The Nanny McPhee Coaching Contract

Nanny_McPheeThere comes a point in every improvement-by-design journey when it is time for the improvement guide to leave.

An experienced improvement coach knows when that time has arrived and the expected departure is in the contract.

The Nanny McPhee Coaching Contract:

“When you need me but do not want me then I have to stay. And when you want me but do not need me then I have to leave.”

The science of improvement can appear like ‘magic’ at first because seemingly impossible simultaneous win-win-win benefits are seen to happen with minimal effort.

It is not magic.  It requires years of training and practice to become a ‘magician’.  So those who have invested in learning the know-how are just catalysts.  When their catalysts-of-change work is done then they must leave to do it elsewhere.

The key to managing this transition is to set this expectation clearly and right at the start; so it does not come as a surprise. And to offer reminders along the way.

And it is important to follow through … when the time is right.

It is not always easy though.

There are three commonly encountered situations that will test the temptation of the guide.

1) When things are going very badly because the coaching contract is being breached; usually by old, habitual, trust-eroding, error-of-omission behaviours such as: not communicating, not sharing learning, and not delivering on commitments. The coach, fearing loss of reputation and face, is tempted to stay longer and to try harder. Often getting angry and frustrated in the process.  This is an error of judgement. If the coaching contract is being persistently breached then the Exit Clause should be activated clearly and cleanly.

2) When things are going OK, it is easy to become complacent and the temptation then is to depart too soon, only to hear later that the solo-flyers “crashed and burned”, because they were not quite ready and could not (or would not) see it.  This is the “need but do not want” part of the Nanny McPhee Coaching Contract.  One role of the coach is to respectfully challenge the assertion that ‘We can do it ourselves‘ … by saying ‘OK, please demonstrate‘.

3) When things are going very well it is tempting to blow the Trumpet of Success too early, attracting the attention of others who will want to take short cuts, to bypass the effort of learning for themselves, and to jump onto someone else’s improvement bus.  The danger here is that they bring their counter-productive, behavioural baggage with them. This can cause the improvement bus to veer off course on the twists and turns of the Nerve Curve; or grind to a halt on the steeper parts of the learning curve.

An experienced improvement coach will respectfully challenge the individuals and the teams to help them develop their experience, competence and confidence. And just as they start to become too comfortable with having someone to defer to for all decisions, the coach will announce their departure and depart as announced.

This is the “want but do not need” part of the Nanny McPhee Coaching Contract.

And experience teaches us that this mutually respectful behaviour works better.

Politicial Purpose

count_this_vote_400_wht_9473The question that is foremost in the mind of a designer is “What is the purpose?”   It is a future-focussed question.  It is a question of intent and outcome. It raises the issues of worth and value.

Without a purpose it impossible to answer the question “Is what we have fit-for-purpose?

And without a clear purpose it is impossible for a fit-for-purpose design to be created and tested.

In the absence of a future-purpose all that remains are the present-problems.

Without a future-purpose we cannot be proactive; we can only be reactive.

And when we react to problems we generate divergence.  We observe heated discussions. We hear differences of opinion as to the causes and the solutions.  We smell the sadness, anger and fear. We taste the bitterness of cynicism. And we are touched to our core … but we are paralysed.  We cannot act because we cannot decide which is the safest direction to run to get away from the pain of the problems we have.

And when the inevitable catastrophe happens we look for somewhere and someone to place and attribute blame … and high on our target-list are politicians.

So the prickly question of politics comes up and we need to grasp that nettle and examine it with the forensic lens of the system designer and we ask “What is the purpose of a politician?”  What is the output of the political process? What is their intent? What is their worth? How productive are they? Do we get value for money?

They will often answer “Our purpose is to serve the public“.  But serve is a verb so it is a process and not a purpose … “To serve the public for what purpose?” we ask. “What outcome can we expect to get?” we ask. “And when can we expect to get it?

We want a service (a noun) and as voters and tax-payers we have customer rights to one!

