Celebrate and Share

There comes a point in every improvement journey when it is time to celebrate and share. This is the most rewarding part of the Improvement Science Practitioner (ISP) coaching role so I am going to share a real celebration that happened this week.

The picture shows Chris Jones holding his well-earned ISP-1 Certificate of Competence.  The “Maintaining the Momentum of Medicines”  redesign project is shown on the poster on the left and it is the tangible Proof of Competence. The hard evidence that the science of improvement delivers.

Chris_Jones_Poster_and_Certificate

Behind us are the A3s for one of the Welsh Health Boards;  ABMU in fact.


An A3 is a way of summarising an improvement project very succinctly – the name comes from the size of paper used.  A3 is the biggest size that will go through an A4 fax machine (i.e. folded over) and the A3 discipline is to be concise and clear at the same time.

The three core questions that the A3 answers are:
Q1: What is the issue?
Q2: What would improvement need to look like?
Q3: How would we know that a change is an improvement?

This display board is one of many in the room, each sharing a succinct story of a different improvement journey and collectively a veritable treasure trove of creativity and discovery.

The A3s were of variable quality … and that is OK and is expected … because like all skills it takes practice. Lots of practice. Perfection is not the goal because it is unachievable. Best is not the goal because only one can be best. Progress is the goal because everyone can progress … and so progress is what we share and what we celebrate.


The event was the Fifth Sharing Event in the Welsh Flow Programme that has been running for just over a year and Chris is the first to earn an ISP-1 Certificate … so we all celebrated with him and shared the story.  It is a team achievement – everyone in the room played a part in some way – as did many more who were not in the room on the day.


stick_figure_look_point_on_cliff_anim_8156Improvement is like mountain walking.

After a tough uphill section we reach a level spot where we can rest; catch our breath; take in the view; reflect on our progress and the slips, trips and breakthroughs along the way; perhaps celebrate with drink and nibble of our chocolate ration; and then get up, look up, and square up for the next uphill bit.

New territory for us.  New challenges and new opportunities to learn and to progress and to celebrate and share our improvement stories.

V.U.T.

figure_pointing_out_chart_data_150_wht_8005It 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).

Over-Egged Expectation

FISH_ISP_eggs_jumpingResistance-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.

Circles

SFQP_enter_circle_middle_15576For a system to be both effective and efficient the parts need to work in synergy. This requires both alignment and collaboration.

Systems that involve people and processes can exhibit complex behaviour. The rules of engagement also change as individuals learn and evolve their beliefs and their behaviours.

The values and the vision should be more fixed. If the goalposts are obscure or oscillate then confusion and chaos is inevitable.


So why is collaborative alignment so difficult to achieve?

One factor has been mentioned. Lack of a common vision and a constant purpose.

Another factor is distrust of others. Our fear of exploitation, bullying, blame, and ridicule.

Distrust is a learned behaviour. Our natural inclination is trust. We have to learn distrust. We do this by copying trust-eroding behaviours that are displayed by our role models. So when leaders display these behaviours then we assume it is OK to behave that way too.  And we dutifully emulate.

The most common trust eroding behaviour is called discounting.  It is a passive-aggressive habit characterised by repeated acts of omission:  Such as not replying to emails, not sharing information, not offering constructive feedback, not asking for other perspectives, and not challenging disrespectful behaviour.


There are many causal factors that lead to distrust … so there is no one-size-fits-all solution to dissolving it.

One factor is ineptitude.

This is the unwillingness to learn and to use available knowledge for improvement.

It is one of the many manifestations of incompetence.  And it is an error of omission.


Whenever we are unable to solve a problem then we must always consider the possibility that we are inept.  We do not tend to do that.  Instead we prefer to jump to the conclusion that there is no solution or that the solution requires someone else doing something different. Not us.

The impossibility hypothesis is easy to disprove.  If anyone has solved the problem, or a very similar one, and if they can provide evidence of what and how then the problem cannot be impossible to solve.

The someone-else’s-fault hypothesis is trickier because proving it requires us to influence others effectively.  And that is not easy.  So we tend to resort to easier but less effective methods … manipulation, blame, bullying and so on.


A useful way to view this dynamic is as a set of four concentric circles – with us at the centre.

The outermost circle is called the ‘Circle of Ignorance‘. The collection of all the things that we do not know we do not know.

Just inside that is the ‘Circle of Concern‘.  These are things we know about but feel completely powerless to change. Such as the fact that the world turns and the sun rises and falls with predictable regularity.

Inside that is the ‘Circle of Influence‘ and it is a broad and continuous band – the further away the less influence we have; the nearer in the more we can do. This is the zone where most of the conflict and chaos arises.

The innermost is the ‘Circle of Control‘.  This is where we can make changes if we so choose to. And this is where change starts and from where it spreads.


SFQP_enter_circle_middle_15576So if we want system-level improvements in safety, flow, quality and productivity (or cost) then we need to align these four circles. Or rather the gaps in them.

We start with the gaps in our circle of control. The things that we believe we cannot do … but when we try … we discover that we can (and always could).

With this new foundation of conscious competence we can start to build new relationships, develop trust and to better influence others in a win-win-win conversation.

And then we can collaborate to address our common concerns – the ones that require coherent effort. We can agree and achieve our common purpose, vision and goals.

And from there we will be able to explore the unknown opportunities that lie beyond. The ones we cannot see yet.

Cumulative Sum

Dr_Bob_Thumbnail[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.

A School for Rebels

Troublemaker_vs_RebelSystem-wide, significant, and sustained improvement implies system-wide change.

And system-wide change implies more than 20% of the people commit to action. This is the cultural tipping point.

These critical 20% have a badge … they call themselves rebels … and they are perceived as troublemakers by those who profit most from the status quo.

But troublemakers and rebels are radically different … as shown in the summary by Lois Kelly.


Rebels share a common, future-focussed purpose.  A mission.  They are passionate, optimistic and creative.  They understand synergy and how to release and align the stored emotional energy of both themselves and others.  And most importantly they are value-led and that makes them attractive.  Values such as honesty, integrity and industry are what make leaders together-effective.

SHCR_logoAnd as we speak there is school for rebels in healthcare gaining momentum …  and their programme is current, open to all and free to access. And the change agent development materials are excellent!

Click here to download their study guide.


Converting possibilities into realities is the essence of design … so our merry band of rebels will also need to learn how to convert their positive rhetoric into practical reality. And that is more physics than psychology.

Streams flow because of physics not because of passion.SFQP_Compass

And this is why the science of improvement is important because it is the synthesis of the people dimension and the process dimension – into a system that delivers significant and sustained improvement.

On all dimensions. Safety, Flow, Quality and Productivity.

The lighthouse is our purpose; the whale represents the magnitude of our challenge; the blue sky is the creative thinking we need … to avoid trying to boil the ocean.

And the noisy, greedy, s****y seagulls are the troublemakers who always will plague us.

[Image by Malaika Art].


SFQP

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.


Quality
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
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?


StElsewhere_Fail
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.

Guess-work or Grunt-work?

back_and_forth_questions_150_wht_8159Improvement flows from change. Change flows from action. Action flows from decision.

And we can make a decision in one of two ways – we can use guess-work or we can use grunt-work.

Of course it does not feel as black and white as that so let us put those two options at the opposite ends of a spectrum. Pure guess-work at one and and pure grunt-work at the other.

Guess-work is the easier end. To guess we just need a random number generator of some sort – like a dice.  Grunt-work is the harder end.  And what exactly is “grunt-work”?


Using available knowledge to work out a decision that will get us to our intended  outcome is grunt-work.  It does not require creativity, imagination, assumptions, beliefs, judgements and all the usual machinery that we humans employ to make decisions. It just requires following the tried-and-tested recipe and doing the grunt-work. A computer does grunt-work. It just follows the recipe we give it.

But experience shows that we even with hard work we do not always get the outcome we intend. So what is going wrong?

When the required knowledge is available and we do not use it we are exhibiting ineptitude. So in that context then we have a clear path of improvement: We invest first in dissolving our own ineptitude. We invest in learning what is already known.  And that is grunt-work. Hard work.

When the required knowledge is not available then we are exhibiting ignorance.  And our ignorance is exposed in two ways: firstly when we cannot make a decision of what to do because we have no option other than to guess. And secondly when what we predicted would happen as a result of our action did not actually happen. Reality disproved our rhetoric.

When we are ignorant we have a different path of improvement – first we need to do research to improve our knowledge and understanding, and only then when we are able to apply the new knowledge to make reliable predictions. We need tested and trusted knowledge to design a path to out intended outcome.

And as Richard Feynman perceptively observed … research starts with an educated guess.  We might call it an hypothesis but it is a guess nevertheless. From that we make predictions and then we do experiments using reality to test our rhetoric. All guesses that fail the reality-check are rejected. So our vast body of scientific knowledge is the accumulation of guesses that did not fail the reality-check.

The critical word in the paragraph above is “educated”. How do researchers make educated guesses?

What does the word “educated” imply?


School is all about learning what is already “known”.  There is no debate.  The teachers are always right, only the students can be wrong. It is assumed.

But most of our learning comes from what we experience before and after school.  We are all enrolled in the University of Life – and the teacher there is reality, not rhetoric.

And when we are tested by reality we are very often found to be lacking something.  Well actually we are always found to be lacking.  Sometimes we flunk the test outright and have to go back to the bottom of the learning ladder. Sometimes we scrape a bare pass … we survive … but we know we came close to failing.  Sometimes we secure a safe pass … and still we know we could have done better.  We can always do better.

But how?  Is it because we were ignorant?  Or was it because we were inept?

Examinations at The University of Rhetoric are designed to measure our ineptitude.

The University of Life is not so didactic or autocratic.  The challenges it presents come from anywhere in the Ignorance-Ineptitude Zone.  We need educated guesswork to survive there.


So one problem we face is how do we differentiate ignorance from ineptitude?

At this point it is important to separate individual ignorance from collective ignorance; and individual ineptitude from collective ineptitude. There are two dimensions at play.

The history of science is characterised by individuals who first resolved their individual ignorance when they discover something new. Only later was it appreciated that they were the first. So long as that discovery is shared then collective ignorance has reduced too. There is no need for everyone to rediscover everything when we share our learning.

Newton’s “discovery” of the Laws of Motion is a good example of an individual discovery quickly becoming collective knowledge. And with that collective knowledge we have proved we are able to land a spaceship on a far distant comet! That is grunt-work.

Einstein’s “discovery” of Relativity did not disprove Newton’s Laws of Motion, it re-framed and re-fined them so that even more profound predictions could be made. Some of the predictions are only now being tested as our technology has evolved to be able to perform the measurements with sufficient precision and accuracy. That is grunt-work.  And it is increasingly collective grunt-work.


We are all born individually ignorant and individually inept.

Through experience and education we become aware of collective knowledge and with that we develop our individual capabilities. We do not re-invent every wheel.

And with that individual capability we are able to survive. We can secure a “pass” in the University of Life Survival Challenge.

But it leaves a lot of room for improvement.

Continuing to build collective knowledge through scientific research into more and more complicated and complex challenges, such as climate change, is necessary. But it is not sufficient. We need more.

Developing  our collective capability to put that knowledge to the service of every living thing on the Earth is our challenge.  And that is not grunt-work because we do not have a recipe to follow. We have to discover how to do that.

And that journey of discovery is called Improvement Science.


People first or Process first?

stick_figure_balance_mind_heart_150_wht_9344A recurring theme this week has been the interplay between the cultural and the technical dimensions of system improvement.

The hearts and the minds.  The people and the process.  The psychology and the physics.

Reflecting on the many conversations what became clear was that both are required but not always in the same amount and in the same sequence.

The context is critical.

In some cases we can start with some technical stuff. Some flow physics and a Gantt and Run chart or two.

In other cases we have to start with some cultural stuff. Some conversations about values, beliefs and behaviours.

And they are both tricky but in different ways.


The technical stuff is counter-intuitive.  We have to engage our logical, rational thinking brains and work it through step-by-step, making every assumption explicit and every definition clear.

If we go with our gut we get it wrong (although we feel it is right) and then we fail, and then we blame others or ourselves. Either way we lose confidence.  The logical thinking is hard work. It makes our heads ache. So we cut corners.

But once we have understood then it gets much easier because we can then translate our hard won understanding into a trusted heuristic.  We do not need to work it out every time. We can just look up the correct recipe.

And there lurks a trap … the problem that was at first unrecognised, then impossible, then difficult, and then doable … becomes easy and even obvious … but only after we have worked out a solution. And that obvious-in-hindsight effect is a source of many dangers …

… we can become complacent, over-confident, and even dismissive of others who have not been through the ‘pain’ of learning. We may be tempted to elevate our status and to inflate our importance by hoarding our hard-won understanding. We risk losing our humility … and when we do that we stop being curious and we stop learning. And then we are part of the problem again.

So to avoid those traps we need to hold ourselves in the role of the teacher and coach. We need to actively share what we have learned and explain how we came to know it.  One step at a time … the blood, the sweat and the tears … the confusion and eureka moments. Not one giant leap from where we started to where we got to.  And when we have the generosity to share our knowledge … it is surprising how much we learn!  We learn more from teaching than by being taught.


The cultural stuff is counter-intuitive too.  We have to engage our emotional, irrational, feeling brains and step back from the objective fine-print to look at the subjective full-picture. We have to become curious. We have to look at the problem from as many perspectives as we can. We have to practice humble inquiry by asking others what they see.

If we go with our gut  and rely only on our learned and habitual beliefs, our untested assumptions and our prejudices … we get it wrong. When we filter reality to match our rhetoric, we leap to invalid conclusions, and we make unwise decisions, and they lead to counter-productive actions.

Our language and behaviour gives the game away … we cannot help it … because all this is happening unconsciously and out of our awareness.

So we need to solicit unfiltered feedback from trusted others who will describe what they see.  And that is tough to do.


So how do we know where to act first? Cultural or technical?

The conclusion I have come to is to use a check-list … the Safe System Improvement check-list so to speak.

Check cultural first – Is there a need to do some people stuff? If so then do it.

Check technical second – Is there a need to do some process stuff? If so then do it.

If neither are needed then we need to get out of the way and let the people redesign the processes. Only they can.

Righteous Indignation

NHS_Legal_CostsThis heading in the the newspaper today caught my eye.

Reading the rest of the story triggered a strong emotional response: anger.

My inner chimp was not happy. Not happy at all.

So I took my chimp for a walk and we had a long chat and this is the story that emerged.

The first trigger was the eye-watering fact that the NHS is facing something like a £26 billion litigation cost.  That is about a quarter of the total NHS annual budget!

The second was the fact that the litigation bill has increased by over £3 billion in the last year alone.

The third was that the extra money will just fall into a bottomless pit – the pockets of legal experts – not to where it is intended, to support overworked and demoralised front-line NHS staff. GPs, nurses, AHPs, consultants … the ones that deliver care.

That is why my chimp was so upset.  And it sounded like righteous indignation rather than irrational fear.


So what is the root cause of this massive bill? A more litigious society? Ambulance chasing lawyers trying to make a living? Dishonest people trying to make a quick buck out of a tax-funded system that cannot defend itself?

And what is the plan to reduce this cost?

Well in the article there are three parts to this:
“apologise and learn when you’re wrong,  explain and vigorously defend when we’re right, view court as a last resort.”

This sounds very plausible but to achieve it requires knowing when we are wrong or right.

How do we know?


Generally we all think we are right until we are proved wrong.

It is the way our brains are wired. We are more sure about our ‘rightness’ than the evidence suggests is justified. We are naturally optimistic about our view of ourselves.

So to be proved wrong is emotionally painful and to do it we need:
1) To make a mistake.
2) For that mistake to lead to psychological or physical harm.
3) For the harm to be identified.
4) For the cause of the harm to be traced back to the mistake we made.
5) For the evidence to be used to hold us to account, (to apologise and learn).

And that is all hunky-dory when we are individually inept and we make avoidable mistakes.

But what happens when the harm is the outcome of a combination of actions that individually are harmless but which together are not?  What if the contributory actions are sensible and are enforced as policies that we dutifully follow to the letter?

Who is held to account?  Who needs to apologise? Who needs to learn?  Someone? Anyone? Everyone? No one?

The person who wrote the policy?  The person who commissioned the policy to be written? The person who administers the policy? The person who follows the policy?

How can that happen if the policies are individually harmless but collectively lethal?


The error here is one of a different sort.

It is called an ‘error of omission’.  The harm is caused by what we did not do.  And notice the ‘we’.

What we did not do is to check the impact on others of the policies that we write for ourselves.

Example:

The governance department of a large hospital designs safety policies that if not followed lead to disciplinary action and possible dismissal.  That sounds like a reasonable way to weed out the ‘bad apples’ and the policies are adhered to.

At the same time the operations department designs flow policies (such as maximum waiting time targets and minimum resource utilisation) that if not followed lead to disciplinary action and possible dismissal.  That also sounds like a reasonable way to weed out the layabouts whose idleness cause queues and delays and the policies are adhered to.

And at the same time the finance department designs fiscal policies (such as fixed budgets and cost improvement targets) that if not followed lead to disciplinary action and possible dismissal. Again, that sounds like a reasonable way to weed out money wasters and the policies are adhered to.

What is the combined effect? The multiple safety checks take more time to complete, which puts extra workload on resources and forces up utilisation. As the budget ceiling is lowered the financial and operational pressures build, the system heats up, stress increases, corners are cut, errors slip through the safety checks. More safety checks are added and the already over-worked staff are forced into an impossible position.  Chaos ensues … more mistakes are made … patients are harmed and justifiably seek compensation by litigation.  Everyone loses (except perhaps the lawyers).


So why was my inner chimp really so unhappy?

Because none of this is necessary. This scenario is avoidable.

Reducing the pain of complaints and the cost of litigation requires setting realistic expectations to avoid disappointment and it requires not creating harm in the first place.

That implies creating healthcare systems that are inherently safe, not made not-unsafe by inspection-and-correction.

And it implies measuring and sharing intended and actual outcomes not  just compliance with policies and rates of failure to meet arbitrary and conflicting targets.

So if that is all possible and all that is required then why are we not doing it?

Simple. We never learned how. We never knew it is possible.

Counter-Productivity

coffee_table_talk_PA_150_wht_6082The Webex icon bounced up and down on Bob’s task bar signalling that Leslie had just joined the weekly ISP coaching session.

<Leslie> Hi Bob. I have been so busy this week that I have not had time to consider a topic to explore.

<Bob> No problem Leslie, I have shelf full of topics we have not touched yet.  So shall we talk about counter-productivity?

<Leslie> Don’t you mean productivity … the fourth dimension of system improvement.

<Bob>They are related of course but we will approach the issue of productivity from a different angle. Rather like we did with safety. To improve safety we considered at the causes of un-safety and focussed our efforts there.

<Leslie> Ah yes, I see.  So to improve productivity we look at the causes of un-productivity … in other words counter-productive beliefs and behaviours that are manifest as system design flaws.

<Bob> Exactly. So remind me what the definition of a productivity metric is from your FISH course.

<Leslie> Productivity is the ratio of a stream metric and a stage metric.  Value-for-Money for example.

<Bob> Good.  So counter-productivity is also a ratio of a stream and a stage metric.

<Leslie> Um, I’m not sure I quite get that. Can you explain a bit more.

<Bob> OK. To explore deeper we need to be clear about how each metric relates to our intended outcome.  Remember in safety-by-design we count the number and severity of risks and harm because  as harm is going up then safety is going down.  So harm is an un-safety stream metric.

<Leslie> Ah! Yes I see.  So if we look at cycle-time, which is a stage metric; as cycle-time increases, the activity falls and productivity falls. So cycle-time is actually a counter-productivity metric.

<Bob>Excellent. You are getting the hang of the concept of counter-productivity.

