increased our flow-capacity and our space-capacity (and our costs) by 50%? I don’t know. That data is not so easy to trawl from the websites. It will be there though … somewhere.

What we have seen is an increase in bed occupancy (the red box on Monitor’s graphic above) … but not a 50% increase … that is impossible if the occupancy is already over 85%. A hospital is like a rigid metal box … it cannot easily expand to accommodate a growing queue … so the inevitable result in an increase in the ‘pressure’ inside. We have created an emergency care pressure cooker. Well lots of them actually.

And that is exactly what the staff who work inside hospitals says it feels like.

And eventually the relentless pressure and daily hammering causes the system to start to weaken and fail, gradually at first then catastrophically … which is exactly what the NHS England data charts are showing.

So what is the solution? More beds?

Nope. More beds will create more space and that will relieve the pressure … for a while … but it will not address the root cause of why we are admitting 50% more patients than we used to; and why we seem to need to increase the pressure inside our hospitals to squeeze the patients through the process and extrude them out of the various exit nozzles.

Those are the questions we need to have understandable and actionable answers to.

Q1: Why are we admitting 5% more of the same A&E arrivals each year rather than delivering what they need in 4 hours or less and returning them home? That is what the patients are asking for.

Q2: Why do we have to push patients through the in-hospital process rather than pulling them through? The staff are willing to work but not inside a pressure cooker.

A more sensible improvement strategy is to look at the flow processes within the hospital and ensure that all the steps and stages are pulling together to the agreed goals and plan for each patient. The clinical management plan that was decided when the patient was first seen in A&E. The intended outcome for each patient and the shortest and quickest path to achieving it.

Our target is not just a departure within 4 hours of arriving in A&E … it is a competent diagnosis (study) and an actionable clinical management plan (plan) within 4 hours of arriving; and then a process that is designed to deliver (do) it … for every patient. Right, first time, on time, in full and at a cost we can afford.

Q: Do we have that?
A: Nope.

Q: Is that within our gift to deliver?
A: Yup.

Q: So what is the reason we are not already doing it?
A: Good question. Who in the NHS is trained how to do system-wide flow design like this?

22/08/2015

Storytelling

Telling a compelling story of improvement is an essential skill for a facilitator and leader of change.

A compelling story has two essential components: cultural and technical. Otherwise known as emotional and factual.

Many of the stories that we hear are one or the other; and consequently are much less effective.

Some prefer emotive language and use stories of dismay and distress to generate an angry reaction: “That is awful we must DO something about that!”

And while emotion is the necessary fuel for action, an angry mob usually attacks the assumed cause rather than the actual cause and can become ‘mindless’ and destructive.

Those who have observed the dangers of the angry mob opt for a more reflective, evidence-based, scientific, rational, analytical, careful, risk-avoidance approach.

And while facts are the necessary informers of decision, the analytical mind often gets stuck in the ‘paralysis of analysis’ swamp as layer upon layer of increasing complexity is exposed … more questions than answers.

So in a compelling story we need a bit of both.

We need a story that fires our emotions … and … we need a story that engages our intellect.

A bit of something for everyone.

And the key to developing this compelling-story-telling skill this is to start with something small enough to be doable in a reasonable period of time. A short story rather than a lengthy legend.

A story, tale or fable.

Aesop’s Fables and Chaucer’s Canterbury Tales are still remembered for their timeless stories.

And here is a taste of such a story … one that has been published recently for all to read and to enjoy.

A Story of Learning Improvement Science

It is an effective blend of cultural and technical, emotional and factual … and to read the full story just follow the ‘Continue’ link.

01/08/2015

Early Adoption

The early phases of a transformation are where most fall by the wayside.

And the failure rate is horrifying – an estimated 80% of improvement initiatives fail to achieve their goals.

The recent history of the NHS is littered with the rusting wreckage of a series of improvement bandwagons. Many who survived the crashes are too scarred and too scared to try again.

Transformation and improvement imply change which implies innovation … new ways of thinking, new ways of behaving, new techniques, new tools, and new ways of working.

And it has been known for over 50 years that innovation spreads in a very characteristic way. This process was described by Everett Rogers in a book called ‘Diffusion of Innovations‘ and is described visually in the diagram above.

The horizontal axis is a measure of individual receptiveness to the specific innovation … and the labels are behaviours: ‘I exhibit early adopter behaviour‘ (i.e. not ‘I am an early adopter’).

What Roger’s discovered through empirical observation was that in all cases the innovation diffuses from left-to-right; from innovation through early adoption to the ‘silent’ majority.

Complete diffusion is not guaranteed though … there are barriers between the phases.

One barrier is between innovation and early adoption.

There are many innovations that we never hear about and very often the same innovation appears in many places and often around the same time.

This innovation-adoption barrier is caused by two things:
1) most are not even aware of the problem … they are blissfully ignorant;
2) news of the innovation is not shared widely enough.

Innovators are sensitive people. They sense there is a problem long before others do. They feel the fear and the excitement of need for innovation. They challenge their own assumptions and they actively seek solutions. They swim against the tide of ignorance, disinterest, skepticism and often toxic cynicism. So when they do discover a way forward they often feel nervous about sharing it. They have learned (the hard way) that the usual reaction is to be dismissed and discounted. Most people do not like to learn about unknown problems and hazards; and they like it even less to learn that there are solutions that they neither recognise nor understand.

But not everyone.

There is a group called the early adopters who, like the innovators, are aware of the problem. They just do not share the innovator’s passion to find a solution … irrespective of the risks … so they wait … their antennae tuned for news that a solution has been found.

Then they act.

And they act in one of two ways:

1) Talkers … re-transmit the news of the problem and the discovery of a generic solution … which is essential in building awareness.

2) Walkers … try the innovative approach themselves and in so doing learn a lot about their specific problem and the new ways to solving it.

And it is the early adopters that do both of these actions that are the most effective and the most valuable to everyone else. Those that talk-the-new-walk and walk-the-new-talk.

And we can identify who they are because they will be able to tell stories of how they have applied the innovation in their world; and the results that they have achieved; and how they achieved them; and what worked well; and what did not; and what they learned; and how they evolved and applied the innovation to meet their specific needs.

They are the leaders, the coaches and the teachers of improvement and transformation.

They See One, Do Some, and Teach Many.

The early adopters are the bridge across the Innovation and Transformation Chasm.

25/07/2015

Not as Easy as it Looks

One of the traps for the inexperienced Improvement Science Practitioner is to believe that applying the science in the real world is as easy as it is in the safety of the training environment.

It isn’t.

The real world is messier and more complicated and it is easy to get lost in the fog of confusion and chaos.

So how do we avoid losing our footing, slipping into the toxic emotional swamp of organisational culture and giving ourselves an unpleasant dunking!

We use safety equipment … to protect ourselves and others from unintended harm.

The Improvement-by-Design framework is like a scaffold. It is there to provide structure and safety. The techniques and tools are like the harnesses, shackles, ropes, crampons, and pitons. They give us flexibility and security.

But we need to know how to use them. We need to be competent as well as confident.

We do not want to tie ourselves up in knots … and we do not want to discover that we have not tied ourselves to something strong enough to support us if we slip. Which we will.

So we need to learn an practice the basics skills to the point that they are second nature.

We need to learn how to tie secure knots, quickly and reliably.

We need to learn how to plan an ascent … identifying the potential hazards and designing around them.

We need to learn how to assemble and check what we will need before we start … not too much and not too little.

We need to learn how to monitor out progress against our planned milestones and be ready to change the plan as we go …and even to abandon the attempt if necessary.

We would not try to climb a real mountain without the necessary training, planning, equipment and support … even though it might look easy.

And we do not try to climb an improvement mountain without the necessary training, planning, tools and support … even though it might look easy.

It is not as easy as it looks.

11/04/2015

The Improvement Pyramid

Developing productive improvement capability in an organisation is like building a pyramid in the desert.

It is not easy and it takes time before there is any visible evidence of success.

The height of the pyramid is a measure of the level of improvement complexity that we can take on.

An improvement of a single step in a system would only require a small pyramid.

Improving the whole system will require a much taller one.

But if we rush and attempt to build a sky-scraper on top of the sand then we will not be surprised when it topples over before we have made very much progress. The Egyptians knew this!

First, we need to dig down and to lay some foundations. Stable enough and strong enough to support the whole structure. We will never see the foundations so it is easy to forget them in our rush but they need to be there and they need to be there first.

It is the same when developing improvement science capability … the foundations are laid first and when enough of that foundation knowledge is in place we can start to build the next layer of the pyramid: the practitioner layer.

It is the the Improvement Science Practitioners (ISPs) who start to generate tangible evidence of progress. The first success stories help to spur us all on to continue to invest effort, time and money in widening our foundations to be able to build even higher – more layers of capability -until we can realistically take on a system wide improvement challenge.

So sharing the first hard evidence of improvement is an important milestone … it is proof of fitness for purpose … and that news should be shared with those toiling in the hot desert sun and with those watching from the safety of the shade.

So here is a real story of a real improvement pyramid achieving this magical and motivating milestone.

06/04/2015

V.U.T.

It was the appointed time for the ISP coaching session and both Bob and Leslie were logged on and chatting about their Easter breaks.

<Bob> OK Leslie, I suppose we had better do some actual work, which seems a shame on such a wonderful spring day.

<Leslie> Yes, I suppose so. There is actually something I would like to ask you about because I came across it by accident and it looked very pertinent to flow design … but you have never mentioned it.

<Bob> That sounds interesting. What is it?

<Leslie> V.U.T.

<Bob> Ah ha! You have stumbled across the Queue Theorists and the Factory Physicists. So, what was your take on it?

<Leslie> Well it all sounded very impressive. The context is I was having a chat with a colleague who is also getting into the improvement stuff and who had been to a course called “Factory Physics for Managers” – and he came away buzzing about the VUT equation … and claimed that it explained everything!

<Bob> OK. So what did you do next?

<Leslie> I looked it up of course and I have to say the more I read the more confused I got. Maybe I am just a bid dim and not up to understanding this stuff.

<Bob> Well you are certainly not dim so your confusion must be caused by something else. Did your colleague describe how the VUT equation is applied in practice?

<Leslie> Um. No, I do not remember him describing an example – just that it explained why we cannot expect to run resources at 100% utilisation.

<Bob> Well he is correct on that point … though there is a bit more to it than that. A more accurate statement is “We cannot expect our system to be stable if there is variation and we run flow-resources at 100% utilisation”.

<Leslie> Well that sounds just like the sort of thing we have been talking about, what you call “resilient design”, so what is the problem with the VUT equation?

<Bob> The problem is that it gives an estimate of the average waiting time in a very simple system called a G/G/1 system.

<Leslie> Eh? What is a G/G/1 system?

<Bob> Arrgh … this is the can of queue theory worms that I was hoping to avoid … but as you brought it up let us grasp the nettle. This is called Kendall’s Notation and it is a short cut notation for describing the system design. The first letter refers to the arrivals or demand and G means a general distribution of arrival times; the second G refers to the size of the jobs or the cycle time and again the distribution is general; and the last number refers to the number of parallel resources pulling from the queue.

<Leslie> OK, so that is a single queue feeding into a single resource … the simplest possible flow system.

<Bob> Yes. But that isn’t the problem. The problem is that the VUT equation gives an approximation to the average waiting time. It tells us nothing about the variation in the waiting time.

<Leslie> Ah I see. So it tells us nothing about the variation in the size of the queue either … so does not help us plan the required space-capacity to hold the varying queue.

<Bob> Precisely. There is another problem too. The ‘U’ term in the VUT equation refers to utilisation of the resource … denoted by the symbol ? or rho. The actual term is ? / (1-?) … so what happens when rho approaches one … or in practical terms the average utilisation of the resource approaches 100%?

<Leslie> Um … 1 divided by (1-1) is 1 divided by zero which is … infinity! The average waiting time becomes infinitely long!

<Bob> Yes, but only if we wait forever – in reality we cannot and anyway – reality is always changing … we live in a dynamic, ever-changing, unstable system called Reality. The VUT equation may be academically appealing but in practice it is almost useless.

<Leslie> Ah ha! Now I see why you never mentioned it. So how do we design for resilience in practice? How do we get a handle on the behaviour of even the G/G/1 system over time?

<Bob> We use an Excel spreadsheet to simulate our G/G/1 system and we find a fit-for-purpose design using an empirical, experimental approach. It is actually quite straightforward and does not require any Queue Theory or VUT equations … just a bit of basic Excel know-how.

<Leslie> Phew! That sounds more up my street. I would like to see an example.

<Bob> Welcome to the first exercise in ISP-2 (Flow).

29/03/2015

Over-Egged Expectation

Resistance-to-change is an oft quoted excuse for improvement torpor. The implied sub-message is more like “We would love to change but They are resisting“.

Notice the Us-and-Them language. This is the observable evidence of an “We‘re OK and They’re Not OK” belief. And in reality it is this unstated belief and the resulting self-justifying behaviour that is an effective barrier to systemic improvement.

This Us-and-Them language generates cultural friction, erodes trust and erects silos that are effective barriers to the flow of information, of innovation and of learning. And the inevitable reactive solutions to this Us-versus-Them friction create self-amplifying positive feedback loops that ensure the counter-productive behaviour is sustained.

One tangible manifestation are DRATs: Delusional Ratios and Arbitrary Targets.

So when a plausible, rational and well-evidenced candidate for an alternative approach is discovered then it is a reasonable reaction to grab it and to desperately spray the ‘magic pixie dust’ at everything.

This a recipe for disappointment: because there is no such thing as ‘improvement magic pixie dust’.

The more uncomfortable reality is that the ‘magic’ is the result of a long period of investment in learning and the associated hard work in practising and polishing the techniques and tools.

It may look like magic but is isn’t. That is an illusion.

And some self-styled ‘magicians’ choose to keep their hard-won skills secret … because by sharing them know that they will lose their ‘magic powers’ in a flash of ‘blindingly obvious in hindsight’.

And so the chronic cycle of despair-hope-anger-and-disappointment continues.

System-wide improvement in safety, flow, quality and productivity requires that the benefits of synergism overcome the benefits of antagonism. This requires two changes to the current hope-and-despair paradigm. Both are necessary and neither are sufficient alone.

1) The ‘wizards’ (i.e. magic folk) share their secrets.
2) The ‘muggles’ (i.e. non-magic folk) invest the time and effort in learning ‘how-to-do-it’.

The transition to this awareness is uncomfortable so it needs to be managed pro-actively … by being open about the risk … and how to mitigate it.

That is what experienced Practitioners of Improvement Science (and ISP) will do. Be open about the challenged ahead.

And those who desperately want the significant and sustained SFQP improvements; and an end to the chronic chaos; and an end to the gaming; and an end to the hope-and-despair cycle …. just need to choose. Choose to invest and learn the ‘how to’ and be part of the future … or choose to be part of the past.

Improvement science is simple … but it is not intuitively obvious … and so it is not easy to learn.

If it were we would be all doing it.

And it is the behaviour of a wise leader of change to set realistic and mature expectations of the challenges that come with a transition to system-wide improvement.

That is demonstrating the OK-OK behaviour needed for synergy to grow.

21/02/2015

Cumulative Sum

[Bing] Bob logged in for the weekly Webex coaching session. Leslie was not yet on line, but joined a few minutes later.

<Leslie> Hi Bob, sorry I am a bit late, I have been grappling with a data analysis problem and did not notice the time.

<Bob> Hi Leslie. Sounds interesting. Would you like to talk about that?

<Leslie> Yes please! It has been driving me nuts!

<Bob> OK. Some context first please.

<Leslie> Right, yes. The context is an improvement-by-design assignment with a primary care team who are looking at ways to reduce the unplanned admissions for elderly patients by 10%.

<Bob> OK. Why 10%?

<Leslie> Because they said that would be an operationally very significant reduction. Most of their unplanned admissions, and therefore costs for admissions, are in that age group. They feel that some admissions are avoidable with better primary care support and a 10% reduction would make their investment of time and effort worthwhile.

<Bob> OK. That makes complete sense. Setting a new design specification is OK. I assume they have some baseline flow data.

<Leslie> Yes. We have historical weekly unplanned admissions data for two years. It looks stable, though rather variable on a week-by-week basis.

<Bob> So has the design change been made?

<Leslie> Yes, over three months ago – so I expected to be able to see something by now but there are no red flags on the XmR chart of weekly admissions. No change. They are adamant that they are making a difference, particularly in reducing re-admissions. I do not want to disappoint them by saying that all their hard work has made no difference!

<Bob> OK Leslie. Let us approach this rationally. What are the possible causes that the weekly admissions chart is not signalling a change?

<Leslie> If there has not been a change in admissions. This could be because they have indeed reduced readmissions but new admissions have gone up and is masking the effect.

<Bob> Yes. That is possible. Any other ideas?

<Leslie> That their intervention has made no difference to re-admissions and their data is erroneous … or worse still … fabricated!

<Bob> Yes. That is possible too. Any other ideas?

<Leslie> Um. No. I cannot think of any.

<Bob> What about the idea that the XmR chart is not showing a change that is actually there?

<Leslie> You mean a false negative? That the sensitivity of the XmR chart is limited? How can that be? I thought these charts will always signal a significant shift.

<Bob> It depends on the degree of shift and the amount of variation. The more variation there is the harder it is to detect a small shift. In a conventional statistical test we would just use bigger samples, but that does not work with an XmR chart because the run tests are all fixed length. Pre-defined sample sizes.

<Leslie> So that means we can miss small but significant changes and come to the wrong conclusion that our change has had no effect! Isn’t that called a Type 2 error?

<Bob> Yes, it is. And we need to be aware of the limitations of the analysis tool we are using. So, now you know that how might you get around the problem?

<Leslie> One way would be to aggregate the data over a longer time period before plotting on the chart … we know that will reduce the sample variation.

<Bob> Yes. That would work … but what is the downside?

<Leslie> That we have to wait a lot longer to show a change, or not. We do not want that.

<Bob> I agree. So what we do is we use a chart that is much more sensitive to small shifts of the mean. And that is called a cusum chart. These were not invented until 30 years after Shewhart first described his time-series chart. To give you an example, do you recall that the work-in-progress chart is much more sensitive to changes in flow than either demand or activity charts?

<Leslie> Yes, and the WIP chart also reacts immediately if either demand or activity change. It is the one I always look at first.

<Bob> That is because a WIP chart is actually a cusum chart. It is the cumulative sum of the difference between demand and activity.

<Leslie> OK! That makes sense. So how do I create and use a cusum chart?

<Bob> I have just emailed you some instructions and a few examples. You can try with your unplanned admissions data. It should only take a few minutes. I will get a cup of tea and a chocolate Hobnob while I wait.

[Five minutes later]

<Leslie> Wow! That is just brilliant! I can see clearly on the cusum chart when the shifts happened and when I split the XmR chart at those points the underlying changes become clear and measurable. The team did indeed achieve a 10% reduction in admissions just as they claimed they had. And I checked with a statistical test which confirmed that it is statistically significant.

<Bob> Good work. Cusum charts take a bit of getting used to and we have be careful about the metric we are plotting and a few other things but it is a useful trick to have up our sleeves for situations like this.

<Leslie> Thanks Bob. I will bear that in mind. Now I just need to work out how to explain cusum charts to others! I do not want to be accused of using statistical smoke-and-mirrors! I think a golf metaphor may work with the GPs.

13/12/2014

Catalyst

[Bing Bong] Bob was already logged into the weekly coaching Webex when Leslie arrived: a little late.

<Bob> Hi Leslie, how has your week been?

<Leslie> Hi Bob, sorry I am a bit late. It has been a very interesting week.

<Bob> My curiosity is pricked … are you willing to share?

<Leslie> Yes indeed! First an update on the improvement project was talked about a few weeks ago.

<Bob> The call centre one?

<Leslie> Yes. The good news is that the improvement has been sustained. It was not a flash in the pan. The chaos is gone and the calm has continued.