On deeper reflection we see a political spectrum come into focus … with Public at one end and Private at the other.  A country generates wealth through commerce … transforming natural and human resources into goods and services. That is the Private part and it has a clear and countable measure of success: profit.  The Public part is the redistribution of some of that wealth for the benefit of all – the tax-paying public. Us.

Unfortunately the Public part does not have quite the same objective test of success: so we substitute a different countable metric: votes. So the objectively measurable outcome of a successful political process is the most votes.

But we are still talking about process … not purpose.  All we have learned so far is that the politicians who attract the most votes will earn for themselves a temporary mandate to strive to achieve their political purpose. Whatever that is.

So what do the public, the voters, the tax-payers (and remember whenever we buy something we pay tax) … the customers of this political process … actually get for their votes and cash?  Are they delighted, satisfied or disappointed? Are they getting value-for-money? Is the political process fit-for-purpose? And what is the purpose? Are we all clear about that?

And if we look at the current “crisis” in health and social care in England then I doubt that “delight” will feature high on the score-sheet for those who work in healthcare or for those that they serve. The patients. The long-suffering tax-paying public.

Are politicians effective? Are they delivering on their pledge to serve the public? What does the evidence show?  What does their portfolio of public service improvement projects reveal?  Welfare, healthcare, education, police, and so on.The_Whitehall_Effect

Well the actual evidence is rather disappointing … a long trail of very expensive taxpayer-funded public service improvement failures.

And for an up-to-date list of some of the “eye-wateringly”expensive public sector improvement train-wrecks just read The Whitehall Effect.

But lurid stories of public service improvement failures do not attract precious votes … so they are not aired and shared … and when they are exposed our tax-funded politicians show their true skills and real potential.

Rather than answering the questions they filter, distort and amplify the questions and fire them at each other.  And then fall over each other avoiding the finger-of-blame and at the same time create the next deceptively-plausible election manifesto.  Their food source is votes so they have to tickle the voters to cough them up. And they are consummate masters of that art.

Politicians sell dreams and serve disappointment.

So when the-most-plausible with the most votes earn the right to wield the ignition keys for the engine of our national economy they deflect future blame by seeking the guidance of experts. And the only place they can realistically look is into the private sector who, in manufacturing anyway, have done a much better job of understanding what their customers need and designing their processes to deliver it. On-time, first-time and every-time.

Politicians have learned to be wary of the advice of academics – they need something more pragmatic and proven.  And just look at the remarkable rise of the manufacturing phoenix of Jaguar-Land-Rover (JLR) from the politically embarrassing ashes of the British car industry. And just look at Amazon to see what information technology can deliver!

So the way forward is blindingly obvious … combine manufacturing methods with information technology and build a dumb-robot manned production-line for delivering low-cost public services via a cloud-based website and an outsourced mega-call-centre manned by standard-script-following low-paid operatives.

But here we hit a bit of a snag.

Designing a process to deliver a manufactured product for a profit is not the same as designing a system to deliver a service to the public.  Not by a long chalk.  Public services are an example of what is now known as a complex adaptive system (CAS).

And if we attempt to apply the mechanistic profit-focussed management mantras of “economy of scale” and “division of labour” and “standardisation of work” to the messy real-world of public service then we actually achieve precisely the opposite of what we intended. And the growing evidence is embarrassingly clear.

We all want safer, smoother, better, and more affordable public services … but that is not what we are experiencing.

Our voted-in politicians have unwittingly commissioned complicated non-adaptive systems that ensure we collectively fail.

And we collectively voted the politicians into power and we are collectively failing to hold them to account.

So the ball is squarely in our court.

Below is a short video that illustrates what happens when politicians and civil servants attempt complex system design. It is called the “Save the NHS Game” and it was created by a surgeon who also happens to be a system designer.  The design purpose of the game is to raise awareness. The fundamental design flaw in this example is “financial fragmentation” which is the the use of specific budgets for each part of the system together with a generic, enforced, incremental cost-reduction policy (the shrinking budget).  See for yourself what happens …

In health care we are in the improvement business and to do that we start with a diagnosis … not a dream or a decision.

We study before we plan, and we plan before we do.

