Transformation

It has been another interesting week.  A bitter-sweet mixture of disappointment and delight. And the central theme has been ‘transformation’.


The source of disappointment was the newsreel images of picket lines of banner-waving junior doctors standing in the cold watching ambulances deliver emergencies to hospitals now run by consultants.

So what about the thousands of elective appointments and operations that were cancelled to release the consultants? If the NHS was failing elective delivery time targets before it is going to be failing them even more now. And who will pay for the “waiting list initiatives” needed to just catch up? Depressing to watch.

The mercurial Roy Lilley summed up the general mood very well in his newsletter on Thursday, the day after the strike.

Roy_Lilley_Transformation

What he is saying is we do not have a health care system, we have a sick care system.  Which is the term coined by the acclaimed systems thinker, the late Russell Ackoff (see the video about half way down).

We aspire to a transformation-to-better but we only appear to be able to achieve a transformation-to-worse. That is depressing.


My source of delight was sharing the stories of those who are stepping up and are transforming themselves and their bits of the world; and how they are doing that by helping each other to learn “how to do it” – a small bite at a time.

Here is one excellent example: a diagnostic study looking at the root cause of the waiting time for school-age pupils to receive a health-protecting immunisation.


So what sort of transformation does the NHS need?

A transformation in the way it delivers care by elimination of the fragmentation that is the primary cause of the distrust, queues, waits, frustration, chaos and ever-increasing costs?

A transformation from purposeless and reactive; to purposeful and proactive?

A transformation from the disappointment that flows from the mismatch between intent and impact; to the delight that flows from discovering that there is a way forward; that there is a well understood science that underpins it; and a growing body of evidence that proves its effectiveness.  The Science of Improvement.


In  a recent blog I shared the story of how it is possible to ‘melt queues‘ or more specifically how it is possible to teach anyone, who wants to learn, how to melt queues.

It is possible to do this for an outpatient clinic in one day.

So imagine what could happen if just 1% of consultants decided improve their outpatient clinics using this quick-and-easy-to-learn-and-apply method?  Those courageous and innovative consultants who are not prepared to drown in the  Victim Vortex of despair and cynicism.  And what could happen if they shared their improvement stories with their less optimistic colleagues?  And what could happen if a just a few of them followed the lead of the innovators?

Would that be a small transformation?  Or the start of a much bigger one? Or both?

Chimp_NoHear_NoSee_NoSpeakLast week I shared a link to Dr Don Berwick’s thought provoking presentation at the Healthcare Safety Congress in Sweden.

Near the end of the talk Don recommended six books, and I was reassured that I already had read three of them. Naturally, I was curious to read the other three.

One of the unfamiliar books was “Overcoming Organizational Defenses” by the late Chris Argyris, a professor at Harvard.  I confess that I have tried to read some of his books before, but found them rather difficult to understand.  So I was intrigued that Don was recommending it as an ‘easy read’.  Maybe I am more of a dimwit that I previously believed!  So fear of failure took over my inner-chimp and I prevaricated. Who would willingly want to discover the true depth of their dimwittedness!


Later in the week, I was forwarded a copy of a recently published paper that was on a topic closely related to a key thread in Dr Don’s presentation:

understanding variation.

The paper was by researchers who had looked at the Board papers of 30 randomly selected NHS Trusts to examine how information on safety and quality was being shared and used.  They were looking for evidence that the Trust Boards understood the importance of variation and the need to separate ‘signal’ from ‘noise’ before making decisions on actions to improve safety and quality performance.  This was a point Don had stressed too, so there was a link.

The randomly selected Trust Board reports contained 1488 charts, of which only 88 demonstrated the contribution of chance effects (i.e. noise). Of these, 72 showed the Shewhart-style control charts that Don demonstrated. And of these, only 8 stated how the control limits were constructed (which is an essential requirement for the chart to be meaningful and useful).

That is a validity yield of 8 out of 1488, or 0.54%, which is for all practical purposes zero. Oh dear!


This chance combination of apparently independent events got me thinking.

Q1: What is the reason that NHS Trust Boards do not use these signal-and-noise separation techniques when it has been demonstrated, for at least 12 years to my knowledge, that they are very effective for facilitating improvement in healthcare? (e.g. Improving Healthcare with Control Charts by Raymond G. Carey was published in 2003).

Q2: Is there some form of “organizational defense” system in place that prevents NHS Trust Boards from learning useful ‘new’ knowledge?