<Bob> That is very good to hear. And how did the team react?

<Leslie> That is one of the interesting things. They went really quiet. There was no celebration, no cheering, no sounds of champagne corks popping. It was almost as if they did not believe what they were seeing and they feared that if they celebrated too early they would somehow trigger a failure … or wake up from a dream.

<Bob> That is a very common reaction. It takes a while for reality to sink in – the reality that they have changed something, that the world did not end, and that their chronic chaos has evaporated. It is like a grief reaction … they have to mourn the loss of their disbelief. That takes time. About six weeks usually.

<Leslie> Yes, that is exactly what has happened – and I know they have now got over the surprise because the message I got this week was simply “OK, that appears to have worked exactly as you predicted it would. Will you help us solve the next impossible problem?“

<Bob> Well done Leslie! You have helped them break through the “Impossibility Barrier”. So what was your answer?

<Leslie> Well I was really tempted to say “Of course, let me at it!” but I did not. Instead I asked a question “What specifically do you need my help to do?“

<Bob> OK. And how was that reply received?

<Leslie> They were surprised, and they said “But we could not have done this on our own. You know what to do right at the start and even with your help it took us months to get to the point where we were ready to make the change. So you can do this stuff much more quickly than we can.“

<Bob> Well they are factually correct.

<Leslie> Yes I know, so I pointed out that although the technical part of the design does not take very long … that was not the problem … what slowed us down was the cultural part of the change. And that is done now so does not need to be repeated. The next study-plan-do cycle will be much quicker and they only need me for the technical bits they have not seen before.

<Bob> Excellent. So how would you now describe your role?

<Leslie> More of a facilitator and coach with a bit of only-when-needed training thrown in.

<Bob> Exactly … and I have a label for this role … I call it a Catalyst.

<Leslie> That is interesting, why so?

<Bob> Because the definition of a catalyst fits rather well. Using the usual scientific definition, a catalyst increases the yield and rate of a chemical reaction. With a catalyst, reactions occur faster and with less energy and catalysts are not consumed, they are recycled, so only tiny amounts are required.

<Leslie> Ah yes, that feels about right. But I am not just catalysing the reaction that produced the desired result am I?

<Bob> No. What else are you doing?

<Leslie> I am also converting some of the substrate into potential future catalysts too.

<Bob> Yes, you are. And that is what is needed for the current paradigm to shift.

<Leslie> Wow! I see that. This is powerful stuff!

<Bob> It is indeed. And the reaction you are catalysing is the combination of wisdom with ineptitude.

<Leslie> Eh? Can you repeat that again. Wisdom and ineptitude? Those are not words that I hear very often. I hear words like dumb, stupid, ignorant, incompetent and incapable. What is the reason you use those words?

<Bob> Simply because the dictionary definitions fit. Ineptitude means not knowing what to do to get the result we want, which is not the same as just not knowing stuff or not having the necessary skills. What we need are decisions which lead to effective actions and to intended outcomes. Wise decisions. If we demonstrate ineptitude we reveal that we lack the wisdom to make those effective decisions. So we need to combine ineptitude with wisdom to get the capability to achieve our purpose.

<Leslie> But why use the word “wisdom”? Why not just “knowledge”?

<Bob> Because knowledge is not enough. Knowledge just implies that I recognise what I am seeing. “I know this. I have seen it before“. Appreciating the implication of what I recognise is something more … it is called “understanding”.

<Leslie> Ah! I know this. I have seen this before. I know what a time-series chart is and I know how to create one but it takes guidance, time and practice to understand the implications of what the chart is saying about the system. But where does wisdom fit?

<Bob>Understanding is past-focussed. We understand how we got to where we are in the present. We cannot change the past so understanding has nothing to do with wise decisions or effective actions or intended outcomes. It is retrospection.

<Leslie> So wisdom is future-focussed. It is prospective. It is the ability to predict the outcome of an action and that ability is necessary to make wise decisions. That is why wisdom is the antidote to ineptitude!

<Bob> Well put! And that is what you did long before you made the change in the call centre … you learned how to make reliable predictions … and the results have confirmed yours was a wise decision. They got their intended outcome. You are not inept.

<Leslie> Ah! Now I understand the difference. I am a catalyst for improvement because I am able to diagnose and treat ineptitude. That is what you did for me. You are a catalyst.

<Bob> Welcome to the world of the Improvement Science Practitioner. You have earned your place.

The word “ineptitude” is coined by Dr Atul Gawande in the first of the 2014 Reith Lectures entitled “Why Do Doctors Fail?“.

Click HERE to listen to his first lecture (30 minutes).

In his second lecture he describes how it is the design of the system that delivers apparently miraculous outcomes. It is the way that the parts work together and the attention to context and to detail that counts.

Click HERE to hear his second lecture “The Century of the System” (30 minutes).

And Atul has a proven track record in system improvement … he is the doctor-surgeon-instigator of the WHO Safer Surgery Check List – a simple idea borrowed from aviation that is now used worldwide and is preventing 1000’s of easily avoidable deaths during and after surgery.

Click HERE to hear his third lecture “The Problem of Hubris” (30 minutes).

Click HERE to hear his fourth lecture “The Idea of Wellbeing” (30 minutes).

15/11/2014

Seeing and Believing

[Beep] It was time again for the weekly Webex coaching session. Bob dialled into the teleconference to find Leslie already there … and very excited.

<Leslie> Hi Bob, I am so excited. I cannot wait to tell you about what has happened this week.

<Bob> Hi Leslie. You really do sound excited. I cannot wait to hear.

<Leslie> Well, let us go back a bit in the story. You remember that I was really struggling to convince the teams I am working with to actually make changes. I kept getting the ‘Yes … but‘ reaction from the sceptics. It was as if they were more comfortable with complaining.

<Bob> That is the normal situation. We are all very able to delude ourselves that what we have is all we can expect.

<Leslie> Well, I listened to what you said and I asked them to work through what they predicted could happen if they did nothing. Their healthy scepticism then worked to build their conviction that doing nothing was a very dangerous choice.

<Bob> OK. And I am guessing that insight was not enough.

<Leslie> Correct. So then I shared some examples of what others had achieved and how they had done it, and I started to see some curiosity building, but no engagement still. So I kept going, sharing stories of ‘what’, and ‘how’. And eventually I got an email saying “We have thought about what you said about a one day experiment and we are prepared to give that a try“.

<Bob> Excellent. How long ago was that?

<Leslie> Three months. And I confess that I was part of the delay. I was so surprised that they said ‘OK‘ that I was not ready to follow on.

<Bob> OK. It sounds like you did not really believe it was possible either. So what did you do next?

<Leslie> Well I knew for sure that we would only get one chance. If the experiment failed then it would be Game Over. So I needed to know before the change what the effect would be. I needed to be able to predict it accurately. I also needed to feel reassured enough to take the leap of faith.

<Bob> Very good, so did you use some of your ISP-2 skills?

<Leslie> Yes! And it was a bit of a struggle because doing it in theory is one thing; doing it in reality is a lot messier.

<Bob> So what did you focus on?

<Leslie> The top niggle of course! At St Elsewhere® we have a call-centre that provides out-of-office-hours telephone advice and guidance – and it is especially busy at weekends. We are required to answer all calls quickly, which we do, and then we categorise them into ‘urgent’ and ‘non-urgent’ and pass them on to the specialists. They call the clients back and provide expert advice and guidance for their specific problem.

<Bob>So you do not use standard scripts?

<Leslie> No, that does not work. The variety of the problems we have to solve is too wide. And the specialist has to come to a decision quite quickly … solve the problem over the phone, arrange a visit to an out of hours clinic, or to dispatch a mobile specialist to the client immediately.

<Bob> OK. So what was the top niggle?

<Leslie> We have contractual performance specifications we have to meet for the maximum waiting time for our specialists to call clients back; and we were not meeting them. That implied that we were at risk of losing the contract and that meant loss of revenue and jobs.

<Bob> So doing nothing was not an option.

<Leslie> Correct. And asking for more resources was not either … the contract was a fixed price one. We got it because we offered the lowest price. If we employed more staff we would go out of business. It was a rock-and-a-hard-place problem.

<Bob> OK. So if this was ranked as your top niggle then you must have had a solution in mind.

<Leslie> I had a diagnosis. The Vitals Chart© showed that we already had enough resources to do the work. The performance failure was caused by a scheduling policy – one that we created – our intuitively-obvious policy.

<Bob> Ah ha! So you suggested doing something that felt counter-intuitive.

<Leslie> Yes. And that generated all the ‘Yes .. but‘ discussion.

<Bob> OK. Do you have the Vitals Chart© to hand? Can you send me the Wait-Time run chart?

<Leslie> Yes, I expected you would ask for that … here it is.

<Bob> OK. So I am looking at the run chart of waiting time for the call backs for one Saturday, and it is in call arrival order, and the blue line is the maximum allowed waiting time is that correct?

<Leslie>Yup. Can you see the diagnosis?

<Bob> Yes. This chart shows the classic pattern of ‘prioritycarveoutosis’. The upper border is the ‘non-urgents’ and the lower group are the ‘urgents’ … the queue jumpers.

<Leslie> Spot on. It is the rising tide of non-urgent calls that spill over the specification limit. And when I shared this chart the immediate reaction was ‘Well that proves we need more capacity!‘

<Bob> And the WIP chart did not support that assertion.

<Leslie> Correct. It showed we had enough total flow-capacity already.

<Bob> So you suggested a change in the scheduling policy would solve the problem without costing any money.

<Leslie> Yes. And the reaction to that was ‘That is impossible. We are already working flat out. We need more capacity because to work quicker will mean cutting corners and it is unsafe to cut-corners‘.

<Bob> So how did you get around that invalid but widely held belief?

<Leslie> I used one of the FISH techniques. I got a few of them to play a table top game where we simulated a much simpler process and demonstrated the same waiting time pattern on a hand-drawn run chart.

<Bob> Excellent. Did that get you to the ‘OK, we will give it a go for one day‘ decision.

<Leslie>Yes. But then I had to come up with a new design and I had test it so I know it would work.

<Bob> Because that was a step too far for them. And It sounds like you achieved that.

<Leslie> Yes. It was tough though because I knew I had to prove to myself I could do it. If I had asked you I know what you would have said – ‘I know you can do this‘. And last Saturday we ran the ‘experiment’. I was pacing up and down like an expectant parent!

<Bob> I expect rather like the ESA team who have just landed Rosetta’s little probe-child on an asteroid travelling at 38,000 miles per hour, billions of miles from Earth after a 10 year journey through deep space! Totally inspiring stuff!

<Leslie> Yes. And that is why I am so excited because OUR DESIGN WORKED! Exactly as predicted.

<Bob> Three cheers for you! You have experienced that wonderful feeling when you see the effect of improvement-by-design with your own eyes. When that happens then you really believe what opportunities become possible.

<Leslie> So I want to show you the ‘after’ chart …

<Bob> Wow! That is a spectacular result! The activity looks very similar, and other than a ‘blip’ between 15:00 and 19:00 the prioritycarveoutosis has gone. The spikes have assignable causes I assume?

<Leslie> Spot on again! The activity was actually well above average for a Saturday. The subjective feedback was that the new design felt calm and under-control. The chaos had evaporated. The performance was easily achieved and everyone was very positive about the whole experience. The sceptics were generous enough to say it had gone better than they expected. And yes, I am now working through the ‘spikes’ and excluding them … but only once I have a root cause that explains them.

<Bob> Well done Leslie! I sense that you now believe what is possible whereas before you just hoped it would be.

<Leslie> Yes! And the most important thing to me is that we did it ourselves. Which means improvement-by-design can be learned. It is not obvious, it feels counter-intuitive, so it is not easy … but it works.

<Bob> Yes. That is the most important message. And you have now earned your ISP Certificate of Competency.

01/11/201406/01/2024

Spring the Trap

[Beeeeeep] It was time for the weekly coaching chat. Bob, a seasoned practitioner of flow science, dialled into the teleconference with Lesley.

<Bob> Good afternoon Lesley, can I suggest a topic today?

<Lesley> Hi Bob. That would be great, and I am sure you have a good reason for suggesting it.

<Bob> I would like to explore the concept of time-traps again because it something that many find confusing. Which is a shame because it is often the key to delivering surprisingly dramatic and rapid improvements; at no cost.

<Lesley> Well doing exactly that is what everyone seems to be clamouring for so it sounds like a good topic to me. I confess that I am still not confident to teach others about time-traps.

<Bob> OK. Let us start there. Can you describe what happens when you try to teach it?

<Lesley> Well, it seems to be when I say that the essence of a time-trap is that the lead time and the flow are independent. For example, the lead time stays the same even though the flow is changing. That really seems to confuse people; and me too if I am brutally honest.

<Bob> OK. Can you share the example that you use?

<Lesley> Well it depends on who I am talking to. I prefer to use an example that they are familiar with. If it is a doctor I might use the example of the ward round. If it is a manager I might use the example of emails or meetings.

<Bob> Assume I am a doctor then – an urgent care physician.

<Lesley> OK. Let us take it that I have done the 4N Chart and the top niggle is ‘Frustration because the post-take ward round takes so long that it delays the discharge of patients who then often have to stay an extra night which then fills up the unit with waiting patients and we get blamed for blocking flow from A&E and causing A&E breaches‘.

<Bob> That sounds like a good example. What is the time-trap in that design?

<Lesley> The post-take ward round.

<Bob> And what justification is usually offered for using that design?

<Lesley> That it is a more efficient use of the expensive doctor’s time if the whole team congregate once a day and work through all the patients admitted over the previous 24 hours. They review the presentation, results of tests, diagnosis, management plans, response to treatment, decide the next steps and do the paperwork.

<Bob> And why is that a time-trap design?

<Lesley> Because it does not matter if one patient is admitted or ten, the average lead time from the perspective of the patient is the same – about one day.

<Bob> Correct. So why is the doctor complaining that there are always lots of patients to see?

<Lesley> Because there are. The emergency short stay ward is usually full by the time the post take ward round happens.

<Bob> And how do you present the data that shows the lead time is independent of the flow?

<Lesley> I use a Gantt chart, but the problem I find is that there is so much variation and queue jumping it is not blindingly obvious from the Gantt chart that there is a time-trap. There is so much else clouding the picture.

<Bob>Is that where the ‘but I do not understand‘ conversation starts?

<Lesley> Yes. And that is where I get stuck too.

<Bob> OK. The issue here is that a Gantt chart is not the ideal visualisation tool when there are lots of crossed-streams, frequently changing priorities, and many other sources of variation. The Gantt chart gets ‘messy’. The trick here is to use a Vitals Chart – and you can derive that from the same data you used for the Gantt chart.

<Lesley> You are right about the Gantt chart getting messy. I have seen massive wall-sized Gantt charts that are veritable works-of-art and that have taken hours to create; and everyone standing looking at it and saying ‘Wow! That is an impressive piece of work. So what does it tell us? How does it help?‘

<Bob> Yes, I have experienced that too. I think what happens is that those who do the foundation training and discover the Gantt chart then try to use it to solve every flow problem – and in their enthusiasm they discount any warning advice. Desperation drives over-inflated expectation which is often the pre-cursor to disappointment, and then disillusionment. The Nerve Curve again.

<Lesley> But a Vitals Chart is an HCSE level technique and you said that we do not need to put everyone through HCSE training.

<Bob>That is correct. I am advocating an HCSE-in-training using a Vitals Chart to explain the concept of a time-trap so that everyone understands it well enough to see the flaw in the design.

<Lesley> Ah ha! Yes, I see. So what is my next step?

<Bob> I will let you answer that.

<Lesley> Um, let me think.

The outcome I want is everyone understands the concept of a time-trap well enough to feel comfortable with trying a time-trap-free design because they can see the benefits for them.

And to get that depth of understanding I need to design a table top exercise that starts with a time-trap design and generates raw data that we can use to build both a Gantt chart and the Vitals Chart; so I can point out and explain the characteristic finger-print of a time trap.

And then we can ‘test’ an alternative time-trap-free design and generate the prognostic Gantt and Vitals Chart and compare with the baseline diagnostic charts to reveal the improvement.

<Bob> That sounds like a good plan to me. And if you do that, and your team apply it to a real improvement exercise, and you see the improvement and you share the story, then that will earn you a coveted HCSE Certificate of Competency.

<Lesley>Ah ha! Now I understand the reason you suggested this topic! I am on the case!

18/10/2014

A Little Law and Order

[Bing bong]. The sound heralded Lesley logging on to the weekly Webex coaching session with Bob, an experienced Improvement Science Practitioner.

<Bob> Good afternoon Lesley. How has your week been and what topic shall we explore today?

<Lesley> Hi Bob. Well in a nutshell, the bit of the system that I have control over feels like a fragile oasis of calm in a perpetual desert of chaos. It is hard work keeping the oasis clear of the toxic sand that blows in!

<Bob> A compelling metaphor. I can just picture it. Maintaining order amidst chaos requires energy. So what would you like to talk about?

<Lesley> Well, I have a small shoal of FISHees who I am guiding through the foundation shallows and they are getting stuck on Little’s Law. I confess I am not very good at explaining it and that suggests to me that I do not really understand it well enough either.

<Bob> OK. So shall we link those two theme – chaos and Little’s Law?

<Lesley> That sounds like an excellent plan!

<Bob> OK. So let us refresh the foundation knowledge. What is Little’s Law?

<Lesley>It is a fundamental Law of process physics that relates flow, with lead time and work in progress.

<Bob> Good. And specifically?

<Lesley> Average lead time is equal to the average flow multiplied by the average work in progress.

<Bob>Yes. And what are the units of flow in your equation?

<Lesley> Ah yes! That is a trap for the unwary. We need to be clear how we express flow. The usual way is to state it as number of tasks in a defined period of time, such as patients admitted per day. In Little’s Law the convention is to use the inverse of that which is the average interval between consecutive flow events. This is an unfamiliar way to present flow to most people.

<Bob> Good. And what is the reason that we use the ‘interval between events’ form?

<Leslie> Because it is easier to compare it with two critically important flow metrics … the takt time and the cycle time.

<Bob> And what is the takt time?

<Leslie> It is the average interval between new tasks arriving … the average demand interval.

<Bob> And the cycle time?

<Leslie> It is the shortest average interval between tasks departing …. and is determined by the design of the flow constraint step.

<Bob> Excellent. And what is the essence of a stable flow design?

<Lesley> That the cycle time is less than the takt time.

<Bob>Why less than? Why not equal to?

<Leslie> Because all realistic systems need some flow resilience to exhibit stable and predictable-within-limits behaviour.

<Bob> Excellent. Now describe the design requirements for creating chronically chaotic system behaviour?

<Leslie> This is a bit trickier to explain. The essence is that for chronically chaotic behaviour to happen then there must be two feedback loops – a destabilising loop and a stabilising loop. The destabilising loop creates the chaos, the stabilising loop ensures it is chronic.

<Bob> Good … so can you give me an example of a destabilising feedback loop?

<Leslie> A common one that I see is when there is a long delay between detecting a safety risk and the diagnosis, decision and corrective action. The risks are often transitory so if the corrective action arrives long after the root cause has gone away then it can actually destabilise the process and paradoxically increase the risk of harm.

<Bob> Can you give me an example?

<Leslie>Yes. Suppose a safety risk is exposed by a near miss. A delay in communicating the niggle and a root cause analysis means that the specific combination of factors that led to the near miss has gone. The holes in the Swiss cheese are not static … they move about in the chaos. So the action that follows the accumulation of many undiagnosed near misses is usually the non-specific mantra of adding yet another safety-check to the already burgeoning check-list. The longer check-list takes more time to do, and is often repeated many times, so the whole flow slows down, queues grow bigger, waiting times get longer and as pressure comes from the delivery targets corners start being cut, and new near misses start to occur; on top of the other ones. So more checks are added and so on.

<Bob> An excellent example! And what is the outcome?