And we have one eye on the problem and one eye on the intended outcome … a healthier patient.  And we often frame improvement in the negative as a ‘we do not want a not sicker patient’ … physically or psychologically. Primum non nocere.  First do no harm.

And 99.9% of the time we do our best given the constraints of the system context that the voted-in politicians have created for us; and that their loyal civil servants have imposed on us.

Politicians are not designers … that is not their role.  Their part is to create and sell realistic dreams in return for votes.

Civil servants are not designers … that is not their role.  Their part is to enact the policy that the vote-seeking politicians cook up.

Doctors are not designers … that is not their role.  Their part is to make the best possible clinical decisions that will direct actions that lead, as quickly as possible, to healthier and happier patients.

So who is doing the complex adaptive system design?  Whose role is that?

And here we expose a gap.  No one.  For the simple reason that no one is trained to … so no one is tasked to.

But there is a group of people who are perfectly placed to create the context for developing this system design capability … the commissioners, the executive boards and the senior managers of our public services.

So that is where we might reasonably start … by inviting our leaders to learn about the science of complex adaptive system improvement-by-design.

And there are now quite a few people who can now teach this science … they are the ones who have done it and can demonstrate and describe their portfolios of successful and sustained public service improvement projects.

Would you vote for that?

Learning Loops

campfire_burning_150_wht_174[Beep Beep] Bob’s phone reminded him that it was time for the remote coaching session with Leslie, one of the CHIPs (community of healthcare improvement science practitioners). He flipped open his laptop and logged in. Leslie was already there.

<Leslie> Hi Bob.  I hope you had a good Xmas.

<Bob> Thank you Leslie. Yes, I did. I was about to ask the same question.

<Leslie> Not so good here I am afraid to say. The whole urgent care system is in meltdown. The hospital is gridlocked, the 4-hour target performance has crashed like the Stock Market on Black Wednesday, emergency admissions have spilled over into the Day Surgery Unit, hundreds of operations have been cancelled, waiting lists are spiralling upwards and the fragile 18-week performance ceiling has been smashed. It is chaos. Dangerous chaos.

<Bob> Oh dear. It sounds as if the butterfly has flapped its wings. Do you remember seeing this pattern of behaviour before?

<Leslie> Sadly yes. When I saw you demonstrate the Save the NHS Game.  This is exactly the chaos I created when I attempted to solve the 4-hour target problem, and the chaos I have seen every doctor, manager and executive create when they do too. We seem to be the root cause!

<Bob> Please do not be too hard on yourself Leslie. I am no different. I had to realise that I was contributing to the chaos I was complaining about, by complaining about it. Paradoxically not complaining about it made no difference. My error was one of omission. I was not learning. I was stuck in a self-justifying delusional blame-bubble of my own making. My humility and curiosity disabled by my disappointment, frustration and anxiety. My inner chimp was running the show!

<Leslie> Wow! That is just how everyone is feeling and behaving. Including me. So how did you escape from the blame-bubble?

<Bob> Well first of all I haven’t completely escaped. I just spend less time there. It is always possible to get sucked back in. The way out started to appear when I installed a “learning loop”.

<Leslie> A what? Is that  like a hearing loop for the partially deaf?

<Bob> Ha! Yes! A very apt metaphor.  Yes, just like that. Very good. I will borrow that if I may.

<Leslie> So what did your learning loop consist of?

<Bob> A journal.  I started a journal. I invested a few minutes each day reflecting and writing it down. The first entries were short and rather “ranty”. I cannot possibly share them in public. It is too embarrassing. But it was therapeutic and over time the anger subsided and a quieter, calmer inner voice could be heard. The voice of curiosity. It was asking one question over and over again. “How?” … not “Why?”.

<Leslie> Like “How did I get myself into this state?

<Bob> Exactly so.  And also “How come I cannot get myself out of this mess?

<Leslie> And what happened next?

<Bob> I started to take more notice of things that I had discounted before. Apparently insignificant things that I discovered had profound implications. Like the “butterflies wing” effect … I discovered that small changes can have big effects.  I also learned to tune in to specific feelings because they were my warning signals.

<Leslie> Niggles you mean?