<Leslie> And we need to be careful because productivity is a ratio so the numerator and denominator metrics work in opposite ways: increasing the magnitude of the numerator is equivalent to decreasing the magnitude of the denominator – the ratio increases.

<Bob> Indeed, there are many hazards with ratios as we have explored before. So let is consider a real and rather useful example.  Let us look at Little’s Law from the perspective of counter-productivity. Remind me of the definition of Little’s Law for a single step system.

<Leslie> Little’s Law is a mathematically proven law of flow physics which states that the average lead-time is the product of the average work-in-progress and the average cycle-time.

LT = WIP * CT

<Bob> Good and I am pleased to see that you have used cycle-time. We are considering a single stream, single stage, single step system.

<Leslie> Yes, I avoided using the unqualified term ‘activity’. I have learned that lesson the hard way too!

<Bob> So how do the terms in Little’s Law relate to streams, stages and systems?

<Leslie> Lead-time is a stream metric, cycle-time is a stage metric and work-in-progress is a …. h’mm. What it is? A stream metric or a stage metric?

<Bob>Or?

<Leslie>A system metric?  WIP is a system metric!

<Bob> Good. So now re-arrange Little’s Law as a productivity formula.

<Leslie> Work-in-Progress equals lead-time divided by cycle-time

WIP = LT / CT

<Bob> So is WIP a productivity or a counter-productivity metric?

<Leslie> H’mmm …. I will need to work this through logically and step-by-step. I do not trust my intuition on this flow stuff.

Increasing cycle-time is counter-productive because it implies activity is falling while costs are not.

But cycle-time is on the bottom of the ratio so it’s effect reverses.

So if lead-time stays the same and cycle-time increases then because it is on the bottom of the ratio that implies a more productive design. And at the same time work in progress must be falling. Urrgh! This is hurting my head.

<Bob> Good, keep going … you are nearly there.

<Leslie> So a falling WIP is a sign of increasing productivity.

<Bob> Good … and that implies?

<Leslie> WIP is a counter-productivity system metric!

<Bob> Well done. Your logic is flawless.

<Leslie> So that  is why we focus on WIP so much!  Whatever causes WIP to increase is counter-productive!

Ahhhh …. that makes complete sense.

Lo-WIP  designs are more productive than Hi-WIP designs.

<Bob> Bravo!  And translating this into financial metrics … it is because a big queue of waiting work incurs costs. Storage cost, maintenance cost, processing cost and so on. So WIP is a liability. It is not an asset!

<Leslie> But doesn’t that imply treating work-in-progress as an asset on the financial balance sheet is counter-productive?

<Bob> It does indeed.

<Leslie> Oh dear! That revelation is going to upset a lot of people in the accounting department!

<Bob> The painful reality is that  the Laws of Flow Physics are completely indifferent to what any of us believe or do not believe.

<Leslie> Wow!  I like this concept of counter-productivity … it really helps to expose some of our invalid assumptions that invisibly block improvement!

<Bob> So here is a question to ponder.  Is zero WIP desirable or even possible?

<Leslie> H’mmm.  I will have to think about that.  I know you would not have asked the question for no reason.

Metamorphosis

butterfly_flying_around_465Some animals undergo a remarkable transformation on their journey to becoming an adult.

This metamorphosis is most obvious with a butterfly: the caterpillar enters the stage and a butterfly emerges.

The capabilities and behaviours of these development stages are very different.  A baby caterpillar crawls and feeds on leaves;  an adult butterfly flies and feeds on nectar.


There are many similarities to the transformation of an organisation from chaotic to calm; from depressed to enthused; and from struggling to flying.

It is the metamorphosis of individuals within organisations that drives the system change – the transformation from inept sceptics to capable advocates.


metamorphosis_1The journey starts with the tiny, hungry, baby caterpillar emerging from the egg.

This like a curious new sceptic emerging from denial and tentatively engaging the the process of learning. Usually triggered by seeing or hearing of a significant and sustained success that disproves their ‘impossibility hypothesis’.


metamorphosis_2A caterpillar is an eating machine. As it grows it sheds its skin and becomes larger. It also changes its appearance and eventually its behaviour.

Our curious improvement sceptic is devouring new information and is visibly growing in knowledge, understanding and confidence. 


metamorphosis_3When the caterpillar sheds the last skin a new form emerges. A pupa. It has a different appearance and behaviour. It is now stationary and it does not move or eat.

This is the contemplative sceptic who appears to have become dormant but is not … they are planning to change. This stage is very variable: it may be minutes or years.


metamorphosis_5Inside the pupa the solid body of the caterpillar is converted to ‘cellular soup’ and the cells are reassembled into a completely new structure called an adult butterfly.

Our healthy sceptic is dissolving their self-limiting beliefs and restructuring their mental model. It is stage of apparent confusion and success is not guaranteed.


metamorphosis_7And suddenly the adult butterfly emerges: fully formed but not yet able to fly. Its wings are not yet ready – they need to be inflated, to dry and be flexed.

So it is with our newly hatched improvement practitioner. They need to pause, prepare, and practice before they feel safe to fly solo.  They start small but are thinking big.


metamorphosis_8After a short rest the new wings are fully expanded and able to lift the butterfly aloft to explore the new opportunities that await. A whole new and exciting world full of flowers and nectar.

Our improvement practitioner can also feel when they are ready to explore. And then they fly – right first time.


An active improvement practitioner will inspire others to emerge, and many of those will hatch into improvement caterpillars who will busily munch on the new knowledge and grow in understanding and confidence. Then it goes quiet and, as if by magic, a new generation of improvement butterflies appear. And they continue to spread the word and the knowledge.

That is how Improvement Science grows and spreads – by metamorphosis.

Seeing and Believing

Flow_Science_Works[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.

StE_CallCentre_Before<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 …

StE_CallCentre_After

<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.

Spring the Trap

trapped_in_question_PA_300_wht_3174[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!

Fit-4-Purpose

F4P_PillsWe all want a healthcare system that is fit for purpose.

One which can deliver diagnosis, treatment and prognosis where it is needed, when it is needed, with empathy and at an affordable cost.

One that achieves intended outcomes without unintended harm – either physical or psychological.

We want safety, delivery, quality and affordability … all at the same time.

And we know that there are always constraints we need to work within.

There are constraints set by the Laws of the Universe – physical constraints.

These are absolute,  eternal and are not negotiable.

Dr Who’s fantastical tardis is fictional. We cannot distort space, or travel in time, or go faster than light – well not with our current knowledge.

There are also constraints set by the Laws of the Land – legal constraints.

Legal constraints are rigid but they are also adjustable.  Laws evolve over time, and they are arbitrary. We design them. We choose them. And we change them when they are no longer fit for purpose.

The third limit is often seen as the financial constraint. We are required to live within our means. There is no eternal font of  limitless funds to draw from.  We all share a planet that has finite natural resources  – and ‘grow’ in one part implies ‘shrink’ in another.  The Laws of the Universe are not negotiable. Mass, momentum and energy are conserved.

The fourth constraint is perceived to be the most difficult yet, paradoxically, is the one that we have most influence over.

It is the cultural constraint.

The collective, continuously evolving, unwritten rules of socially acceptable behaviour.


Improvement requires challenging our unconscious assumptions, our beliefs and our habits – and selectively updating those that are no longer fit-4-purpose.

To learn we first need to expose the gaps in our knowledge and then to fill them.

We need to test our hot rhetoric against cold reality – and when the fog of disillusionment forms we must rip up and rewrite what we have exposed to be old rubbish.

We need to examine our habits with forensic detachment and we need to ‘unlearn’ the ones that are limiting our effectiveness, and replace them with new habits that better leverage our capabilities.

And all of that is tough to do. Life is tough. Living is tough. Learning is tough. Leading is tough. But it energising too.

Having a model-of-effective-leadership to aspire to and a peer-group for mutual respect and support is a critical piece of the jigsaw.

It is not possible to improve a system alone. No matter how smart we are, how committed we are, or how hard we work.  A system can only be improved by the system itself. It is a collective and a collaborative challenge.


So with all that in mind let us sketch a blueprint for a leader of systemic cultural improvement.

What values, beliefs, attitudes, knowledge, skills and behaviours would be on our ‘must have’ list?

What hard evidence of effectiveness would we ask for? What facts, figures and feedback?

And with our check-list in hand would we feel confident to spot an ‘effective leader of systemic cultural improvement’ if we came across one?


This is a tough design assignment because it requires the benefit of  hindsight to identify the critical-to-success factors: our ‘must have and must do’ and ‘must not have and must not do’ lists.

H’mmmm ….

So let us take a more pragmatic and empirical approach. Let us ask …

“Are there any real examples of significant and sustained healthcare system improvement that are relevant to our specific context?”

And if we can find even just one Black Swan then we can ask …

Q1. What specifically was the significant and sustained improvement?
Q2. How specifically was the improvement achieved?
Q3. When exactly did the process start?
Q4. Who specifically led the system improvement?

And if we do this exercise for the NHS we discover some interesting things.

First let us look for exemplars … and let us start using some official material – the Monitor website (http://www.monitor.gov.uk) for example … and let us pick out ‘Foundation Trusts’ because they are the ones who are entrusted to run their systems with a greater degree of capability and autonomy.

And what we discover is a league table where those FTs that are OK are called ‘green’ and those that are Not OK are coloured ‘red’.  And there are some that are ‘under review’ so we will call them ‘amber’.

The criteria for deciding this RAG rating are embedded in a large balanced scorecard of objective performance metrics linked to a robust legal contract that provides the framework for enforcement.  Safety metrics like standardised mortality ratios, flow metrics like 18-week and 4-hour target yields, quality metrics like the friends-and-family test, and productivity metrics like financial viability.

A quick tally revealed 106 FTs in the green, 10 in the amber and 27 in the red.

But this is not much help with our quest for exemplars because it is not designed to point us to who has improved the most, it only points to who is failing the most!  The league table is a name-and-shame motivation-destroying cultural-missile fuelled by DRATs (delusional ratios and arbitrary targets) and armed with legal teeth.  A projection of the current top-down, Theory-X, burn-the-toast-then-scrape-it management-of-mediocrity paradigm. Oh dear!

However,  despite these drawbacks we could make better use of this data.  We could look at the ‘reds’ and specifically at their styles of cultural leadership and compare with a random sample of all the ‘greens’ and their models for success. We could draw out the differences and correlate with outcomes: red, amber or green.

That could offer us some insight and could give us the head start with our blueprint and check-list.


It would be a time-consuming and expensive piece of work and we do not want to wait that long. So what other avenues are there we can explore now and at no cost?

Well there are unofficial sources of information … the ‘grapevine’ … the stuff that people actually talk about.

What examples of effective improvement leadership in the NHS are people talking about?

Well a little blue bird tweeted one in my ear this week …

And specifically they are talking about a leader who has learned to walk-the-improvement-walk and is now talking-the-improvement-walk: and that is Sir David Dalton, the CEO of Salford Royal.

Here is a copy of the slides from Sir David’s recent lecture at the Kings Fund … and it is interesting to compare and contrast it with the style of NHS Leadership that led up to the Mid Staffordshire Failure, and to the Francis Report, and to the Keogh Report and to the Berwick Report.

Chalk and cheese!


So if you are an NHS employee would you rather work as part of an NHS Trust where the leaders walk-DD’s-walk and talk-DD’s-talk?

And if you are an NHS customer would you prefer that the leaders of your local NHS Trust walked Sir David’s walk too?


We are the system … we get the leaders that we deserve … we make the  choice … so we need to choose wisely … and we need to make our collective voice heard.

Actions speak louder than words.  Walk works better than talk.  We must be the change we want to see.

A Little Law and Order

teamwork_puzzle_build_PA_150_wht_2341[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.

Wacky Language

wacky_languageAll 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?

A Sisyphean Nightmare

cardiogram_heart_signal_150_wht_5748[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.

Sisyphus_Cartoon

<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 …

The 85% Optimum Occupancy Myth

egg_face_spooked_400_wht_13421There 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.

Big Data

database_transferring_data_150_wht_10400The Digital Age is changing the context of everything that we do – and that includes how we use information for improvement.

Historically we have used relatively small, but carefully collected, samples of data and we subjected these to rigorous statistical analysis. Or rather the statisticians did.  Statistics is a dark and mysterious art to most people.

As the digital age ramped up in the 1980’s the data storage, data transmission and data processing power became cheap and plentiful.  The World Wide Web appeared; desktop computers with graphical user interfaces appeared; data warehouses appeared, and very quickly we were all drowning in the data ocean.

Our natural reaction was to centralise but it became quickly obvious that even an army of analysts and statisticians could not keep up.

So our next step was to automate and Business Intelligence was born; along with its beguiling puppy-faced friend, the Performance Dashboard.

The ocean of data could now be boiled down into a dazzling collection of animated histograms, pie-charts, trend-lines, dials and winking indicators. We could slice-and-dice,  we could zoom in-and-out, and we could drill up-and-down until our brains ached.

And none of it has helped very much in making wiser decisions that lead to effective actions that lead to improved outcomes.

Why?

The reason is that the missing link was not a lack of data processing power … it was a lack of an effective data processing paradigm.

The BI systems are rooted in the closed, linear, static, descriptive statistics of the past … trend lines, associations, correlations, p-values and so on.

Real systems are open, non-linear and dynamic; they are eternally co-evolving. Nothing stays still.

And it is real systems that we live in … so we need a new data processing paradigm that suits our current reality.

Some are starting to call this the Big Data Era and it is very different.

  • Business Intelligence uses descriptive statistics and data with high information density to measure things, detect trends etc.;
  • Big Data uses inductive statistics and concepts from non-linear system identification to infer laws (regressions, non-linear relationships, and causal effects) from large data sets to reveal relationships, dependencies and perform predictions of outcomes and behaviours.

And each of us already has a powerful Big Data processor … the 1.3 kg of caveman wet-ware sitting between our ears.

Our brain processes billions of bits of data every second and looks for spatio-temporal relationships to identify patterns, to derive models, to create action options, to predict short-term outcomes and to make wise survival decisions.

The problem is that our Brainy Big Data Processor is easily tricked when we start looking at time-dependent systems … data from multiple simultaneous flows that are interacting dynamically with each other.

It did not evolve to do that … it evolved to help us to survive in the Wild – as individuals.

And it has been very successful … as the burgeoning human population illustrates.

But now we have a new collective survival challenge  and we need new tools … and the out-of-date Business Intelligence Performance Dashboard is just not going to cut the mustard!

Big Data on TED Talks

 

Perfect Storm

lightning_strike_150_wht_5809[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.

Seeing-by-Doing

OneStopBeforeGanttFlow 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.

 

Economy-of-Scale vs Economy-of-Flow

We_Need_Small_HospitalsThis was an interesting headline to see on the front page of a newspaper yesterday!

The Top Man of the NHS is openly challenging the current Centralisation-is-The-Only-Way-Forward Mantra;  and for good reason.

Mass centralisation is poor system design – very poor.

Q: So what is driving the centralisation agenda?

A: Money.

Or to be more precise – rather simplistic thinking about money.

The misguided money logic goes like this:

1. Resources (such as highly trained doctors, nurses and AHPs) cost a lot of money to provide.
[Yes].

2. So we want all these resources to be fully-utilised to get value-for-money.
[No, not all – just the most expensive].

3. So we will gather all the most expensive resources into one place to get the Economy-of-Scale.
[No, not all the most expensive – just the most specialised]

4. And we will suck /push all the work through these super-hubs to keep our expensive specialist resources busy all the time.
[No, what about the growing population of older folks who just need a bit of expert healthcare support, quickly, and close to home?]

This flawed logic confuses two complementary ways to achieve higher system productivity/economy/value-for-money without  sacrificing safety:

Economies of Scale (EoS) and Economies of Flow (EoF).

Of the two the EoF is the more important because by using EoF principles we can increase productivity in huge leaps at almost no cost; and without causing harm and disappointment. EoS are always destructive.

But that is impossible. You are talking rubbish … because if it were possible we would be doing it!

It is not impossible and we are doing it … but not at scale and pace in healthcare … and the reason for that is we are not trained in Economy-of-Flow methods.

And those who are trained and who have have experienced the effects of EoF would not do it any other way.

Example:

In a recent EoF exercise an ISP (Improvement Science Practitioner) helped a surgical team to increase their operating theatre productivity by 30% overnight at no cost.  The productivity improvement was measured and sustained for most of the last year. [it did dip a bit when the waiting list evaporated because of the higher throughput, and again after some meddlesome middle management madness was triggered by end-of-financial-year target chasing].  The team achieved the improvement using Economy of Flow principles and by re-designing some historical scheduling policies. The new policies  were less antagonistic. They were designed to line the ducks up and as a result the flow improved.


So the specific issue of  Super Hospitals vs Small Hospitals is actually an Economy of Flow design challenge.

But there is another critical factor to take into account.

Specialisation.

Medicine has become super-specialised for a simple reason: it is believed that to get ‘good enough’ at something you have to have a lot of practice. And to get the practice you have to have high volumes of the same stuff – so you need to specialise and then to sort undifferentiated work into separate ‘speciologist’ streams or sequence the work through separate speciologist stages.

Generalists are relegated to second-class-citizen status; mere tripe-skimmers and sign-posters.

Specialisation is certainly one way to get ‘good enough’ at doing something … but it is not the only way.

Another way to learn the key-essentials from someone who already knows (and can teach) and then to continuously improve using feedback on what works and what does not – feedback from everywhere.

This second approach is actually a much more effective and efficient way to develop expertise – but we have not been taught this way.  We have only learned the scrape-the-burned-toast-by-suck-and-see method.

We need to experience another way.

We need to experience rapid acquisition of expertise!

And being able to gain expertise quickly means that we can become expert generalists.

There is good evidence that the broader our skill-set the more resilient we are to change, and the more innovative we are when faced with novel challenges.

In the Navy of the 1800’s sailors were “Jacks of All Trades and Master of One” because if only one person knew how to navigate and they got shot or died of scurvy the whole ship was doomed.  Survival required resilience and that meant multi-skilled teams who were good enough at everything to keep the ship afloat – literally.


Specialisation has another big drawback – it is very expensive and on many dimensions. Not just Finance.

Example:

Suppose we have six-step process and we have specialised to the point where an individual can only do one step to the required level of performance (safety/flow/quality/productivity).  The minimum number of people we need is six and the process only flows when we have all six people. Our minimum costs are high and they do not scale with flow.

If any one of the six are not there then the whole process stops. There is no flow.  So queues build up and smooth flow is sacrificed.

Out system behaves in an unstable and chaotic feast-or-famine manner and rapidly shifting priorities create what is technically called ‘thrashing’.

And the special-six do not like the constant battering.

And the special-six have the power to individually hold the whole system to ransom – they do not even need to agree.

And then we aggravate the problem by paying them the high salary that it is independent of how much they collectively achieve.

We now have the perfect recipe for a bigger problem!  A bunch of grumpy, highly-paid specialists who blame each other for the chaos and who incessantly clamour for ‘more resources’ at every step.

This is not financially viable and so creates the drive for economy-of-scale thinking in which to get us ‘flow resilience’ we need more than one specialist at each of the six steps so that if one is on holiday or off sick then the process can still flow.  Let us call these tribes of ‘speciologists’ there own names and budgets, and now we need to put all these departments somewhere – so we will need a big hospital to fit them in – along with the queues of waiting work that they need.

Now we make an even bigger design blunder.  We assume the ‘efficiency’ of our system is the same as the average utilisation of all the departments – so we trim budgets until everyone’s utilisation is high; and we suck any-old work in to ensure there is always something to do to keep everyone busy.