So I surfed the Web to learn more about Chris Argyris and to explore in greater depth his concept of Single Loop and Double Loop learning.  I was feeling like a dimwit again because to me it is not a very descriptive title!  I suspect it is not to many others too.

I sensed that I needed to translate the concept into the language of healthcare and this is what emerged.

Single Loop learning is like treating the symptoms and ignoring the disease.

Double Loop learning is diagnosing the underlying disease and treating that.


So what are the symptoms?
The NHS Trust Board pain of performance failure on all dimensions – safety, delivery, quality and productivity (i.e. affordability for a not-for-profit enterprise).

And what are the signs?
The tell-tale sign is more subtle. It is what is not present that is important. A serious omission. The missing bits are valid time-series charts in the Trust Board reports that show clearly what is signal and what is noise. This diagnostic decision is critical because the strategies for addressing them are quite different – as Julian Simcox eloquently describes in his latest essay.  If we get this wrong and we act on our decision, then we stand a very high chance of making the problem worse and demoralizing ourselves and our whole workforce in the process! Does that sound at all familiar?

And what is the disease?
Undiscussables.  Emotive subjects that are taboo in the Board Room.  And the issue of what is discussable is one of the undiscussables so we have a self-sustaining system.  Anyone who attempts to discuss an undiscussable is breaking the unspoken social code.  Another undiscussable is behaviour, and our social code is that we must not upset anyone so we cannot discuss it.  But by avoiding the issue (the undiscussable disease) we fail to address the root cause and end up upsetting everyone.  We achieve exactly what we are striving to avoid, which is the technical definition of incompetence.  Chris Argyris calls this ‘skilled incompetence’.


Does an apparent lack of awareness of what is already possible fully explain why NHS Trust Boards do not use the tried-and-tested tool called a system behaviour chart to help them diagnose, design and deliver effective improvements in safety, flow, quality and productivity?

Or are there other forces at play as well?

Some more undiscussables perhaps?

 

Don_Berwick_2016

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


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

Deming_SOPK

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

Here are the summaries of these parts – psychology first …

Deming_SOPK_Psychology

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


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

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

Deming_SOPK_PDSA

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

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

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

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

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

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

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


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

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


So where does psychology come in?

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

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

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


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

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


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

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

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

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


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

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

engineers_turbine_engine_16758The NHS is falling.

All the performance indicators on the NHSE cockpit dashboard show that it is on a downward trajectory.

The NHS engines are no longer effective enough or efficient enough to keep the NHS airship safely aloft.

And many sense the impending crash.

Scuffles are breaking out in the cockpit as scared pilots and anxious politicians wrestle with each other for the controls. The passengers and patients appear to be blissfully ignorant of the cockpit conflict.

But the cockpit chaos only serves to accelerate their collective rate of descent towards the hard reality of the Mountain of Doom.


So what is needed to avoid the crash?

Well, some calm and credible leadership in the cockpit would help; some coordinated crash avoidance would help too; and some much more effective and efficient engines to halt the descent and to lift us back to a safe altitude would help too. In fact the new NHS engines are essential.

But who is able to design, build, test and install these new healthcare system engines?


We need competent and experienced healthcare system engineers.


And clearly we do not have enough because if we had, we would not be in a CFIT scenario (cee fit = controlled flight into terrain).

So why do we not have enough healthcare system engineers?

Surely there are appropriate candidates and surely there are enough accredited courses with proven track records?

I looked.  There are no accredited courses in the UK and there are no proven track records. But there appear to be no shortage of suitable candidates from all corners of the NHS.

How can this be?

The answer seems to be that the complex flow system engineering science needed to do this is actually quite new … it is called complex adaptive system design (CASD) … and it has not diffused into healthcare.

More worryingly, even basic flow engineering science has not either, and that seems to be because healthcare is so insular.

So what can we do?

The answer would seem to be clear.  First, we need to find some people who, by chance, are dual-trained in healthcare and system engineering.  And there are a few of them, but not many.


People like Dr Kate Silvester who trained as an ophthalmic surgeon then retrained as a manufacturing systems engineer with Lucas and Airbus. Kate brought these priceless flow engineering skills back in to the NHS in the days of the Modernisation Agency and since then has proved that they work in practice – as described in the Health Foundation Flow-Cost-Quality Programme Report.


Second, we need to ask this small band of seasoned practitioners to design and to deliver a pragmatic, hands-on, learning-by-doing Healthcare Systems Engineer Development Programme.