<Bob> Yes. Niggles are flashes of negative emotion that signal a design flaw. They are usually followed by an untested assumption, an invalid conclusion, an unwise decision and a counter-productive action. It all happens unconsciously and very fast so we are only aware of the final action – the MR ANGRY reply to the email that we stupidly broadcast via the Reply All button!

<Leslie> So you learned to tune into the niggle to avoid the chain reaction that led to hitting the Red Button.

<Bob> Sort of. What actually happened is that the passion unleashed by the niggle got redirected into a more constructive channel – via my Curiosity Centre to power up the Improvement Engine. It was a bit rusty! It had not been used for a long while.

<Leslie> And once the “engine” was running it sucked in niggles that were now a source of fuel! You started harvesting them using the 4N Chart! So what was the output?

<Bob> Purposeful, focused, constructive, rational actions. Not random, destructive, emotional explosions.

<Leslie> Constructive actions such as?

<Bob> Well designing and building the FISH course is one, and this ISP programme is another.

<Leslie> More learning loops!

<Bob> Yup.

<Leslie> OK. So I can see that a private journal can help an individual to build their own learning loop. How does that work with groups? We do not all need to design and build a FISH-equivalent surely!

<Bob> No indeed. What we do is we share stories. We gather together in small groups around camp fires and we share what we are learning … as we are learning it. We contribute our perspective to the collective awareness … and we all gain from everyone’s learning. We learn and teach together.

<Leslie> So the stories are about what we are learning, not what we achieved with that learning.

<Bob> Well put! The “how” we achieved it is more valuable knowledge than “what” we achieved. The “how” is the process, the “what” is just the product. And the “how” we failed to achieve is even more valuable.

<Leslie> Wow! So are you saying that the chaos we are experiencing is the expected effect of not installing enough learning loops! A system-wide error of omission.

<Bob> I would say that is a reasonable diagnosis.

<Leslie> So a rational and reasonable course of treatment becomes clear.  I am on the case!


SFQPThe flavour of the week has been “chaos”.  Again!

Chaos dissipates energy faster than calm so chaotic behaviour is a symptom of an inefficient design.

And we would like to improve our design to restore a state of ‘calm efficiency’.

Chaos is a flow phenomenon … but that is not where the improvement by design process starts.  There is a step before that … Safety.

Safety First
If a design is unsafe it generates harm.  So we do not want to improve the smooth efficiency of the harm generator … that will only produce more harm!  First we must consider if our system is safe enough.

Despite what many claim, our healthcare systems are actually very safe.  For sure there are embarrassing exceptions and we can always improve safety further, but we actually have quite a safe design.

It is not a very efficient design though.  There is a lot of checking and correcting which uses up time and resources … but it helps to ensure safety is good enough for now.

Having done the safety sanity check we can move on to Flow.

Flow Second
Flow comes before quality because it is impossible to deliver a high quality experience in a chaotic system.  First we need to calm any chaos.  Or rather we need to diagnose the root causes of the chaotic behaviour and do some flow re-design to restore the calm.

Chaos is funny stuff.  It does not behave intuitively.  Time is always a factor.  The butterflies wing effect is ever present.  Small causes can have big effects, both good and bad.  Big causes can have no effect.  Causes can be synergistic and they can be antagonistic.  The whole is not the sum of the parts.  This confusing and counter-intuitive behaviour is called “non linear” and we are all rubbish at getting a mental handle on it.  Our brains did not evolve that way.

The good news is that when chaos reigns it is usually possible to calm it with a small number of carefully placed, carefully timed, carefully designed, synergistic, design “tweaks”.

The problem is that when we do what intuitively feels “right” we can too easily make poor improvement decisions that lead to ineffective actions.  The chaos either does not go away or it gets worse.  So, we have learned from our ineptitude to just put up with the chaos and to accept the inefficiency, the high cost-of-chaos.

To calm the chaos we have to learn how to use the tools designed to do that.  And they do exist.

Safety and Flow represent the “absolute” half of the SFQP cycle.  Harm is an absolute metric. We can devise absolute definitions and count harmful events.  Mortality.  Mistakes.  Hospital  acquired infections.  That sort of stuff.