<Leslie> Chronic chaos which is more dangerous, more disordered and more expensive. Lose lose lose.

<Bob> And how do the people feel who work in the system?

<Leslie> Chronically naffed off! Angry. Demotivated. Cynical.

<Bob>And those feelings are the key symptoms. Niggles are not only symptoms of poor process design, they are also symptoms of a much deeper problem: a violation of values.

<Leslie> I get the first bit about poor design; but what is that second bit about values?

<Bob> We all have a set of values that we learned when we were very young and that have bee shaped by life experience. They are our source of emotional energy, and our guiding lights in an uncertain world. Our internal unconscious check-list. So when one of our values is violated we know because we feel angry. How that anger is directed varies from person to person … some internalise it and some externalise it.

<Leslie> OK. That explains the commonest emotion that people report when they feel a niggle … frustration which is the same as anger.

<Bob>Yes. And we reveal our values by uncovering the specific root causes of our niggles. For example if I value ‘Hard Work’ then I will be niggled by laziness. If you value ‘Experimentation’ then you may be niggled by ‘Rigid Rules’. If someone else values ‘Safety’ then they may value ‘Rigid Rules’ and be niggled by ‘Innovation’ which they interpret as risky.

<Leslie> Ahhhh! Yes, I see. This explains why there is so much impassioned discussion when we do a 4N Chart! But if this behaviour is so innate then it must be impossible to resolve!

<Bob> Understanding how our values motivate us actually helps a lot because we are naturally attracted to others who share the same values – because we have learned that it reduces conflict and stress and improves our chance of survival. We are tribal and tribes share the same values.

<Leslie> Is that why different departments appear to have different cultures and behaviours and why they fight each other?

<Bob> It is one factor in the Silo Wars that are a characteristic of some large organisations. But Silo Wars are not inevitable.

<Leslie> So how are they avoided?

<Bob> By everyone knowing what common purpose of the organisation is and by being clear about what values are aligned with that purpose.

<Leslie> So in the healthcare context one purpose is avoidance of harm … primum non nocere … so ‘safety’ is a core value. Which implies anything that is felt to be unsafe generates niggles and well-intended but potentially self-destructive negative behaviour.

<Bob> Indeed so, as you described very well.

<Leslie> So how does all this link to Little’s Law?

<Bob>Let us go back to the foundation knowledge. What are the four interdependent dimensions of system improvement?

<Leslie> Safety, Flow, Quality and Productivity.

<Bob> And one measure of productivity is profit. So organisations that have only short term profit as their primary goal are at risk of making poor long term safety, flow and quality decisions.

<Leslie> And flow is the key dimension – because profit is just the difference between two cash flows: income and expenses.

<Bob> Exactly. One way or another it all comes down to flow … and Little’s Law is a fundamental Law of flow physics. So if you want all the other outcomes … without the emotionally painful disorder and chaos … then you cannot avoid learning to use Little’s Law.

<Leslie> Wow! That is a profound insight. I will need to lie down in a darkened room and meditate on that!

<Bob> An oasis of calm is the perfect place to pause, rest and reflect.

11/10/2014

Feel the Fear

We spend a lot of time in a state of anxiety and fear. It is part and parcel of life because there are many real threats that we need to detect and avoid.

For our own safety and survival.

Unfortunately there are also many imagined threats that feel just as real and just as terrifying.

In these cases it is our fear that does the damage because it paralyses our decision making and triggers our ‘fright’ then ‘fight’ or ‘flight’ reaction.

Fear is not bad … the emotional energy it releases can be channelled into change and improvement. Just as anger can.

So we need to be able to distinguish the real fears from the imaginary ones. And we need effective strategies to defuse the imaginary ones. Because until we do that we will find it very difficult to listen, learn, experiment, change and improve.

So let us grasp the nettle and talk about a dozen universal fears …

Fear of dying before one’s time.
Fear of having one’s basic identity questioned.
Fear of poverty or loss of one’s livelihood.
Fear of being denied one’s fundamental rights and liberties.

Fear of being unjustly accused of wrongdoing.
Fear of public humiliation.
Fear of being unjustly seen as lacking character.
Fear of being discovered as inauthentic – a fraud.

Fear of radical change.
Fear of feedback.
Fear of failure.
Fear of the unknown.

Notice that some of these fears are much ‘deeper’ than others … this list is approximately in depth order. Some relate to ‘self’; some relate to ‘others’ and all are inter-related to some degree. Fear of failure links to fear of humiliation and to fear of loss-of-livelihood.

Of these the four that are closest to the surface are the easiest to tackle … fear of radical change, fear of feedback, fear of failure, and fear of the unknown. These are the Four Fears that block personal improvement.

Fear of the unknown is the easiest to defuse. We just open the door and look … from an emotionally safe distance so that we can run away if our worst fears are realised … which does not happen when the fear is imagined.

This is an effective strategy for defusing the emotionally and socially damaging effects of self-generated phobias.

And we find overcoming fear-of-the-unknown exhilarating … that is how theme parks and roller-coaster rides work.

First we open our eyes, we look, we see, we observe, we reflect, we learn and we convert the unknown to the unfamiliar and then to the familiar. We may not conquer our fear completely … there may be some reasonable residual anxiety … but we have learned to contain it and to control it. We have made friends with our inner Chimp. We climb aboard the roller coaster that is called ‘life’.

Fear of failure is next. We defuse this by learning how to fail safely so that we can learn-by-doing and by that means we reduce the risk of future failures. We make frequent small safe failures in order to learn how to avoid the rare big unsafe ones!

Many people approach improvement from an academic angle. They sit on the fence. They are the reflector-theorists. And this may because they are too fearful-of-failing to learn the how-by-doing. So they are unable to demonstrate the how and their fear becomes the fear-of-fraud and the fear-of-humiliation. They are blocked from developing their pragmatist/activist capability by their self-generated fear-of-failure.

So we start small, we stay focussed, we stay inside our circle of control, and we create a safe zone where we can learn how to fail safely – first in private and later in public.

One of the most inspiring behaviours of an effective leader is the courage to learn in public and to make small failures that demonstrate their humility and humanity.

Those who insist on ‘perfect’ leaders are guaranteed to be disappointed.

And one thing that we all fail repeatedly is to ask for, to give and to receive effective feedback. This links to the deeper fear-of-humiliation.

And it is relatively easy to defuse this fear-of-feedback too … we just need a framework to support us until we find our feet and our confidence.

The key to effective feedback is to make it non-judgemental.

And that can only be done by developing our ability to step back and out of the Drama Triangle and to cultivate an I’m OK- You’re OK mindset.

The mindset of mutual respect. Self-respect and Other-respect.

And remember that Other-respect does not imply trust, alignment, agreement, or even liking.

Sworn enemies can respect each other while at the same time not trusting, liking or agreeing with each other.

Judgement-free feedback (JFF) is a very effective technique … both for defusing fear and for developing mutual respect.

And from that foundation radical change becomes possible, even inevitable.

04/10/2014

Wacky Language

All innovative ideas are inevitably associated with new language.

Familiar words used in an unfamiliar context so that the language sounds ‘wacky’ to those in the current paradigm.

Improvement science is no different.

A problem arises when familiar words are used in a new context and therefore with a different meaning. Confusion.

So we try to avoid this cognitive confusion by inventing new words, or by using foreign words that are ‘correct’ but unfamiliar.

This use of novel and foreign language exposes us to another danger: the evolution of a clique of self-appointed experts who speak the new and ‘wacky’ language.

This self-appointed expert clique can actually hinder change because it can result yet another us-and-them division. Another tribe. More discussion. More confusion. Less improvement.

So it is important for an effective facilitator-of-improvement to define any new language using the language of the current paradigm. This can be achieved by sharing examples of new concepts and their language in familiar contexts and with familiar words, because we learn what words mean from their use-in-context.

For example:

The word ‘capacity’ is familiar and we all know what we think it means. So when we link it to another familiar word, ‘demand’, then we feel comfortable that we understand what the phrase ‘demand-and-capacity’ means.

But do we?

The act of recognising a word is a use of memory or knowledge. Understanding what a word means requires more … it requires knowing the context in which the word is used. It means understanding the concept that the word is a label for.

To a practitioner of flow science the word ‘capacity’ is confusing – because it is too fuzzy. There are many different forms of capacity: flow-capacity, space-capacity, time-capacity, and so on. Each has a different unit and they are not interchangeable. So the unqualified term ‘capacity’ will trigger the question:

What sort of capacity are you referring to?

[And if that is not the reaction then you may be talking to someone who has little understanding of flow science].

Then there are the foreign words that are used as new labels for old concepts.

Lean zealots seem particularly fond of peppering their monologues with Japanese words that are meaningless to anyone else but other Lean zealots. Words like muda and muri and mura which are labels for important and useful flow science concepts … but the foreign name gives no clue as to what that essential concept is!

[And for a bit of harmless sport ask a Lean zealot to explain what these three words actually mean but only using language that you understand. If they cannot to your satisfaction then you have exposed the niggle. And if they can then it is worth asking ‘What is the added value of the foreign language?’]

And for those who are curious to know the essential concepts that these four-letter M words refer to:

muda means ‘waste’ and refers to the effects of poor process design in terms of the extra time (and cost) required for the process to achieve its intended purpose. A linked concept is a ‘niggle’ which is the negative emotional effect of a poor process design.

muri means ‘overburdening’ and can be illustrated with an example. Suppose you work in a system where there is always a big backlog of work waiting to be done … a large queue of patients in the waiting room … a big heap of notes on the trolley. That ‘burden’ generates stress and leads to other risky behaviours such as rushing, corner-cutting, deflection and overspill. It is also an outcome of poor process design, so is avoidable.

mura means variation or uncertainty. Again an example helps. Suppose we are running an emergency service then, by definition, a we have no idea what medical problem the next patient that comes through the door will present us with. It could be trivial or life-threatening. That is unplanned and expected variation and is part of the what we need our service to be designed to handle. Suppose when we arrive for our shift that we have no idea how many staff will be available to do the work because people phone in sick at the last minute and there is no resilience on the staffing capacity. Our day could be calm-and-capable (and rewarding) or chaotic-and-incapable (and unrewarding). It is the stress of not knowing that creates the emotional and cultural damage, and is the expected outcome of incompetent process design. And is avoidable.

And finally we come to words that are not foreign but are not very familiar either.

Words like praxis.

This sounds like ‘practice’ but is not spelt the same. So is the the same?

And it sounds like a medical condition called dyspraxia which means: poor coordination of movement.

And when we look up praxis in an English dictionary we discover that one definition is:

the practice and practical side of a profession or field of study, as opposed to theory.

Ah ah! So praxis is a label for the the concept of ‘how to’ … and someone who has this ‘know how’ is called a practitioner. That makes sense.

On deeper reflection we might then describe our poor collective process design capability as dyspraxic or uncoordinated. That feels about right too.

An improvement science practitioner (ISP) is someone who knows the science of improvement; and can demonstrate their know-how in practice; and can explain the principles that underpin their praxis using the language of the learner. Without any wacky language.

So if we want to diagnose and treat our organisational dyspraxia;

… and if we want smooth and efficient services (i.e. elimination of chaos and reduction of cost);

… and if we want to learn this know-how, practice or praxis;

… then we could study the Foundations of Improvement Science in Healthcare (FISH);

… and we could seek the wisdom of the growing Community of Healthcare Improvement Practitioners (CHIPs).

FISH & CHIPs … a new use for a familiar phrase?

20/09/2014

A Sisyphean Nightmare

[Beep] It was time for the weekly e-mentoring session so Bob switched on his laptop, logged in to the virtual meeting site and found that Lesley was already there.

<Bob> Hi Lesley. What shall we talk about today?

<Lesley> Hello Bob. Another old chestnut I am afraid. Queues. I keep hitting the same barrier where people who are fed up with the perpetual queue chaos have only one mantra “If you want to avoid long waiting times then we need more capacity.“

<Bob> So what is the problem? You know that is not the cause of chronic queues.

<Lesley> Yes, I know that mantra is incorrect – but I do not yet understand how to respectfully challenge it and how to demonstrate why it is incorrect and what the alternative is.

<Bob> OK. I understand. So could you outline a real example that we can work with.

<Lesley> Yes. Another old chestnut: the Emergency Department 4-hour breaches.

<Bob> Do you remember the Myth of Sisyphus?

<Leslie> No, I do not remember that being mentioned in the FISH course.

<Bob> Ho ho! No indeed, it is much older. In Greek mythology Sisyphus was a king of Ephyra who was punished by the Gods for chronic deceitfulness by being compelled to roll an immense boulder up a hill, only to watch it roll back down, and then to repeat this action forever.

<Lesley> Ah! I see the link. Yes, that is exactly how people in the ED feel. Everyday it feels like they are pushing a heavy boulder uphill – only to have to repeat the same labour the next day. And they do not believe it can ever be any better with the resources they have.

<Bob> A rather depressing conclusion! Perhaps a better metaphor is the story in the film “Ground Hog Day” where Bill Murray plays the part of a rather arrogant newsreader who enters a recurring nightmare where the same day is repeated, over and over. He seems powerless to prevent it. He does eventually escape when he learns the power of humility and learns how to behave differently.

<Lesley> So the message is that there is a way out of this daily torture – if we are humble enough to learn the ‘how’.

<Bob> Well put. So shall we start?

<Lesley> Yes please!

<Bob> OK. As you know very well it is important not to use the unqualified term ‘capacity’. We must always state if we are referring to flow-capacity or space-capacity.

<Lesley> Because they have different units and because they are intimately related to lead time by Little’s Law.

<Bob> Yes. Little’s Law is mathematically proven Law of flow physics – it is not negotiable.

<Lesley> OK. I know that but how does it solve problem we started with?

<Bob> Little’s Law is necessary but it is not sufficient. Little’s Law relates to averages – and is therefore just the foundation. We now need to build the next level of understanding.

<Lesley> So you mean we need to introduce variation?

<Bob> Yes. And the tool we need for this is a particular form of time-series chart called a Vitals Chart.

<Lesley> And I am assuming that will show the relationship between flow, lead time and work in progress … over time ?

<Bob> Exactly. It is the temporal patterns on the Vitals Chart that point to the root causes of the Sisyphean Chaos. The flow design flaws.

<Lesley> Which are not lack of flow-capacity or space-capacity.

<Bob> Correct. If the chaos is chronic then there must already be enough space-capacity and flow-capacity. Little’s Law shows that, because if there were not the system would have failed completely a long time ago. The usual design flaw in a chronically chaotic system is one or more misaligned policies. It is as if the system hardware is OK but the operating software is not.

<Lesley> So to escape from the Sisyphean Recurring ED 4-Hour Breach Nightmare we just need enough humility and enough time to learn how to diagnose and redesign some of our ED system operating software? Some of our own policies? Some of our own mantras?

<Bob> Yup. And not very much actually. Most of the software is OK. We need to focus on the flaws.

<Lesley> So where do I start?

<Bob> You need to do the ISP-1 challenge that is called Brainteaser 104. That is where you learn how to create a Vitals Chart.

<Lesley> OK. Now I see what I need to do and the reason: understanding how to do that will help me explain it to others. And you are not going to just give me the answer.

<Bob> Correct. I am not going to just give you the answer. You will not fully understand unless you are able to build your own Vitals Chart generator. You will not be able to explain the how to others unless you demonstrate it to yourself first.

<Lesley> And what else do I need to do that?

<Bob> A spreadsheet and your raw start and finish event data.

<Lesley> But we have tried that before and neither I nor the database experts in our Performance Department could work out how to get the real time work in progress from the events – so we assumed we would have to do a head count or a bed count every hour which is impractical.

<Bob> It is indeed possible as you are about to discover for yourself. The fact that we do not know how to do something does not prove that it is impossible … humility means accepting our inevitable ignorance and being open to learning. Those who lack humility will continue to live the Sisyphean Nightmare of ED Ground Hog Day. The choice to escape is ours.

<Lesley> I choose to learn. Please send me BT104.

<Bob> It is on its way …

06/09/2014

The Jigsaw

Systems are made of interdependent parts that link together – rather like a jigsaw.

If pieces are distorted, missing, or in the wrong place then the picture is distorted and the system does not work as well as it could.

And if pieces of one jigsaw are mixed up with those of another then it is even more difficult to see any clear picture.

A system of improvement is just the same.

There are many improvement jigsaws each of which have pieces that fit well together and form a synergistic whole. Lean, Six Sigma, and Theory of Constraints are three well known ones.

Each improvement jigsaw evolved in a different context so naturally the picture that emerges is from a particular perspective: such as manufacturing.

So when the improvement context changes then the familiar jigsaws may not work as well: such as when we shift context from products to services, and from commercial to public.

A public service such as healthcare requires a modified improvement jigsaw … so how do we go about getting that?

One way is to ‘evolve’ an old jigsaw into a new context. That is tricky because it means adding new pieces and changing old pieces and the ‘zealots’ do not like changing their familiar jigsaw so they resist.

Another way is to ‘combine’ several old jigsaws in the hope that together they will provide enough perspectives. That is even more tricky because now you have several tribes of zealots who resist having their familiar jigsaws modified.

What about starting with a blank canvas and painting a new picture from scratch? Well it is actually very difficult to create a blank canvas for learning because we cannot erase what we already know. Our current mental model is the context we need for learning new knowledge.

So what about using a combination of the above?

What about first learning a new creative approach called design? And within that framework we can then create a new improvement jigsaw that better suits our specific context using some of the pieces of the existing ones. We may need to modify the pieces a bit to allow them to fit better together, and we may need to fashion new pieces to fill the gaps that we expose. But that is part of the fun.

The improvement jigsaw shown here is a new hybrid.

It has been created from a combination of existing improvement knowledge and some innovative stuff.

Pareto analysis was described by Vilfredo Pareto over 100 years ago. So that is tried and tested!

Time-series charts were invented by Walter Shewhart almost 100 years ago. So they are tried and tested too!

The combination of Pareto and Shewhart tools have been used very effectively for over 50 years. The combination is well proven.

The other two pieces are innovative. They have different parents and different pedigrees. And different purposes.

The Niggle-o-Gram® is related to 2-by-2, FMEA and EIQ and the 4N Chart®. It is the synthesis of them that creates a powerful lens for focussing our improvement efforts on where the greatest return-on-investment will be.

The Right-2-Left Map® is a descendent of the Design family and has been crossed with Graph Theory and Causal Network exemplars to introduce their best features. Its purpose is to expose errors of omission.

The emergent system is synergistic … much more effective than each part individually … and more even than their linear sum.

So when learning this new Science of Improvement we have to focus first on learning about the individual pieces and we do that by seeing examples of them used in practice. That in itself is illuminating!

As we learn about more pieces a fog of confusion starts to form and we run the risk of mutating into a ‘tool-head’. We know about the pieces in detail but we still do not see the bigger picture.

To avoid the tool-head trap we must balance our learning wheel and ensure that we invest enough time in learning-by-doing.

Then one day something apparently random will happen that triggers a ‘click’. Familiar pieces start to fit together in a unfamiliar way and as we see the relationships, the sequences, and the synergy – then a bigger picture will start to emerge. Slowly at first and then more quickly as more pieces aggregate.

Suddenly we feel a big CLICK as the final pieces fall into place. The fog of confusion evaporates in the bright sunlight of a paradigm shift in our thinking.

The way forward that was previously obscured becomes clearly visible.

Ah ha!

And we are off on the next stage of our purposeful journey of improvement.

23/08/2014

Learning in Style

Improvement implies learning – new experiences, new insights, new models and new ways of doing things.

So understanding the process of learning is core to the science of improvement.

What many people do not fully appreciate is that we differ in the way we prefer to learn. These are habitual behaviours that we have acquired.

The diagram shows one model – the Honey and Mumford model that evolved from an earlier model described by Kolb.

One interesting feature of this diagram is the two dimensions – Perception and Processing which are essentially the same as the two core dimensions in the Myers-Briggs Type Index.