And in so doing we sacrifice all our Economy of Flow opportunities and we then scratch our heads and wonder why our total costs and queues are escalating,  safety and quality are falling, the chaos continues, and our tribes of highly-paid specialists are as grumpy as ever they were!   It must be an impossible-to-solve problem!


Now contrast that with having a pool of generalists – all of whom are multi-skilled and can do any of the six steps to the required level of expertise.  A pool of generalists is a much more resilient-flow design.

And the key phrase here is ‘to the required level of expertise‘.

That is how to achieve Economy-of-Flow on a small scale without compromising either safety or quality.

Yes, there is still a need for a super-level of expertise to tackle the small number of complex problems – but that expertise is better delivered as a collective-expertise to an individual problem-focused process.  That is a completely different design.

Designing and delivering a system that that can achieve the synergy of the pool-of-generalists and team-of-specialists model requires addressing a key error of omission first: we are not trained how to do this.

We are not trained in Complex-Adaptive-System Improvement-by-Design.

So that is where we must start.

 

SuDoKu

sudokuAn Improvement-by-Design challenge is very like a Sudoku puzzle. The rules are deceptively simple but the solving the puzzle is not so simple.

For those who have never tried a Sudoku puzzle the objective is to fill in all the empty boxes with a number between 1 and 9. The constraint is that each row, column and 3×3 box (outlined in bold) must include all the numbers between 1 and 9 i.e. no duplicates.

What you will find when you try is that, at each point in the puzzle solving process there are more than one choice for  most empty cells.

The trick is to find the empty cells that have only one option and fill those in. That changes the puzzle and makes it ‘easier’.

And when you keep following this strategy, and so long as you do not make any mistakes, then you will solve the puzzle.  It just takes concentration, attention to detail, and discipline.

In the example above, the top-right cell in the left-box on the middle-row can only hold a 6; and the top-middle cell in the middle-box on the bottom-row must be a 3.

So we can see already there are three ways ‘into’ the solution – put the 6 in and see where that takes us; put the 3 in and see where that takes us; or put both in and see where that takes us.

The final solution will be the same – so there are multiple paths from where we are to our objective.  Some may involve more mental work than others but all will involve completing the same number of empty cells.

What is also clear is that the sequence order that we complete the empty cells is not arbitrary. Usually the boxes and rows with the fewest empty cells get competed earlier and those with the most empty cells at the start get completed later.

And even if the final configuration is the same, if we start with a different set of missing cells the solution path will be different. It may be very easy, very hard or even impossible without some ‘guessing’ and hoping for the best.


Exactly the same is true of improvement-by-design challenges.

The rules of flow science  are rather simple; but when we have a system of parallel streams (the rows) interacting with parallel stages (the columns); and when we have safety, delivery, and economy constraints to comply with at every part of the system … then finding and ‘improvement plan’ that will deliver our objective is a tough challenge.

But it is possible with concentration, attention-to-detail and discipline; and that requires some flow science training and some improvement science practice.

OK – I am off for lunch and then maybe indulge in a Sudoku puzzle or two – just for fun – and then maybe design an improvement plan to two – just for fun!

 

Alignment of Purpose

woman_back_and_forth_questions_150_wht_12477<Lesley> Hi Bob, how are you today?

<Bob> I’m OK thanks Lesley. Having a bit of a break from the daily grind.

<Lesley> Oh! I am sorry, I had no idea you were on holiday. I will call when you are back at work.

<Bob> No need Lesley. Our chats are always a welcome opportunity to reflect and learn.

<Lesley> OK, if you are sure.  The top niggle on my list at the moment is that I do not feel my organisation values what I do.

<Bob> OK. Have you done the diagnostic Right-2-Left Map® backwards from that top niggle?

<Lesley>Yes. The final straw was that I was asked to justify my improvement role.

<Bob> OK, and before that?

<Lesley> There have been some changes in the senior management team.

<Bob> OK. This sounds like the ‘New Brush Sweeps Clean’ effect.

<Lesley> I have heard that phrase before. What does it mean in this context?

<Bob> Senior management changes are very disruptive events. The more senior the change the more disruptive it is.  Let us call it a form of ‘Disruptive Innovation’.  The trigger for the change is important.  One trigger might be a well-respected and effective leader retiring or moving to an even more senior role.  This leaves a leadership gap which is an opportunity for someone to grow and develop.  Another trigger might be a less-respected  and ineffective leader moving on and leaving a trail of rather-too-visible failures. It is the latter tends to be associated with the New Broom effect.

<Lesley> How is that?

<Bob>Well, put yourself in the shoes of the New Leader who has inherited a Trail of Disappointment – you need to establish your authority and expectation quickly and decisively. Ambiguity and lack of clarity will only contribute to further disappointment.  So you have to ask everyone to justify what they do.  And if they cannot then you need to know that.  And if they can then you need to decide if what they do is aligned with your purpose.  This is the New Brush.

<Lesley> So what if I can justify what I do and that does not fit with the ‘New Leader’s Plan’?

<Bob> If what you do is aligned to your Life Purpose but not with the New Brush then you have to choose.  And experience shows that the road to long term personal happiness is the one the aligns with your individual purpose.  And often it is just a matter of timing. The New Brush is indiscriminate and impatient – anything that does not fit neatly into the New Plan has to go.

<Lesley> OK my purpose is to improve the safety, flow, quality and productivity of healthcare processes – for the benefit of all. That is not negotiable. It is what fires my passion and fuels my day.  So does it matter really where or how I do that?

<Bob> Not really.  You do need be mindful of the pragmatic constraints though … your life circumstances.  There are many paths to your Purpose, so it is wise to choose one that is low enough risk to both you and those you love.

<Lesley> Ah! Now I see why you say that timing is important. You need to prepare to be able to make the decision.  You do not what to be caught by surprise and off balance.

<Bob>Yes. That is why as an ISP you always start with your own Purpose and your own Right-2-Left Map®.  Then you will know what to prepare and in what order so that you have the maximum number of options when you have to make a choice.  Sometimes the Universe will create the trigger and sometimes you have to initiate it yourself.

<Lesley> So this is just another facet of Improvement Science?

<Bob>  Yes.

Firewall

buncefield_fireFires are destructive, indifferent, and they can grow and spread very fast.

The picture is of  the Buncefield explosion and conflagration that occurred on 11th December 2005 near Hemel Hempstead in the UK.  The root cause was a faulty switch that failed to prevent tank number 912 from being overfilled. This resulted in an initial 300 gallon petrol spill which created the perfect conditions for an air-fuel explosion.  The explosion was triggered by a spark and devastated the facility. Over 2000 local residents needed to be evacuated and the massive fuel fire took days to bring under control. The financial cost of the accident has been estimated to run into tens of millions of pounds.

The Great Fire of London in September 1666 led directly to the adoption of new building standards – notably brick and stone instead of wood because they are more effective barriers to fire.

A common design to limit the spread of a fire is called a firewall.

And we use the same principle in computer systems to limit the spread of damage when a computer system goes out of control.


Money is the fuel that keeps the wheels of healthcare systems turning.  And healthcare is an expensive business so every drop of cash-fuel is precious.  Healthcare is also a risky business – from both a professional and a financial perspective. Mistakes can quickly lead to loss of livelihood, expensive recovery plans and huge compensation claims. The social and financial equivalent of a conflagration.

Financial fires spread just like real ones – quickly. So it makes good sense not to have all the cash-fuel in one big pot.  It makes sense to distribute it to smaller pots – in each department – and to distribute the cash-fuel intermittently. These cash-fuel silos are separated by robust financial firewalls and they are called Budgets.

The social sparks that ignite financial fires are called ‘Niggles‘.  They are very numerous but we have effective mechanisms for containing them. The problem happens when a multiple sparks happen at the same time and place and together create a small chain reaction. Then we get a complaint. A ‘Not Again‘.  And we are required to spend some of our precious cash-fuel investigating and apologizing.  We do not deal with the root cause, we just scrape the burned toast.

And then one day the chain reaction goes a bit further and we get a ‘Near Miss‘.  That has a different  reporting mechanism so it stimulates a bigger investigation and it usually culminates in some recommendations that involve more expensive checking, documenting and auditing of the checking and documentation.  The root cause, the Niggles, go untreated – because there are too many of them.

But this check-and-correct reaction is also  expensive and we need even more cash-fuel to keep the organizational engine running – but we do not have any more. Our budgets are capped. So we start cutting corners. A bit here and a bit there. And that increases the risk of more Niggles, Not Agains, and Near Misses.

Then the ‘Never Event‘ happens … a Safety and Quality catastrophe that triggers the financial conflagration and toasts the whole organization.


So although our financial firewalls, the Budgets, are partially effective they also have downsides:

1. Paradoxically they can create the perfect condition for a financial conflagration when too small a budget leads to corner-cutting on safety.

2. They lead to ‘off-loading’ which means that too-expensive-to-solve problems are chucked over the financial firewalls into the next department.  The cost is felt downstream of the source – in a different department – and is often much larger. The sparks are blown downwind.

For example: a waiting list management department is under financial pressure and is running short staffed as a recruitment freeze has been imposed. The overburdening of the remaining staff leads to errors in booking patients for operations. The knock on effect that is patients being cancelled on the day and the allocated operating theatre time is wasted.  The additional cost of wasted theatre time is orders of magnitude greater than the cost-saving achieved in the upstream stage.  The result is a lower quality service, a greater cost to the whole system, and the risk that safety corners will be cut leading to a Near Miss or a Never Event.

The nature of real systems is that small perturbations can be rapidly amplified by a ‘tight’ financial design to create a very large and expensive perturbation called a ‘catastrophe’.  A silo-based financial budget design with a cost-improvement thumbscrew feature increases the likelihood of this universally unwanted outcome.

So if we cannot use one big fuel tank or multiple, smaller, independent fuel tanks then what is the solution?

We want to ensure smooth responsiveness of our healthcare engine, we want healthcare  cash-fuel-efficiency and we want low levels of toxic emissions (i.e. complaints) at the same time. How can we do that?

Fuel-injection.

fuel_injectorsElectronic Fuel Injection (EFI) designs have now replaced the old-fashioned, inefficient, high-emission  carburettor-based engines of the 1970’s and 1980’s.

The safer, more effective and more efficient cash-flow design is to inject the cash-fuel where and when it is needed and in just the right amount.

And to do that we need to have a robust, reliable and rapid feedback system that controls the cash-injectors.

But we do not have such a feedback system in healthcare so that is where we need to start our design work.

Designing an automated cash-injection system requires understanding how the Seven Flows of any  system work together and the two critical flows are Data Flow and Cash Flow.

And that is possible.

Our Iceberg Is Melting

hold_your_ground_rope_300_wht_6223[Dring Dring] The telephone soundbite announced the start of the coaching session.

<Bob> Good morning Leslie. How are you today?

<Leslie> I have been better.

<Bob> You seem upset. Do you want to talk about it?

<Leslie> Yes, please. The trigger for my unhappiness is that last week I received an email demanding that I justify the time I spend doing improvement work and  a summons to a meeting to ‘discuss some issues that have been raised‘.

<Bob> OK. I take it that you do not know what or who has triggered this inquiry.

<Leslie> You are correct. My working hypothesis is that it is the end of the financial year and budget holders are looking for opportunities to do some pruning – to meet their cost improvement program targets!

<Bob> So what is the problem? You have shared the output of your work. You have demonstrated significant improvements in safety, flow, quality and productivity and you have described both them and the methodology clearly.

<Leslie> I know. That us why I was so upset to get this email. It is as if everything that we have achieved has been ignored. It is almost as if it is resented.

<Bob> Ah! You may well be correct.  This is the nature of paradigm shifts. Those who have the greatest vested interest in the current paradigm get spooked when they feel it start to wobble. Each time you share the outcome of your improvement work you create emotional shock-waves. The effects are cumulative and eventually there will be is a ‘crisis of confidence’ in those who feel most challenged by the changes that you are demonstrating are possible.  The whole process is well described in Thomas Kuhn’s The Structure of Scientific Revolutions. That is not a book for an impatient reader though – for those who prefer something lighter I recommend “Our Iceberg is Melting” by John Kotter.

<Leslie> Thanks Bob. I will get a copy of Kotter’s book – that sounds more my cup of tea. Will that tell me what to do?

<Bob> It is a parable – a fictional story of a colony of penguins who discover that their iceberg is melting and are suddenly faced with a new and urgent potential risk of not surviving the storms of the approaching winter. It is not a factual account of a real crisis or a step-by-step recipe book for solving all problems  – it describes some effective engagement strategies in general terms.

<Leslie> I will still read it. What I need is something more specific to my actual context.

<Bob> This is an improvement-by-design challenge. The only difference from the challenges you have done already is that this time the outcome you are looking for is a smooth transition from the ‘old’ paradigm to the ‘new’ one.  Kuhn showed that this transition will not start to happen until there is a new paradigm because individuals choose to take the step from the old to the new and they do not all do that at the same time.  Your work is demonstrating that there is a new paradigm. Some will love that message, some will hate it. Rather like Marmite.

<Leslie> Yes, that make sense.  But how do I deal with an unseen enemy who is stirring up trouble behind my back?

<Bob> Are you are referring to those who have ‘raised some issues‘?

<Leslie> Yes.

<Bob> They will be the ones who have most invested in the current status quo and they will not be in senior enough positions to challenge you directly so they are going around spooking the inner Chimps of those who can. This is expected behaviour when the relentlessly changing reality starts to wobble the concrete current paradigm.

<Leslie> Yes! That is  exactly how it feels.

<Bob> The danger lurking here is that your inner Chimp is getting spooked too and is conjuring up Gremlins and Goblins from the Computer! Left to itself your inner Chimp will steer you straight into the Victim Vortex.  So you need to take it for a long walk, let it scream and wave its hairy arms about, listen to it, and give it lots of bananas to calm it down. Then put your put your calmed-down Chimp into its cage and your ‘paradigm transition design’ into the Computer. Only then will you be ready for the ‘so-justify-yourself’ meeting.  At the meeting your Chimp will be out of its cage like a shot and interpreting everything as a threat. It will disable you and go straight to the Computer for what to do – and it will read your design and follow the ‘wise’ instructions that you have put in there.

<Leslie> Wow! I see how you are using the Chimp Paradox metaphor to describe an incredibly complex emotional process in really simple language. My inner Chimp is feeling happier already!

<Bob> And remember that you are in all in the same race. Your collective goal is to cross the finish line as quickly as possible with the least chaos, pain and cost.  You are not in a battle – that is lose-lose inner Chimp thinking.  The only message that your interrogators must get from you is ‘Win-win is possible and here is how we can do it‘. That will be the best way to soothe their inner Chimps – the ones who fear that you are going to sink their boat by rocking it.

<Leslie> That is really helpful. Thank you again Bob. My inner Chimp is now snoring gently in its cage and while it is asleep I have some Improvement-by-Design work to do and then some Computer programming.

Jiggling

hurry_with_the_SFQP_kit[Dring] Bob’s laptop signaled the arrival of Leslie for their regular ISP remote coaching session.

<Bob> Hi Leslie. Thanks for emailing me with a long list of things to choose from. It looks like you have been having some challenging conversations.

<Leslie> Hi Bob. Yes indeed! The deepening gloom and the last few blog topics seem to be polarising opinion. Some are claiming it is all hopeless and others, perhaps out of desperation, are trying the FISH stuff for themselves and discovering that it works.  The ‘What Ifs’ are engaged in war of words with the ‘Yes Buts’.

<Bob> I like your metaphor! Where would you like to start on the long list of topics?

<Leslie> That is my problem. I do not know where to start. They all look equally important.

<Bob> So, first we need a way to prioritise the topics to get the horse-before-the-cart.

<Leslie> Sounds like a good plan to me!

<Bob> One of the problems with the traditional improvement approaches is that they seem to start at the most difficult point. They focus on ‘quality’ first – and to be fair that has been the mantra from the gurus like W.E.Deming. ‘Quality Improvement’ is the Holy Grail.

<Leslie>But quality IS important … are you saying they are wrong?

<Bob> Not at all. I am saying that it is not the place to start … it is actually the third step.

<Leslie>So what is the first step?

<Bob> Safety. Eliminating avoidable harm. Primum Non Nocere. The NoNos. The Never Events. The stuff that generates the most fear for everyone. The fear of failure.

<Leslie> You mean having a service that we can trust not to harm us unnecessarily?

<Bob> Yes. It is not a good idea to make an unsafe design more efficient – it will deliver even more cumulative harm!

<Leslie> OK. That makes perfect sense to me. So how do we do that?

<Bob> It does not actually matter.  Well-designed and thoroughly field-tested checklists have been proven to be very effective in the ‘ultra-safe’ industries like aerospace and nuclear.

<Leslie> OK. Something like the WHO Safe Surgery Checklist?

<Bob> Yes, that is a good example – and it is well worth reading Atul Gawande’s book about how that happened – “The Checklist Manifesto“.  Gawande is a surgeon who had published a lot on improvement and even so was quite skeptical that something as simple as a checklist could possibly work in the complex world of surgery. In his book he describes a number of personal ‘Ah Ha!’ moments that illustrate a phenomenon that I call Jiggling.

<Leslie> OK. I have made a note to read Checklist Manifesto and I am curious to learn more about Jiggling – but can we stick to the point? Does quality come after safety?

<Bob> Yes, but not immediately after. As I said, Quality is the third step.

<Leslie> So what is the second one?

<Bob> Flow.

There was a long pause – and just as Bob was about to check that the connection had not been lost – Leslie spoke.

<Leslie> But none of the Improvement Schools teach basic flow science.  They all focus on quality, waste and variation!

<Bob> I know. And attempting to improve quality before improving flow is like papering the walls before doing the plastering.  Quality cannot grow in a chaotic context. The flow must be smooth before that. And the fear of harm must be removed first.

<Leslie> So the ‘Improving Quality through Leadership‘ bandwagon that everyone is jumping on will not work?

<Bob> Well that depends on what the ‘Leaders’ are doing. If they are leading the way to learning how to design-for-safety and then design-for-flow then the bandwagon might be a wise choice. If they are only facilitating collaborative agreement and group-think then they may be making an unsafe and ineffective system more efficient which will steer it over the edge into faster decline.

<Leslie>So, if we can stabilize safety using checklists do we focus on flow next?

<Bob>Yup.

<Leslie> OK. That makes a lot of sense to me. So what is Jiggling?

<Bob> This is Jiggling. This conversation.

<Leslie> Ah, I see. I am jiggling my understanding through a series of ‘nudges’ from you.

<Bob>Yes. And when the learning cogs are a bit rusty, some Improvement Science Oil and a bit of Jiggling is more effective and much safer than whacking the caveman wetware with a big emotional hammer.

<Leslie>Well the conversation has certainly jiggled Safety-Flow-Quality-and-Productivity into a sensible order for me. That has helped a lot. I will sort my to-do list into that order and start at the beginning. Let me see. I have a plan for safety, now I can focus on flow. Here is my top flow niggle. How do I design the resource capacity I need to ensure the flow is smooth and the waiting times are short enough to avoid ‘persecution’ by the Target Time Police?

<Bob> An excellent question! I will send you the first ISP Brainteaser that will nudge us towards an answer to that question.

<Leslie> I am ready and waiting to have my brain-teased and my niggles-nudged!

The Speed of Trust

London_UndergroundSystems are built from intersecting streams of work called processes.

This iconic image of the London Underground shows a system map – a set of intersecting transport streams.

Each stream links a sequence of independent steps – in this case the individual stations.  Each step is a system in itself – it has a set of inner streams.

For a system to exhibit stable and acceptable behaviour the steps must be in synergy – literally ‘together work’. The steps also need to be in synchrony – literally ‘same time’. And to do that they need to be aligned to a common purpose.  In the case of a transport system the design purpose is to get from A to B safety, quickly, in comfort and at an affordable cost.