The good news is that, not surprisingly, they have already diagnosed this skill gap and have been quietly designing, building and testing.

And they have come up with a name: The Phoenix Programme.

And because TPP is a highly disruptive innovation they know that it is impossible to assign a price-tag to it, so they have generously offered a limited number of free tickets to the first part of TPP to clinicians and clinical scientists.

The first step is called the Foundations of Improvement Science in Healthcare online course, and better known to those who have completed it as FISH.

This vanguard of innovators have shared their feedback.

And, for those who are frustrated and curious enough to explore outside their comfort zones, there are still some #freeFISH tickets.


So, if you are attracted by the opportunity of dual-training as a clinician and as a Healthcare Systems Engineer then we invite you to step this way.


And not surprisingly, this is not a new idea … see here and here.

The Harvard Business Review is worth reading because many of its articles challenge deeply held assumptions, and then back up the challenge with the pragmatic experience of those who have succeeded to overcome the limiting beliefs.

So the heading on the April 2016 copy that awaited me on my return from an Easter break caught my eye: YOU CAN’T FIX CULTURE.


 

HBR_April_2016

The successful leaders of major corporate transformations are agreed … the cultural change follows the technical change … and then the emergent culture sustains the improvement.

The examples presented include the Ford Motor Company, Delta Airlines, Novartis – so these are not corporate small fry!

The evidence suggests that the belief of “we cannot improve until the culture changes” is the mantra of failure of both leadership and management.


A health care system is characterised by a culture of risk avoidance. And for good reason. It is all too easy to harm while trying to heal!  Primum non nocere is a core tenet – first do no harm.

But, change and improvement implies taking risks – and those leaders of successful transformation know that the bigger risk by far is to become paralysed by fear and to do nothing.  Continual learning from many small successes and many small failures is preferable to crisis learning after a catastrophic failure!

The UK healthcare system is in a state of chronic chaos.  The evidence is there for anyone willing to look.  And waiting for the NHS culture to change, or pushing for culture change first appears to be a guaranteed recipe for further failure.

The HBR article suggests that it is better to stay focussed; to work within our circles of control and influence; to learn from others where knowledge is known, and where it is not – to use small, controlled experiments to explore new ground.


And I know this works because I have done it and I have seen it work.  Just by focussing on what is important to every member on the team; focussing on fixing what we could fix; not expecting or waiting for outside help; gathering and sharing the feedback from patients on a continuous basis; and maintaining patient and team safety while learning and experimenting … we have created a micro-culture of high safety, high efficiency, high trust and high productivity.  And we have shared the evidence via JOIS.

The micro-culture required to maintain the safety, flow, quality and productivity improvements emerged and evolved along with the improvements.

It was part of the effect, not the cause.


So the concept of ‘fix the system design flaws and the continual improvement culture will emerge’ seems to work at macro-system and at micro-system levels.

We just need to learn how to diagnose and treat healthcare system design flaws. And that is known knowledge.

So what is the next excuse?  Too busy?

frailsafeSafe means avoiding harm, and safety is an emergent property of a well-designed system.

Frail means infirm, poorly, wobbly and at higher risk of harm.

So we want our healthcare system to be a FrailSafe Design. But is it? How would we know? And what could we do to improve it?


About ten years ago I was involved in a project to improve the safety design of a specific clinical stream flowing through the hospital that I work in.

The ‘at risk’ group of patients were the frail elderly admitted as an emergency after a fall and who had suffered a fractured thigh bone. The neck of the femur.

Historically, the outcome for these patients is poor.  Many do not survive, and many of the survivors will never return to independent living. They become even more frail.


The project was undertaken during an organisational transition, the hospital was being ‘taken over’ by a bigger one.  This created a window of opportunity for some disruptive innovation, and the project was labelled as a ‘lean’ because we had been inspired by similar work done at Bolton some years before.

The actual design change was small and it was a zero-cost flow design tweak.

First we asked two flow questions:
Q1: How many of these high-risk frail patients do we admit a year?
A1: About one per day on average.
Q2: What is the safety critical time for these patients?
A2: The first four days.  The sooner they have hip surgery and are able to be actively mobilised the better their outcome.

Second we applied Little’s Law which showed the average number of patients in this critical phase is four. This was the ‘work in progress’ or WIP.

And we knew that variation is always present, and we knew that having all these patients in one place would make it easier for the multi-disciplinary teams to provide timely care and to avoid potentially harmful delays.