What the diagram above does not show so well is that the process of learning is a cycle – the clockwise direction in this diagram – Pragmatist then Activist then Reflector then Theorist and back to Pragmatist.

This is the PART sequence. And it can start at any point … ARTP, RTPA, TPAR.

We all use all of these learning styles – but we have a preference for some more than others – our preferred learning styles are our learning comfort zones.

The large observational studies conducted in the 1980’s using the PART model revealed that most people have moderate to strong preferences for only one or two of these styles. Less than 20% have a preference for three and very few feel equally comfortable with all four.

The commonest patterns are illustrated by the left and right sides of the diagram: the Pragmatist-Activist combination and the Reflector-Theorist combination.

It is not that one is better than the other … all four are synergistic and an effective and efficient learning process requires being comfortable with using all four in a continuous sequence.

Imagine this as a wheel – an imbalance between the four parts represents a distorted wheel. So when this learning wheel ‘turns’ it delivers an emotionally bumpy ‘ride’. Past experience of being pushed through this pain-and-gain process will tend to inhibit or even block learning completely.

So to get a more comfortable learning journey we first need to balance our PART wheel – and that implies knowing what our preferred styles are and then developing the learning styles that we use least to build our competence and confidence with them. And that is possible because these are learned habits. With guidance, focus and practice we can all strengthen our less favoured learning ‘muscles’.

Those with a preference for planning-and-doing would focus on developing their reflection and then their abstraction skills. For example by monitoring the effects of their actions in reality and using that evidence to challenge their underlying assumptions and to generate new ‘theories’ for pragmatic experimentation. Actively seeking balanced feedback and reflecting on it is one way to do that.

Those with a preference for studying-and-abstracting would focus on developing their design and then their delivery skills and become more comfortable with experimenting to test their rhetoric against reality. Actively seeking opportunities to learn-by-doing is one way.

And by creating the context for individuals to become more productive self-learners we can see how learning organisations will follow naturally. And that is what we need to deliver system-wide improvement at scale and pace.

16/08/2014

The 85% Optimum Occupancy Myth

There seems to be a belief among some people that the “optimum” average bed occupancy for a hospital is around 85%.

More than that risks running out of beds and admissions being blocked, 4 hour breaches appearing and patients being put at risk. Less than that is inefficient use of expensive resources. They claim there is a ‘magic sweet spot’ that we should aim for.

Unfortunately, this 85% optimum occupancy belief is a myth.

So, first we need to dispel it, then we need to understand where it came from, and then we are ready to learn how to actually prevent queues, delays, disappointment, avoidable harm and financial non-viability.

Disproving this myth is surprisingly easy. A simple thought experiment is enough.

Suppose we have a policy where we keep patients in hospital until someone needs their bed, then we discharge the patient with the longest length of stay and admit the new one into the still warm bed – like a baton pass. There would be no patients turned away – 0% breaches. And all our the beds would always be full – 100% occupancy. Perfection!

And it does not matter if the number of admissions arriving per day is varying – as it will.

And it does not matter if the length of stay is varying from patient to patient – as it will.

We have disproved the hypothesis that a maximum 85% average occupancy is required to achieve 0% breaches.

The source of this specific myth appears to be a paper published in the British Medical Journal in 1999 called “Dynamics of bed use in accommodating emergency admissions: stochastic simulation model“

So it appears that this myth was cooked up by academic health economists using a computer model.

And then amateur queue theory zealots jump on the band-wagon to defend this meaningless mantra and create a smoke-screen by bamboozling the mathematical muggles with tales of Poisson processes and Erlang equations.

And they are sort-of correct … the theoretical behaviour of the “ideal” stochastic demand process was described by Poisson and the equations that describe the theoretical behaviour were described by Agner Krarup Erlang. Over 100 years ago before we had computers.

BUT …

The academics and amateurs conveniently omit one minor, but annoying, fact … that real world systems have people in them … and people are irrational … and people cook up policies that ride roughshod over the mathematics, the statistics and the simplistic, stochastic mathematical and computer models.

And when creative people start meddling then just about anything can happen!

So what went wrong here?

One problem is that the academic hefalumps unwittingly stumbled into a whole minefield of pragmatic process design traps.

Here are just some of them …

1. Occupancy is a ratio – it is a meaningless number without its context – the flow parameters.

2. Using linear, stochastic models is dangerous – they ignore the non-linear complex system behaviours (chaos to you and me).

3. Occupancy relates to space-capacity and says nothing about the flow-capacity or the space-capacity and flow-capacity scheduling.

4. Space-capacity utilisation (i.e. occupancy) and systemic operational efficiency are not equivalent.

5. Queue theory is a simplification of reality that is needed to make the mathematics manageable.

6. Ignoring the fact that our real systems are both complex and adaptive implies that blind application of basic queue theory rhetoric is dangerous.

And if we recognise and avoid these traps and we re-examine the problem a little more pragmatically then we discover something very useful:

That the maximum space capacity requirement (the number of beds needed to avoid breaches) is actually easily predictable.

It does not need a black-magic-box full of scary queue theory equations or rather complicated stochastic simulation models to do this … all we need is our tried-and-trusted tool … a spreadsheet.

And we need something else … some flow science training and some simulation model design discipline.

When we do that we discover something else …. that the expected average occupancy is not 85% … or 65%, or 99%, or 95%.

There is no one-size-fits-all optimum occupancy number.

And as we explore further we discover that:

The expected average occupancy is context dependent.

And when we remember that our real system is adaptive, and it is staffed with well-intended, well-educated, creative people (who may have become rather addicted to reactive fire-fighting), then we begin to see why the behaviour of real systems seems to defy the predictions of the 85% optimum occupancy myth:

Our hospitals seem to work better-than-predicted at much higher occupancy rates.

And then we realise that we might actually be able to design proactive policies that are better able to manage unpredictable variation; better than the simplistic maximum 85% average occupancy mantra.

And finally another penny drops … average occupancy is an output of the system …. not an input. It is an effect.

And so is average length of stay.

Which implies that setting these output effects as causal inputs to our bed model creates a meaningless, self-fulfilling, self-justifying delusion.

Ooops!

Now our challenge is clear … we need to learn proactive and adaptive flow policy design … and using that understanding we have the potential to deliver zero delays and high productivity at the same time.

And doing that requires a bit more than a spreadsheet … but it is possible.

23/07/2014

Perfect Storm

[Drrrrring Drrrrring]

<Bob> Hi Lesley! How are you today?

<Leslie> Hi Bob. Really good. I have just got back from a well earned holiday so I am feeling refreshed and re-energised.

<Bob> That is good to hear. It has been a bit stormy here over the past few weeks. Apparently lots of hot air hitting cold reality and forming a fog of disillusionment and storms of protest.

<Leslie> Is that a metaphor?

<Bob> Yes! A good one do you think? And it leads us into our topic for this week. Perfect storms.

<Leslie> I am looking forward to it. Can you be a bit more specific?

<Bob> Sure. Remember the ISP exercise where I asked you to build a ‘chaos generator’?

<Leslie> I sure do. That was an eye-opener! I had no idea how easy it is to create chaotic performance in a system – just by making the Flaw of Averages error and adding a pinch of variation. Booom!

<Bob> Good. We are going to use that model to demonstrate another facet of system design. How to steer out of chaos.

<Leslie> OK – what do I need to do.

<Bob> Start up that model and set the cycle time to 10 minutes with a sigma of 1.5 minutes.

<Leslie> OK.

<Bob> Now set the demand interval to 10 minutes and the sigma of that to 2.0 minutes.

<Leslie> OK. That is what I had before.

<Bob> Set the lead time upper specification limit to 30 minutes. Run that 12 times and record the failure rate.

<Leslie> OK. That gives a chaotic picture! All over the place.

<Bob> OK now change just the average of the demand interval. Start with a value of 8 minutes, run 12 times, and then increase to 8.5 minutes and repeat that up to 12 minutes.

<Leslie> OK. That will repeat the run for 10 minutes. Is that OK.

<Bob> Yes.

<Leslie> OK … it will take me a few minutes to run all these. Do you want to get a cup of tea while I do that?

<Bob> Good idea.

[5 minutes later]

<Leslie> OK I have done all that – 108 data points. Do I plot that as a run chart?

<Bob> You could. I suggest plotting as a scattergram.

<Leslie> With the average demand interval on the X axis and the Failure % on the Y axis?

<Bob> Yes. Exactly so. And just the dots, no lines.

<Leslie> OK. Wow! That is amazing! Now I see why you get so worked up about the Flaw of Averages!

<Bob> What you are looking at is called a performance curve. Notice how steep and fuzzy it is. That is called a chaotic transition. The perfect storm. And when fall into the Flaw of Averages trap we design our systems to be smack in the middle of it.

<Leslie> Yes I see what you are getting at. And that implies that to calm the chaos we do not need very much resilient flow capacity … and we could probably release that just from a few minor design tweaks.

<Bob> Yup.

<Leslie> That is so cool. I cannot wait to share this with the team. Thanks again Bob.

12/07/2014

Seeing-by-Doing

Flow improvement-by-design requires being able to see the flows; and that is trickier than it first appears.

We can see movement very easily.

Seeing flows is not so easy – particularly when they are mixed-up and unsteady.

One of the most useful tools for visualising flow was invented over 100 years ago by Henry Laurence Gantt (1861-1919).

Henry Gantt was a mechanical engineer from Johns Hopkins University and an early associate of Frederick Taylor. Gantt parted ways with Taylor because he disagreed with the philosophy of Taylorism which was that workers should be instructed what to do by managers (=parent-child). Gantt saw that workers and managers could work together for mutual benefit of themselves and their companies (=adult-adult). At one point Gantt was invited to streamline the production of munitions for the war effort and his methods were so successful that the Ordinance Department was the most productive department of the armed forces. Gantt favoured democracy over autocracy and is quoted to have said “Our most serious trouble is incompetence in high places. The manager who has not earned his position and who is immune from responsibility will fail time and again, at the cost of the business and the workman“.

Henry Gantt invented a number of different charts – not just the one used in project management which was actually invented 20 years earlier by Karol Adamieki and re-invented by Gantt. It become popularised when it was used in the Hoover Dam project management; but that was after Gantt’s death in 1919.

The form of Gantt chart above is called a process template chart and it is designed to show the flow of tasks through a process. Each horizontal line is a task; each vertical column is an interval of time. The colour code in each cell indicates what the task is doing and which resource the task is using during that time interval. Red indicates that the task is waiting. White means that the task is outside the scope of the chart (e.g. not yet arrived or already departed).

The Gantt chart shows two “red wedges”. A red wedge that is getting wider from top to bottom is the pattern created by a flow constraint. A red wedge that is getting narrower from top to bottom is the pattern of a policy constraint. Both are signs of poor scheduling design.

A Gantt chart like this has three primary uses:
1) Diagnosis – understanding how the current flow design is creating the queues and delays.
2) Design – inventing new design options.
3) Prognosis – testing the innovative designs so the ‘fittest’ can be chosen for implementation.

These three steps are encapsulated in the third “M” of 6M Design® – the Model step.

In this example the design flaw was the scheduling policy. When that was redesigned the outcome was zero-wait performance. No red on the chart at all. The same number of tasks were completed in the same with the same resources used. Just less waiting. Which means less space is needed to store the queue of waiting work (i.e. none in this case).

That this is even possible comes as a big surprise to most people. It feels counter-intuitive. It is however an easy to demonstrate fact. Our intuition tricks us.

And that reduction in the size of the queue implies a big cost reduction when the work-in-progress is perishable and needs constant attention [such as patients lying on A&E trolleys and in hospital beds].

So what was the cost of re-designing this schedule?

A pinch of humility. A few bits of squared paper and some coloured pens. A couple hours of time. And a one-off investment in learning how to do it. Peanuts in comparison with the recurring benefit gained.

21/06/2014

N-N-N-N Feedback

One of the essential components of an adaptive system is effective feedback.

Without feedback we cannot learn – we can only guess and hope.

So the design of our feedback loops is critical-to-success.

Many people do not like getting feedback because they live in a state of fear: fear of criticism. This is a learned behaviour.

Many people do not like giving feedback because they too live in a state of fear: fear of conflict. This is a learned behaviour.

And what is learned can be unlearned; with training, practice and time.

But before we will engage in unlearning our current habit we need to see the new habit that will replace it. The one that will work better for us. The one that is more effective. The one that will require less effort. The one that is more efficient use of our most precious resource: life-time.

There is an effective and efficient feedback technique called The 4N Chart®. And I know it works because I have used it and demonstrated to myself and others that it works. And I have seen others use it and demonstrate to themselves and others that it works too.

The 4N Chart® has two dimensions – Time (Now and Future) and Emotion (Happy and Unhappy).

This gives four combinations each of which is given a label that begins with the letter ‘N’ – Niggles, Nuggets, NoNos and NiceIfs.

The N has a further significance … it reminds us which order to move through the chart.

We start bottom left with the Niggles. What is happening now that causes us to feel unhappy. What are these root causes of our niggles? And more importantly, which of these do we have control over? Knowing that gives us a list of actions that we can do that will have the effect of reducing our niggles. And we can start that immediately because we do not need permission.

Next we move top-left to the Nuggets. What is happening now that causes us to feel happy? What are the root causes of our nuggets? Which of these do we control? We need to recognise these too and to celebrate them. We need to give ourselves a pat on the back for them because that helps reinforce the habit to keep doing them.

Now we look to the future – and we need to consider two things: what we do not want to feel in the future and what we do want to feel in the future. These are our NoNos and our NiceIfs. It does not matter which order we do this … but we must consider both.

Many prefer to consider dangers and threats first … that is SAFETY FIRST thinking and is OK. First Do No Harm. Primum non nocere.

So with the four corners of our 4N Chart® filled in we have a balanced perspective and we can set off on the journey of improvement with confidence. Our 4N Chart® will help us stay on track. And we will update it as we go, as we study, as we plan and as we do things. As we convert NiceIfs into Nuggets and Niggles into NoNos.

It sounds simple. It is in theory. It is not quite as easy to do.

It takes practice … particularly the working backwards from the effect (the feeling) to the cause (the facts). This is done step-by-step using Reality as a guide – not our rhetoric. And we must be careful not to make assumptions in lieu of evidence. We must be careful not to jump to unsupported conclusions. That is called pre-judging. Prejudice.

But when you get the hang of using The 4N Chart® you will be amazed at how much more easily and more quickly you make progress.

07/06/2014

A Stab At The Vitals

[Drrring Drrring] The phone heralded the start of the weekly ISP mentoring session.

<Bob> Hi Leslie, how are you today?

<Leslie> Hi Bob. To be honest I am not good. I am drowning. Drowning in data!

<Bob> Oh dear! I am sorry to hear that. Can I help? What led up to this?

<Leslie> Well, it was sort of triggered by our last chat and after you opened my eyes to the fact that we habitually throw most of our valuable information away by thresholding, aggregating and normalising. Then we wonder why we make poor decisions … and then we get frustrated because nothing seems to improve.

<Bob> OK. What happened next?

<Leslie> I phoned our Performance Team and asked for some raw data. Three months worth.

<Bob> And what was their reaction?

<Leslie> They said “OK, here you go!” and sent me a twenty megabyte Excel spreadsheet that clogged my email inbox! I did manage to unclog it eventually by deleting loads of old junk. But I could swear that I heard the whole office laughing as they hung up the phone! Maybe I am paranoid?

<Bob> OK. And what happened next?

<Leslie> I started drowning! The mega-file had a row of data for every patient that has attended A&E for the last three months as I had requested, but there were dozens of columns! Trying to slice-and-dice it was a nightmare! My computer was smoking and each step took ages for it to complete. In the end I gave up in frustration. I now have a lot more respect for the Performance Team I can tell you! They do this for a living?

<Bob> OK. It sounds like you are ready for a Stab At the Vitals.

<Leslie> What? That sounds rather piratical! Are you making fun of my slicing-and-dicing metaphor?

<Bob> No indeed. I am deadly serious! Before we leap into the data ocean we need to be able to swim; and we also need a raft that will keep us afloat; and we need a sail to power our raft; and we need a way to navigate our raft to our desired destination.

<Leslie> OK. I like the nautical metaphor but how does it help?

<Bob> Let me translate. Learning to use system behaviour charts is equivalent to learning the skill of swimming. We have to do that first and practice until we are competent and confident. Let us call our raft “ISP” – you are already aboard. The sail you also have already – your Excel software. The navigation aid is what I refer to as Vitals. So we need to have a “stab at the vitals”.

<Leslie> Do you mean we use a combination of time-series charts, ISP and Excel to create a navigation aid that helps avoid the Depths of Data and the Rocks of DRAT?

<Bob> Exactly.

<Leslie> Can you demonstrate with an example?

<Bob> Sure. Send me some of your data … just the arrival and departure events for one day – a typical one.

<Leslie> OK … give me a minute! … It is on its way. How long will it take for you to analyse it?

<Bob> About 2 seconds. OK, here is your email … um … copy … paste … copy … reply

<Leslie> What the ****? That was quick! Let me see what this is … the top left chart is the demand, activity and work-in-progress for each hour; the top right chart is the lead time by patient plotted in discharge order; the table bottom left includes the 4 hour breach rate. Those I do recognise. What is the chart on the bottom right?

<Bob> It is a histogram of the lead times … and it shows a problem. Can you see the spike at 225 to 240 minutes?

<Leslie> Is that the fabled Horned Gaussian?

<Bob> Yes. That is the sign that the 4-hour performance target is distorting the behaviour of the system. And this is yet another reason why the Breach Rate is a dangerous management metric. The adaptive reaction it triggers amplifies the variation and fuels the chaos.

<Leslie> Wow! And you did all that in Excel using my data in two seconds? That must need a whole host of clever macros and code!

<Bob> “Yes” it was done in Excel and “No” it does not need any macros or code. It is all done using simple formulae.

<Leslie> That is fantastic! Can you send me a copy of your Excel file?

<Bob> Nope.

<Leslie>Whaaaat? Why not? Is this some sort of evil piratical game?

<Bob> Nope. You are going to learn how to do this yourself – you are going to build your own Vitals Chart Generator – because that is the only way to really understand how it works.

<Leslie> Phew! You had me going for a second there! Bring it on! What do I do next?

<Bob> I will send you the step-by-step instructions of how to build, test and use a Vitals Chart Generator.

<Leslie> Thanks Bob. I cannot wait to get started! Weigh anchor and set the sails! Ha’ harrrr me hearties.

24/05/2014

Ratio Hazards

[Bzzzzz Bzzzzz] Bob’s phone was on silent but the desktop amplified the vibration and heralded the arrival of Leslie’s weekly ISP coaching call.

<Bob> Hi Leslie. How are you today and what would you like to talk about?

<Leslie> Hi Bob. I am well and I have an old chestnut to roast today … target-driven-behaviour!

<Bob> Excellent. That is one of my favorite topics. Is there a specific context?

<Leslie> Yes. The usual desperate directive from on-high exhorting everyone to “work harder to hit the target” and usually accompanied by a RAG table of percentages that show just who is failing and how badly they are doing.

<Bob> OK. Red RAGs irritating the Bulls eh? Percentages eh? Have we talked about Ratio Hazards?

<Leslie> We have talked about DRATs … Delusional Ratios and Arbitrary Targets as you call them. Is that the same thing?

<Bob> Sort of. What happened when you tried to explain DRATs to those who are reacting to these ‘desperate directives’?

<Leslie> The usual reply is ‘Yes, but that is how we are required to report our performance to our Commissioners and Regulatory Bodies.’

<Bob> And are the key performance indicators that are reported upwards and outwards also being used to manage downwards and inwards? If so, then that is poor design and is very likely to be contributing to the chaos.

<Leslie> Can you explain that a bit more? It feels like a very fundamental point you have just made.

<Bob> OK. To do that let us work through the process by which the raw data from your system is converted into the externally reported KPI. Choose any one of your KPIs

<Leslie> Easy! The 4-hour A&E target performance.