In large socioeconomic systems called ‘organisations’ the steps represent groups of people with special knowledge and skills that collectively create the desired product or service.  This creates an inevitable need for ‘handoffs’ as partially completed work flows through the system along streams from one step to another. Each step contributes to the output. It is like a series of baton passes in a relay race.

This creates the requirement for a critical design ingredient: trust.

Each step needs to be able to trust the others to do their part:  right-first-time and on-time.  All the steps are directly or indirectly interdependent.  If any one of them is ‘untrustworthy’ then the whole system will suffer to some degree. If too many generate dis-trust then the system may fail and can literally fall apart. Trust is like social glue.

So a critical part of people-system design is the development and the maintenance of trust-bonds.

And it does not happen by accident. It takes active effort. It requires design.

We are social animals. Our default behaviour is to trust. We learn distrust by experiencing repeated disappointments. We are not born cynical – we learn that behaviour.

The default behaviour for inanimate systems is disorder – and it has a fancy name – it is called ‘entropy’. There is a Law of Physics that says that ‘the average entropy of a system will increase over time‘. The critical word is ‘average’.

So, if we are not aware of this and we omit to pay attention to the hand-offs between the steps we will observe increasing disorder which leads to repeated disappointments and erosion of trust. Our natural reaction then is ‘self-protect’ which implies ‘check-and-reject’ and ‘check and correct’. This adds complexity and bureaucracy and may prevent further decline – which is good – but it comes at a cost – quite literally.

Eventually an equilibrium will be achieved where our system performance is limited by the amount of check-and-correct bureaucracy we can afford.  This is called a ‘mediocrity trap’ and it is very resilient – which means resistant to change in any direction.


To escape from the mediocrity trap we need to break into the self-reinforcing check-and-reject loop and we do that by developing a design that challenges ‘trust eroding behaviour’.  The strategy is to develop a skill called  ‘smart trust’.

To appreciate what smart trust is we need to view trust as a spectrum: not as a yes/no option.

At one end is ‘nonspecific distrust’ – otherwise known as ‘cynical behaviour’. At the other end is ‘blind trust’ – otherwise  known and ‘gullible behaviour’.  Neither of these are what we need.

In the middle is the zone of smart trust that spans healthy scepticism  through to healthy optimism.  What we need is to maintain a balance between the two – not to eliminate them. This is because some people are ‘glass-half-empty’ types and some are ‘glass-half-full’. And both views have a value.

The action required to develop smart trust is to respectfully challenge every part of the organisation to demonstrate ‘trustworthiness’ using evidence.  Rhetoric is not enough. Politicians always score very low on ‘most trusted people’ surveys.

The first phase of this smart trust development is for steps to demonstrate trustworthiness to themselves using their own evidence, and then to share this with the steps immediately upstream and downstream of them.

So what evidence is needed?

SFQP1Safety comes first. If a step cannot be trusted to be safe then that is the first priority. Safe systems need to be designed to be safe.

Flow comes second. If the streams do not flow smoothly then we experience turbulence and chaos which increases stress,  the risk of harm and creates disappointment for everyone. Smooth flow is the result of careful  flow design.

Third is Quality which means ‘setting and meeting realistic expectations‘.  This cannot happen in an unsafe, chaotic system.  Quality builds on Flow which builds on Safety. Quality is a design goal – an output – a purpose.

Fourth is Productivity (or profitability) and that does not automatically follow from the other three as some QI Zealots might have us believe. It is possible to have a safe, smooth, high quality design that is unaffordable.  Productivity needs to be designed too.  An unsafe, chaotic, low quality design is always more expensive.  Always. Safe, smooth and reliable can be highly productive and profitable – if designed to be.

So whatever the driver for improvement the sequence of questions is the same for every step in the system: “How can I demonstrate evidence of trustworthiness for Safety, then Flow, then Quality and then Productivity?”

And when that happens improvement will take off like a rocket. That is the Speed of Trust.  That is Improvement Science in Action.

The Time Trap

clock_hands_spinning_import_150_wht_3149[Hmmmmmm] The desk amplified the vibration of Bob’s smartphone as it signaled the time for his planned e-mentoring session with Leslie.

[Dring Dring]

<Bob> Hi Leslie, right-on-time, how are you today?

<Leslie> Good thanks Bob. I have a specific topic to explore if that is OK. Can we talk about time traps.

<Bob> OK – do you have a specific reason for choosing that topic?

<Leslie> Yes. The blog last week about ‘Recipe for Chaos‘ set me thinking and I remembered that time-traps were mentioned in the FISH course but I confess, at the time, I did not understand them. I still do not.

<Bob> Can you describe how the ‘Recipe for Chaos‘ blog triggered this renewed interest in time-traps?

<Leslie> Yes – the question that occurred to me was: ‘Is a time-trap a recipe for chaos?’

<Bob> A very good question! What do you feel the answer is?

<Leslie>I feel that time-traps can and do trigger chaos but I cannot explain how. I feel confused.

<Bob>Your intuition is spot on – so can you localize the source of your confusion?

<Leslie>OK. I will try. I confess I got the answer to the MCQ correct by guessing – and I wrote down the answer when I eventually guessed correctly – but I did not understand it.

<Bob>What did you write down?

<Leslie>“The lead time is independent of the flow”.

<Bob>OK. That is accurate – though I agree it is perhaps a bit abstract. One source of confusion may be that there are different causes of of time-traps and there is a lot of overlap with other chaos-creating policies. Do you have a specific example we can use to connect theory with reality?

<Leslie> OK – that might explain my confusion.  The example that jumped to mind is the RTT target.

<Bob> RTT?

<Leslie> Oops – sorry – I know I should not use undefined abbreviations. Referral to Treatment Time.

<Bob> OK – can you describe what you have mapped and measured already?

<Leslie> Yes.  When I plot the lead-time for patients in date-of-treatment order the process looks stable but the histogram is multi-modal with a big spike just underneath the RTT target of 18 weeks. What you describe as the ‘Horned Gaussian’ – the sign that the performance target is distorting the behaviour of the system and the design of the system is not capable on its own.

<Bob> OK and have you investigated why there is not just one spike?

<Leslie> Yes – the factor that best explains that is the ‘priority’ of the referral.  The  ‘urgents’ jump in front of the ‘soons’ and both jump in front of the ‘routines’. The chart has three overlapping spikes.

<Bob> That sounds like a reasonable policy for mixed-priority demand. So what is the problem?

<Leslie> The ‘Routine’ group is the one that clusters just underneath the target. The lead time for routines is almost constant but most of the time those patients sit in one queue or another being leap-frogged by other higher-priority patients. Until they become high-priority – then they do the leap frogging.

<Bob> OK – and what is the condition for a time trap again?

<Leslie> That the lead time is independent of flow.

<Bob>Which implies?

<Leslie> Um. let me think. That the flow can be varying but the lead time stays the same?

<Bob> Yup. So is the flow of routine referrals varying?

<Leslie> Not over the long term. The chart is stable.

<Bob> What about over the short term? Is demand constant?

<Leslie>No of course not – it varies – but that is expected for all systems. Constant means ‘over-smoothed data’ – the Flaw of Averages trap!

<Bob>OK. And how close is the average lead time for routines to the RTT maximum allowable target?

<Leslie> Ah! I see what you mean. The average is about 17 weeks and the target is 18 weeks.

<Bob>So what is the flow variation on a week-to-week time scale?

<Leslie>Demand or Activity?

<Bob>Both.

<Leslie>H’mm – give me a minute to re-plot flow as a weekly-aggregated chart. Oh! I see what you mean – both the weekly activity and demand are both varying widely and they are not in sync with each other. Work in progress must be wobbling up and down a lot! So how can the lead time variation be so low?

<Bob>What do the flow histograms look like?

<Leslie> Um. Just a second. That is weird! They are both bi-modal with peaks at the extremes and not much in the middle – the exact opposite of what I expected to see! I expected a centered peak.

<Bob>What you are looking at is the characteristic flow fingerprint of a chaotic system – it is called ‘thrashing’.

<Leslie> So I was right!

<Bob> Yes. And now you know the characteristic pattern to look for. So what is the policy design flaw here?

<Leslie>The DRAT – the delusional ratio and arbitrary target?

<Bob> That is part of it – that is the external driver policy. The one you cannot change easily. What is the internally driven policy? The reaction to the DRAT?

<Leslie> The policy of leaving routine patients until they are about to breach then re-classifying them as ‘urgent’.

<Bob>Yes! It is called a ‘Prevarication Policy’ and it is surprisingly and uncomfortably common. Ask yourself – do you ever prevaricate? Do you ever put off ‘lower priority’ tasks until later and then not fill the time freed up with ‘higher priority tasks’?

<Leslie> OMG! I do that all the time! I put low priority and unexciting jobs on a ‘to do later’ heap but I do not sit idle – I do then focus on the high priority ones.

<Bob> High priority for whom?

<Leslie> Ah! I see what you mean. High priority for me. The ones that give me the biggest reward! The fun stuff or the stuff that I get a pat on the back for doing or that I feel good about.

<Bob> And what happens?

<Leslie> The heap of ‘no-fun-for-me-to-do’ jobs gets bigger and I await the ‘reminders’ and then have to rush round in a mad panic to avoid disappointment, criticism and blame. It feels chaotic. I get grumpy. I make more mistakes and I deliver lower-quality work. If I do not get a reminder I assume that the job was not that urgent after all and if I am challenged I claim I am too busy doing the other stuff.

<Bob> Have you avoided disappointment?

<Leslie> Ah! No – that I needed to be reminded meant that I had already disappointed. And when I do not get a reminded does not prove I have not disappointed either. Most people blame rather than complain. I have just managed to erode other people’s trust in my reliability. I have disappointed myself. I have achieved exactly the opposite of what I intended. Drat!

<Bob> So what is the reason that you work this way? There will be a reason.  A good reason.

<Leslie> That is a very good question! I will reflect on that because I believe it will help me understand why others behave this way too.

<Bob> OK – I will be interested to hear your conclusion.  Let us return to the question. What is the  downside of a ‘Prevarication Policy’?

<Leslie> It creates stress, chaos, fire-fighting, last minute changes, increased risk of errors,  more work and it erodes both quality, confidence and trust.

<Bob>Indeed so – and the impact on productivity?

<Leslie> The activity falls, the system productivity falls, revenue falls, queues increase, waiting times increase and the chaos increases!

<Bob> And?

<Leslie> We treat the symptoms by throwing resources at the problem – waiting list initiatives – and that pushes our costs up. Either way we are heading into a spiral of decline and disappointment. We do not address the root cause.

<Bob> So what is the way out of chaos?

<Leslie> Reduce the volume on the destabilizing feedback loop? Stop the managers meddling!

<Bob> Or?

<Leslie> Eh? I do not understand what you mean. The blog last week said management meddling was the problem.

<Bob> It is a problem. How many feedback loops are there?

<Leslie> Two – that need to be balanced.

<Bob> So what is another option?

<Leslie> OMG! I see. Turn UP the volume of the stabilizing feedback loop!

<Bob> Yup. And that is a lot easier to do in reality. So that is your other challenge to reflect on this week. And I am delighted to hear you using the terms ‘stabilizing feedback loop’ and ‘destabilizing feedback loop’.

<Leslie> Thank you. That was a lesson for me after last week – when I used the terms ‘positive and negative feedback’ it was interpreted in the emotional context – positive feedback as encouragement and negative feedback as criticism.  So ‘reducing positive feedback’ in that sense is the exact opposite of what I was intending. So I switched my language to using ‘stabilizing and destabilizing’ feedback loops that are much less ambiguous and the confusion and conflict disappeared.

<Bob> That is very useful learning Leslie … I think I need to emphasize that distinction more in the blog. That is one advantage of online media – it can be updated!

 <Leslie> Thanks again Bob!  And I have the perfect opportunity to test a new no-prevarication-policy design – in part of the system that I have complete control over – me!

The Recipe for Chaos

boxes_group_PA4_150_wht_4916There are only four ingredients required to create Chaos.

The first is Time.

All processes and systems are time-dependent.

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

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

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

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

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

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

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

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

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

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

For example:

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

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

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

The destabilizing ingredient is the hit-the-arbitrary-target policy which drives a performance management feedback loop.

This policy typically involves:
(1) Setting a performance target that is desirable but impossible for the current design to achieve reliably;
(2) inspecting how close to the target we are; then
(3) using the real-time data to justify threats of dire consequences for failure.

Now we have a perfect Recipe for Chaos.

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

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

This equation is called the logistic equation.

Here is the abstract of his seminal paper.

Nature 261, 459-467 (10 June 1976)

Simple mathematical models with very complicated dynamics

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

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

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

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

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

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

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

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

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

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

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

And that comes as a really BIG surprise!

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

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

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

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

Software First

computer_power_display_glowing_150_wht_9646A healthcare system has two inter-dependent parts. Let us call them the ‘hardware’ and the ‘software’ – terms we are more familiar with when referring to computer systems.

In a computer the critical-to-success software is called the ‘operating system’ – and we know that by the brand labels such as Windows, Linux, MacOS, or Android. There are many.

It is the O/S that makes the hardware fit-for-purpose. Without the O/S the computer is just a box of hot chips. A rather expensive room heater.

All the programs and apps that we use to to deliver our particular information service require the O/S to manage the actual hardware. Without a coordinator there would be chaos.

In a healthcare system the ‘hardware’ is the buildings, the equipment, and the people.  They are all necessary – but they are not sufficient on their own.

The ‘operating system’ in a healthcare system are the management policies: the ‘instructions’ that guide the ‘hardware’ to do what is required, when it is required and sometimes how it is required.  These policies are created by managers – they are the healthcare operating system design engineers so-to-speak.

Change the O/S and you change the behaviour of the whole system – it may look exactly the same – but it will deliver a different performance. For better or for worse.


In 1953 the invention of the transistor led to the first commercially viable computers. They were faster, smaller, more reliable, cheaper to buy and cheaper to maintain than their predecessors. They were also programmable.  And with many separate customer programs demanding hardware resources – an effective and efficient operating system was needed. So the understanding of “good” O/S design developed quickly.

In the 1960’s the first integrated circuits appeared and the computer world became dominated by mainframe computers. They filled air-conditioned rooms with gleaming cabinets tended lovingly by white-coated technicians carrying clipboards. Mainframes were, and still are, very expensive to build and to run! The valuable resource that was purchased by the customers was ‘CPU time’.  So the operating systems of these machines were designed to squeeze every microsecond of value out of the expensive-to-maintain CPU: for very good commercial reasons. Delivering the “data processing jobs” right, on-time and every-time was paramount.

The design of the operating system software was critical to the performance and to the profit.  So a lot of brain power was invested in learning how to schedule jobs; how to orchestrate the parts of the hardware system so that they worked in harmony; how to manage data buffers to smooth out flow and priority variation; how to design efficient algorithms for number crunching, sorting and searching; and how to switch from one task to the next quickly and without wasting time or making errors.

Every modern digital computer has inherited this legacy of learning.

In the 1970’s the first commercial microprocessors appeared – which reduced the size and cost of computers by orders of magnitude again – and increased their speed and reliability even further. Silicon Valley blossomed and although the first micro-chips were rather feeble in comparison with their mainframe equivalents they ushered in the modern era of the desktop-sized personal computer.

In the 1980’s players such as Microsoft and Apple appeared to exploit this vast new market. The only difference was that Microsoft only offered just the operating system for the new IBM-PC hardware (called MS-DOS); while Apple created both the hardware and software as a tightly integrated system – the Apple I.

The ergonomic-seamless-design philosophy at Apple led to the Apple Mac which revolutionised personal computing. It made them usable by people who had no interest in the innards or in programming. The Apple Macs were the “designer”computers and were reassuringly more expensive. The innovations that Apple designed into the Mac are now expected in all personal computers as well as the latest generations of smartphones and tablets.

Today we carry more computing power in our top pocket than a mainframe of the 1970’s could deliver! The design of the operating system has hardly changed though.

It was the O/S  design that leveraged the maximum potential of the very expensive hardware.  And that is still the case – but we take it for completely for granted.


Exactly the same principle applies to our healthcare systems.

The only difference is that the flow is not 1’s and 0’s – it is patients and all the things needed to deliver patient care. The ‘hardware’ is the expensive part to assemble and run – and the largest cost is the people.  Healthcare is a service delivered by people to people. Highly-trained nurses, doctors and allied healthcare professionals are expensive.

So the key to healthcare system performance is high quality management policy design – the healthcare operating system (HOS).

And here we hit a snag.

Our healthcare management policies have not been designed using the same rigor as the operating systems for our computers. They have not been designed using the well-understood principles of flow physics. The various parts of our healthcare system do not work well together. The flows are fractured. The silos work independently. And the ubiquitous symptom of this dysfunction is confusion, chaos and conflict.  The managers and the doctors are at each others throats. And this is because the management policies have evolved through a largely ineffective and very inefficient strategy called “burn-and-scrape”. Firefighting.

The root cause of the poor design is that neither healthcare managers nor the healthcare workers are trained in operational policy design. Design for Safety. Design for Quality. Design for Delivery. Design for Productivity.

And we are all left with a lose-lose-lose legacy: a system that is no longer fit-for-purpose and a generation of managers and clinicians who have never learned how to design the operational and clinical policies that ensure the system actually delivers what the ‘hardware’ is capable of delivering.


For example:

Suppose we have a simple healthcare system with three stages called A, B and C.  All the patients flow through A, then to B and then to C.  Let us assume these three parts are managed separately as departments with separate budgets and that they are free to use whatever policies they choose so long as they achieve their performance targets -which are (a) to do all the work and (b) to stay in budget and (c) to deliver on time.  So far so good.

Now suppose that the work that arrives at Department B from Department  A is not all the same and different tasks require different pathways and different resources. A Radiology, Pathology or Pharmacy Department for example.

Sorting the work into separate streams and having expensive special-purpose resources sitting idle waiting for work to arrive is inefficient and expensive. It will push up the unit cost – the total cost divided by the total activity. This is called ‘carve-out’.

Switching resources from one pathway to another takes time and that change-over time implies some resources are not able to do the work for a while.  These inefficiencies will contribute to the total cost and therefore push up the “unit-cost”. The total cost for the department divided by the total activity for the department.

So Department B decides to improve its “unit cost” by deploying a policy called ‘batching’.  It starts to sort the incoming work into different types of task and when a big enough batch has accumulated it then initiates the change-over. The cost of the change-over is shared by the whole batch. The “unit cost” falls because Department B is now able to deliver the same activity with fewer resources because they spend less time doing the change-overs. That is good. Isn’t it?

But what is the impact on Departments A and C and what effect does it have on delivery times and work in progress and the cost of storing the queues?

Department A notices that it can no longer pass work to B when it wants because B will only start the work when it has a full batch of requests. The queue of waiting work sits inside Department A.  That queue takes up space and that space costs money but the queue cost is incurred by Department A – not Department B.

What Department C sees is the order of the work changed by Department B to create a bigger variation in lead times for consecutive tasks. So if the whole system is required to achieve a delivery time specification – then Department C has to expedite the longest waiters and delay the shortest waiters – and that takes work,  time, space and money. That cost is incurred by Department C not by Department B.

The unit costs for Department B go down – and those for A and C both go up. The system is less productive as a whole.  The queues and delays caused by the policy change means that work can not be completed reliably on time. The blame for the failure falls on Department C.  Conflict between the parts of the system is inevitable. Lose-Lose-Lose.

And conflict is always expensive – on all dimensions – emotional, temporal and financial.


The policy design flaw here looks like it is ‘batching’ – but that policy is just a reaction to a deeper design flaw. It is a symptom.  The deeper flaw is not even the use of ‘unit costing’. That is a useful enough tool. The deeper flaw is the incorrect assumption that by improving the unit costs of the stages independently will always get an improvement in whole system productivity.