So we suggested that one six-bedded bay be designated the Fractured Neck Of Femur bay.

That was the flow design done.

The safety design was created by the multi-disciplinary teams who looked after these patients: the geriatricians, the anaesthetists, the perioperative emergency care team, the trauma and orthopaedic team, the physiotherapists, and so on.

They designed checklists to ensure that all patients got what they needed when they needed it and so that nothing important was left to chance.

And that was basically it.

And what happened was remarkable. The stream flowed. And one measured outcome was a dramatic and highly statistically significant reduction in mortality.

Injury_2011_Results
The full paper was published in Injury 2011; 42: 1234-1237.

We had created a FrailSafe Design … which implied that what was happening before was not frailsafe!


And the improvement in outcome for the patients who survived was also dramatic: A far larger proportion rehabilitated and returned to independent living, and a far smaller proportion required long-term institutional care.

By learning how to create and implement a FrailSafe Design we had added both years-to-life and life-to-years.

It cost nothing to achieve and the message was clear; this quotation is from the 2011 paper …

Injury_2011_Message

What was disappointing was the gap of four years between delivering the dramatic improvement and sharing the story.  Why was that?


What is exciting is that the concept of FrailSafe is growing, evolving and spreading.

figure_pointing_out_chart_data_150_clr_8005It was the time for Bob and Leslie’s regular Improvement Science coaching session.

<Leslie> Hi Bob, how are you today?

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

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

<Bob> OK. What is the context?

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

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

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

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

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

<Bob> And?

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

<Bob> Which charts, specifically?

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

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

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

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

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

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

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

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

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

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

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

<Bob> An excellent analogy!

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

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

<Leslie> Which is?

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

<Leslie> OK, I am on the case!

Pearl_and_OysterThe word pearl is a metaphor for something rare, beautiful, and valuable.

Pearls are formed inside the shell of certain mollusks as a defense mechanism against a potentially threatening irritant.

The mollusk creates a pearl sac to seal off the irritation.


And so it is with change and improvement.  The growth of precious pearls of improvement wisdom – the ones that develop slowly over time – are triggered by an irritant.

Someone asking an uncomfortable question perhaps, or presenting some information that implies that an uncomfortable question needs to be asked.


About seven years ago a question was asked “Would improving healthcare flow and quality result in lower costs?”

It is a good question because some believe that it would and some believe that it would not.  So an experiment to test the hypothesis was needed.

The Health Foundation stepped up to the challenge and funded a three year project to find the answer. The design of the experiment was simple. Take two oysters and introduce an irritant into them and see if pearls of wisdom appeared.

The two ‘oysters’ were Sheffield Hospital and Warwick Hospital and the irritant was Dr Kate Silvester who is a doctor and manufacturing system engineer and who has a bit-of-a-reputation for asking uncomfortable questions and backing them up with irrefutable information.


Two rare and precious pearls did indeed grow.

In Sheffield, it was proved that by improving the design of their elderly care process they improved the outcome for their frail, elderly patients.  More went back to their own homes and fewer left via the mortuary.  That was the quality and safety improvement. They also showed a shorter length of stay and a reduction in the number of beds needed to store the work in progress.  That was the flow and productivity improvement.

What was interesting to observe was how difficult it was to get these profoundly important findings published.  It appeared that a further irritant had been created for the academic peer review oyster!

The case study was eventually published in Age and Aging 2014; 43: 472-77.

The pearl that grew around this seed is the Sheffield Microsystems Academy.


In Warwick, it was proved that the A&E 4 hour performance could be improved by focussing on improving the design of the processes within the hospital, downstream of A&E.  For example, a redesign of the phlebotomy and laboratory process to ensure that clinical decisions on a ward round are based on todays blood results.

This specific case study was eventually published as well, but by a different path – one specifically designed for sharing improvement case studies – JOIS 2015; 22:1-30

And the pearls of wisdom that developed as a result of irritating many oysters in the Warwick bed are clearly described by Glen Burley, CEO of Warwick Hospital NHS Trust in this recent video.


Getting the results of all these oyster bed experiments published required irritating the Health Foundation oyster … but a pearl grew there too and emerged as the full Health Foundation report which can be downloaded here.


So if you want to grow a fistful of improvement and a bagful of pearls of wisdom … then you will need to introduce a bit of irritation … and Dr Kate Silvester is a proven source of grit for your oyster!