<Bob> What is the raw data that goes in to that?

<Leslie> The percentage of patients who breach 4-hours per day.

<Bob> And where does that ratio come from?

<Leslie> Oh! I see what you mean. That comes from a count of the number of patients who are in A&E for more than 4 hours divided by a count of the number of patients who attended.

<Bob> And where do those counts come come from?

<Leslie> We calculate the time the patient is in A&E and use the 4-hour target to label them as breaches or not.

<Bob> And what data goes into the calculation of that time?

<Leslie>The arrival and departure times for each patient. The arrive and depart events.

<Bob>OK. Is that the raw data?

<Leslie>Yes. Everything follows from that.

<Bob> Good. Each of these two events is a time – which is a continuous metric. In principle, we could in record it to any degree of precision we like – milliseconds if we had a good enough enough clock.

<Leslie> Yes. We record it to an accuracy of of seconds – it is when the patient is ‘clicked through’ on the computer.

<Bob> Careful Leslie, do not confuse precision with accuracy. We need both.

<Leslie> Oops! Yes I remember we had that conversation before.

<Bob> And how often is the A&E 4-hour target KPI reported externally?

<Leslie> Quarterly. We either succeed or fail each quarter of the financial year.

<Bob> That is a binary metric. An “OK or not OK”. No gray zone.

<Leslie> Yes. It is rather blunt but that is how we are contractually obliged to report our performance.

<Bob> OK. And how many patients per day on average come to A&E?

<Leslie> About 200 per day.

<Bob> So the data analysis process is boiling down about 36,000 pieces of continuous data into one Yes-or-No bit of binary data.

<Leslie> Yes.

<Bob> And then that one bit is used to drive the action of the Board: if it is ‘OK last quarter’ then there is no ‘desperate directive’ and if it is a ‘Not OK last quarter’ then there is.

<Leslie> Yes.

<Bob> So you are throwing away 99.9999% of your data and wondering why what is left is not offering much insight in what to do.

<Leslie>Um, I guess so … when you say it like that. But how does that relate to your phrase ‘Ratio Hazards’?

<Bob> A ratio is just one of the many ways that we throw away information. A ratio requires two numbers to calculate it; and it gives one number as an output so we are throwing half our information away. And this is an irreversible act. Two specific numbers will give one ratio; but that ratio can be created by an infinite number possible pairs of numbers and we have no way of knowing from the ratio what specific pair was used to create it.

<Leslie> So a ratio is an exercise in obfuscation!

<Bob> Well put! And there is an even more data-wasteful behaviour that we indulge in. We aggregate.

<Leslie> By that do you mean we summarise a whole set of numbers with an average?

<Bob> Yes. When we average we throw most of the data away and when we average over time then we abandon our ability to react in a timely way.

<Leslie>The Flaw of Averages!

<Bob> Yes. One of them. There are many.

<Leslie>No wonder it feels like we are flying blind and out of control!

<Bob> There is more. There is an even worse data-wasteful behaviour. We threshold.

<Leslie>Is that when we use a target to decide if the lead time is OK or Not OK.

<Bob> Yes. And using an arbitrary target makes it even worse.

<Leslie> Ah ha! I see what you are getting at. The raw event data that we painstakingly collect is a treasure trove of information and potential insight that we could use to help us diagnose, design and deliver a better service. But we throw all but one single solitary binary digit when we put it through the DRAT Processor.

<Bob> Yup.

<Leslie> So why could we not do both? Why could we not use use the raw data for ourselves and the DRAT processed data for external reporting.

<Bob> We could. So what is stopping us doing just that?

<Leslie> We do not know how to effectively and efficiently interpret the vast ocean of raw data.

<Bob> That is what a time-series chart is for. It turns the thousands of pieces of valuable information onto a picture that tells a story – without throwing the information away in the process. We just need to learn how to interpret the pictures.

<Leslie> Wow! Now I understand much better why you insist we ‘plot the dots’ first.

<Bob> And now you understand the Ratio Hazards a bit better too.

<Leslie> Indeed so. And once again I have much to ponder on. Thank you again Bob.

17/05/2014

The Learning Labyrinth

There is an amazing phenomenon happening right now – a whole generation of people are learning to become system designers and they are doing it by having fun.

There is a game called Minecraft which millions of people of all ages are rapidly discovering. It is creative, fun and surprisingly addictive.

This is what it says on the website.

“Minecraft is a game about breaking and placing blocks. At first, people built structures to protect against nocturnal monsters, but as the game grew players worked together to create wonderful, imaginative things.”

The principle is that before you can build you have to dig … you have to gather the raw materials you need … and then you have to use what you have gathered in novel and imaginative ways. You need tools too, and you need to learn what they are used for, and what they are useless for. And the quickest way to learn the necessary survival and creative skills is by exploring, experimenting, seeking help, and sharing your hard-won knowledge and experience with others.

The same principles hold in the real world of Improvement Science.

The treasure we are looking for is less tangible though … but no less difficult to find … unless you know where to look.

The treasure we seek is learning; how to achieve significant and sustained improvement on all dimensions.

And there is a mountain of opportunity that we can mine into. It is called Reality.

And when we do that we uncover nuggets of knowledge, jewels of understanding, and pearls of wisdom.

There are already many tunnels that have been carved out by others who have gone before us. They branch and join to form a vast cave network. A veritable labyrinth. Complicated and not always well illuminated or signposted.

And stored in the caverns is a vast treasure trove of experience we can dip into – and an even greater horde of new treasure waiting to be discovered.

But even now there there is no comprehensive map of the labyrinth. So it is easy to get confused and to get lost. Not all junctions have signposts and not all the signposts are correct. There are caves with many entrances and exits, there are blind-ending tunnels, and there are many hazards and traps for the unwary.

So to enter the Learning Labyrinth and to return safety with Improvement treasure we need guides. Those who know the safe paths and the unsafe ones. And as we explore we all need to improve the signage and add warning signs where hazards lurk.

And we need to work at the edge of knowledge to extend the tunnels further. We need to seal off the dead-ends, and to draw and share up-to-date maps of the paths.

We need to grow a Community of Improvement Science Minecrafters.

And the first things we need are some basic improvement tools and techniques … and they can be found here.

15/03/2014

Reducing Avoidable Harm

“Primum non nocere” is Latin for “First do no harm”.

It is a warning mantra that had been repeated by doctors for thousands of years and for good reason.

Doctors can be bad for your health.

I am not referring to the rare case where the doctor deliberately causes harm. Such people are criminals and deserve to be in prison.

I am referring to the much more frequent situation where the doctor has no intention to cause harm – but harm is the outcome anyway.

Very often the risk of harm is unavoidable. Healthcare is a high risk business. Seriously unwell patients can be very unstable and very unpredictable. Heroic efforts to do whatever can be done can result in unintended harm and we have to accept those risks. It is the nature of the work. Much of the judgement in healthcare is balancing benefit with risk on a patient by patient basis. It is not an exact science. It requires wisdom, judgement, training and experience. It feels more like an art than a science.

The focus of this essay is not the above. It is on unintentionally causing avoidable harm.

Or rather unintentionally not preventing avoidable harm which is not quite the same thing.

Safety means prevention of avoidable harm. A safe system is one that does that. There is no evidence of harm to collect. A safe system does not cause harm. Never events never happen.

Safe systems are designed to be safe. The root causes of harm are deliberately designed out one way or another. But it is not always easy because to do that we need to understand the cause-and-effect relationships that lead to unintended harm. Very often we do not.

In 1847 a doctor called Ignaz Semmelweis made a very important discovery. He discovered that if the doctors and medical students washed their hands in disinfectant when they entered the labour ward, then the number of mothers and babies who died from infection was reduced.

And the number dropped a lot.

It fell from an annual average of 10% to less than 2%! In really bad months the rate was 30%.

The chart below shows the actual data plotted as a time-series chart. The yellow flag in 1848 is just after Semmelweis enforced a standard practice of hand-washing.

Semmelweis did not know the mechanism though. This was not a carefully designed randomised controlled trial (RCT). He was desperate. And he was desperate because this horrendous waste of young lives was only happening on the doctors ward. On the nurses ward, which was just across the corridor, the maternal mortality was less than 2%.

The hospital authorities explained it away as ‘bad air’ from outside. That was the prevailing belief at the time. Unavoidable. A risk that had to be just accepted.

Semmeleis could not do a randomized controlled trial because they were not invented until a century later.

And Semmelweis suspected that the difference between the mortality on the nurses and the doctors wards was something to do with the Mortuary. Only the doctors performed the post-mortems and the practice of teaching anatomy to medical students using post-mortem dissection was an innovation pioneered in Vienna in 1823 (the first yellow flag on the chart above). But Semmelweis did not have this data in 1847. He collated it later and did not publish it until 1861.

What Semmelweis demonstrated was the unintended and avoidable deaths were caused by ignorance of the mechanism of how microorganisms cause disease. We know that now. He did not.

It would be another 20 years before Louis Pasteur demonstrated the mechanism using the famous experiment with the swan neck flask. Pasteur did not discover microorganisms; he proved that they did not appear spontaneously in decaying matter as was believed. He proved that by killing the bugs by boiling, the broth in the flask stayed fresh even though it was exposed to the air. That was a big shock but it was a simple and repeatable experiment. He had a mechanism. He was believed. Germ theory was born. A Scottish surgeon called Joseph Lister read of this discovery and surgical antisepsis was born.

Semmelweis suspected that some ‘agent’ may have been unwittingly transported from the dead bodies to the live mothers and babies on the hands of the doctors. It was a deeply shocking suggestion that the doctors were unwittingly killing their patients.

The other doctors did not take this suggestion well. Not well at all. They went into denial. They discounted the message and they discharged the messenger. Semmelweis never worked in Vienna again. He went back to Hungary and repeated the experiment. It worked.

Even today the message that healthcare practitioners can unwittingly bring avoidable harm to their patients is disturbing. We still seek solace in denial.

Hospital acquired infections (HAI) are a common cause of harm and many are avoidable using simple, cheap and effective measures such as hand-washing.

The harm does not come from what we do. It comes from what we do not do. It happens when we omit to follow the simple safety measures that have be proven to work. Scientifically. Statistically Significantly. Understood and avoidable errors of omission.

So how is this “statistically significant scientific proof” acquired?

By doing experiments. Just like the one Ignaz Semmelweis conducted. But the improvement he showed was so large that it did not need statistical analysis to validate it. And anyway such analysis tools were not available in 1847. If they had been he might have had more success influencing his peers. And if he had achieved that goal then thousands, if not millions, of deaths from hospital acquired infections may have been prevented. With the clarity of hindsight we now know this harm was avoidable.

No. The problem we have now is because the improvement that follows a single intervention is not very large. And when the causal mechanisms are multi-factorial we need more than one intervention to achieve the improvement we want. The big reduction in avoidable harm. How do we do that scientifically and safely?

About 20% of hospital acquired infections occur after surgical operations.

We have learned much since 1847 and we have designed much safer surgical systems and processes. Joseph Lister ushered in the era of safe surgery, much has happened since.

We routinely use carefully designed, ultra-clean operating theatres, sterilized surgical instruments, gloves and gowns, and aseptic techniques – all to reduce bacterial contamination from outside.

But surgical site infections (SSIs) are still common place. Studies show that 5% of patients on average will suffer this complication. Some procedures are much higher risk than others, despite the precautions we take. And many surgeons assume that this risk must just be accepted.

Others have tried to understand the mechanism of SSI and their research shows that the source of the infections is the patients themselves. We all carry a ‘bacterial flora’ and normally that is no problem. Our natural defense – our skin – is enough. But when that biological barrier is deliberately breached during a surgical operation then we have a problem. The bugs get in and cause mischief. They cause surgical site infections.

So we have done more research to test interventions to prevent this harm. Each intervention has been subject to well-designed, carefully-conducted, statistically-valid and very expensive randomized controlled trials. And the results are often equivocal. So we repeat the trials – bigger, better controlled trials. But the effects of the individual interventions are small and they easily get lost in the noise. So we pool the results of many RCTs in what is called a ‘meta-analysis’ and the answer from that is very often ‘not proven’ – either way. So individual surgeons are left to make the judgement call and not surprisingly there is wide variation in practice. So is this the best that medical science can do?

No. There is another way. What we can do is pool all the learning from all the trials and design a multi-facetted intervention. A bundle of care. And the idea of a bundle is that the separate small effects will add or even synergise to create one big effect. We are not so much interested in the mechanism as the outcome. Just like Ignaz Semmelweiss.

And we can now do something else. We can test our bundle of care using statistically robust tools that do not require a RCT. They are just as statistically valid as a RCT but a different design.

And the appropriate tool for this to measure the time interval between adverse the events – and then to plot this continuous metric as a time-series chart.

But we must be disciplined. First we must establish the baseline average interval and then we introduce our bundle and then we just keep measuring the intervals.

If our bundle works then the interval between the adverse events gets longer – and we can easily prove that using our time-series chart. The longer the interval the more ‘proof’ we have. In fact we can even predict how long we need to observe to prove that ‘no events’ is a statistically significant improvement. That is an elegant an efficient design.

Here is a real and recent example.

The time-series chart below shows the interval in days between surgical site infections following routine hernia surgery. These are not life threatening complications. They rarely require re-admission or re-operation. But they are disruptive for patients. They cause pain, require treatment with antibiotics, and the delay recovery and return to normal activities. So we would like to avoid them if possible.

The green and red lines show the baseline period. The green line says that the average interval between SSIs is 14 days. The red line says that an interval more than about 60 days would be surprisingly long: valid statistical evidence of an improvement. The end of the green and red lines indicates when the intervention was made: when the evidence-based designer care bundle was adopted together with the discipline of applying it to every patient. No judgement. No variation.

The chart tells the story. No complicated statistical analysis is required. It shows a statistically significant improvement. And the SSI rate fell by over 80%. That is a big improvement.

We still do not know how the care bundle works. We do not know which of the seven simultaneous simple and low-cost interventions we chose are the most important or even if they work independently or in synergy. Knowledge of the mechanism was not our goal.

Our goal was to improve outcomes for our patients – to reduce avoidable harm – and that has been achieved. The evidence is clear.

That is Improvement Science in action.

And to read the full account of this example of the Science of Improvement please go to:

http://www.journalofimprovementscience.org

It is essay number 18.

And avoid another error of omission. If you have read this far please share this message – it is important.

08/03/2014

The Battle of the Chimps

Improvement implies change.
Change implies action.
Action implies decision.

So how is the decision made?
With Urgency?
With Understanding?

Bitter experience teaches us that often there is an argument about what to do and when to do it. An argument between two factions. Both are motivated by a combination of anger and fear. One side is motivated more by anger than fear. They vote for action because of the urgency of the present problem. The other side is motivated more by fear than anger. They vote for inaction because of their fear of future failure.

The outcome is unhappiness for everyone.

If the ‘action’ party wins the vote and a failure results then there is blame and recrimination. If the ‘inaction’ party wins the vote and a failure results then there is blame and recrimination. If either party achieves a success then there is both gloating and resentment. Lose Lose.

The issue is not the decision and how it is achieved.The problem is the battle.

Dr Steve Peters is a psychiatrist with 30 years of clinical experience. He knows how to help people succeed in life through understanding how the caveman wetware between their ears actually works.

In the run up to the 2012 Olympic games he was the sports psychologist for the multiple-gold-medal winning UK Cycling Team. The World Champions. And what he taught them is described in his book – “The Chimp Paradox“.

Steve brilliantly boils the current scientific understanding of the complexity of the human mind down into a simple metaphor.

One that is accessible to everyone.

The metaphor goes like this:

There are actually two ‘beings’ inside our heads. The Chimp and the Human. The Chimp is the older, stronger, more emotional and more irrational part of our psyche. The Human is the newer, weaker, logical and rational part. Also inside there is the Computer. It is just a memory where both the Chimp and the Human store information for reference later. Beliefs, values, experience. Stuff like that. Stuff they use to help them make decisions.

And when some new information arrives through our senses – sight and sound for example – the Chimp gets first dibs and uses the Computer to look up what to do. Long before the Human has had time to analyse the new information logically and rationally. By the time the Human has even started on solving the problem the Chimp has come to a decision and signaled it to the Human and associated it with a strong emotion. Anger, Fear, Excitement and so on. The Chimp operates on basic drives like survival-of-the-self and survival-of-the-species. So if the Chimp gets spooked or seduced then it takes control – and it is the stronger so it always wins the internal argument.

But the human is responsible for the actions of the Chimp. As Steve Peters says ‘If your dog bites someone you cannot blame the dog – you are responsible for the dog‘. So it is with our inner Chimps. Very often we end up apologising for the bad behaviour of our inner Chimp.

Because our inner Chimp is the stronger we cannot ‘control’ it by force. We have to learn how to manage the animal. We need to learn how to soothe it and to nurture it. And we need to learn how to remove the Gremlins that it has programmed into the Computer. Our inner Chimp is not ‘bad’ or ‘mad’ it is just a Chimp and it is an essential part of us.

Real chimpanzees are social, tribal and territorial. They live in family groups and the strongest male is the boss. And it is now well known that a troop of chimpanzees in the wild can plan and wage battles to acquire territory from neighbouring troops. With casualties on both sides. And so it is with people when their inner Chimps are in control.

Which is most of the time.

Scenario:
A hospital is failing one of its performance targets – the 18 week referral-to-treatment one – and is being threatened with fines and potential loss of its autonomy. The fear at the top drives the threat downwards. Operational managers are forced into action and do so using strategies that have not worked in the past. But they do not have time to learn how to design and test new ones. They are bullied into Plan-Do mode. The hospital is also required to provide safe care and the Plan-Do knee-jerk triggers fear-of-failure in the minds of the clinicians who then angrily oppose the diktat or quietly sabotage it.

This lose-lose scenario is being played out in 100’s if not 1000’s of hospitals across the globe as we speak. The evidence is there for everyone to see.

The inner Chimps are in charge and the outcome is a turf war with casualties on all sides.

So how does The Chimp Paradox help dissolve this seemingly impossible challenge?

First it is necessary to appreciate that both sides are being controlled by their inner Chimps who are reacting from a position of irrational fear and anger. This means that everyone’s behaviour is irrational and their actions likely to be counter-productive.

What is needed is for everyone to be managing their inner Chimps so that the Humans are back in control of the decision making. That way we get wise decisions that lead to effective actions and win-win outcomes. Without chaos and casualties.

To do this we all need to learn how to manage our own inner Chimps … and that is what “The Chimp Paradox” is all about. That is what helped the UK cyclists to become gold medalists.

In the scenario painted above we might observe that the managers are more comfortable in the Pragmatist-Activist (PA) half of the learning cycle. The Plan-Do part of PDSA – to translate into the language of improvement. The clinicians appear more comfortable in the Reflector-Theorist (RT) half. The Study-Act part of PDSA. And that difference of preference is fueling the firestorm.

Improvement Science tells us that to achieve and sustain improvement we need all four parts of the learning cycle working smoothly and in sequence.

So what at first sight looks like it must be pitched battle which will result in two losers; in reality is could be a three-legged race that will result in everyone winning. But only if synergy between the PA and the RT halves can be achieved.

And that synergy is achieved by learning to respect, understand and manage our inner Chimps.

11/01/2014

What is my P.A.R.T?

Improvement implies change, but change does not imply improvement.

Change follows action. Action follows planning. Effective planning follows from an understanding of the system because it is required to make the wise decisions needed to achieve the purpose.

The purpose is the intended outcome.

Learning follows from observing the effect of change – whatever it is. Understanding follows from learning to predict the effect of both actions and in-actions.

All these pieces of the change jigsaw are different and they are inter-dependent. They fit together. They are a system.

And we can pick out four pieces: the Plan piece, the Action piece, the Observation piece and the Learning piece – and they seem to follow that sequence – it looks like a learning cycle.

This is not a new idea.