This is incorrect. This error is the result of ‘linear thinking’.

The Laws of Flow Physics do not work like this. Real systems are non-linear.

To design the management policies for a non-linear system using linear-thinking is guaranteed to fail. Disappointment and conflict is inevitable. And that is what we have. As system designers we need to use ‘systems-thinking’.

This discovery comes as a bit of a shock to management accountants. They feel rather challenged by the assertion that some of their cherished “cost improvement policies” are actually making the system less productive. Precisely the opposite of what they are trying to achieve.

And it is the senior management that decide the system-wide financial policies so that is where the linear-thinking needs to be challenged and the ‘software patch’ applied first.

It is not a major management software re-write. Just a minor tweak is all that is required.

And the numbers speak for themselves. It is not a difficult experiment to do.


So that is where we need to start.

We need to learn Healthcare Operating System design and we need to learn it at all levels in healthcare organisations.

And that system-thinking skill has another name – it is called Improvement Science.

The good news is that it is a lot easier to learn than most people believe.

And that is a big shock too – because how to do this has been known for 50 years.

So if you would like to see a real and current example of how poor policy design leads to falling productivity and then how to re-design the policies to reverse this effect have a look at Journal Of Improvement Science 2013:8;1-20.

And if you would like to learn how to design healthcare operating policies that deliver higher productivity with the same resources then the first step is FISH.

Space-and-Time

line_figure_phone_400_wht_9858<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.

Temperament Treacle

stick_figure_help_button_150_wht_9911If 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?”

Doctor_Temperament 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.

Sorting_HatAnd 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.

The Mirror

mirror_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.

Three Essentials

There are three necessary parts before ANY improvement-by-design effort will gain traction. Omit any one of them and nothing happens.

stick_figure_drawing_three_check_marks_150_wht_5283

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.

The Black Curtain

Black_Curtain_and_DoorA 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?

Black_Curtain

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.

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

20131019_TTchart

21:22 [MC] Here’s the same clinic from patients’ point if view, wait time. Much longer than I thought or would like

20131019_WTchart

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

20131019_Ganttchart

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

 

20131019_WIPchart

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.

The Victim Vortex

[Beep Beep] Bob tapped the “Answer” button on his smartphone – it was Lesley calling in for their regular ISP coaching session.

<Bob>Hi Lesley. How are you today? And which tunnel in the ISP Learning Labyrinth shall we explore today?

<Lesley>Hi Bob. I am OK thank you. Can we invest some time in the Engagement Maze?

<Bob>OK. Do you have a specific example?

<Lesley>Sort of. This week I had a conversation with our Chief Executive about the potential of Improvement Science and the reply I got was “I am convinced by what you say but it is your colleagues who need to engage. If you have not succeeded in convincing them then how can I?” I was surprised by that response and slightly niggled because it had an uncomfortable nugget of truth in it.

<Bob>That sounds like the wisdom of a leader who understands that the “power” to make things happen does not sit wholly in the lap of those charged with accountability.

<Lesley> I agree.  And at the same time everything that the “Top Team” suggest gets shot down in flames by a small and very vocal group of my more skeptical colleagues.

<Bob>Ah ha!  It sounds like the Victim Vortex is causing trouble here.

<Lesley>The Victim Vortex?

<Bob>Yes.  Let me give you an example.  One of the common initiators of the Victim Vortex is the data flow part of a complex system design.  The Sixth Flow.  So can I ask you: “How are new information systems developed in your organization?

<Lesley>Wow!  You hit the nail on the head first time!  Just this week there has been another firestorm of angry emails triggered by yet another silver-bullet IT system being foisted on us!

<Bob>Interesting use of language Lesley.  You sound quite “niggled”.

<Lesley>I am.  Not by the constant “drizzle of IT magic” – that is irritating enough – but more by the vehemently cynical reaction of my peers.

<Bob>OK.  This sounds like good enough example of the Victim Vortex.  What do you expect the outcome will be?

<Lesley>Well, if past experience is a predictor for future performance – an expensive failure, more frustration and a deeper well of cynicism.

<Bob>Frustrating for whom?

<Lesley>Everyone.  The IT department as well.  It feels like we are all being sucked into a lose-lose-lose black hole of depression and despair!

<Bob>A very good description of the Victim Vortex.

<Lesley>So the Victim Vortex is an example of the Drama Triangle acting on an organizational level?

tornada_150_wht_10155<Bob>Yes. Visualize a cultural tornado.  The energy that drives it is the emotional  currency spent in playing the OK – Not OK Games.  It is a self-fueling system, a stable design, very destructive and very resistant to change.

<Lesley>That metaphor works really well for me!

<Bob>A similar one is a whirlpool – a water vortex.  If you were out swimming and were caught up in a whirlpool what are your exit strategy options?

<Lesley>An interesting question.  I have never had that experience and would not want it – it sounds rather hazardous.  Let me think.  If I do nothing I will just get swept around in the chaos and I am at risk of  getting bashed, bruised and then sucked under.

<Bob>Yes – you would probably spend all your time and energy just treading water and dodging the flotsam and jetsam that has been sucked into the Vortex.  That is what most people do.  It is called the Hamster Wheel effect.

<Lesley>So another option is to actively swim towards the middle of the Vortex – the end would at least be quick! But that is giving up and adopting the Hopelessness attitude of burned out Victim.  That would be the equivalent of taking voluntary redundancy or early retirement.  It is not my style!

<Bob>Yes.  It does not solve the problem either.  The Vortex is always hoovering up new Victims.  It is insatiable.

<Lesley> And another option would be to swim with the flow to avoid being “got” from behind.  That would be seem sensible and is possible; and at least I would feel better for doing something. I might even escape if I swim fast enough!

<Bob>That is indeed what some try.  The movers and shakers.  The pace setters.  The optimists.  The extrovert leaders.  The problem is that it makes the Vortex spin even faster.  It actually makes the Vortex bigger,  more chaotic and more dangerous than before.

<Lesley>Yes – I can see that.  So my other option is to swim against the flow in an attempt to slow the Vortex down.  Would that work?

<Bob>If everyone did that at the same time it might but that is unlikely to happen spontaneously.  If you could achieve that degree of action alignment you would not have a Victim Vortex in the first place.  Trying to do it alone is ineffective, you tire very quickly, the other Victims bash into you, you slow them down, and then you all get sucked down the Plughole of Despair.

<Lesley>And I suppose a small group of like-minded champions who try to swim-against the flow might last longer if they stick together but even then eventually they would get bashed up and broken up too.  I have seen that happen.  And that is probably where our team are heading at the moment.  I am out of options.  Is it impossible to escape the Victim Vortex?

<Bob>There is one more direction you can swim.

<Lesley>Um?  You mean across the flow heading directly away from the center?

<Bob>Exactly.  Consider that option.

<Lesley>Well, it would still be hard work and I would still be going around with the Vortex and I would still need to watch out for flotsam but every stroke I make would take me further from the center.  The chaos would get gradually less and eventually I would be in clear water and out of danger.  I could escape the Victim Vortex!

<Bob>Yes. And what would happen if others saw you do that and did the same?

<Lesley>The Victim Vortex would dissipate!

<Bob>Yes.  So that is your best strategy.  It is a win-win-win strategy too. You can lead others out of the Victim Vortex.

<Lesley>Wow!  That is so cool!  So how would I apply that metaphor to the Information System niggle?

<Bob>I will leave you to ponder on that.  Think about it as a design assignment.  The design of the system that generates IT solutions that are fit-for-purpose.

<Lesley> Somehow I knew you were going to say that!  I have my squared-paper and sharpened pencil at the ready.  Yes – an improvement-by-design assignment.  Thank you once again Bob.  This ISP course is the business!

DRAT!

[Bing Bong]  The sound bite heralded Leslie joining the regular Improvement Science mentoring session with Bob.  They were now using web-technology to run virtual meetings because it allows a richer conversation and saves a lot of time. It is a big improvement.

<Bob> Hi Lesley, how are you today?

<Leslie> OK thank you Bob.  I have a thorny issue to ask you about today. It has been niggling me even since we started to share the experience we are gaining from our current improvement-by-design project.

<Bob> OK. That sounds interesting. Can you paint the picture for me?

<Leslie> Better than that – I can show you the picture, I will share my screen with you.

DRAT_01 <Bob> OK. I can see that RAG table. Can you give me a bit more context?

<Leslie> Yes. This is how our performance management team have been asked to produce their 4-weekly reports for the monthly performance committee meetings.

<Bob> OK. I assume the “Period” means sequential four week periods … so what is Count, Fail and Fail%?

<Leslie> Count is the number of discharges in that 4 week period, Fail is the number whose length of stay is longer than the target, and Fail% is the ratio of Fail/Count for each 4 week period.

<Bob> It looks odd that the counts are all 28.  Is there some form of admission slot carve-out policy?

<Leslie> Yes. There is one admission slot per day for this particular stream – that has been worked out from the average historical activity.

<Bob> Ah! And the Red, Amber, Green indicates what?

<Leslie> That is depends where the Fail% falls in a set of predefined target ranges; less than 5% is green, 5-10% is Amber and more than 10% is red.

<Bob> OK. So what is the niggle?

<Leslie>Each month when we are in the green we get no feedback – a deafening silence. Each month we are in amber we get a warning email.  Each month we are in the red we have to “go and explain ourselves” and provide a “back-on-track” plan.

<Bob> Let me guess – this feedback design is not helping much.

<Leslie> It is worse than that – it creates a perpetual sense of fear. The risk of breaching the target is distorting people’s priorities and their behaviour.

<Bob> Do you have any evidence of that?

<Leslie> Yes – but it is anecdotal.  There is a daily operational meeting and the highest priority topic is “Which patients are closest to the target length of stay and therefore need to have their  discharge expedited?“.

<Bob> Ah yes.  The “target tail wagging the quality dog” problem. So what is your question?

<Leslie> How do we focus on the cause of the problem rather than the symptoms?  We want to be rid of the “fear of the stick”.

<Bob> OK. What you have hear is a very common system design flaw. It is called a DRAT.

<Leslie> DRAT?

<Bob> “Delusional Ratio and Arbitrary Target”.

<Leslie> Ha! That sounds spot on!  “DRAT” is what we say every time we miss the target!

<Bob> Indeed.  So first plot this yield data as a time series chart.

<Leslie> Here we go.

DRAT_02<Bob>Good. I see you have added the cut-off thresholds for the RAG chart. These 5% and 10% thresholds are arbitrary and the data shows your current system is unable to meet them. Your design looks incapable.

<Leslie>Yes – and it also shows that the % expressed to one decimal place is meaningless because there are limited possibilities for the value.

<Bob> Yes. These are two reasons that this is a Delusional Ratio; there are quite a few more.

DRAT_03<Leslie> OK  and if I plot this as an Individuals charts I can see that this variation is not exceptional.

<Bob> Careful Leslie. It can be dangerous to do this: an Individuals chart of aggregate yield becomes quite insensitive with aggregated counts of relatively rare events, a small number of levels that go down to zero, and a limited number of points.  The SPC zealots are compounding the problem and plotting this data as a C-chart or a P-chart makes no difference.

This is all the effect of the common practice of applying  an arbitrary performance target then counting the failures and using that as means of control.

It is poor feedback loop design – but a depressingly common one.

<Leslie> So what do we do? What is a better design?

<Bob> First ask what the purpose of the feedback is?

<Leslie> To reduce the number of beds and save money by forcing down the length of stay so that the bed-day load is reduced and so we can do the same activity with fewer beds and at the same time avoid cancellations.

<Bob> OK. That sounds reasonable from the perspective of a tax-payer and a patient. It would also be a more productive design.

<Leslie> I agree but it seems to be having the opposite effect.  We are focusing on avoiding breaches so much that other patients get delayed who could have gone home sooner and we end up with more patients to expedite. It is like a vicious circle.  And every time we fail we get whacked with the RAG stick again. It is very demoralizing and it generates a lot of resentment and conflict. That is not good for anyone – least of all the patients.

<Bob>Yes.  That is the usual effect of a DRAT design. Remember that senior managers have not been trained in process improvement-by-design either so blaming them is also counter-productive.  We need to go back to the raw data. Can you plot actual LOS by patient in order of discharge as a run chart.

DRAT_04

<Bob> OK – is the maximum LOS target 8 days?

<Leslie> Yes – and this shows  we are meeting it most of the time.  But it is only with a huge amount of effort.

<Bob> Do you know where 8 days came from?

<Leslie> I think it was the historical average divided by 85% – someone read in a book somewhere that 85%  average occupancy was optimum and put 2 and 2 together.

<Bob> Oh dear! The “85% Occupancy is Best” myth combined with the “Flaw of Averages” trap. Never mind – let me explain the reasons why it is invalid to do this.

<Leslie> Yes please!

<Bob> First plot the data as a run chart and  as a histogram – do not plot the natural process limits yet as you have done. We need to do some validity checks first.

DRAT_05

<Leslie> Here you go.

<Bob> What do you see?

<Leslie> The histogram  has more than one peak – and there is a big one sitting just under the target.

<Bob>Yes. This is called the “Horned Gaussian” and is the characteristic pattern of an arbitrary lead-time target that is distorting the behaviour of the system.  Just as you have described subjectively. There is a smaller peak with a mode of 4 days and are a few very long length of stay outliers.  This multi-modal pattern means that the mean and standard deviation of this data are meaningless numbers as are any numbers derived from them. It is like having a bag of mixed fruit and then setting a maximum allowable size for an unspecified piece of fruit. Meaningless.

<Leslie> And the cases causing the breaches are completely different and could never realistically achieve that target! So we are effectively being randomly beaten with a stick. That is certainly how it feels.

<Bob> They are certainly different but you cannot yet assume that their longer LOS is inevitable. This chart just says – “go and have a look at these specific cases for a possible cause for the difference“.

<Leslie> OK … so if they are from a different system and I exclude them from the analysis what happens?

<Bob> It will not change reality.  The current design of  this process may not be capable of delivering an 8 day upper limit for the LOS.  Imposing  a DRAT does not help – it actually makes the design worse! As you can see. Only removing the DRAT will remove the distortion and reveal the underlying process behaviour.

<Leslie> So what do we do? There is no way that will happen in the current chaos!

<Bob> Apply the 6M Design® method. Map, Measure and Model it. Understand how it is behaving as it is then design out all the causes of longer LOS and that way deliver with a shorter and less variable LOS. Your chart shows that your process is stable.  That means you have enough flow capacity – so look at the policies. Draw on all your FISH training. That way you achieve your common purpose, and the big nasty stick goes away, and everyone feels better. And in the process you will demonstrate that there is a better feedback design than DRATs and RAGs. A win-win-win design.

<Leslie> OK. That makes complete sense. Thanks Bob!  But what you have described is not part of the FISH course.

<Bob> You are right. It is part of the ISP training that comes after FISH. Improvement Science Practitioner.

<Leslie> I think we will need to get a few more people trained in the theory, techniques and tools of Improvement Science.

<Bob> That would appear to be the case. They will need a real example to see what is possible.

<Leslie> OK. I am on the case!

The Art of Juggling

figure_juggling_balls_150_wht_4301Improvement 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.

juggling_at_Keele_June_2013Those 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.

fish
And for an apprentice Improvement Scientist the first step on the Unlearning Journey is FISH.

Closing the Two Loops

Over the past few weeks I have been conducting an Improvement Science Experiment (ISE).  I do that a lot.  This one is a health improvement experiment. I do that a lot too.  Specifically – improving my own health. Ah! Not so diligent with that one.

The domain of health that I am focusing on is weight – for several reasons:
(1) because a stable weight that is within “healthy” limits is a good idea for many reasons and
(2) because weight is very easy to measure objectively and accurately.

But like most people I have constraints: motivation constraints, time constraints and money constraints.  What I need is a weight reduction design that requires no motivation, no time, and no money.  That sounds like a tough design challenge – so some consideration is needed.

Design starts with a specific purpose and a way of monitoring progress.  And I have a purpose – weight within acceptable limits; a method for monitoring progress – a dusty set of digital scales. What I need is a design for delivering the improvement and a method for maintaining it. That is the challenge.

So I need a tested design that will deliver the purpose.  I could invent something here but it is usually quicker to learn from others who have done it, or something very similar.  And there is lots of knowledge and experience out there.  And they fall into two broad schools – Eat Healthier or Exercise More and usually Both.

Eat Healthier is sold as  Eat Less of the Yummy Bad Stuff and more of the Yukky Good Stuff. It sounds like a Puritanical Policy and is not very motivating. So with zero motivation as  a constraint this is a problem.  And Yukky Good Stuff seems to come with a high price tag. So with zero budget as a constraint this is a problem too.

Exercise More is sold as Get off Your Bottom and Go for a Walk. It sounds like a Macho Man Mantra. Not very motivating either. It takes time to build up a “healthy” sweat and I have no desire to expose myself as a health-desperado by jogging around my locality in my moth-eaten track suit.  So with zero time as a constraint this is a problem. Gym subscriptions and the necessary hi-tech designer garb do not come cheap.  So with a zero budget constraint this is another problem.

So far all the conventional wisdom is failing to meet any of my design constraints. On all dimensions.

Oh dear!

The rhetoric is not working.  That packet of Chocolate Hob Nobs is calling to me from the cupboard. And I know I will feel better if I put them out of their misery. Just one will not do any harm. Yum Yum.  Arrrgh!!!  The Guilt. The Guilt.

OK – get a grip – time for Improvement Scientist to step in – we need some Science.

[Improvement Science hat on]

The physics and physiology are easy on this one:

(a) What we eat provides us with energy to do necessary stuff (keep warm, move about, think, etc). Food energy  is measured in “Cals”; work energy is measured in “Ergs”.
(b) If we eat more Cals than we burn as Ergs then the difference is stored for later – ultimately as blubber (=fat).
(c) There are four contributors to or weight: dry (bones and stuff), lean (muscles and glands of various sorts), fluid (blood, wee etc), and blubber (fat).
(d) The sum of the dry, lean, and fluids should be constant – we need them – we do not store energy there.
(e) The fat component varies. It is stored energy. Work-in-progress so to speak.
(f) One kilogram of blubber is equivalent to about 9000 Cals.
(g) An adult of average weight, composition, and activity uses between 2000 and 2500 Cals per day – just to stay at a stable weight.

These facts are all we need to build an energy flow model.

Food Cals = Energy In.
Work Ergs = Energy Out.
Difference between Energy In and Energy Out is converted to-and-from blubber at a rate of 1 gram per 9 Cal.
Some of our weight is the accumulated blubber – the accumulated difference between Cals-In and Ergs-Out

The Laws Of Physics are 100% Absolute and 0% Negotiable. The Behaviours of People are 100% Relative and 100% Negotiable.  Weight loss is more about behaviour. Habits. Lifestyle.

Bit more Science needed now:

Which foods have the Cals?

(1) Fat (9 Cal per gram)
(2) Carbs (4 Cal per gram)
(3) Protein (4 Cal per gram)
(4) Water, Vitamins, Minerals, Fibre, Air, Sunshine, Fags, Motivation (0 Cal per gram).

So how much of each do we get from the stuff we nosh?

It is easy enough to work out – but it is very tedious to do so.  This is how calorie counting weight loss diets work. You weigh everything that goes in, look up the Cal conversions per gram in a big book, do some maths and come up with a number.  That takes lots of time. Then you convert to points and engage in a pseudo-accounting game where you save points up and cash them in as an occasional cream cake.  Time is a constraint and Saving-the-Yummies-for-Later is not changing a habit – it is feeding it!

So it is just easier for me to know what a big bowel of tortilla chips translates to as Cals. Then I can make an informed choice. But I do not know that.

Why not?