Learning how to manage is as vital as learning how to lead.

by Julian Simcox

Recently I blogged to introduce the re-publication of my 10 year old essay:

“Intervening into Personal and Organisational Systems by Powerfully Leading and Wisely Managing”

The key ideas in that essay were seven fold:

  1. Aiming to develop Leadership separately from Management is likely to confuse anyone targeted by a separatist training programme, the reality being that everyone in organisational life is necessarily and simultaneously both Managing and Leading (M/L) and often desperately trying to integrate them as two very different action-logics.
  2. Managing and Leading are not roles but ways of thinking and acting that need to be intently chosen, according to the particular learning context (one of three) that any Managerial Leader (12) is facing.
  3. Like in Stephen Covey’s “Maturity Continuum” (8) M/L capability evolves over time (see the diagram below) and makes possible a transformational outcome, if supported in one’s organisation by sufficient and timely post-conventional thinking.
  4. Such an outcome (9,10,11,14,17,19,20,21,23) occurred in Toyota from 1950, making it possible for the organisation to evolve into what Peter Senge (18) calls a “Learning Organisation” – one in which improvement science (4) ensues continually from the bottom-up, within a structure that has evolved top-down.
  5. In Toyota’s case it was W. Edwards Deming who is most credited with having been the catalyst. Jim Collins (6) evidences eleven other examples of an organisational transformation sparked by an individual with a post-conventional world view that transcended a pre-existing conventional one.
  6. Deming talked a lot about ways of thinking – paradigms – that, like Euclidian geometry, make sense in their own world, but not outside it. When speaking with anyone in a client organisation he always aimed at being empathic to a person’s individual frame of reference. He was interested in how individuals make their own common sense because he had learned that it is this that often negatively impacts an individual’s decision-making process and hence their impact on an organisational system that needs to continually learn – a phenomenon he called “tampering”.
  7. The diagram seeks to capture the ways in which paradigms (world views) collectively and sequentially evolve. It combines the research of several practitioners (2,7,15,16) who sought to empirically trace the archetypal evolution of individual sense-making.

JS_Blog_20160307_Fig1

In 2013, Don Berwick (5) recommended to the UK government that, in order to prioritise quality and safety, the National Health Service must become a Deming-style learning organisation. The NHS however is not one single organisation, it is a thousand organisations – both privately and publically owned.  Yet if structured with “Liberating Disciplines” (22) via appropriately set central standards (e.g. tools that prompt thinking that is scientifically methodical), each can be invited as a single organisation to transform themselves into a body with learning its core value. Berwick seems to appreciate that out of the apparently sufficient conventional thinking, enough post-conventional managerial leadership will then have a chance to take root, and in time bloom.

The purpose of this blog is to introduce a second essay:

“Managerial Leadership: Five action-logics viewed via two developmental lenses.”

In the first essay I used P-D-S-A as the integrative link between Managing and Leading – offering a total of just three learning contexts, but this always felt a little over-simplistic and in 2005 when coaching my daughter Josie – then in her sandwich year as an undergraduate trainee in the hospitality industry – I was persuaded by her to further sub-divide the two M/L modes – replacing two with four:

  1. maintaining
  2. continually improving
  3. innovating
  4. transforming.

Applying this new 4 action-logic model, Josie succeeded in transforming the fortunes of her hotel – winning a national award for her efforts – and this made me wonder if she might be on to something important?

I decided to use the new version of the model to explore what it would look like through first a “conventional” lens, and then second a “post-conventional” lens – illustrating the kinds of paradigm shifts that one might see in action when inside a learning organisation, in particular the way that accountabilities for performance are handled.

It is hard to describe a post-conventional way of seeing things to someone who developmentally has discovered only the conventional way – about 85% of adults. It is as if the instructions about how to get out of the box are on the outside. It is hoped that this essay may help some individuals unlock this conundrum. In a learning organisation for example it turns out that real-time data and feedback are essential for continually prompting individuals and organisations to rapidly evolve a new way of seeing.

BaseLine® for example is a tool that has been designed with this in mind. It allows conventional organisations and individuals, even those considering themselves relatively innumerate, to develop post-conventional habits; simply by using the time-series data that in many cases is already being collected – albeit usually for reasons of top-down accountability rather than methodical improvement. In this way, healthy developmental conversation gets sparked – and at all organisational levels: bottom, middle and top.