It is the same sequence as the Scientific Method: hypothesis, experiment, analysis, conclusion. The preferred tool of Academics – the Thinkers.

It is also the same sequence as the Shewhart Cycle: plan, do, check, act. The preferred tool of the Pragmatists – the Doers.

So where does all the change conflict come from? What is the reason for the perpetual debate between theorists and activists? The incessant game of “Yes … but!”

One possible cause was highlighted by David Kolb in his work on ‘experiential learning’ which showed that individuals demonstrate a learning style preference.

We tend to be thinkers or doers and only a small proportion us say that we are equally comfortable with both.

The effect of this natural preference is that real problems bounce back-and-forth between the Tribe of Thinkers and the Tribe of Doers. Together we are providing separate parts of the big picture – but as two tribes we appear to be unaware of the synergistic power of the two parts. We are blocked by a power struggle.

The Experiential Learning Model (ELM) was promoted and developed by Peter Honey and Alan Mumford (see learning styles) and their work forms the evidence behind the Learning Style Questionnaire that anyone can use to get their ‘score’ on the four dimensions:

Pragmatist – the designer and planner
Activist – the action person
Reflector – the observer and analyst
Theorist – the abstracter and hypothesis generator

The evidence from population studies showed that individuals have a preference for one of these styles, sometimes two, occasionally three and rarely all four.

That observation, together with the fact that learning from experience requires moving around the whole cycle, leads to an awareness that both individuals and groups can get ‘stuck’ in their learning preference comfort zone. If the learning wheel is unbalanced it will deliver a bumpy ride when it turns! So it may be more comfortable just to remain stationary and not to learn.

Which means not to change. Which means not to improve.

So if we are embarking on an improvement exercise – be it individual or collective – then we are committed to learning. So where do we start on the learning cycle?

The first step is action. To do something – and the easiest and safest thing to do is just look. Observe what is actually happening out there in the real world – outside the office – outside our comfort zone. We need to look outside our rhetorical inner world of assumptions, intuition and pre-judgements. The process starts with Study.

The next step is to reflect on what we see – we look in the mirror – and we compare what are actually seeing with what we expected to see. That is not as easy as it sounds – and a useful tool to help is to draw charts. To make it visual. All sorts of charts.

The result is often a shock. There is often a big gap between what we see and what we perceive; between what we expect and what we experience; between what we want and what we get; between our intent and our impact.

That emotional shock is actually what we need to power us through the next phase – the Realm of the Theorist – where we ask three simple questions:
Q1: What could be causing the reality that I am seeing?
Q2: How would I know which of the plausible causes is the actual cause?
Q3: What experiment can I do to answer my question and clarify my understanding of Reality?

This is the world of the Academic.

The third step is design an experiment to test our new hypothesis. The real world is messy and complicated and we need to be comfortable with ‘good enough’ and ‘reasonable uncertainty’. Design is about practicalities – making something that works well enough in practice – in the real world. Something that is fit-for-purpose. We are not expecting perfection; not looking for optimum; not striving for best – just significantly better than what we have now. And the more we can test our design before we implement it the better because we want to know what to expect before we make the change and we want to avoid unintended negative consequences – the NoNos. This is Plan.

Then we act … and the cycle of learning has come one revolution … but we are not back at the start – we have moved forward. Our understanding is already different from when were were at this stage before: it is deeper and wider. We are following the trajectory of a spiral – our capability for improvement is expanding over time.

So we need to balance our learning wheel before we start the journey or we will have a slow, bumpy and painful ride!

We need to study, then plan, then do, then study the impact.

One plausible approach is to stay inside our comfort zones, play to our strengths and to say “What we need is a team made of people with complementary strengths. We need a Department of Action for the Activists; a Department of Analysis for the Reflectors; a Department of Research for the Theorists and a Department of Planning for the Pragmatists.“

But that is what we have now and what is the impact? The Four Departments have become super-specialised and more polarised. There is little common ground or shared language. There is no common direction, no co-ordination, no oil on the axle of the wheel of change. We have ground to a halt. We have chaos. Each part is working but independently of the others in an unsynchronised mess.

We have cultural fibrillation. Change output has dropped to zero.

A better design is for everyone to focus first on balancing their own learning wheel by actively redirecting emotional energy from their comfort zone, their strength, into developing the next step in their learning cycle.

Pragmatists develop their capability for Action.
Activists develop their capability for Reflection.
Reflectors develop their capability for Hypothesis.
Theorists develop their capability for Design.

The first step in the improvement spiral is Action – so if you are committed to improvement then investing £10 and 20 minutes in the 80-question Learning Style Questionnaire will demonstrate your commitment to yourself. And that is where change always starts.

30/11/2013

Seeing Inside the Black Box

Improvement Science requires the effective, efficient and coordinated use of diagnosis, design and delivery tools.

Experience has also taught us that it is not just about the tools – each must be used as it was designed.

The craftsman knows his tools and knows what instrument to use, where and when the context dictates; and how to use it with skill.

Some tools are simple and effective – easy to understand and to use. The kitchen knife is a good example. It does not require an instruction manual to use it.

Other tools are more complex. Very often because they have a specific purpose. They are not generic. And they may not be intuitively obvious how to use them. Many labour-saving household appliances have specific purposes: the microwave oven, the dish-washer and so on – but they have complex controls and settings that we need to manipulate to direct the “domestic robot” to deliver what we actually want. Very often these controls are not intuitively obvious – we are dealing with a black box – and our understanding of what is happening inside is vague.

Very often we do not understand how the buttons and dials that we can see and touch – the inputs – actually influence the innards of the box to determine the outputs. We do not have a mental model of what is inside the Black Box. We do not know – we are ignorant.

In this situation we may resort to just blindly following the instructions; or blindly copying what someone else does; or blindly trying random combinations of inputs until we get close enough to what we want. No wiser at the end than we were at the start. The common thread here is “blind”. The box is black. We cannot see inside.

And the complex black box is deliberately made so – because the supplier of the super-tool does not want their “secret recipe” to be known to all – least of all their competitors.

This is a perfect recipe for confusion and for conflict. Lose-Lose-Lose.

Improvement Science is dedicated to eliminating confusion and conflict – so Black Box Tools are NOT on the menu.

Improvement Scientists need to understand how their tools work – and the best way to achieve that level of understanding is to design and build their own.

This may sound like re-inventing the wheel but it is not about building novel tools – it is about re-creating the tried and tested tools – for the purpose of understanding how they work. And understanding their strengths, their weaknesses, their opportunities and their risks or threats.

And doing that requires guidance from a mentor who has been through this same learning journey. Starting with simple, intuitive tools, and working step-by-step to design, build and understand the more complex ones.

So where do we start?

In the FISH course the first tool we learn to use is a Gantt Chart.

It was invented by Henry Laurence Gantt about 100 years ago and requires nothing more than pencil and paper. Coloured pencils and squared paper are even better.

This is an example of a Gantt Chart for a Day Surgery Unit.

At the top are the “tasks” – patients 1 and 2; and at the bottom are the “resources”.

Time runs left to right.

Each coloured bar appears twice: once on each chart.

The power of a Gantt Chart is that it presents a lot of information in a very compact and easy-to-interpret format. That is what Henry Gantt intended.

A Gantt Chart is like the surgeon’s scalpel. It is a simple, generic easy-to-create tool that has a wide range of uses. The skill is knowing where, when and how to use it: and just as importantly where-not, when-not and how-not.

The second tool that an Improvement Scientist learns to use is the Shewhart or time-series chart.

It was invented about 90 years ago.

This is a more complex tool and as such there is a BIG danger that it is used as a Black Box with no understanding of the innards. The SPC and Six-Sigma Zealots sell it as a Magic Box. It is not.

We could paste any old time-series data into a bit of SPC software; twiddle with the controls until we get the output we want; and copy the chart into our report. We could do that and hope that no-one will ask us to explain what we have done and how we have done it. Most do not because they do not want to appear ‘ignorant’. The elephant is in the room though. There is a conspiracy of silence.

The elephant-in-the-room is the risk we take when use Black Box tools – the risk of GIGO. Garbage In Garbage Out.

And unfortunately we have a tendency to blindly trust what comes out of the Black Box that a plausible Zealot tells us is “magic”. This is the Emporer’s New Clothes problem. Another conspiracy of silence follows.

The problem here is not the tool – it is the desperate person blindly wielding it. The Zealots know this and they warn the Desperados of the risk and offer their expensive Magician services. They are not interested in showing how the magic trick is done though! They prefer the Box to stay Black.

So to avoid this cat-and-mouse scenario and to understand both the simpler and the more complex tools, and to be able to use them effectively and safely, we need to be able to build one for ourselves.

And the know-how to do that is not obvious – if it were we would have already done it – so we need guidance.

And once we have built our first one – a rough-and-ready working prototype – then we can use the existing ones that have been polished with long use. And we can appreciate the wisdom that has gone into their design. The Black Box becomes Transparent.

So learning how the build the essential tools is the first part of the Improvement Science Practitioner (ISP) training – because without that knowledge it is difficult to progress very far. And without that understanding it is impossible to teach anyone anything other than to blindly follow a Black Box recipe.

Of course Magic Black Box Solutions Inc will not warm to this idea – they may not want to reveal what is inside their magic product. They are fearful that their customers may discover that it is much simpler than they are being told. And we can test that hypothesis by asking them to explain how it works in language that we can understand. If they cannot (or will not) then we may want to keep looking for someone who can and will.

23/11/2013

Space-and-Time

<Lesley>Hi Bob! How are you today?

<Bob>OK thanks Lesley. And you?

<Lesley>I am looking forward to our conversation. I have two questions this week.

<Bob>OK. What is the first one?

<Lesley>You have taught me that improvement-by-design starts with the “purpose” question and that makes sense to me. But when I ask that question in a session I get an “eh?” reaction and I get nowhere.

<Bob>Quod facere bonum opus et quomodo te cognovi unum?

<Lesley>Eh?

<Bob>I asked you a purpose question.

<Lesley>Did you? What language is that? Latin? I do not understand Latin.

<Bob>So although you recognize the language you do not understand what I asked, the words have no meaning. So you are unable to answer my question and your reaction is “eh?”. I suspect the same is happening with your audience. Who are they?

<Lesley>Front-line clinicians and managers who have come to me to ask how to solve their problems. There Niggles. They want a how-to-recipe and they want it yesterday!

<Bob>OK. Remember the Temperament Treacle conversation last week. What is the commonest Myers-Briggs Type preference in your audience?

<Lesley>It is xSTJ – tough minded Guardians. We did that exercise. It was good fun! Lots of OMG moments!

<Bob>OK – is your “purpose” question framed in a language that the xSTJ preference will understand naturally?

<Lesley>Ah! Probably not! The “purpose” question is future-focused, conceptual , strategic, value-loaded and subjective.

<Bob>Indeed – it is an iNtuitor question. xNTx or xNFx. Pose that question to a roomful of academics or executives and they will debate it ad infinitum.

<Lesley>More Latin – but that phrase I understand. You are right. And my own preference is xNTP so I need to translate my xNTP “purpose” question into their xSTJ language?

<Bob>Yes. And what language do they use?

<Lesley>The language of facts, figures, jobs-to-do, work-schedules, targets, budgets, rational, logical, problem-solving, tough-decisions, and action-plans. Objective, pragmatic, necessary stuff that keep the operational-wheels-turning.

<Bob>OK – so what would “purpose” look like in xSTJ language?

<Lesley>Um. Good question. Let me start at the beginning. They came to me in desperation because they are now scared enough to ask for help.

<Bob>Scared of what?

<Lesley>Unintentionally failing. They do not want to fail and they do not need beating with sticks. They are tough enough on themselves and each other.

<Bob>OK that is part of their purpose. The “Avoid” part. The bit they do not want. What do they want? What is the “Achieve” part? What is their “Nice If”?

<Lesley>To do a good job.

<Bob>Yes. And that is what I asked you – but in an unfamiliar language. Translated into English I asked “What is a good job and how do you know you are doing one?”

<Lesley>Ah ha! That is it! That is the question I need to ask. And that links in the first map – The 4N Chart®. And it links in measurement, time-series charts and BaseLine© too. Wow!

<Bob>OK. So what is your second question?

<Lesley>Oh yes! I keep getting asked “How do we work out how much extra capacity we need?” and I answer “I doubt that you need any more capacity.”

<Bob>And their response is?

<Lesley>Anger and frustration! They say “That is obvious rubbish! We have a constant stream of complaints from patients about waiting too long and we are all maxed out so of course we need more capacity! We just need to know the minimum we can get away with – the what, where and when so we can work out how much it will cost for the business case.”

<Bob>OK. So what do they mean by the word “capacity”. And what do you mean?

<Lesley>Capacity to do a good job?

<Bob>Very quick! Ho ho! That is a bit imprecise and subjective for a process designer though. The Laws of Physics need the terms “capacity”, “good” and “job” clearly defined – with units of measurement that are meaningful.

<Lesley>OK. Let us define “good” as “delivered on time” and “job” as “a patient with a health problem”.

<Bob>OK. So how do we define and measure capacity? What are the units of measurement?

<Lesley>Ah yes – I see what you mean. We touched on that in FISH but did not go into much depth.

<Bob>Now we dig deeper.

<Lesley>OK. FISH talks about three interdependent forms of capacity: flow-capacity, resource-capacity, and space-capacity.

<Bob>Yes. They are the space-and-time capacities. If we are too loose with our use of these and treat them as interchangeable then we will create the confusion and conflict that you have experienced. What are the units of measurement of each?

<Lesley>Um. Flow-capacity will be in the same units as flow, the same units as demand and activity – tasks per unit time.

<Bob>Yes. Good. And space-capacity?

<Lesley>That will be in the same units as work in progress or inventory – tasks.

<Bob>Good! And what about resource-capacity?

<Lesley>Um – Will that be resource-time – so time?

<Bob>Actually it is resource-time per unit time. So they have different units of measurement. It is invalid to mix them up any-old-way. It would be meaningless to add them for example.

<Lesley>OK. So I cannot see how to create a valid combination from these three! I cannot get the units of measurement to work.

<Bob>This is a critical insight. So what does that mean?

<Lesley>There is something missing?

<Bob>Yes. Excellent! Your homework this week is to work out what the missing pieces of the capacity-jigsaw are.

<Lesley>You are not going to tell me the answer?

<Bob>Nope. You are doing ISP training now. You already know enough to work it out.

<Lesley>OK. Now you have got me thinking. I like it. Until next week then.

<Bob>Have a good week.

16/11/2013

Temperament Treacle

If the headlines in the newspapers are a measure of social anxiety then healthcare in the UK is in a state of panic: “Hospitals Fear The Winter Crisis Is Here Early“.

The Panic Button is being pressed and the Patient Safety Alarms are sounding.

Closer examination of the statement suggests that the winter crisis is not unexpected – it is just here early. So we are assuming it will be worse than last year – which was bad enough.

The evidence shows this fear is well founded. Last year was the worst on the last 5 years and this year is shaping up to be worse still.

So if it is a predictable annual crisis and we have a lot of very intelligent, very committed, very passionate people working on the problem – then why is it getting worse rather than better?

One possible factor is Temperament Treacle.

This is the glacially slow pace of effective change in healthcare – often labelled as “resistance to change” and implying deliberate scuppering of the change boat by powerful forces within the healthcare system.

Resistance to the flow of change is probably a better term. We could call that cultural viscosity. Treacle has a very high viscosity – it resists flow. Wading through treacle is very hard work. So pushing change though cultural treacle is hard work. Many give up in exhaustion after a while.

So why the term “Temperament Treacle“?

Improvement Science has three parts – Processes, Politics and Systems.

Process Science is applied physics. It is an objective, logical, rational science. The Laws of Physics are not negotiable. They are absolute.

Political Science is applied psychology. It is a subjective, illogical, irrational science. The Laws of People are totally negotiable. They are arbitrary.

Systems Science is a combination of Physics and Psychology. A synthesis. A synergy. A greater-than-the-sum-of-the-parts combination.

The Swiss physician Carl Gustav Jung studied psychology – and in 1920 published “Psychological Types“. When this ground-breaking work was translated into English in 1923 it was picked up by Katherine Cook Briggs and made popular by her daughter Isabel. Isabel Briggs married Clarence Myers and in 1942 Isabel Myers learned about the Humm-Wadsworth Scale, a tool for matching people with jobs. So using her knowledge of psychological type differences she set out to develop her own “personality sorting tool”. The first prototype appeared in 1943; in the 1950’s she tested the third iteration and measured the personality types of 5,355 medical students and over 10,000 nurses. The Myers-Briggs Type Indicator was published 1962 and since then the MBTI® has been widely tested and validated and is the most extensively used personality type instrument. In 1980 Isabel Myers finished writing Gifts Differing just before she died at the age of 82 after a twenty year long battle with cancer.

The essence of Jung’s model is that an individual’s temperament is largely innate and the result of a combination of three dimensions:

1. The input or perceiving process (P). The poles are Intuitor (N) or Sensor (S).
2. The decision or judging process (J). The poles are Thinker (T) or Feeler (F).
3. The output or doing process. The poles are Extraversion (E) or Intraversion (I).

Each of Jung’s dimensions had two “opposite” poles so when combined they gave eight types. Isabel Myers, as a result of her extensive empirical testing, added a fourth dimension – which gives the four we see in the modern MBTI®. The fourth dimension linked the other three together – it describes if the J or the P process is the one shown to the outside world. So the MBTI® has sixteen broad personality types. In 1998 a book called “Please Understand Me II” written by David Keirsey, the MBTI® is put into an historical context and Keirsey concluded that there are four broad Temperaments – and these have been described since Ancient times.

When Isabel Myers measured different populations using her new tool she discovered a consistent pattern: that the proportions of the sixteen MBTI® types were consistent across a wide range of societies. Personality type is, as Jung had suggested, an innate part of the “human condition”. She also saw that different types clustered in different occupations. Finding the “right job” appeared to be a process of natural selection: certain types fitted certain roles better than others and people self-selected at an early age. If their choice was poor then the person would be unhappy and would not achieve their potential.

Isabel’s work also showed that each type had both strengths and weaknesses – and that people performed better and felt happier when their role played to their temperament strengths. It also revealed that considerable conflict could be attributed to type-mismatch. Polar opposite types have the least psychological “common ground” – so when they attempt to solve a common problem they do so by different routes and using different methods and language. This generates confusion and conflict. This is why Isabel Myers gave her book the title of “Gifts Differing” and her message was that just having awareness of and respect for the innate type differences was a big step towards reducing the confusion and conflict.

So what relevance does this have to change and improvement?

Well it turns out that certain types are much more open to change than others and certain types are much more resistant. If an organisation, by the very nature of its work, attracts the more change resistant types then that organisation will be culturally more viscous to the flow of change. It will exhibit the cultural characteristics of temperament treacle.

The key to understanding Temperament and the MBTI® is to ask a series of questions:

Q1. Does the person have the N or S preference on their perceiving function?

A1=N then Q2: Does the person have a T or F preference on their judging function?
A2=T gives the xNTx combination which is called the Rational or phlegmatic temperament.
A2=F gives the xNFx combination which is called the Idealist or choleric temperament.

A1=S then Q3: Does the person show a J or P preference to the outside world?
A3=J gives the xSxJ combination which is called the Guardian or melancholic temperament.
A3=P gives the xSxP combination which is called the Artisan or sanguine temperament.

So which is the most change resistant temperament? The answer may not be a big surprise. It is the Guardians. The melancholics. The SJ’s.

Bureaucracies characteristically attract SJ types. The upside is that they ensure stability – the downside is that they prevent agility. Bureaucracies block change.

The NF Idealists are the advocates and the mentors: they love initiating and facilitating transformations with the dream of making the world a better place for everyone. They light the emotional bonfire and upset the apple cart. The NT Rationals are the engineers and the architects. They love designing and building new concepts and things – so once the Idealists have cracked the bureaucratic carapace they can swing into action. The SP Sanguines are the improvisors and expeditors – they love getting the new “concept” designs to actually work in the messy real world.