Flow is absolute too in the sense that the Laws of Physics determine what happens, and they are absolute too. And non-negotiable.

Quality is relative.  It is the ratio of experience and expectation and both of these are subjective but that is not the point.  The point is that it is a ratio and that makes it a relative metric.  My expectation influences my perception of quality, as does what I experience.  And this has important implications.  For example we can reduce disappointment by lowering expectation; or we can reduce disappointment by improving experience.  Lowering expectation is the easier option because to do that we only have to don the “black hat” and paint a grisly picture of a worst case scenario.  Some call it “informed consent”; I call it “abdication of empathy” and “fear-mongering”.

Variable quality can  come from variable experience, variable expectation or both.  So, to reduce quality variation we can focus on either input to the ratio; and the easiest is expectation.  Setting a realistic expectation just requires measuring experience retrospectively and sharing it prospectively.  Not satisfaction mind you – Experience. Satisfaction surveys are largely meaningless as an improvement tool because just setting a lower expectation will improve satisfaction!

And this is why quality follows flow … because if flow is chaotic then expectation becomes a lottery, and quality does too.  The chaotic behaviour of the St.Elsewhere’s® A&E Department that we saw last week implies that we cannot set any other expectation than “It might be OK or it might be Not OK … we cannot predict. So fingers crossed.”  It is a quality lottery!

But with calm and efficient flow we experience less variation and with that we can set a reasonable expectation.  Quality becomes predictable-within-limits.

Productivity is also a relative concept.  It is the ratio of what we get out of the system divided by what we put in.  Revenue divided by expense for example.

And it does not actually emerge last.  As soon as safety, flow or quality improve then they will have an immediate impact on productivity.  Work gets easier.  The cost of harm, chaos and disappointment will fall (and they are surprisingly large costs!).

The reason that productivity-by-design comes last is because we are talking about focussed productivity improvement-by-design.  Better value for money.  And that requires a specific design focus.  And it comes last because we need some head-space and some life-time to learn and do good system design.

And SFQP is a cycle so after doing the Productivity improvement we go back to Safety and ask “How can we make our design even safer and even simpler?” And so on, round and round the SFQP loop.

Do no harm, restore the calm, delight for all, and costs will fall.

And if you would like a full-size copy of the SFQP cycle diagram to use and share just click here.

Magnum Chaos

Magnum_ChaosThe title of this alter piece by Lorenzo Lotto is Magnum Chaos. It was painted in the first half of the 16th Century.

Chaos was the Greek name for the primeval state of existence from which everything that has order was created. Similar concepts exist in all ancient mythologies.

The sudden appearance of order from chaos is the subject of much debate and current astronomical science refers to it as the Big Bang … which is the sense that this 500 year old image captures.  Except that it appears to have happened bout 13.5 thousand million years ago.

So it is surprising to learn that the Science of Chaos did not really get going until about 50 years ago – shortly after the digital computer was developed.

The timing is no co-incidence.  The theoretical roots of chaos had been known for much longer – since Isaac Newton formulated the concept of gravity. About 200 years ago it became the “Three-Body Problem”. The motion of the Earth, Moon and Sun is a three-body gravitational problem.

And in 1887, mathematicians Ernst Bruns and Henri Poincaré showed that there is no general analytical solution for the three-body problem given by algebraic expressions and integrals. The motion of three bodies is generally non-repeating, except in special cases. No simple equation describes it.

The implication of this is that the only way to solve this sort of problem is by grunt-work, empirically, with thousands of millions of small calculations.  And in 1887 the technology was not available to do this.

So when the high-speed transistorised digital computer appeared in the 1960’s it became possible to revisit this old niggle … and the nature of chaos became much better understood.  The modern legacy of this pioneering work is the surprising accuracy that we can now predict the weather – at least over the short term – using powerful digital computers running chaotic system simulation models. Weather is a chaotic flow system.

So given the knowledge that exists about the nature of flow in naturally chaotic systems … it is surprising that not much of this understanding has diffused into the design of man-made systems; such as healthcare.