It also turns out that Continuous Improvement when seen though the second lens is not the same as Continual Improvement (mode 2) – and this is another one of the paradigm shifts that in the essay gets explained. Here is the model as it then appears:

JS_Blog_20160307_Fig2

Note that a fifth action-logic mode, modelling, is also now included. This emerged out of conversations I was having with Simon Dodds when writing the final draft in 2011. The essence of this mode is embodied in a phrase coined by the late Russell Ackoff – “idealized design” (1) – using modern computing technology to facilitate transformative change within tolerable levels of risk.

People often readily admit to spending much of their life in mode 1 (maintaining), whilst really preferring to be in mode 3 (innovating) – even admitting to seeing mode 1 as relatively boring, or at best as overly bureaucratic. Such individuals are especially prone to tampering, and may even shun regimes in which they feel overly controlled. What the post-conventional worldview offers however is not the prospect of being controlled, but the prospect of being in control – whilst simultaneously letting go – a paradox that is not easy to get unless developmentally ready – hence the 2005 essay. This goes for the tools too – especially when being deployed with the full cultural support that can flow from an organisation imbued with sufficient post-conventional design.

If the organisation can be designed to sufficiently support the right people to take control of each critical process or sub-system, who at the right level (usually the lowest point in the hierarchy that accountability may be accepted), may feel safely equipped to make sound decisions, genuine empowerment then becomes possible. Essentially, people then feel safe enough to self-empower and take charge of their system.

Toyota are an exemplar “learning organisation” – actually a system of organisations that work so harmoniously as a whole that by continually adapting to its changing environment, risk can be smoothly managed. Their preoccupation from bottom to top is understanding in real time what is changing so that changes (to the system) can then be proactively and wisely made. Each employee at each organisational level is educated to both manage and lead.

This approach has enabled them to grow to become the largest volume car maker in the world – and largely via organic growth alone. They have achieved this simply by constantly delivering what the customer wants with low variation (hence high reliability) and by continually studying that variation to uncover the real causes of problems. Performance is continually assessed over time and seen largely as pertaining to the system rather than being down to any one individual. Job hoppers – who though charismatic may also be practiced at being able to avoid having to live with the longer-term consequences of their actions – are not appointed to key roles.

Some will read the essay and say to themselves that little of this applies to me or my organisation – “we’re not Toyota, we’re not a private company, and we’re not even in manufacturing”. That however is likely to be a conventional view. The post-conventional principles described in the essay apply as much to service industries as to the public sector – both commissioners and providers – some of whom would intentionally evolve a post-conventional culture if given the space to do so.

At the very least I hope to have succeeded in convincing you, even if you don’t buy in to the notion of a Berwick-style learning system, that schooling people in management or leadership separately, or without a workable definition of each, is likely to be both cruel to the individual and to court dysfunction in the organisation.

References

  1. Ackoff R. Why so few organisations adopt systems thinking – 2007
  2. Beck D.E & Cowan C.C. – Spiral Dynamics – Mastering Values, Leadership, and Change – 1996
  3. Berwick D. – The Science of Improvement – 2008 : http://www.allhealth.org/BriefingMaterials/JAMA-Berwick-1151.pdf
  4. Berwick D. – The Science of Improvement – 2008 : http://www.allhealth.org/BriefingMaterials/JAMA-Berwick-1151.pdf
  5. Berwick Donald M. – Berwick Review into patient safety – 2013
  6. Collins J.C. – Level 5 Leadership: The triumph of Humility and Fierce Resolve – HBR Jan 2001
  7. Cook-Greuter. S. – Maps for living: ego-Development Stages Symbiosis to Conscious Universal Embeddedness – 1990
  8. Covey. S.R. – The 7 habits of Highly Effective People – 1989   (ISBN 0613191455)
  9. Delavigne K.T & Robertson J. D. – Deming’s profound changes – 1994
  10. Deming W. Edwards – Out of the Crisis – 1986 (ISBN 0-911379-01-0)
  11. Deming W.Edwards – The New Economics – 1993 (ISBN 0-911379-07-X) First edition
  12. Jaques. E. – Requisite Organisation: A Total System for Effective Managerial Organisation and Managerial Leadership for the 21st Century 1998 (ISBN 1886436045)
  13. Kotter. J. P. – A Force for Change: How Leadership Differs from Management – 1990
  14. Liker J.K & Meier D. – The Toyota Way Fieldbook – 2006
  15. Rooke D and Torbert W.R. – Organisational Transformation as a function of CEO’s Development Stage 1998 (Organisation Development Journal, Vol. 6.1)
  16. Rooke D and Torbert W.R. – Seven Transformations of Leadership – Harvard Business Review April 2005
  17. Scholtes Peter R. The Leader’s Handbook: Making Things Happen, Getting Things Done – 1998
  18. Senge. P. M. – The Fifth Discipline 1990 ISBN 10 – 0385260946
  19. Spear. S and Bowen H. K- Decoding the DNA of the Toyota Production System – Harvard Business Review Sept/Oct 1999
  20. Spear. S. – Learning to Lead at Toyota – Harvard Business Review – May 2004
  21. Takeuchi H, Osono E, Shimizu N. The contradictions that drive Toyota’s success. Harvard Business Review: June 2008
  22. Torbert W.R. & Associates – Action Inquiry – The secret of timely and transforming leadership – 2004
  23. Wheeler Donald J. – Advanced Topics in Statistical Process Control – the power of Shewhart Charts – 1995