Unfortunately the grand designs dreamed up by the ‘N’s often do not work in practice – and the scene is set for the we-told-you-so game, and the name-shame-blame game.

So if initiating and facilitating change is the Achilles Heel of the SJ’s then what is their strength?

Let us approach this from a different perspective:

Let us put ourselves in the shoes of patients and ask ourselves: “What do we want from a System of Healthcare and from those who deliver that care – the doctors?”

1. Safe?
2. Reliable?
3. Predictable?
4. Decisive?
5. Dependable?
6. All the above?

These are the strengths of the SJ temperament. So how do doctors measure up?

In a recent observational study, 168 doctors who attended a leadership training course completed their MBTI® self-assessments as part of developing insight into temperament from the perspective of a clinical leader. From the collective data we can answer our question: “Are there more SJ types in the medical profession than we would expect from the general population?”

The table shows the results – 60% of doctors were SJ compared with 35% expected for the general population.

Statistically this is highly significant difference (p<0.0001). Doctors are different.

It is of enormous practical importance well.

We are reassured that the majority of doctors have a preference for the very traits that patients want from them. That may explain why the Medical Profession always ranks highest in the league table of “trusted professionals”. We need to be able to trust them – it could literally be a matter of life or death.

The table also shows where the doctors were thin on the ground: in the mediating, improvising, developing, constructing temperaments. The very set of skills needed to initiate and facilitate effective and sustained change.

So when the healthcare system is lurching from one predictable crisis to another – the innate temperament of the very people we trust to deliver our health care are the least comfortable with changing the system of care itself.

That is a problem. A big problem.

Studies have show that when we get over-stressed, fearful and start to panic then in a desperate act of survival we tend to resort to the aspects of our temperament that are least well developed. An SJ who is in panic-mode may resort to NP tactics: opinion-led purposeless conceptual discussion and collective decision paralysis. This is called the “headless chicken and rabbit in the headlights” mode. We have all experienced it.

A system that is no longer delivering fit-for-purpose performance because its purpose has shifted requires redesign. The temperament treacle inhibits the flow of change so the crisis is not averted. The crisis happens, invokes panic and triggers ineffective and counter-productive behaviour. The crisis deepens and performance can drop catastrophically when the red tape is cut. It was the only thing holding the system together!

But while the bureaucracy is in disarray then innovation can start to flourish. And the next cycle starts.

It is a painful, slow, wasteful process called “reactionary evolution by natural selection“.

Improvement Science is different. It operates from a “proactive revolution through collective design” that is enjoyable, quick and efficient but it requires mastery of synergistic political science and process science. We do not have that capability – yet.

The table offers some hope. It shows the majority of doctors are xSTJ. They are Logical Guardians. That means that they solve problems using tried-tested-and-trustworthy logic. So they have no problem with the physics. Show them how to diagnose and design processes and they are inside their comfort zone.

Their collective weak spot is managing the politics – the critical cultural dimension of change. Often the result is manipulation rather than motivation. It does not work. The improvement stalls. Cynicism increases. The treacle gets thicker.

System-redesign requires synergistic support, development, improvisation and mediation. These strengths do exist in the medical profession – but they appear to be in short supply – so they need to be identified, and nurtured. And change teams need to assemble and respect the different gifts.

One further point about temperament. It is not immutable. We can all develop a broader set of MBTI® capabilities with guidance and practice – especially the ones that fill the gaps between xSTJ and xNFP. Those whose comfort zone naturally falls nearer the middle of the four dimensions find this easier. And that is one of the goals of Improvement Science training.

And if you are in a hurry then you might start today by identifying the xSFJ “supporters” and the xNFJ “mentors” in your organisation and linking them together to build a temporary bridge over the change culture chasm.

So to find your Temperament just click here to download the Temperament Sorter.

09/11/2013

The Mirror

[Dring Dring]

The phone announced the arrival of Leslie for the weekly ISP mentoring conversation with Bob.

<Leslie> Hi Bob.

<Bob> Hi Leslie. What would you like to talk about today?

<Leslie> A new challenge – one that I have not encountered before.

<Bob>Excellent. As ever you have pricked my curiosity. Tell me more.

<Leslie> OK. Up until very recently whenever I have demonstrated the results of our improvement work to individuals or groups the usual response has been “Yes, but“. The habitual discount as you call it. “Yes, but your service is simpler; Yes, but your budget is bigger; Yes, but your staff are less militant.” I have learned to expect it so I do not get angry any more.

<Bob> OK. The mantra of the skeptics is to be expected and you have learned to stay calm and maintain respect. So what is the new challenge?

<Leslie>There are two parts to it. Firstly, because the habitual discounting is such an effective barrier to diffusion of learning; our system has not changed; the performance is steadily deteriorating; the chaos is worsening and everything that is ‘obvious’ has been tried and has not worked. More red lights are flashing on the patient-harm dashboard and the Inspectors are on their way. There is an increasing turnover of staff at all levels – including Executive. There is an anguished call for “A return to compassion first” and “A search for new leaders” and “A cultural transformation“.

<Bob> OK. It sounds like the tipping point of awareness has been reached, enough people now appreciate that their platform is burning and radical change of strategy is required to avoid the ship sinking and them all drowning. What is the second part?

<Leslie> I am getting more emails along the line of “What would you do?”

<Bob> And your reply?

<Leslie> I say that I do not know because I do not have a diagnosis of the cause of the problem. I do know a lot of possible causes but I do not know which plausible ones are the actual ones.

<Bob> That is a good answer. What was the response?

<Leslie>The commonest one is “Yes, but you have shown us that Plan-Do-Study-Act is the way to improve – and we have tried that and it does not work for us. So we think that improvement science is just more snake oil!”

<Bob>Ah ha. And how do you feel about that?

<Leslie>I have learned the hard way to respect the opinion of skeptics. PDSA does work for me but not for them. And I do not understand why that is. I would like to conclude that they are not doing it right but that is just discounting them and I am wary of doing that.

<Bob>OK. You are wise to be wary. We have reached what I call the Mirror-on-the-Wall moment. Let me ask what your understanding of the history of PDSA is?

<Leslie>It was called Plan-Do-Check-Act by Walter Shewhart in the 1930’s and was presented as a form of the scientific method that could be applied on the factory floor to improving the quality of manufactured products. W Edwards Deming modified it to PDSA where the “Check” was changed to “Study”. Since then it has been the key tool in the improvement toolbox.

<Bob>Good. That is an excellent summary. What the Zealots do not talk about are the limitations of their wonder-tool. Perhaps that is because they believe it has no limitations. Your experience would seem to suggest otherwise though.

<Leslie>Spot on Bob. I have a nagging doubt that I am missing something here. And not just me.

<Bob>The reason PDSA works for you is because you are using it for the purpose it was designed for: incremental improvement of small bits of the big system; the steps; the points where the streams cross the stages. You are using your FISH training to come up with change plans that will work because you understand the Physics of Flow better. You make wise improvement decisions. In fact you are using PDSA in two separate modes: discovery mode and delivery mode. In discovery mode we use the Study phase to build your competence – and we learn most when what happens is not what we expected. In delivery mode we use the Study phase to build our confidence – and that grows most when what happens is what we predicted.

<Leslie>Yes, that makes sense. I see the two modes clearly now you have framed it that way – and I see that I am doing both at the same time, almost by second nature.

<Bob>Yes – so when you demonstrate it you describe PDSA generically – not as two complimentary but contrasting modes. And by demonstrating success you omit to show that there are some design challenges that cannot be solved with either mode. That hidden gap attracts some of the “Yes, but” reactions.

<Leslie>Do you mean the challenges that others are trying to solve and failing?

<Bob>Yes. The commonest error is to discount the value of improvement science in general; so nothing is done and the inevitable crisis happens because the system design is increasingly unfit for the evolving needs. The toast is not just burned it is on fire and is now too late to use the discovery mode of PDSA because prompt and effective action is needed. So the delivery mode of PDSA is applied to a emergent, ill-understood crisis. The Plan is created using invalid assumptions and guesswork so it is fundamentally flawed and the Do then just makes the chaos worse. In the ensuing panic the Study and Act steps are skipped so all hope of learning is lost and and a vicious and damaging spiral of knee-jerk Plan-Do-Plan-Do follows. The chaos worsens, quality falls, safety falls, confidence falls, trust falls, expectation falls and depression and despair increase.

<Leslie>That is exactly what is happening and why I feel powerless to help. What do I do?

<Bob>The toughest bit is past. You have looked squarely in the mirror and can now see harsh reality rather than hasty rhetoric. Now you can look out of the window with different eyes. And you are now looking for a real-world example of where complex problems are solved effectively and efficiently. Can you think of one?

<Leslie>Well medicine is one that jumps to mind. Solving a complex, emergent clinical problems requires a clear diagnosis and prompt and effective action to stabilise the patient and then to cure the underlying cause: the disease.

<Bob>An excellent example. Can you describe what happens as a PDSA sequence?

<Leslie>That is a really interesting question. I can say for starters that it does not start with P – we have learned are not to have a preconceived idea of what to do at the start because it badly distorts our clinical judgement. The first thing we do is assess the patient to see how sick and unstable they are – we use the Vital Signs. So that means that we decide to Act first and our first action is to Study the patient.

<Bob>OK – what happens next?

<Leslie>Then we will do whatever is needed to stabilise the patient based on what we have observed – it is called resuscitation – and only then we can plan how we will establish the diagnosis; the root cause of the crisis.

<Bob> So what does that spell?

<Leslie> A-S-D-P. It is the exact opposite of P-D-S-A … the mirror image!

<Bob>Yes. Now consider the treatment that addresses the root cause and that cures the patient. What happens then?

<Leslie>We use the diagnosis is used to create a treatment Plan for the specific patient; we then Do that, and we Study the effect of the treatment in that specific patient, using our various charts to compare what actually happens with what we predicted would happen. Then we decide what to do next: the final action. We may stop because we have achieved our goal, or repeat the whole cycle to achieve further improvement. So that is our old friend P-D-S-A.

<Bob>Yes. And what links the two bits together … what is the bit in the middle?

<Leslie>Once we have a diagnosis we look up the appropriate treatment options that have been proven to work through research trials and experience; and we tailor the treatment to the specific patient. Oh I see! The missing link is design. We design a specific treatment plan using generic principles.

<Bob>Yup. The design step is the jam in the improvement sandwich and it acts like a mirror: A-S-D-P is reflected back as P-D-S-A

<Leslie>So I need to teach this backwards: P-D-S-A and then Design and then A-S-P-D!

<Bob>Yup – and you know that by another name.

<Leslie> 6M Design®! That is what my Improvement Science Practitioner course is all about.

<Bob> Yup.

<Leslie> If you had told me that at the start it would not have made much sense – it would just have confused me.

<Bob>I know. That is the reason I did not. The Mirror needs to be discovered in order for the true value to appreciated. At the start we look in the mirror and perceive what we want to see. We have to learn to see what is actually there. Us. Now you can see clearly where P-D-S-A and Design fit together and the missing A-S-D-P component that is needed to assemble a 6M Design® engine. That is Improvement-by-Design in a nine-letter nutshell.

<Leslie> Wow! I can’t wait to share this.

<Bob> And what do you expect the response to be?

<Leslie>”Yes, but”?

<Bob> From the die hard skeptics – yes. It is the ones who do not say “Yes, but” that you want to engage with. The ones who are quiet. It is always the quiet ones that hold the key.

02/11/2013

Three Essentials

There are three necessary parts before ANY improvement-by-design effort will gain traction. Omit any one of them and nothing happens.

1. A clear purpose and an outline strategic plan.

2. Tactical measurement of performance-over-time.

3. A generic Improvement-by-Design framework.

These are necessary minimum requirements to be able to safely delegate the day-to-day and week-to-week tactical stuff the delivers the “what is needed”.

These are necessary minimum requirements to build a self-regulating, self-sustaining, self-healing, self-learning win-win-win system.

And this is not a new idea. It was described by Joseph Juran in the 1960’s and that description was based on 20 years of hands-on experience of actually doing it in a wide range of manufacturing and service organisations.

That is 20 years before the terms “Lean” or “Six Sigma” or “Theory of Constraints” were coined. And the roots of Juran’s journey were 20 years before that – when he started work at the famous Hawthorne Works in Chicago – home of the Hawthorne Effect – and where he learned of the pioneering work of Walter Shewhart.

And the roots of Shewhart’s innovations were 20 years before that – in the first decade of the 20th Century when innovators like Henry Ford and Henry Gantt were developing the methods of how to design and build highly productive processes.

Ford gave us the one-piece-flow high-quality at low-cost production paradigm. Toyota learned it from Ford. Gantt gave us simple yet powerful visual charts that give us an understanding-at-a-glance of the progress of the work. And Shewhart gave us the deceptively simple time-series chart that signals when we need to take more notice.

These nuggets of pragmatic golden knowledge have been buried for decades under a deluge of academic mud. It is nigh time to clear away the detritus and get back to the bedrock of pragmatism. The “how-to-do-it” of improvement. Just reading Juran’s 1964 “Managerial Breakthrough” illustrates just how much we now take for granted. And how ignorant we have allowed ourselves to become.

Acquired Arrogance is a creeping, silent disease – we slip from second nature to blissful ignorance without noticing when we divorce painful reality and settle down with our own comfortable collective rhetoric.

The wake-up call is all the more painful as a consequence: because it is all the more shocking for each one of us; and because it affects more of us.

The pain is temporary – so long as we treat the cause and not just the symptom.

The first step is to acknowledge the gap – and to start filling it in. It is not technically difficult, time-consuming or expensive. Whatever our starting point we need to put in place the three foundation stones above:

1. Common purpose.
2. Measurement-over-time.
3. Method for Improvement.

Then the rubber meets the road (rather than the sky) and things start to improve – for real. Lots of little things in lots of places at the same time – facilitated by the Junior Managers. The cumulative effect is dramatic. Chaos is tamed; calm is restored; capability builds; and confidence builds. The cynics have to look elsewhere for their sport and the skeptics are able to remain healthy.

Then the Middle Managers feel the new firmness under their feet – where before there were shifting sands. They are able to exert their influence again – to where it makes a difference. They stop chasing Scotch Mist and start reporting real and tangible improvement – with hard evidence. And they rightly claim a slice of the credit.

And the upwelling of win-win-win feedback frees the Senior Managers from getting sucked into reactive fire-fighting and the Victim Vortex; and that releases the emotional and temporal space to start learning and applying System-level Design. That is what is needed to deliver a significant and sustained improvement.

And that creates the stable platform for the Executive Team to do Strategy from. Which is their job.

It all starts with the Three Essentials:

1. A Clear and Common Constancy of Purpose.
2. Measurement-over-time of the Vital Metrics.
3. A Generic Method for Improvement-by-Design.

25/10/2013

The Black Curtain

A couple of weeks ago an important event happened. A Masterclass in Demand and Capacity for NHS service managers was run by an internationally renown and very experienced practitioner of Improvement Science.

The purpose was to assist the service managers to develop their capability for designing quality, flow and cost improvement using tried and tested operations management (OM) theory, techniques and tools.

It was assumed that as experienced NHS service managers that they already knew the basic principles of OM and the foundation concepts, terminology, techniques and tools.

It was advertised as a Masterclass and designed accordingly.

On the day it was discovered that none of the twenty delegates had heard of two fundamental OM concepts: Little’s Law and Takt Time.

These relate to how processes are designed-to-flow. It was a Demand and Capacity Master Class; not a safety, quality or cost one. The focus was flow.

And it became clear that none of the twenty delegates were aware before the day that there is a well-known and robust science to designing systems to flow.

So learning this fact came as a bit of a shock.

The implications of this observation are profound and worrying:

“if a significant % of senior NHS operational managers are unaware of the foundations of operations management then the NHS may have problem it was not aware of …“

because …

“if transformational change of the NHS into a stable system that is fit-for-purpose (now and into the future) requires the ability to design processes and systems that deliver both high effectiveness and high efficiency ...”

then …

“it raises the question of whether the current generation of NHS managers are fit-for-this-future-purpose“.

No wonder that discovering a Science of Improvement actually exists came as a bit of a shock!

And saying “Yes, but clinicians do not know this science either!” is a defensive reaction and not a constructive response. They may not but they do not call themselves “operational managers”.

[PS. If you are reading this and are employed by the NHS and do not know what Little’s Law and Takt Time are then it would be worth doing that first. Wikipedia is a good place to start].

And now we have another question:

“Given there are thousands of operational managers in the NHS; what does one sample of 20 managers tell us about the whole population?”

Now that is a good question.

It is also a question of statistics. More specifically quite advanced statistics.

And most people who work in the NHS have not studied statistics to that level. So now we have another do-not-know-how problem.

But it is still an important question that we need to understand the answer to – so we need to learn how and that means taking this learning path one step at a time using what we do know, rather than what we do not.

Step 1:

What do we know? We have one sample of 20 NHS service managers. We know something about our sample because our unintended experiment has measured it: that none of them had heard of Little’s Law or Takt Time. That is 0/20 or 0%.

This is called a “sample statistic“.

What we want to know is “What does this information tell us about the proportion of the whole population of all NHS managers who do have this foundation OM knowledge?”

This proportion of interest is called the unknown “population parameter“.

And we need to estimate this population parameter from our sample statistic because it is impractical to measure a population parameter directly: That would require every NHS manager completing an independent and accurate assessment of their basic OM knowledge. Which seems unlikely to happen.

The good news is that we can get an estimate of a population parameter from measurements made from small samples of that population. That is one purpose of statistics.

Step 2:

But we need to check some assumptions before we attempt this statistical estimation trick.

Q1: How representative is our small sample of the whole population?

If we chose the delegates for the masterclass by putting the names of all NHS managers in a hat and drawing twenty names out at random, as in a tombola or lottery, than we have what is called a “random sample” and we can trust our estimate of the wanted population parameter. This is called “random sampling”.

That was not the case here. Our sample was self-selecting. We were not conducting a research study. This was the real world … so there is a chance of “bias”. Our sample may not be representative and we cannot say what the most likely bias is.

It is possible that the managers who selected themselves were the ones struggling most and therefore more likely than average to have a gap in their foundation OM knowledge. It is also possible that the managers who selected themselves are the most capable in their generation and are very well aware that there is something else that they need to know.

We may have a biased sample and we need to proceed with some caution.

Step 3:

So given the fact that none of our possibly biased sample of mangers were aware of the Foundation OM Knowledge then it is possible that no NHS service managers know this core knowledge. In other words the actual population parameter is 0%. It is also possible that the managers in our sample were the only ones in the NHS who do not know this. So, in theory, the sought-for population parameter could be anywhere between 0% and very nearly 100%. Does that mean it is impossible to estimate the true value?

It is not impossible. In fact we can get an estimate that we can be very confident is accurate. Here is how it is done.

Statistical estimates of population parameters are always presented as ranges with a lower and an upper limit called a “confidence interval” because the sample is not the population. And even if we have an unbiased random sample we can never be 100% confident of our estimate. The only way to be 100% confident is to measure the whole population. And that is not practical.

So, we know the theoretical limits from consideration of the extreme cases … but what happens when we are more real-world-reasonable and say – “let us assume our sample is actually a representative sample, albeit not a randomly selected one“. How does that affect the range of our estimate of the elusive number – the proportion of NHS service managers who know basic operation management theory?

Step 4:

To answer that we need to consider two further questions:

Q2. What is the effect of the size of the sample? What if only 5 managers had come and none of them knew; what if had been 50 or 500 and none of them knew?

Q3. What if we repeated the experiment more times? With the same or different sample sizes? What could we learn from that?

Our intuition tells us that the larger the sample size and the more often we do the experiment then the more confident we will be of the result. In other words narrower the range of the confidence interval around our sample statistic.

Our intuition is correct because if our sample was 100% of the population we could be 100% confident.