Because I never invested time in learning.  Like everyone else I gossip, I guess, and I generalise.  I say “Yummy stuff is bad because it is Hi-Cal; Yukky stuff is good because it is Lo-Cal“.  And from this generalisation I conclude “Cutting Cals feels bad“. Which is a problem because my motivation is already rock bottom.  So I do nothing,  and my weight stays the same, and I still feel bad.

The Get-Thin-Quick industry knows this … so they use Shock Tactics to motivate us.  They scare us with stories of fat young people having heart attacks and dying wracked with regret. Those they leave behind are the real victims. The industry bludgeons us into fearful submission and into coughing up cash for their Get Thin Quick Panaceas.  Their real goal is the repeat work – the loyal customers. And using scare mongering and a few whale-to-waif conversions as rabble-rousing  zealots they cook up the ideal design to achieve that.  They know that, for most of us, as soon as the fear subsides, the will weakens, the chips are down (the neck), the blubber builds, and we are back with our heads hung low and our wallets open.

I have no motivation – that is a constraint.  So flogging an over-weight and under-motivated middle-aged curmudgeon will only get a more over-weight, ego-bruised-and-depressed, middle-aged cynic. I may even seek solace in the Chocolate Hob Nob jar.

Nah! I need a better design.

[Improvement Scientist hat back on]

First Rule of Improvement – Check the Assumptions.

Assumption 1:
Yummy => Hi-Cal => Bad for Health
Yukky => Lo-Cal => Good for Health

It turns out this is a gross over-simplification.  Lots of Yummy things are Lo-Cal; lots of Yukky things are Hi-Cal. Yummy and Yukky are subjective. Cals are not.

OK – that knowledge is really useful because if I know which-is-which then I can made wiser decisions. I can do swaps so that the Yummy Score goes higher and the Cals Score goes lower.  That sounds more like it! My Motiv-o-Meter twitches.

Assumption 2:
Hi-Cal => Cheap => Good for Wealth
Lo-Cal => Expensive => Bad for Wealth

This is a gross over-simplification too. Lots of Expensive things are Hi-Cal; lots of Cheap things are Lo-Cal.

OK so what about the combination?

Bingo!  There are lots of Yummy+Cheap+Lo-Cal things out there !  So my process is to swap the Lose-Lose-Lose for the Win-Win-Win. I feel a motivation surge. The needle on my Motiv-o-Meter definitely moved this time.

But how much? And for how long? And how will I know if it is working?

[Improvement Science hat back on]

Second Rule of Improvement Science – Work from the Purpose

We need an output  specification.  What weight reduction in what time-scale?

OK – I work out my target weight – using something called the BMI (body mass index) which uses my height and a recommended healthy BMI range to give a target weight range. I plumb for 75 kg – not just “10% reduction” – I need an absolute goal. (PS. The BMI chart I used is at the end of the blog).

OK – I now I need a time-scale – and I know that motivation theory shows that if significant improvement is not seen within 15 repetitions of a behaviour change then it does not stick. It will not become a new habit. I need immediate feedback. I need to see a significant weight reduction within two weeks. I need a quick win to avoid eroding my fragile motivation.  And so long as a get that I will keep going. And how long to get to target weight?  One or two lunar cycles feels about right. Let us compromise on six weeks.

And what is a “significant improvement”?

Ah ha! Now I am on familiar ground – I have a tool for answering that question – a system behaviour chart (SBC).  I need to measure my weight and plot it on a time-series chart using BaseLine.  And I know that I need 9 points to show a significant shift, and I know I must not introduce variation into my measurements. So I do four things – I ensure my scales have high enough precision (+/- 0.1 kg); I do the weighing under standard conditions (same time of day and same state of dress);  I weigh myself every day or every other day; and I plot-the-dots.

OK – how am I doing on my design checklist?
1. Purpose – check
2. Process – check
3. Progress – check

Anything missing?

Yes – I need to measure the energy input – the Cals per day going in – but I need a easy, quick and low-cost way of doing it.

Time for some brainstorming. What about an App? That fancy new smartphone can earn its living for a change. Yup – lots of free ones for tracking Cals.  Choose one. Works OK. Another flick on the Motiv-o-Meter needle.

OK – next bit of the jigsaw. What is my internal process metric (IPM)?  How many fewer Cals per day on average do I need to achieve … quick bit of beer-mat maths … that many kg reduction times Cal per kg of blubber divided by 6 weeks gives  … 1300 Cals per day less than now (on average).  So what is my daily Cals input now?  I dunno. I do not have a baseline.  And I do not fancy measuring it for a couple of weeks to get one. My feeble motivation will not last that long. I need action. I need a quick win.

OK – I need to approach this a different way.  What if I just change the input to more Yummy+Cheap+Lo-Cal stuff and less Yummy+Cheap+Hi-Cal stuff and just measure what happens.  What if I just do what I feel able to? I can measure the input Cals accurately enough and also the output weight. My curiosity is now pricked too and my Inner Nerd starts to take notice and chips in “You can work out the rest from that. It is a simple S&F model” . Thanks Inner Nerd – you do come in handy occasionally. My Motiv-o-Meter is now in the green – enough emotional fuel for a decision and some action.

I have all the bits of the design jigsaw – Purpose, Process, Progress and Pieces.  Studying, and Planning over – time for Doing.

So what happened?

It is an ongoing experiment – but so far it has gone exactly as the design dictated (and the nerdy S&F model predicted).

And the experience has helped me move some Get-Thin-Quick mantras to the rubbish bin.

I have counted nine so far:

Mantra 1. Do not weight yourself every day –  rubbish – weigh yourself every day using a consistent method and plot the dots.
Mantra 2. Focus on the fatrubbish – it is Cals that count whatever the source – fat, carbs, protein (and alcohol).
Mantra 3. Five fresh fruit and veg a dayrubbish – they are just Hi-Cost+Low-Cal stocking fillers.
Mantra 4. Only eat balanced mealsrubbish –  it is OK to increase protein and reduce both carbs and fat.
Mantra 5. It costs money to get healthyrubbish – it is possible to reduce cost by switching to Yummy+Cheap+Lo-Cal stuff.
Mantra 6. Cholesterol is badrubbish – we make more cholesterol than we eat – just stay inside a recommended range.
Mantra 7. Give up all alcohol – rubbish – just be sensible – just stay inside a recommended range.
Mantra 8. Burn the fat with exercise rubbish – this is scraping-the-burnt-toast thinking – less Cals in first.
Mantra 9. Eat less every dayrubbish – it is OK to have Lo-Cal days and OK-Cal days – it is the average Cals that count.

And the thing that has made the biggest difference is the App.  Just being able to quickly look up the Cals in a “Waitrose Potato Croquette” when-ever and where-ever I want to is what I really needed. I have quickly learned what-is-in-what and that helps me make “Do I need that Chocolate Hob-Nob or not?” decisions on the fly. One tiny, insignificant Chocolate Hob-Nob = 95 Cals. Ouch! Maybe not.

I have been surprised by what I have learned. I now know that before I was making lots of unwise decisions based on completely wrong assumptions. Doh!

The other thing that has helped me build motivation is seeing the effect of those wiser design decisions translated into a tangible improvement – and quickly!  With a low-variation and high-precision weight measurement protocol I can actually see the effect of the Cals ingested yesterday on the Weight recorded today.  Our bodies obey the Laws of Physics. We are what we eat.

So what is the lesson to take away?

That there are two feedback loops that need to be included in all Improvement Science challenges – and both loops need to be closed so information flows if the Improvement exercise is to succeed and to sustain.

First the Rhetoric Feedback loop – where new, specific, knowledge replaces old, generic gossip. We want to expose the myths and mantras and reveal novel options.  Challenge assumptions with scientifically valid evidence. If you do not know then look it up.

Second the Reality Feedback loop – where measured outcomes verifies the wisdom of the decision – the intended purpose was achieved.  Measure the input, internal and output metrics and plot all as time-series charts. Seeing is believing.

So the design challenge has been achieved and with no motivation, no time and no budget.

Now where is that packet of Chocolate Hob Nobs. I think I have earned one. Yum yum.

[PS. This is not a new idea – it is called “double loop learning“.  Do not know of it? Worth looking it up?]


bmi_chart

“When the Student is ready …”

Improvement Science is not a new idea.  The principles are enduring and can be traced back as far as recorded memory – for Millennia. This means that there is a deep well of ancient wisdom that we can draw from.  Much of this wisdom is condensed into short sayings which capture a fundamental principle or essence.

One such saying is attributed to Zen Buddhism and goes “When the Student is ready the Teacher will appear.

This captures the essence of a paradigm shift – a term made popular by Thomas S Kuhn in his seminal 1962 book – The Structure of Scientific Revolutions.  It was written just over 50 years ago.

System-wide change takes time and the first stage is the gradual build up of dissatisfaction with the current paradigm.  The usual reaction from the Guardians of the Status Quo is to silence the first voices of dissent, often brutally. As the pressure grows there are too many voices to silence individually so more repressive Policies and Policing are introduced. This works for a while but does not dissolve the drivers of dissatisfaction. The pressure builds and the cracks start to appear.  This is a dangerous phase.

There are three ways out: repression, revolution, and evolution.  The last one is the preferred option – and it requires effective leadership to achieve.  Effective leaders are both Teachers and Students. Knowledge and understanding flow through them as they acquire Wisdom.

The first essence of the message is that the solutions to the problems are already known – but the reason they are not widely known and used is our natural affection for the familiar and our distrust of the unfamiliar.  If we are comfortable then why change?

It is only when we are uncomfortable enough that we will start to look for ways to regain comfort – physical and psychological.

The second essence of the message is that to change we need to learn something and that means we have to become Students, and to seek the guidance of a Teacher. Someone who understands the problems, their root causes, the solutions, the benefits and most importantly – how to disseminate that knowledge and understanding.  A Teacher that can show us how not just tell us what.

The third essence of the message is that the Students become Teachers themselves as they put into practice what they have learned and prove to themselves that it works, and it is workable.  The new understanding flows along the Optimism-Skepticism gradient until the Tipping Point is reached.  It is then unstoppable and the Paradigm flips. Often remarkably quickly.

The risk is that change means opportunity and there are many who can sniff out an opportunity to cash in on the change chaos. They are the purveyors of Snakeoil – and they prey on the dissatisfied and desperate.

So how does a Student know a True-Teacher from a Snakeoil Salesperson?

Simple – the genuine Teacher will be able to show a portfolio of successes and delighted ex-students; will be able to explain and demonstrate how they were both achieved; will be willing to share their knowledge; and will respectfully decline to teach someone who they feel is not yet ready to learn.

The Green Shoots of Improvement

one_on_one_challenge_150_wht_8069Improvement is a form of innovation and it obeys the same Laws of Innovation.

One of these Laws describes how innovation diffuses and it is called Rogers’ Law.

The principle is that innovations diffuse according to two opposing forces – the Force of Optimism and the Force of Skepticism.  As individuals we differ in our balance of these two preferences.

When we are in status quo the two forces are exactly balanced.

As the Force of Optimism builds (usually from increasing dissatisfaction with the status quo driving Necessity-the-Mother-of-Invention) then the Force of Skepticism tends to build too. It feels like being in a vice that is slowly closing. The emotional stress builds, the strain starts to show and the cracks begin to appear.  Sometimes the Optimism jaw of the vice shatters first, sometimes the Skepticism jaw does – either way the pent-up-tension is relieved. At least for a while.

The way to avoid the Vice is to align the forces of Optimism and Skepticism so that they both pull towards the common goal, the common purpose, the common vision.  And there always is one. People want a win-win-win outcome, they vary in daring to dream that it is possible. It is.

The importance of pull is critical. When we have push forces and a common goal we do get movement – but there is a danger – because things can veer out of control quickly.  Pull is much easier to steer and control than push.  We all know this from our experience of the real world.

And When the status quo starts to move in the direction of the common vision we are seeing tangible evidence of the Green Shoots of Improvement breaking through the surface into our conscious awareness.  Small signs first, tender green shoots, often invisible among the overgrowth, dead wood and weeds.

Sometimes the improvement is a reduction of the stuff we do not want – and that can be really difficult to detect if it is gradual because we adapt quickly and do not notice diffuse, slow changes.

We can detect the change by recording how it feels now then reviewing our records later (very few of us do that – very few of us keep a personal reflective journal). We can also detect change by comparing ourselves with others – but that is a minefield of hidden traps and is much less reliable (but we do that all the time!).

Improvement scientists prepare the Soil-of-Change, sow the Seeds of Innovation, and wait for the Spring to arrive.  As the soil thaws (the burning platform of a crisis may provide some energy for this) some of the Seeds will germinate and start to grow.  They root themselves in past reality and they shoot for the future rhetoric.  But they have a finite fuel store for growth – they need to get to the surface and to sunlight before their stored energy runs out. The preparation, planting and timing are all critical.

plant_growing_anim_150_wht_9902And when the Green Shoots of Improvement appear the Improvement Scientist switches role from Germinator to Grower – providing the seedlings with emotional sunshine in the form of positive feedback, encouragement, essential training, and guidance.  The Grower also has to provide protection from toxic threats that can easily kill a tender improvement seedling – the sources of Cynicide that are always present. The disrespectful sneers of “That will never last!” and “You are wasting your time – nothing good lasts long around here!”

The Improvement Scientist must facilitate harnessing the other parts of the system so that they all pull in the direction of the common vision – at least to some degree.  And the other parts add up to about 85% of it so they collectively they have enough muscle to create movement in the direction of the shared vision. If they are aligned.

And each other part has a different, significant and essential role.

The Disruptive Innovators provide the new ideas – they are always a challenge because they are always questioning “Why do we do it that way?” “What if we did it differently?” “How could we change?”  We do not want too many disruptive innovators because they are – disruptive.  Frustrated disruptive innovations can easily flip to being Cynics – so it is wise not to ignore them.

The Early Adopters provide the filter – they test the new ideas; they reject the ones that do not work; and they shape the ones that do. They provide the robust evidence of possibility. We need more Adopters than Innovators because lots of the ideas do not germinate. Duff seed or hostile soil – it does not matter which.  We want Green Shoots of Improvement.

The Majority provide the route to sharing the Adopter-Endorsed ideas, the Green Shoots of Improvement. They will sit on the fence, consider the options, comment, gossip, listen, ponder and eventually they will commit and change. The Early Majority earlier and the Late Majority later. The Late Majority are also known as the Skeptics. They are willing to be convinced but they need the most evidence. They are most risk-averse and for that reason they are really useful – because they can help guide the Shoots of  Improvement around the Traps. They will help if asked and given a clear role – “Tell us if you see gaps and risks and tell us why so that we can avoid them at the design and development stage”.  And you can tell if they are a True Skeptic or a Cynic-in-Skeptic clothing – because the Cynics will decline to help saying that they are too busy.

The last group, the Cynics, are a threat to significant and sustained improvement. And they can be managed using one or more the these four tactics:

1. Ignore them. This has the advantage of not wasting time but it tends to enrage them and they get noisier and more toxic.
2. Isolate them. This is done by establishing peer group ground rules that are is based on Respectful Challenge.
3. Remove them. This needs senior intervention and a cast-iron case with ample evidence of bad behaviour. Last resort.
4. Engage them. This is the best option if it can be achieved – invite the Cynics to be Skeptics. The choice is theirs.

It is surprising how much improvement follows from just turning blocking some of the sources of Cynicide!

growing_blue_vine_dissolve_150_wht_244So the take home message is a positive one:

  • Look for the Green Shoots of Improvement,
  • Celebrate every one you find,
  • Nurture and Protect them

and they will grow bigger and stronger and one day will flower, fruit and create their own Seeds of Innovation.

Do Not Give Up Too Soon

clock_hands_spinning_import_150_wht_3149Tangible improvement takes time. Sometimes it takes a long time.

The more fundamental the improvement the more people are affected. The more people involved the greater the psychological inertia. The greater the resistance the longer it takes to show tangible effects.

The advantage of deep-level improvement is that the cumulative benefit is greater – the risk is that the impatient Improvementologist may give up too early – sometimes just before the benefit becomes obvious to all.

The seeds of change need time to germinate and to grow – and not all good ideas will germinate. The green shoots of innovation do not emerge immediately – there is often a long lag and little tangible evidence for a long time.

This inevitable  delay is a source of frustration, and the impatient innovator can unwittingly undo their good work.  By pushing too hard they can drag a failure from the jaws of success.

Q: So how do we avoid this trap?

The trick is to understand the effect of the change on the system.  This means knowing where it falls on our Influence Map that is marked with the Circles of Control, Influence and Concern.

Our Circle of Concern includes all those things that we are aware of that present a threat to our future survival – such as a chunk of high-velocity space rock smashing into the Earth and wiping us all out in a matter of milliseconds. Gulp! Very unlikely but not impossible.

Some concerns are less dramatic – such as global warming – and collectively we may have more influence over changing that. But not individually.

Our Circle of Influence lies between the limit of our individual control and the limit of our collective control. This a broad scope because “collective” can mean two, twenty, two hundred, two thousand, two million, two billion and so on.

Making significant improvements is usually a Circle of Influence challenge and only collectively can we make a difference.  But to deliver improvement at this level we have to influence others to change their knowledge, understanding, attitudes, beliefs and behaviour. That is not easy and that is not quick. It is possible though – with passion, plausibility, persistence, patience – and an effective process.

It is here that we can become impatient and frustrated and are at risk of giving up too soon – and our temperaments influence the risk. Idealists are impatient for fundamental change. Rationals, Guardians and Artisans do not feel the same pain – and it is a rich source of conflict.

So if we need to see tangible results quickly then we have to focus closer to home. We have to work inside our Circle of Individual Influence and inside our Circle of Control.  The scope of individual influence varies from person-to-person but our Circle of Control is the same for all of us: the outer limit is our skin.  We all choose our behaviour and it is that which influences others: for better or for worse.  It is not what we think it is what we do. We cannot read or control each others minds. We can all choose our attitudes and our actions.

So if we want to see tangible improvement quickly then we must limit the scope of our action to our Circle of Individual Influence and get started.  We do what we can and as soon as we can.

Choosing what to do and what not do requires wisdom. That takes time to develop too.


Making an impact outside the limit of our Circle of Individual Influence is more difficult because it requires influencing many other people.

So it is especially rewarding for to see examples of how individual passion, persistence and patience have led to profound collective improvement.  It proves that it is still possible. It provides inspiration and encouragement for others.

One example is the recently published Health Foundation Quality, Cost and Flow Report.

This was a three-year experiment to test if the theory, techniques and tools of Improvement Science work in healthcare: specifically in two large UK acute hospitals – Sheffield and Warwick.

The results showed that Improvement Science does indeed work in healthcare and it worked for tough problems that were believed to be very difficult if not impossible to solve. That is very good news for everyone – patients and practitioners.

But the results have taken some time to appear in published form – so it is really good news to report that the green shoots of improvement are now there for all to see.

The case studies provide hard evidence that win-win-win outcomes are possible and achievable in the NHS.

The Impossibility Hypothesis has been disproved. The cynics can step off the bus. The skeptics have their evidence and can now become adopters.

And the report offers a lot of detail on how to do it including two references that are available here:

  1. A Recipe for Improvement PIE
  2. A Study of Productivity Improvement Tactics using a Two-Stream Production System Model

These references both describe the fundamentals of how to align financial improvement with quality and delivery improvement to achieve the elusive win-win-win outcome.

A previously invisible door has opened to reveal a new Land of Opportunity. A land inhabited by Improvementologists who mark the path to learning and applying this new knowledge and understanding.

There are many who do not know what to do to solve the current crisis in healthcare – they now have a new vista to explore.

Do not give up too soon –  there is a light at the end of the dark tunnel.