 

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

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

Some of us spend some of our time in a different mental state called denial. And we enter that from yet another mental state called painful awareness.

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

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

Denial is flight.  We run away from a new and uncomfortable reality.

Fight means anger; directed at others (blame) and ourselves (guilt).

It is that passion that we must channel and focus as determination to learn.


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

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

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

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

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

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

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

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

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


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

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

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


puzzle_lightbulb_build_PA_150_wht_4587The second step in the process of improvement is to show the door to the next phase of learning; the phase called ‘know how’.

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

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

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


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


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

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

Raising awareness is the first step to improvement.
Choosing the path out of the pain towards knowledge is the second.

british_pound_money_three_bundled_stack_400_wht_2425This week I conducted an experiment – on myself.

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

It is easy enough to grasp the concept that fire-fighting to maintain patient safety amidst the chaos of healthcare would cost more in terms of tears and time, but it is tricky to translate that concept into hard numbers: cash.


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

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


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

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

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

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

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

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

by Julian Simcox

Actually, it doesn’t much matter because everyone needs to be able to choose between managing and leading – as distinct and yet mutually complementary action/ logics – and to argue that one is better than the other, or worse to try to school people about just one of them on its own, is inane. The UK’s National Health Service for example is currently keen on convincing medics that they should become “clinical leaders”, the term “clinical manager” being rarely heard, yet if anything the NHS suffers more from a shortage of management skill.

It is not only healthcare that is short on management. In the first half of my career I held the title “manager” in seven different roles, and in three different organisations, and had even completed an Exec MBA, but still didn’t properly get what it meant. The people I reported into also had little idea about what “managing well” actually meant, and even if they had possessed an inclination to coach me, would have merely added to my confusion.

If however you are fortunate enough to be working in an organisation that over time has been purposefully developed as a “Learning Culture” you will have acquired an appreciation of the vital distinction between managing and leading, and just what a massive difference this makes to your effectiveness, for it requires you, before you act, to understand (11) how your system is really flowing and performing. Only then will you be ready to choose whether to manage or to lead.

It is therefore not your role’s title that matters but whether the system you are running is stable, and whether it is capable of producing the outcomes needed by your customers. It also matters how risk is to be handled by you and your organisation when you are making changes. Outcomes will depend heavily upon you and your team’s accumulated levels of learning – as well, as it turns out, upon your personal world view/ developmental stage (more of which later).

Here is a diagram that illustrates that there are three basic learning contexts that a “managerial leader” (7) needs to be adept at operating within if they are to be able to nimbly choose between them.

JS_Blog_20160221_Fig1

Depending on one’s definitions of the processes of managing and leading, most people would agree that the first learning context pertains to the process of managing, and the third to the process of leading. The second context         (P-D-S-A) which helpfully for NHS employees is core to the NHS “Model of Improvement” turns out to be especially vital for effective managerial leadership for it binds the other two contexts together – as long as you know how?

Following the Mid-Staffs Hospital disaster, David Cameron asked Professor Don Berwick to recommend how to enhance public safety in the UK’s healthcare system. Unusually for a clinician he gets the importance of understanding your system and knowing moment-to-moment whether managing or leading is the right course of action. He recommends that to evolve a system to be as safe as it can be, all NHS employees should “Learn, master and apply the modern methods of quality control, quality improvement and quality planning” (1). He makes this recommendation because without the thinking that accompanies modern quality control methods, clinical managerial leadership is lame.