So given we have not yet found an NHS service manager who has the OM Knowledge then we cannot exclude 0%. Our challenge narrows to finding a reasonable estimate of the upper limit of our confidence interval.

Step 5

Before we move on let us review where we have got to already and our purpose for starting this conversation: We want enough NHS service managers who are knowledgeable enough of design-for-flow methods to catalyse a transition to a fit-for-purpose and self-sustaining NHS.

One path to this purpose is to have a large enough pool of service managers who do understand this Science well enough to act as advocates and to spread both the know-of and the know-how. This is called the “tipping point“.

There is strong evidence that when about 20% of a population knows about something that is useful for the whole population – then that knowledge will start to spread through the grapevine. Deeper understanding will follow. Wiser decisions will emerge. More effective actions will be taken. The system will start to self-transform.

And in the Brave New World of social media this message may spread further and faster than in the past. This is good.

So if the NHS needs 20% of its operational managers aware of the Foundations of Operations Management then what value is our morsel of data from one sample of 20 managers who, by chance, were all unaware of the Knowledge. How can we use that data to say how close to the magic 20% tipping point we are?

Step 6:

To do that we need to ask the question in a slightly different way.

Q4. What is the chance of an NHS manager NOT knowing?

We assume that they either know or do not know; so if 20% know then 80% do not.

This is just like saying: if the chance of rolling a “six” is 1-in-6 then the chance of rolling a “not-a-six” is 5-in-6.

Next we ask:

Q5. What is the likelihood that we, just by chance, selected a group of managers where none of them know – and there are 20 in the group?

This is rather like asking: what is the likelihood of rolling twenty “not-a-sixes” in a row?

Our intuition says “an unlikely thing to happen!”

And again our intuition is sort of correct. How unlikely though? Our intuition is a bit vague on that.

If the actual proportion of NHS managers who have the OM Knowledge is about the same chance of rolling a six (about 16%) then we sense that the likelihood of getting a random sample of 20 where not one knows is small. But how small? Exactly?

We sense that 20% is too a high an estimate of a reasonable upper limit. But how much too high?

The answer to these questions is not intuitively obvious.

We need to work it out logically and rationally. And to work this out we need to ask:

Q6. As the % of Managers-who-Know is reduced from 20% towards 0% – what is the effect on the chance of randomly selecting 20 all of whom are not in the Know? We need to be able to see a picture of that relationship in our minds.

The good news is that we can work that out with a bit of O-level maths. And all NHS service managers, nurses and doctors have done O-level maths. It is a mandatory requirement.

The chance of rolling a “not-a-six” is 5/6 on one throw – about 83%;
and the chance of rolling only “not-a-sixes” in two throws is 5/6 x 5/6 = 25/36 – about 69%
and the chance of rolling only “not-a-sixes” in three throws is 5/6 x 5/6 x 5/6 – about 58%… and so on.

[This is called the “chain rule” and it requires that the throws are independent of each other – i.e. a random, unbiased sample]

If we do this 20 times we find that the chance of rolling no sixes at all in 20 throws is about 2.6% – unlikely but far from impossible.

We need to introduce a bit of O-level algebra now.

Let us call the proportion of NHS service managers who understand basic OM, our unknown population parameter something like “p”.

So if p is the chance of a “six” then (1-p) is a chance of a “not-a-six”.

Then the chance of no sixes in one throw is (1-p)

and no sixes after 2 throws is (1-p)(1-p) = (1-p)^2 (where ^ means raise to the power)

and no sixes after three throws is (1-p)(1-p)(1-p) = (1-p)^3 and so on.

So the likelihood of “no sixes in n throws” is (1-p)^n

Let us call this “t”

So the equation we need to solve to estimate the upper limit of our estimate of “p” is

t=(1-p)^20

Where “t” is a measure of how likely we are to choose 20 managers all of whom do not know – just by chance. And we want that to be a small number. We want to feel confident that our estimate is reasonable and not just a quirk of chance.

So what threshold do we set for “t” that we feel is “reasonable”? 1 in a million? 1 in 1000? 1 in 100? 1 in10?

By convention we use 1 in 20 (t=0.05) – but that is arbitrary. If we are more risk-averse we might choose 1:100 or 1:1000. It depends on the context.

Let us be reasonable – let is say we want to be 95% confident our our estimated upper limit for “p” – which means we are calculating the 95% confidence interval. This means that will accept a 1:20 risk of our calculated confidence interval for “p” being wrong: a 19:1 odds that the true value of “p” falls outside our calculated range. Pretty good odds! We will be reasonable and we will set the likelihood threshold for being “wrong” at 5%.

So now we need to solve:

0.05= (1-p)^20

And we want a picture of this relationship in our minds so let us draw a graph of t for a range of values of p.

We know the value of p must be between 0 and 1.0 so we have all we need and we can generate this graph easily using Excel. And every senior NHS operational manager knows how to use Excel. It is a requirement. Isn’t it?

The Excel-generated chart shows the relationship between p (horizontal axis) and t (vertical axis) using our equation:

t=(1-p)^20.

Step 7

Let us first do a “sanity check” on what we have drawn. Let us “check the extreme values”.

If 0% of managers know then a sample of 20 will always reveal none – i.e. the leftmost point of the chart. Check!

If 100% of managers know then a sample of 20 will never reveal none – i.e. way off to the right. Check!

What is clear from the chart is that the relationship between p and t is not a straight line; it is non-linear. That explains why we find it difficult to estimate intuitively. Our brains are not very good at doing non-linear analysis. Not very good at all.

So we need a tool to help us. Our Excel graph. We read down the vertical “t” axis from 100% to the 5% point, then trace across to the right until we hit the line we have drawn, then read down to the corresponding value for “p”. It says about 14%.

So that is the upper limit of our 95% confidence interval of the estimate of the true proportion of NHS service managers who know the Foundations of Operations Management. The lower limit is 0%.

And we cannot say better than somewhere between 0%-14% with the data we have and the assumptions we have made.

To get a more precise estimate, a narrower 95% confidence interval, we need to gather some more data.

[Another way we can use our chart is to ask “If the actual % of Managers who know is x% the what is the chance that no one of our sample of 20 will know?” Solving this manually means marking the x% point on the horizontal axis then tracing a line vertically up until it crosses the drawn line then tracing a horizontal line to the left until it crosses the vertical axis and reading off the likelihood.]

So if in reality 5% of all managers do Know then the chance of no one knowing in an unbiased sample of 20 is about 35% – really quite likely.

Now we are getting a feel for the likely reality. Much more useful than just dry numbers!

But we are 95% sure that 86% of NHS managers do NOT know the basic language of flow-improvement-science.

And what this chart also tells us is that we can be VERY confident that the true value of p is less than 2o% – the proportion we believe we need to get to transformation tipping point.

Now we need to repeat the experiment experiment and draw a new graph to get a more accurate estimate of just how much less – but stepping back from the statistical nuances – the message is already clear that we do have a Black Curtain problem.

A Black Curtain of Ignorance problem.

Many will now proclaim angrily “This cannot be true! It is just statistical smoke and mirrors. Surely our managers do know this by a different name – how could they not! It is unthinkable to suggest the majority of NHS manages are ignorant of the basic science of what they are employed to do!“

If that were the case though then we would already have an NHS that is fit-for-purpose. That is not what reality is telling us.

And it quickly become apparent at the master class that our sample of 20 did not know-this-by-a-different-name.

The good news is that this knowledge gap could hiding the opportunity we are all looking for – a door to a path that leads to a radical yet achievable transformation of the NHS into a system that is fit-for-purpose. Now and into the future.

A system that delivers safe, high quality care for those who need it, in full, when they need it and at a cost the country can afford. Now and for the foreseeable future.

And the really good news is that this IS knowledge gap may be and extensive deep but it is not wide … the Foundations are is easy to learn, and to start applying immediately. The basics can be learned in less than a week – the more advanced skills take a bit longer. And this is not untested academic theory – it is proven pragmatic real-world problem solving know-how. It has been known for over 50 years outside healthcare.

Our goal is not acquisition of theoretical knowledge – is is a deep enough understanding to make wise enough decisions to achieve good enough outcomes. For everyone. Starting tomorrow.

And that is the design purpose of FISH. To provide those who want to learn a quick and easy way to do so.

Stop Press: Further feedback from the masterclass is that some of the managers are grasping the nettle, drawing back their own black curtains, opening the door that was always there behind it, and taking a peek through into a magical garden of opportunity. One that was always there but was hidden from view.

19/10/2013

Improvement-by-Twitter

Sat 5th October

It started with a tweet.

08:17 [JG] The NHS is its people. If you lose them, you lose the NHS.

09:15 [DO] We are in a PEOPLE business – educating people and creating value.

Sun 6th October

08:32 [SD] Who isn’t in people business? It is only people who buy stuff. Plants, animals, rocks and machines don’t.

09:42 [DO] Very true – it is people who use a service and people who deliver a service and we ALL know what good service is.

09:47 [SD] So onus is on us to walk our own talk. If we don’t all improve our small bits of the NHS then who can do it for us?

Then we were off … the debate was on …

10:04 [DO] True – I can prove I am saving over £160 000.00 a year – roll on PBR !?

10:15 [SD] Bravo David. I recently changed my surgery process: productivity up by 35%. Cost? Zero. How? Process design methods.

11:54 [DO] Exactly – cost neutral because we were thinking differently – so how to persuade the rest?

12:10 [SD] First demonstrate it is possible then show those who want to learn how to do it themselves. http://www.saasoft.com/fish/course

We had hard evidence it was possible … and now MC joined the debate …

12:48 [MC] Simon why are there different FISH courses for safety, quality and efficiency? Shouldn’t good design do all of that?

12:52 [SD] Yes – goal of good design is all three. It just depends where you are starting from: Governance, Operations or Finance.

A number of parallel threads then took off and we all had lots of fun exploring each others knowledge and understanding.

17:28 MC registers on the FISH course.

And that gave me an idea. I emailed an offer – that he could have a complimentary pass for the whole FISH course in return for sharing what he learns as he learns it. He thought it over for a couple of days then said “OK”.

Weds 9th October

06:38 [MC] Over the last 4 years of so, I’ve been involved in incrementally improving systems in hospitals. Today I’m going to start an experiment.

06:40 [MC] I’m going to see if we can do less of the incremental change and more system redesign. To do this I’ve enrolled in FISH

Fri 11th October

06:47 [MC] So as part of my exploration into system design, I’ve done some studies in my clinic this week. Will share data shortly.

21:21 [MC] Here’s a chart showing cycle time of patients in my clinic. Median cycle time 14 mins, but much longer in 2 pic.twitter.com/wu5MsAKk80

21:22 [MC] Here’s the same clinic from patients’ point if view, wait time. Much longer than I thought or would like pic.twitter.com/iVLTDsbG8m

21:24 [MC] Two patients needed to discuss surgery or significant news, that takes time and can’t be rushed.

21:25 [MC] So, although I started on time, worked hard and finished on time. People were waited ages to see me. Template is wrong!

21:27 [MC] By the time I had seen the the 3rd patient, people were waiting 45 mins to see me. That’s poor.

21:28 [MC] The wait got progressively worse until the end of the clinic.

Sunday 13th October

16:02 [MC] As part of my homework on systems, I’ve put my clinic study data into a Gantt chart. Red = waiting, green = seeing me pic.twitter.com/iep2PDoruN

16:34 [SD] Hurrah! The visual power of the Gantt Chart. Worth adding the booked time too – there are Seven Sins of Scheduling to find.

16:36 [SD] Excellent – good idea to sort into booked time order – it makes the planned rate of demand easier to see.

16:42 [SD] Best chart is Work In Progress – count the number of patients at each time step and plot as a run chart.

17:23 [SD] Yes – just count how many lines you cross vertically at each time interval. It can be automated in Excel

17:38 [MC] Like this? pic.twitter.com/fTnTK7MdOp

This is the work-in-progress chart. The most useful process monitoring chart of all. It shows the changing size of the queue over time. Good flow design is associated with small, steady queues.

18:22 [SD] Perfect! You’re right not to plot as XmR – this is a cusum metric. Not a healthy WIP chart this!

There was more to follow but the “ah ha” moment had been seen and shared.

Weds 16th October

MC completes the Online FISH course and receives his well-earned Certificate of Achievement.

This was his with-the-benefit-of-hindsight conclusion:

I wish I had known some of this before. I will have totally different approach to improvement projects now. Key is to measure and model well before doing anything radical.

Improvement Science works.
Improvement-by-Design is a skill that can be learned quickly.
FISH is just a first step.

05/10/2013

The Power of the Converted Skeptic

One of the biggest challenges in Improvement Science is diffusion of an improvement outside the circle of control of the innovator.

It is difficult enough to make a significant improvement in one small area – it is an order of magnitude more difficult to spread the word and to influence others to adopt the new idea!

One strategy is to shame others into change by demonstrating that their attitude and behaviour are blocking the diffusion of innovation.

This strategy does not work. It generates more resistance and amplifies the differences of opinion.

Another approach is to bully others into change by discounting their opinion and just rolling out the “obvious solution” by top-down diktat.

This strategy does not work either. It generates resentment – even if the solution is fit-for-purpose – which it usually is not!

So what does work?

The key to it is to convert some skeptics because a converted skeptic is a powerful force for change.

But doesn’t that fly in the face of established change management theory?

Innovation diffuses from innovators to early-adopters, then to the silent majority, then to the laggards and maybe even dinosaurs … doesn’t it?

Yes – but that style of diffusion is incremental, slow and has a very high failure rate. What is very often required is something more radical, much faster and more reliable. For that it needs both push from the Confident Optimists and pull from some Converted Pessimists. The tipping point does not happen until the silent majority start to come off the fence in droves: and they do that when the noisy optimists and equally noisy pessimists start to agree.

The fence-sitters jump when the tug-o-war stalemate stops and the force for change becomes aligned in the direction of progress.

So how is a skeptic converted?

Simple. By another Converted Skeptic.

Here is a real example.

We are all skeptical about many things that we would actually like to improve.

Personal health for instance. Something like weight. Yawn! Not that Old Chestnut!

We are bombarded with shroud-waver stories that we are facing an epidemic of obesity, rapidly rising rates of diabetes, and all the nasty and life-shortening consequences of that. We are exhorted to eat “five portions of fruit and veg a day” … or else! We are told that we must all exercise our flab away. We are warned of the Evils of Cholesterol and told that overweight children are caused by bad parenting.

The more gullible and fearful are herded en-masse in the direction of the Get-Thin-Quick sharks who then have a veritable feeding frenzy. Their goal is their short-term financial health not the long-term health of their customers.

The more insightful, skeptical and frustrated seek solace in the chocolate Hob Nob jar.

For their part, the healthcare professionals are rewarded for providing ineffective healthcare by being paid-for-activity not for outcome. They dutifully measure the decline and hand out ineffective advice. Their goal is survival too.

The outcome is predictable and seemingly unavoidable.

So when a disruptive innovation comes along that challenges the current dogma and status quo, the healthy skeptics inevitably line up and proclaim that it will not work.

Not that it does not work. They do not know that because they never try it. They are skeptics. Someone else has to prove it to them.

And I am a healthy skeptic about many things.

I am skeptical about diets – the evidence suggests that their proclaimed benefit is difficult to achieve and even more difficult to sustain: and that is the hall-mark of either a poor design or a deliberate, profit-driven, yet legal scam.

So I decided to put an innovative approach to weight loss to the test. It is not a diet – it is a design to achieve and sustain a healthier weight to height ratio. And for it to work it must work for me because I am a diet skeptic.

The start of the story is HERE

I am now a Converted Healthier Skeptic.

I call the innovative design a “2 out of 7 Lo-CHO” policy and what that means is for two days a week I just cut out as much carbohydrate (CHO) as feasible. Stuff like bread, potatoes, rice, pasta and sugar. The rest of the time I do what I normally do. There is no need for me to exercise and no need for me to fill up on Five Fruit and Veg.

The chart above is the evidence of what happened. It shows a 7 kg reduction in weight over 140 days – and that is impressive given that it has required no extra exercise and no need to give up tasty treats completely and definitely no need to boost the bottom-line of a Get-Thin-Quick shark!

It also shows what to expect. The weight loss starts steeper then tails off as it approaches a new equilibrium weight. This is the classic picture of what happens to a “system” when one of its “operational policies” is wisely re-designed.

Patience, persistence and a time-series chart are all that is needed. It takes less than a minute per day to monitor the improvement.

Even I can afford to invest a minute per day.

The BaseLine© chart clearly shows that the day-to-day variation is quite high: and that is expected – it is inherent in the 2-out-of-7 Lo-CHO design. It is the not the short-term change that is the measure of success – it is the long-term improvement that is important.

It is important to measure daily – because it is the daily habit that keeps me mindful, aligned, and on-goal. It is not the measurement itself that is the most important thing – it is the conscious act of measuring and then plotting the dot in the context of the previous dots. The picture tells the story. No further “statistical” analysis is required.

The power of this chart is that it provides hard evidence that is very effective for nudging other skeptics like me into giving the innovative idea a try. I know because I have done that many times now. I have converted other skeptics. It is an innovation infection.

And the same principle appears to apply to other areas. What is critical to success is tangible and visible proof of progress. That is what skeptics need. Then a rational and logical method and explanation that respects their individual opinion and requirements. The design has to work for them. And it must make sense.

They will come out with a string of “Yes … buts” and that is OK because that is how skeptics work. Just answer their questions with evidence and explanations. It can get a bit wearing I admit but it is worth the effort.

An effective Improvement Scientist needs to be a healthy skeptic too – i.e. an open minded one.

13/07/2013

The Art of Juggling

Improvement Science is like three-ball juggling.

And there are different sets of three things that an Improvementologist needs to juggle:

the Quality-Flow-Cost set and
the Governance-Operations-Finance set and
the Customer-Staff-Organization set.

But the problem with juggling is that it looks very difficult to do – so almost impossible to learn – so we do not try. We give up before we start. And if we are foolhardy enough to try (by teaching ourselves using the suck-it-and-see or trial-and-error method) then we drop all the balls very quickly. We succeed in reinforcing our impossible-for-me belief with evidence. It is a self-fulfilling prophesy. Only the most tenacious, self-motivated and confident people succeed – which further reinforces the I-Can’t-Do belief of everyone else.

The problem here is that we are making an Error of Omission.

We are omitting to ask ourselves two basic questions “How does a juggler learn their art?” and “How long does it take?“

The answer is surprising.

It is possible for just about anyone to learn to juggle in about 10 minutes. Yes – TEN MINUTES.

Skeptical? Sure you are – if it was that easy we would all be jugglers. That is the “I Can’t Do” belief talking. Let us silence that confidence-sapping voice once and for all.

Here is how …

You do need to have at least one working arm and one working eyeball and something connecting them … and it is a bit easier with two working arms and two working eyeballs and something connecting them.

And you need something to juggle – fruit is quite good – oranges and apples are about the right size, shape, weight and consistency (and you can eat the evidence later too).

And you need something else.

You need someone to teach you.

And that someone must be able to juggle and more importantly they must be able to teach someone else how to juggle which is a completely different skill.

Those are the necessary-and-sufficient requirements to learn to juggle in 10 minutes.

The recent picture shows an apprentice Improvement Scientist at the “two orange” stage – just about ready to move to the “three orange” stage.

Exactly the same is true of learning the Improvement Science juggling trick.

The ability to improve Quality, Flow and Cost at the same time.

The ability to align Governance, Operations and Finance into a win-win-win synergistic system.

The ability to delight customers, motivate staff and support leaders at the same time.

And the trick to learning to juggle is called step-by-step unlearning. It is counter-intuitive.

To learn to juggle you just “unlearn” what is stopping you from juggling. You unlearn the unconscious assumptions and habits that are getting in the way.

And that is why you need a teacher who knows what needs to be unlearned and how to help you do it.

And for an apprentice Improvement Scientist the first step on the Unlearning Journey is FISH.