And to get there safely and quickly we just need to learn and apply the Foundations of Improvement Science in Healthcare – and we first learn to FISH in our own ponds first.

fish

Life or Death Decisions

The Improvement Science blog this week is kindly provided by Julian Simcox and Terry Weight.

What can surgeons learn from other professions about making life or death decisions?

http://www.bbc.co.uk/news/health-21862527

Dr Kevin Fong is on a mission to find out what can be done to reduce the number of mistakes being made by surgeons in the operating theatre.

He starts out with an example of a mistake in an operation that involved a problematic tracheotomy and subsequently, despite there being plenty of extra expert advice on hand, sadly the patient died. Crucially, a nurse had been ignored who if listened to might have provided the solution that could have saved the patient’s life.

Whilst looking at other walks of life – this example is used to explore how under similar pressures such mistakes can be avoided. For example, in aviation and in fire-fighting more robust and resilient cultures and systems have evolved – but how?

The Horizon editors highlight the importance of six things and we make some comments:

1. The aviation industry continually designs out hazards and risk.

Aviation was once a very hazardous pursuit. Nowadays the trip to the airport is much riskier than the flight itself, because over the decades aviators have learned how to learn-from-mistakes and to reduce future incidents. They have learned that blaming individuals for systemic failure gets in the way of accumulating the system-wide knowledge that makes the most difference.

Peter Jordan reminds us that in the official report into the 1989 Kegworth air disaster: 31 recommendations for improved safety were made – mainly to do with patient safety during crashes – an even then the report could not resist pointing the finger at the two pilots who, when confronted with a blow-out in one of their two engines, had wrongly interpreted a variety of signals and talked themselves into switching off the wrong engine. On publication of the report they were summarily dismissed, but much later successfully claimed damages for unfair dismissal.

http://en.wikipedia.org/wiki/Kegworth_air_disaster

2. Checklists can make a difference if the Team is engaged

The programme then refers to recent research by the World Health Organisation on the use of checklists that when implemented showed a large (35%) reduction in surgical complications across a range of countries and hospitals.

In University College Hospital London we see checklists being used by the clinical team to powerful effect. The specific example given concerns the process of patient hand-over after an operation from the surgical team to the intensive care unit. Previously this process had been ill-defined and done differently by lots of people – and had not been properly overseen by anyone.

No reference is made however to the visual display of data that helps teams see the effect of their actions on their system over time, and there is no mention of whether the checklists have been designed by outsiders or by the team themselves.

In our experience these things make a critical difference to ongoing levels of engagement – and to outcomes – especially in the NHS where checklists have historically been used more as a way of ensuring compliance with standards and targets imposed from the top. Too often checklists are felt to be instruments of persecution and are therefore fiercely (and justifiably) resisted.

We see plenty of scope in the NHS for clarifying and tightening process definitions, but checklists are only one way of prompting this. Our concern is that checklists could easily become a flavour-of-the-month thing – seen as one more edict from above. And all-too-quickly becoming yet another layer of the tick-box bureaucracy, of the kind that most people say they want to get away from.

We also see many potentially powerful ideas flowing form the top of the NHS, raining down on a system that has become moribund – wearied by one disempowering change initiative after another.

3. Focussing on the team and the process – instead of the hierarchy – enhances cooperation and reduces deferential behaviour.

Learning from the Formula One Pit Stop Team processes, UCH we are told have flattened their hierarchy ensuring that at each stage of the process there is clear leadership, and well understood roles to perform. After studying their process they have realised that most of the focus had previously been on only the technically demanding work rather than on the sequence of steps and the need for ensuring clear communication between each one of those steps. We are told that flattening the hierarchy in order to prioritise team working has also helped – deference to seniority (e.g. nurses to doctors) is now seen as obstructing safer practice.

Achieving role clarity goes hand-in-hand with simplification of the system – which all starts with careful process definition undertaken collaboratively by the team as a whole. In the featured operation every individual appears to know their role and the importance of keeping things simple and consistent. In our experience this is all the more powerful when the team agree to standardise procedures as soon as any new way has been shown to be more effective.

4. Situational Awareness is an inherent human frailty.

We see how fire officers are specifically trained to deal with situations that require both a narrow focus and an ability to stand back and connect to the whole – a skill which for most people does not come naturally. Under pressure we each too often fail to appreciate either the context or the bigger picture, losing situational awareness and constraining our span of attention.

In the aviation industry we see how pilot training is nowadays considered critically important to outcomes and to the reductions of pilot error in emergencies. Flight simulators and scenario simulation now play a vital role, and this is becoming more commonplace in senior doctor training.

It seems common sense that people being trained should experience the real system whilst being able to making mistakes. Learning comes from experimentation (P-D-C-A). In potentially life-and-death situations simulation allows the learning and the building of needed experience to be done safely off-line. Nowadays, new systems containing multiple processes and lots of people can be designed using computer simulations, but these skills are as yet in short supply in the NHS.

http://www.saasoft.com/6Mdesign/index.php

5. Understand the psychology of how people respond to their mistakes.

Using some demonstrations using playing cards, we see how people who have a non-reactive attitude to mistakes respond better to making them and are then less likely to make the same mistake again. Conversely some individuals seem to be less resilient – we would say becoming unstable – taking longer to correct their mistakes and subsequently making more of them. Recruitment of doctors is now starting to include the use of simulators to test for this psychological ability.

6. Innovation more easily flows from systems that are stable.

Due to a bird strike a few minutes after take-off, stopping both engines, an aircraft in 2008 was forced to crash land. The landing – in to New York’s Hudson River – was an innovative novel manoeuvre, and incredibly led to the survival of all the passengers and crew. An innovation that was safely executed by the pilot who in the moment kept his cool by sticking to the procedures and checklists he had been trained in.

This capability we are told had been acquired over more than three decades by the pilot Captain “Sully” Sullenberger, who sees himself as part of an industry that over time institutionalises emerging knowledge. He tells us that he had faith in the robustness and resilience of this knowledge that had accumulated by using the lessons from the past to build a safer future. He suggests it would be immoral not to learn from historical experience. To him it was “this robustness that made it possible to innovate when the unknown occurred”.

Standardisation often spawns innovation – something which for many people remains a counter-intuitive notion.

Sullenberger was subsequently lauded as a hero, but he himself tells us that he merely stuck to the checklist procedures and that this helped him to keep his cool whilst realising he needed to think outside the box.

The programme signs off with the message that human error is always going to be with us, and that it is how we deal with human error that really matters. In aviation there is a continual search for progress, rather than someone to blame. By accepting our psychological fallibility we give ourselves – in the moment – the best possible chance.

The programme attempts to balance the actions of the individual with collective action over time to design and build a better system – one in which all individuals can play their part well. Some viewers may have ended up remembering most the importance of the “heroic” individual. In our view more emphasis could have placed on the design of the system as a whole – such that it more easily maintains its stability without needing to rely either on the heroic acts of any one individual or on finding the one scapegoat.

If heroes need to exist they are the individuals who understand their role and submit themselves to the needs of team and to achieving the outcomes that are needed by the wider system. We like that the programme ends with the following words:

Search for progress, not someone to blame!

 

 

 

Time-Reversed Insight

stick_figure_wheels_turning_150_wht_4572Thinking-in-reverse sounds like an odd thing to do but it delivers more insight and solves tougher problems than thinking forwards.  That is the reason it is called Time-Reversed Insight.   And once we have mastered how to do it, we discover that it comes in handy in all sorts of problematic situations where thinking forwards only hits a barrier or even makes things worse.

Time-reversed thinking is not the same thing as undoing what you just did. It is reverse thinking – not reverse acting.

We often hear the advice “Start with the end in mind …” and that certainly sounds like it might be time-reversed thinking, but it is often followed by “… to help guide your first step.” The second part tells us it is not. Jumping from outcome to choosing the first step is actually time-forward thinking.

Time-forward thinking comes in many other disguises: “Seeking your True North” is one and “Blue Sky Thinking” is another. They are certainly better than discounting the future and they certainly do help us to focus and to align our efforts – but they are still time-forward thinking. We know that because the next question is always “What do we do first? And then? And then?” in other words “What is our Plan?”.

This is not time-reversed insightful thinking: it is good old, tried-and-tested, cause-and-effect thinking. Great for implementation but a largely-ineffective, and a hugely-inefficient way to dissolve “difficult” problems. In those situation it becomes keep-busy behaviour. Plan-Do-Plan-Do-Plan-Do ……..


In time-reversed thinking the first question looks similar. It is a question about outcome but it is very specific.  It is “What outcome do we want? When do we want it? and How would we know we have got it?”  It is not a direction. It is a destination. The second question in time-reversed thinking is the clincher. It is  “What happened just before?” and is followed by “And before that? And before that?“.

We actually do this all the time but we do it unconsciously and we do it very fast.  It is called the “blindingly obvious in hindsight” phenomenon.  What happens is we feel the good or bad outcome and then we flip to the cause in one unconscious mental leap. Ah ha!

And we do this because thinking backwards in a deliberate, conscious, sequential way is counter-intuitive.

Our unconscious mind seems to have no problem doing it though. And that is because it is wired differently. Some psychologists believe that we literally have “two brains”: one that works sequentially in the direction of forward time – and one that works in parallel and in a forward-and backward in time fashion. It is the sequential one that we associate with conscious thinking; it is the parallel one that we associate with unconscious feeling. We do both and usually they work in synergy – but not always. Sometimes they antagonise each other.

The problem is that our sequential, conscious brain does not  like working backwards. Just like we do not like walking backwards, or driving backwards.  We have evolved to look, think, and move forwards. In time.

So what is so useful about deliberate, conscious, time-reversed thinking?

It can give us an uniquely different perspective – one that generates fresh insight – and that new view enables us to solve problems that we believed were impossible when looked at in a time-forward way.


An example of time-reverse thinking:

The 4N Chart is an emotional mapping tool.  More specifically it is an emotion-over-time mapping technique. The way it is used is quite specific and quite counter-intuitive.  If we ask ourselves the question “What is my top Niggle?” our reply is usually something like “Not enough time!” or “Person x!” or “Too much work!“.  This is not how The 4N Chart is designed to be used.  The question is “What is my commonest negative feeling?” and then the question “What happened just before I felt it?“.  What was the immediately preceding cause of  the Niggle? And then the questions continue deliberately and consciously to think backwards: “And before that?”, “And before that?” until the root causes are laid bare.

A typical Niggle-cause exposing dialog might be:

Q: What is my most commonest negative feeling?
A: I feel angry!
Q: What happened just before?
A: My boss gives me urgent jobs to do at half past 4 on Friday afternoon!
Q: And before that?
A: Reactive crisis management meetings are arranged at very short notice!
Q: And before that?
A: We have regular avoidable crises!
Q: And before that?
A: We are too distracted with other important work to spot each crisis developing!
Q: And before that?
A: We were not able to recruit when a valuable member of staff left.
Q: And before that?
A: Our budget was cut!

This is time-reversed  thinking and we can do this reasonably easily because we are working backwards from the present – so we can use our memory to help us. And we can do this individually and collectively. Working backwards from the actual outcome is safer because we cannot change the past.

It is surprisingly effective though because by doing this time-reverse thinking consciously we uncover where best to intervene in the cause-and-effect pathway that generates our negative emotions. Where it crosses the boundary of our Circle of Control. And all of us have the choice to step-in just before the feeling is triggered. We can all choose if we are going to allow the last cause to trigger to a negative feeling in us. We can all learn to dodge the emotional hooks. It takes practice but it is possible. And having deflected the stimulus and avoided being hijacked by our negative emotional response we are then able to focus our emotional effort into designing a way to break the cause-effect-sequence further upstream.

We might leave ourselves a reminder to check on something that could develop into a crisis without us noticing. Averting just one crisis would justify all the checking!

This is what calm-in-a-crisis people do. They disconnect their feelings. It is very helpful but it has a risk.

robot_builder_textThe downside is that they can disconnect all their feelings – including the positive ones. They can become emotionless, rational, logical, tough-minded robots.  And that can be destructive to individual and team morale. It is the antithesis of improvement.

So be careful when disconnecting emotional responses – do it only for defense – never for attack.


A more difficult form of time-reversed thinking is thinking backwards from future-to-present.  It is more difficult for many reasons, one of which is because we do not have a record of what actually happened to help us.  We do however have experience of  similar things from the past so we can make a good guess at the sort of things that could cause a future outcome.

Many people do this sort of thinking in a risk-avoidance way with the objective of blocking all potential threats to safety at an early stage. When taken to extreme it can manifest as turgid, red-taped, blind bureaucracy that impedes all change. For better or worse.

Future-to-present thinking can be used as an improvement engine – by unlocking potential opportunity at an early stage. Innovation is a fragile flower and can easily be crushed. Creative thinking needs to be nurtured long enough to be tested.

Change is deliberately destablising so this positive form of future-to-present thinking can also be counter-productive if taken to extreme when it becomes incessant meddling. Change for change sake is also damaging to morale.

So, either form of future-to-present thinking is OK in moderation and when used in synergy the effect is like magic!

Synergistic future-to-present time-reversed thinking is called Design Thinking and one formulation is called 6M Design.

The Seventh Flow

texting_a_friend_back_n_forth_150_wht_5352Bing Bong

Bob looked up from the report he was reading and saw the SMS was from Leslie, one of his Improvement Science Practitioners.

It said “Hi Bob, would you be able to offer me your perspective on another barrier to improvement that I have come up against.”

Bob thumbed a reply immediately “Hi Leslie. Happy to help. Free now if you would like to call. Bob

Ring Ring

<Bob> Hello, Bob here.

<Leslie> Hi Bob. Thank you for responding so quickly. Can I describe the problem?

<Bob> Hi Leslie – Yes, please do.

<Leslie> OK. The essence of it is that I have discovered that our current method of cash-flow control is preventing improvements in safety, quality, delivery and paradoxically in productivity too. I have tried to talk to the Finance department and all I get back is “We have always done it this way. That is what we are taught. It works. The rules are not negotiable and the problem is not Finance“. I am at a loss what to do.

<Bob> OK. Do not worry. This is a common issue that every ISP discovers at some point. What led you to your conclusion that the current methods are creating a barrier to change?

<Leslie> Well, the penny dropped when I started using the modelling tools you have shown me.  In particular when predicting the impact of process improvement-by-design changes on the financial performance of the system.

<Bob> OK. Can you be more specific?

<Leslie> Yes. The project was to design a new ambulatory diagnostic facility that will allow much more of the complex diagnostic work to be done on an outpatient basis.  I followed the 6M Design approach and looked first at the physical space design. We needed that to brief the architect.

<Bob> OK. What did that show?

<Leslie> It showed that the physical layout had a very significant impact on the flow in the process and that by getting all the pieces arranged in the right order we could create a physical design that felt spacious without actually requiring a lot of space. We called it the “Tardis Effect“. The most marked impact was on the size of the waiting areas – they were really small compared with what we have now which are much bigger and yet still feel cramped and chaotic.

<Bob> OK. So how does that physical space design link to the finance question?

<Leslie> Well, the obvious links were that the new design would have a smaller physical foot-print and at the same time give a higher throughput. It will cost less to build and will generate more activity than if we just copied the old design into a shiny new building.

<Bob> OK. I am sure that the Capital Allocation Committee and the Revenue Generation Committee will have been pleased with that outcome. What was the barrier?

<Leslie> Yes, you are correct. They were delighted because it left more in the Capital Pot for other equally worthy projects. The problem was not capital it was revenue.

<Bob> You said that activity was predicted to increase. What was the problem?

<Leslie>Yes – sorry, I was not clear – it was not the increased activity that was the problem – it was how to price the activity and  how to distribute the revenue generated. The Reference Cost Committee and Budget Allocation Committee were the problem.

<Bob> OK. What was the problem?

<Leslie> Well the estimates for the new operational budgets were basically the current budgets multiplied by the ratio of the future planned and historical actual activity. The rationale was that the major costs are people and consumables so the running costs should scale linearly with activity. They said the price should stay as it is now because the quality of the output is the same.

<Bob> OK. That does sound like a reasonable perspective. The variable costs will track with the activity if nothing else changes. Was it apportioning the overhead costs as part of the Reference Costing that was the problem?

<Leslie> No actually. We have not had that conversation yet. The problem was more fundamental. The problem is that the current budgets are wrong.

<Bob> Ah! That statement might come across as a bit of a challenge to the Finance Department. What was their reaction?

<Leslie> To para-phrase it was “We are just breaking even in the current financial year so the current budget must be correct. Please do not dabble in things that you clearly do not understand.”

<Bob> OK. You can see their point. How did you reply?

<Leslie> I tried to explain the concepts of the Cost-Of-The-Queue and how that cost was incurred by one part of the system with one budget but that the queue was created by a different part of the system with a different budget. I tried to explain that just because the budgets were 100% utilised does not mean that the budgets were optimal.

<Bob> How was that explanation received?

<Leslie> They did not seem to understand what I was getting at and kept saying “Inventory is an asset on the balance sheet. If profit is zero we must have planned our budgets perfectly. We cannot shift money between budgets within year if the budgets are already perfect. Any variation will average out. We have to stick to the financial plan and projections for the year. It works. The problem is not Finance – the problem is you.

<Bob> OK. Have you described the Seventh Flow and put it in context?

<Leslie> Arrrgh! No! Of course! That is how I should have approached it. Budgets are Cash-Inventories and what we need is Cash-Flow to where and when it is needed and in just the right amount according to the Principle of Parsimonious Pull. Thank you. I knew you would ask the crunch question. That has given me a fresh perspective on it. I will have another go.

<Bob> Let know how you get on. I am curious to hear the next instalment of the story.

<Leslie> Will do. Bye for now.

Drrrrrrrr

construction_blueprint_meeting_150_wht_10887Creating a productive and stable system design requires considering Seven Flows at the same time. The Seventh Flow is cash flow.

Cash is like energy – it is only doing useful work when it is flowing.

Energy is often described as two forms – potential energy and and kinetic energy.  The ‘doing’ happens when one form is being converted from potential to kinetic. Cash in the budget is like potential energy – sitting there ready to do some business.  Cash flow is like kinetic energy – it is the business.

The most versatile form of energy that we use is electrical energy. It is versatile because it can easily be converted into other forms – e.g. heat, light and movement. Since the late 1800’s our whole society has become highly dependent on electrical energy.  But electrical energy is tricky to store and even now our battery technology is pretty feeble. So, if we want to store energy we use a different form – chemical energy.  Gas, oil and coal – the fossil fuels – are all ancient stores of chemical energy that were originally derived from sunlight captured by vast carboniferous forests over millions of years. These carbon-rich fossil fuels are convenient to store near where they are needed, and when they are needed. But fossil fuels have a number of drawbacks: One is that they release their stored carbon when they are “burned”.  Another is that they are not renewable.  So, in the future we will need to develop better ways to capture, transport, use and store the energy from the Sun that will flow in glorious abundance for millions of years to come.

Plants discovered millions of years ago how to do this sunlight-to-chemical energy conversion and that biological legacy is built into every cell in every plant on the planet. Animals just do the reverse trick – they convert chemical-to-electrical. Every cell in every animal on the planet is a microscopic electrical generator that “burns” chemical fuel – carbohydrate. The other products are carbon dioxide and water. Plants use sunlight to recycle and store the carbon dioxide. It is a resilient and sustainable design.

plant_growing_anim_150_wht_9902Plants seemingly have it easy – the sunlight comes to them – they just sunbathe all day!  The animals have to work a bit harder – they have to move about gathering their chemical fuel. Some animals just feed on plants, others feed on other animals, and we do a bit of both. This food-gathering is a more complicated affair – and it creates a problem. Animals need a constant supply of energy – so they have to carry a store of chemical fuel around with them. That store is heavy so it needs energy to move it about.  Herbivors can be bigger and less intelligent because their food does not run away.  Carnivors need to be more agile; both physically and mentally. A balance is required. A big enough fuel store but not too big.  So, some animals have evolved additional strategies. Animals have become very good at not wasting energy – because the more that is wasted the more food that is needed and the greater the risk of getting eaten or getting too weak to catch the next meal.