It has probably not escaped most people’s attention that the NHS is suffering yet another “winter crisis” … despite the fact that the NHS budget has doubled over the last 15 years.

If we can predict the weather, but not control it, then why cannot we avoid the annual NHS crisis – which is a much simpler system that we can influence?

The chart above shows the actual behaviour of a healthcare system – a medium sized hospital that we shall call St.Elsewhere’s®.  It could be called St.Anywhere’s.  The performance metric that is being plotted over time is the % of patients who arrive each day in the A&E department and who are there for more than 4 hours. The infamous 4-hour A&E target.  The time-span on the horizontal axis is just over 5 years – and the data has been segmented by financial year.

The behaviour of this system over time is not random.  It is chaotic.

There are repeating but non-identical cyclical patterns in the data … for example the first half of the year (April to September) is “better” than the second half. And this cyclical pattern appears to be changing as time passes.

The thin blue line is the arbitrary ‘target’.  And it does require a statistical expert to conclude that this system has never come close to achieving the ‘target’.  The system design is not capable of achieving it … so beating the system with a stick is not going to help. It amounts to the Basil Fawlty tactic of beating the broken-down car with a tree branch!

The system needs to be re-designed in order to achieve the requirement of consistently less than a 5% failure rate on the 4-hour A&E target. Exhortation is ineffective.

And this is not a local problem … it is a systemic one … BBC News

To re-design a system to achieve improved performance we first need to understand why the current design is not demonstrating the behaviour we want. Guessing is not design. It is guess-work. Generating a hypothesis is not design. It is guess-work too.

Design requires understanding.

A common misunderstanding is that the primary cause of deteriorating A&E performance is increasing demand. Reality does not support this rhetoric.

StElsewhere_DemandThis system behaviour chart (SBC) shows the A&E daily demand for the same period segmented by financial year. Over time there has indeed been an increase in the average demand, but that association does not prove causality.  If increasing demand caused performance failure we would expect to see matching cyclical patterns on both charts. But it is rather obvious that there is little relation between the two charts – the periods of highest demand do not correlate with the periods of highest failure. If anything there is a negative correlation – there is actually less demand in the second half of the financial year compared with the first.

So there must be more to it than just the average A&E demand.  Could there be a chicken-and-egg problem here? Higher breach rates leading to lower demand? Word gets round about a poor quality service!  What about the weather?  What about the effect of day-length? What about holidays? What about annual budgets?

What is uncomfortably obvious is that the chaotic behaviour has been going on for a long time. That is because it is an inherent part of the design.  We created it because we designed the NHS.

One surprising lesson that Chaos Theory teaches us is that chaos is predictable.  A system can be designed to behave chaotically … and rather easily too. It does not required a complicated design – a mechanically simple system can behave chaotically – a hinged pendulum for example.

So if we can deliberately design a system to behave chaotically then surely we can understand what design features are critical to delivering chaos and what are not. And with that insight might we then examine the design of man-made systems that we do not want to behave chaotically … such as our healthcare system?

And when we do that we discover something rather uncomfortable – that our healthcare system has been nearly perfectly designed to generate chaotic behaviour.  That may not have been the intention but it is the outcome.

So how did we get ourselves into this mess … and how do we get ourselves out of it?

To understand chaotic flow behaviour we need to consider two effects: the first is called a destabilising effect, the other is a stabilising effect.

The golden rule of chaos is that if the destabilising effect dominates then we get bumpy behaviour, if the stabilizing effect dominates then we get smooth flow.

So to eliminate the chaos all we need to do is to adjust the balance of these two effects … increase the stabilisers and reduce the destabilisers.

And because of the counter-intuitive nature of non-linear flow systems, only a small change in this balance can have a big effect: it can flip us from stable to chaotic, and it can also flip us back.

The trick is knowing how to tweak the design to create the flip.  Tweak at the wrong place or wrong time and nothing improves … as our chart above illustrates.

We need chaotic-flow-diagnostic and anti-chaotic-flow-design capability … and that is clearly lacking … because if it were present we would not be having this conversation.

And that capability exists … it is called Improvement Science. We just need to learn it.