To illustrate how amazing animals are at energy conservation we just need to look at an animal structure like the heart. The heart is there to pump blood around. Blood carries chemical nutrients and waste from one “department” of the body to another – just like ships, rail, roads and planes carry stuff around the world.

cardiogram_heart_working_150_wht_5747Blood is a sticky, viscous fluid that requires considerable energy to pump around the body and, because it is pumped continuously by the heart, even a small improvement in the energy efficiency of the circulation design has a big long-term cumulative effect. The flow of blood to any part of the body must match the requirements of that part.  If the blood flow to your brain slows down for even few seconds the brain cannot work properly and you lose consciousness – it is called “fainting”.

If the flow of blood to the brain is stopped for just a few minutes then the brain cells actually die. That is called a “stroke”. Our brains use a lot of electrical energy to do their job and our brain cells do not have big stores of fuel – so they need constant re-supply. And our brains are electrically active all the time – even when we are sleeping.

Other parts of the body are similar. Muscles for instance. The difference is that the supply of blood that muscles need is very variable – it is low when resting and goes up with exercise. It has been estimated that the change in blood flow for a muscle can be 30 fold!  That variation creates a design problem for the body because we need to maintain the blood flow to brain at all times but we only want blood to be flowing to the muscles in just the amount that they need, where they need it and when they need it. And we want to minimise the energy required to pump the blood at all times. How then is the total and differential allocation of blood flow decided and controlled?  It is certainly not a conscious process.

stick_figure_turning_valve_150_wht_8583The answer is that the brain and the muscles control their own flow. It is called autoregulation.  They open the tap when needed and just as importantly they close the tap when not needed. It is called the Principle of Parsimonious Pull. The brain directs which muscles are active but it does not direct the blood supply that they need. They are left to do that themselves.

So, if we equate blood-flow and energy-flow to cash-flow then we arrive at a surprising conclusion. The optimal design, the most energy and cash efficient, is where the separate parts of the system continuously determine the energy/cash flow required for them to operate effectively. They control the supply. They autoregulate their cash-flow. They pull only what they need when they need it.

BUT

For this to work then every part of the system needs to have a collaborative and parsimonious pull-design philosophy – one that wastes as little energy and cash as possible.  Minimum waste of energy requires careful design – it is called ergonomic design. Minimum waste of cash requires careful design – it is called economic design.

business_figures_accusing_anim_150_wht_9821Many socioeconomic systems are fragmented and have parts that behave in a “greedy” manner and that compete with each other for resources. It is a dog-eat-dog design. They would use whatever resources they can get for fear of being starved. Greed is Good. Collaboration is Weak.  In such a competitive situation a rigid-budget design is a requirement because it helps prevent one part selfishly and blindly destabilising the whole system for all. The problem is that this rigid financial design blocks change so it blocks improvement.

This means that greedy, competitive, selfish systems are unable to self-improve.

So, when the world changes too much and their survival depends on change then they risk becoming extinct just as the dinosaurs did.

red_arrow_down_crash_400_wht_2751Many will challenge this assertion by saying “But competition drives up performance“.  Actually, it is not as simple as that. Competition will weed out the weakest who “die” and remove themselves from the equation – apparently increasing the average. What actually drives improvement is customer choice. Organisations that are able to self-improve will create higher-quality and lower-cost products and in a globally-connected-economy the customers will vote with their wallets. The greedy and selfish competition lags behind.

So, to ensure survival in a global economy the Seventh Flow cannot be rigidly restricted by annually allocated departmental budgets. It is a dinosaur design.

And there is no difference between public and private organisations. The laws of cash-flow physics are universal.

How then is the cash flow controlled?

The “trick” is to design a monitoring and feedback component into the system design. This is called the Sixth Flow – and it must be designed so that just the right amount of cash is pulled to the just the right places and at just the right time and for just as long as needed to maximise the revenue.  The rest of the design – First Flow to Fifth Flow ensure the total amount of cash needed is a minimum.  All Seven Flows are needed.

So the essential ingredient for financial stability and survival is Sixth and Seventh Flow Design capability. That skill has another name – it is called Value Stream Accounting which is a component of complex adaptive systems engineering (CASE).

What? Never heard of Value Stream Accounting?

Maybe that is just another Error of Omission?

Creep-Crack-Crunch

The current crisis of confidence in the NHS has all the hallmarks of a classic system behaviour called creep-crack-crunch.

The first obvious crunch may feel like a sudden shock but it is usually not a complete surprise and it is actually one of a series of cracks that are leading up to a BIG CRUNCH. These cracks are an early warning sign of pressure building up in parts of the system and causing localised failures. These cracks weaken the whole system. The underlying cause is called creep.

SanFrancisco_PostEarthquake

Earthquakes are a perfect example of this phenomemon. Geological time scales are measured in thousands of years and we now know that the surface of the earth is a dynamic structure with vast contient-sized plates of solid rock floating on a liquid core of molten magma. Over millions of years the continents have moved huge distances and the world we see today on our satellite images is just a single frame in a multi-billion year geological video.  That is the geological creep bit. The cracks first appear at the edges of these tectonic plates where they smash into each other, grind past each other or are pulled apart from each other.  The geological hot-spots are marked out on our global map by lofty mountain ranges, fissured earthquake zones, and deep mid-ocean trenches. And we know that when a geological crunch arrives it happens in a blink of the geological eye.

The panorama above shows the devastation of San Francisco caused by the 1906 earthquake. San Francisco is built on the San Andreas Fault – the junction between the Pacific plate and the North American plate. The dramatic volcanic eruption in Iceland in 2010 came and went in a matter of weeks but the irreversible disruption it caused for global air traffic will be felt for years. The undersea earthquakes that caused the devastating tsunamis in 2006 and 2011 lasted only a few minutes; the deadly shock waves crossed an ocean in a matter of hours; and when they arrived the silent killer wiped out whole shoreside communities in seconds. Tens of thousands of lives were lost and the social after-shocks of that geological-crunch will be felt for decades.

These are natural disasters. We have little or no influence over them. Human-engineered disasters are a different matter – and they are just as deadly.

The NHS is an example. We are all painfully aware of the recent crisis of confidence triggered by the Francis Report. Many could see the cracks appearing and tried to blow their warning whistles but with little effect – they were silenced with legal gagging clauses and the opening cracks were papered over. It was only after the crunch that we finally acknowledged what we already knew and we started to search for the creep. Remorse and revenge does not bring back those who have been lost.  We need to focus on the future and not just point at the past.

UK_PopulationPyramid_2013Socio-economic systems evolve at a pace that is measured in years. So when a social crunch happens it is necessary to look back several decades for the tell-tale symptoms of creep and the early signs of cracks appearing.

Two objective measures of a socio-economic system are population and expenditure.

Population is people-in-progress; and national expenditure is the flow of the cash required to keep the people-in-progress watered, fed, clothed, housed, healthy and occupied.

The diagram above is called a population pyramid and it shows the distribution by gender and age of the UK population in 2013. The wobbles tell a story. It does rather look like the profile of a bushy-eyebrowed, big-nosed, pointy-chinned old couple standing back-to-back and maybe there is a hidden message for us there?

The “eyebrow” between ages 67 and 62 is the increase in births that happened 62 to 67 years ago: betwee 1946 and 1951. The post WWII baby boom.  The “nose” of 42-52 year olds are the “children of the 60’s” which was a period of rapid economic growth and new optimism. The “upper lip” at 32-42 correlates with the 1970’s that was a period of stagnant growth,  high inflation, strikes, civil unrest and the dark threat of global thermonuclear war. This “stagflation” is now believed to have been triggered by political meddling in the Middle-East that led to the 1974 OPEC oil crisis and culminated in the “winter of discontent” in 1979.  The “chin” signals there was another population expansion in the 1980s when optimism returned (SALT-II was signed in 1979) and the economy was growing again. Then the “neck” contraction in the 1990’s after the 1987 Black Monday global stock market crash.  Perhaps the new optimism of the Third Millenium led to the “chest” expansion but the financial crisis that followed the sub-prime bubble to burst in 2008 has yet to show its impact on the population chart. This static chart only tells part of the story – the animated chart reveals a significant secondary expansion of the 20-30 year old age group over the last decade. This cannot have been caused by births and is evidence of immigration of a large number of young couples – probably from the expanding Europe Union.

If this “yo-yo” population pattern is repeated then the current economic downturn will be followed by a contraction at the birth end of the spectrum and possibly also net emigration. And that is a big worry because each population wave takes a 100 years to propagate through the system. The most economically productive population – the  20-60 year olds  – are the ones who pay the care bills for the rest. So having a population curve with lots of wobbles in it causes long term socio-economic instability.

Using this big-picture long-timescale perspective; evidence of an NHS safety and quality crunch; silenced voices of cracks being papered-over; let us look for the historical evidence of the creep.

Nowadays the data we need is literally at our fingertips – and there is a vast ocean of it to swim around in – and to drown in if we are not careful.  The Office of National Statistics (ONS) is a rich mine of UK socioeconomic data – it is the source of the histogram above.  The trick is to find the nuggets of knowledge in the haystack of facts and then to convert the tables of numbers into something that is a bit more digestible and meaningful. This is what Russ Ackoff descibes as the difference between Data and Information. The data-to-information conversion needs context.

Rule #1: Data without context is meaningless – and is at best worthless and at worse is dangerous.

boxes_connected_PA_150_wht_2762With respect to the NHS there is a Minotaur’s Labyrinth of data warehouses – it is fragmented but it is out there – in cyberspace. The Department of Health publishes some on public sites but it is a bit thin on context so it can be difficult to extract the meaning.

Relying on our memories to provide the necessary context is fraught with problems. Memories are subject to a whole range of distortions, deletions, denials and delusions.  The NHS has been in existence since 1948 and there are not many people who can personally remember the whole story with objective clarity.  Fortunately cyberspace again provides some of what we need and with a few minutes of surfing we can discover something like a website that chronicles the history of the NHS in decades from its creation in 1948 – http://www.nhshistory.net/ – created and maintained by one person and a goldmine of valuable context. The decade that is of particular interest is 1998-2007 – Chapter 6

With just some data and some context it is possible to pull together the outline of the bigger picture of the decade that led up to the Mid Staffordshire healthcare quality crunch.

We will look at this as a NHS system evolving over time within its broader UK context. Here is the time-series chart of the population of England – the source of the demand on the NHS.

Population_of_England_1984-2010This shows a significant and steady increase in population – 12% overall between 1984 an 2012.

This aggregate hides a 9% increase in the under 65 population and 29% growth in the over 65 age group.

This is hard evidence of demographic creep – a ticking health and social care time bomb. And the curve is getting steeper. The pressure is building.

The next bit of the map we need is a measure of the flow through hospitals – the activity – and this data is available as the annual HES (Hospital Episodes Statistics) reports.  The full reports are hundreds of pages of fine detail but the headline summaries contain enough for our present purpose.

NHS_HES_Admissions_1997-2011

The time- series chart shows a steady increase in hospital admissions. Drilling into the summaries revealed that just over a third are emergency admissions and the rest are planned or maternity.

In the decade from 1998 to 2008 there was a 25% increase in hospital activity. This means more work for someone – but how much more and who for?

But does it imply more NHS beds?

Beds require wards, buildings and infrastructure – but it is the staff that deliver the health care. The bed is just a means of storage.  One measure of capacity and cost is the number of staffed beds available to be filled.  But this like measuring the number of spaces in a car park – it does not say much about flow – it is a just measure of maximum possible work in progress – the available space to hold the queue of patients who are somewhere between admission and discharge.

Here is the time series chart of the number of NHS beds from 1984 to 2006. The was a big fall in the number of beds in the decade after 1984 [Why was that?]

NHS_Beds_1984-2006

Between 1997 and 2007 there was about a 10% fall in the number of beds. The NHS patient warehouse was getting smaller.

But the activity – the flow – grew by 25% over the same time period: so the Laws Of Physics say that the flow must have been faster.

The average length of stay must have been falling.

This insight has another implication – fewer beds must mean smaller hospitals and lower costs – yes?  After all everyone seems to equate beds-to-cost; more-beds-cost-more less-beds-cost-less. It sounds reasonable. But higher flow means more demand and more workload so that would require more staff – and that means higher costs. So which is it? Less, the same or more cost?

NHS_Employees_1996_2007The published data says that staff headcount  went up by 25% – which correlates with the increase in activity. That makes sense.

And it looks like it “jumped” up in 2003 so something must have triggered that. More cash pumped into the system perhaps? Was that the effect of the Wanless Report?

But what type of staff? Doctors? Nurses? Admin and Clerical? Managers?  The European Working Time Directive (EWTD) forced junior doctors hours down and prompted an expansion of consultants to take on the displaced service work. There was also a gradual move towards specialisation and multi-disciplinary teams. What impact would that have on cost? Higher most likely. The system is getting more complex.

Of course not all costs have the same impact on the system. About 4% of staff are classified as “management” and it is this group that are responsible for strategic and tactical planning. Managers plan the work – workers work the plan.  The cost and efficiency of the management component of the system is not as useful a metric as the effectiveness of its collective decision making. Unfortuately there does not appear to be any published data on management decision making qualty and effectiveness. So we cannot estimate cost-effectiveness. Perhaps that is because it is not as easy to measure effectiveness as it is to count admissions, discharges, head counts, costs and deaths. Some things that count cannot easily be counted. The 4% number is also meaningless. The human head represents about 4% of the bodyweight of an adult person – and we all know that it is not the size of our heads that is important it is the effectiveness of the decisions that it makes which really counts!  Effectiveness, efficiency and costs are not the same thing.

Back to the story. The number of beds went down by 10% and number of staff went up by 25% which means that the staff-per-bed ratio went up by nearly 40%.  Does this mean that each bed has become 25% more productive or 40% more productive or less productive? [What exactly do we mean by “productivity”?]

To answer that we need to know what the beds produced – the discharges from hospital and not just the total number, we need the “last discharges” that signal the end of an episode of hospital care.

NHS_LastDischarges_1998-2011The time-series chart of last-discharges shows the same pattern as the admissions: as we would expect.

This output has two components – patients who leave alive and those who do not.

So what happened to the number of deaths per year over this period of time?

That data is also published annually in the Hospital Episode Statistics (HES) summaries.

This is what it shows ….

NHS_Absolute_Deaths_1998-2011The absolute hospital mortality is reducing over time – but not steadily. It went up and down between 2000 and 2005 – and has continued on a downward trend since then.

And to put this into context – the UK annual mortality is about 600,000 per year. That means that only about 40% of deaths happen in hospitals. UK annual mortality is falling and births are rising so the population is growing bigger and older.  [My head is now starting to ache trying to juggle all these numbers and pictures in it].

This is not the whole story though – if the absolute hospital activity is going up and the absolute hospital mortality is going down then this raw mortality number may not be telling the whole picture. To correct for those effects we need the ratio – the Hospital Mortality Ratio (HMR).

NHS_HospitalMortalityRatio_1998-2011This is the result of combining these two metrics – a 40% reduction in the hospital mortality ratio.

Does this mean that NHS hospitals are getting safer over time?

This observed behaviour can be caused by hospitals getting safer – it can also be caused by hospitals doing more low-risk work that creates a dilution effect. We would need to dig deeper to find out which. But that will distract us from telling the story.

Back to productivity.

The other part of the productivity equation is cost.

So what about NHS costs?  A bigger, older population, more activity, more staff, and better outcomes will all cost more taxpayer cash, surely! But how much more?  The activity and head count has gone up by 25% so has cost gone up by the same amount?

NHS_Annual_SpendThis is the time-series chart of the cost per year of the NHS and because buying power changes over time it has been adjusted using the Consumer Price Index using 2009 as the reference year – so the historical cost is roughly comparable with current prices.

The cost has gone up by 100% in one decade!  That is a lot more than 25%.

The published financial data for 2006-2010 shows that the proportion of NHS spending that goes to hospitals is about 50% and this has been relatively stable over that period – so it is reasonable to say that the increase in cash flowing to hospitals has been about 100% too.

So if the cost of hospitals is going up faster than the output then productivity is falling – and in this case it works out as a 37% drop in productivity (25% increase in activity for 100% increase in cost = 37% fall in productivity).

So the available data which anyone with a computer, an internet connection, and some curiosity can get; and with bit of spreadsheet noggin can turn into pictures shows that over the decade of growth that led up to the the Mid Staffs crunch we had:

1. A slightly bigger population; and a
2. significantly older population; and a
3. 25% increase in NHS hospital activity; and a
4. 10% fall in NHS beds; and a
5. 25% increase in NHS staff; which gives a
6. 40% increase in staff-per-bed ratio; an an
7. 8% reduction in absolute hospital mortality; which gives a
8. 40% reduction in relative hospital mortality; and a
9. 100% increase in NHS  hospital cost; which gives a
10. 37% fall drop in “hospital productivity”.

An experienced Improvement Scientist knows that a system that has been left to evolve by creep-crack-and-crunch can be re-designed to deliver higher quality and higher flow at lower total cost.

The safety creep at Mid-Staffs is now there for all to see. A crack has appeared in our confidence in the NHS – and raises a couple of crunch questions:

Where Has All The Extra Money Gone?

 How Will We Avoid The BIG CRUNCH?

The huge increase in NHS funding over the last decade was the recommendation of the Wanless Report but the impact of implementing the recommendations has never been fully explored. Healthcare is a service system that is designed to deliver two intangible products – health and care. So the major cost is staff-time – particularly the clinical staff.  A 25% increase in head count and a 100% increase in cost implies that the heads are getting more expensive.  Either a higher proportion of more expensive clinically trained and registered staff, or more pay for the existing staff or both.  The evidence shows that about 50% of NHS Staff are doctors and nurses and over the last decade there has been a bigger increase in the number of doctors than nurses. Added to that the Agenda for Change programme effectively increased the total wage bill and the new contracts for GPs and Consultants added more upward wage pressure.  This is cost creep and it adds up over time. The Kings Fund looked at the impact in 2006 and suggested that, in that year alone, 72% of the additional money was sucked up by bigger wage bills and other cost-pressures! The previous year they estimated 87% of the “new money” had disappeared hte same way. The extra cash is gushing though the cracks in the bottom of the fiscal bucket that had been clumsily papered-over. And these are recurring revenue costs so they add up over time into a future financial crunch.  The biggest one may be yet to come – the generous final-salary pensions that public-sector employees enjoy!

So it is even more important that the increasingly expensive clinical staff are not being forced to spend their time doing work that has no direct or indirect benefit to patients.

Trying to do a good job in a poorly designed system is both frustrating and demotivating – and the outcome can be a cynical attitude of “I only work here to pay the bills“. But as public sector wages go up and private sector pensions evaporate the cynics are stuck in a miserable job that they cannot afford to give up. And their negative behaviour poisons the whole pool. That is the long term cumulative cultural and financial cost of poor NHS process design. That is the outcome of not investing earlier in developing an Improvement Science capability.

The good news is that the time-series charts illustrate that the NHS is behaving like any other complex, adaptive, human-engineered value system. This means that the theory, techniques and tools of Improvement Science and value system design can be applied to answer these questions. It means that the root causes of the excessive costs can be diagnosed and selectively removed without compromising safety and quality. It means that the savings can be wisely re-invested to improve the resilience of some parts and to provide capacity in other parts to absorb the expected increases in demand that are coming down the population pipe.

This is Improvement Science. It is a learnable skill.

18/03/2013: Update

The question “Where Has The Money Gone?” has now been asked at the Public Accounts Committee