The Journal of Improvement Science has recently re-published my 10 year old essay called:

“Intervening into Personal and Organisational Systems by Powerfully Leading and Wisely Managing”

Originally written from the perspective of a practising executive coach, and as a retrospective on the work of W. Edwards Deming, the essay describes just what it is that a few extraordinary Managerial Leaders seem to possess that enables them to simultaneously Manage and Lead Transformation – first of themselves, and second of their organisation. The essay culminates in a comparison of “conventional” and “post-conventional” organisations. Toyota (9,12) in which Deming’s influence continues to be profound, is used as an example of the latter. Using the 3 generic intervention modes/ learning contexts, and the way that these corresponds to an executive’s evolving developmental stage I illustrate how this works and with it what a massive difference it makes. It is only in the later (post-conventional) stages for example that the processes of managing and leading are seen as two sides of the same coin. Dee Hock (6) called these heightened levels of awareness “chaordic” and Jim Collins (2) calls the level of power this brings “Level 5 Leadership”.

JS_Blog_20160221_Fig2

Berwick, borrowing from Deming (4,5) knows that to be structured-to-learn organisations need systems thinking (11) – and that organisations need Managerial Leaders who are sufficiently developed to know how to think and intervene systemically – in other words he recognises the need for personally developing the capability to lead and manage.

Deming in particular seemed to understand the importance of developing empathy for different worldviews – he knew that each contains coherence, just as in its own flat-earth world Euclidian geometry makes perfect sense. When consulting he spent much of his time listening and asking people questions that might develop paradigmatic understanding – theirs and his. Likewise in my own work, primed with knowledge about the developmental stage of key individual players, I am more able to give my interventions teeth.

Possessing a definition of managerial leadership that can work at all the stages is also vital:

Managing =  keeping things flowing, and stable – and hence predictable – so you can consistently and confidently deliver what you’re promising. Any improvement comes from noticing what causes instability and eliminating that cause, or from learning what causes it via experimentation.

Leading  =  changing things, or transforming them, which risks a temporary loss of stability/ predictability in order to shift performance to a new and better level – a level that can then be managed and sustained.

If you resonate with the first essay you need to know that after publishing it I continued to develop the managerial leadership model into one that would work equally well for Managerial Leaders in either developmental epoch – conventional and post-conventional – whilst simultaneously balancing the level of change needed with the level of risk that’s politically tolerable – and all framed by the paradigm-shifts that typically characterise these two epochs. This revised model is described in detail in the essay:

Managerial Leadership: Five action logics viewed via two developmental lenses

– also soon to be made available via the Journal of Improvement Science.

References

  1. Berwick Donald M. – Berwick Review into patient safety (2013)
  2. Collins J.C. – Level 5 Leadership: The triumph of Humility and Fierce Resolve – HBR Jan 2001
  3. Covey. S.R. – The 7 habits of Highly Effective People – 1989 (ISBN 0613191455)
  4. Deming W. Edwards – Out of the Crisis – 1986   (ISBN 0-911379-01-0)
  5. Deming W.E – The New Economics – 1993 (ISBN 0-911379-07-X) First edition
  6. Hock. D. – The birth of the Chaordic Age 2000 (ISBN: 1576750744)
  7. Jaques. E. – Requisite Organisation: A Total System for Effective Managerial Organisation and Managerial Leadership for the 21st Century 1998 (ISBN 1886436045)
  8. Kotter. J. P. – A Force for Change: How Leadership Differs from Management – 1990
  9. Liker J.K & Meier D. – The Toyota Way Fieldbook. 2006
  10. Scholtes Peter R. The Leader’s Handbook: Making Things Happen, Getting Things Done. 1998
  11. Senge. P. M. – The Fifth Discipline 1990   ISBN 10-0385260946
  12. Spear. S. – Learning to Lead at Toyota – Harvard Business Review – May 2004

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

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

Can we disprove this hypothesis?

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

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


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

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

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


But there is another critical factor to take into account.

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

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

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

And that changes everything!


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


Which brings us back to our hypothesis.

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

What if we are unintentionally and unconsciously generating it?

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

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


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

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

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

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

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

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

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

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

Last week I shared the evidence that proved the one-stop clinic chaos was iatrogenic.

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

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

The hypothesis had been disproved.

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

Everywhere?


For more about complexity science see Santa Fe Institute

custom_meter_15256[Drrrrrrring]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

<Leslie> You bet I do!

FreshMeatOldBonesEvolution is an amazing process.

Using the same building blocks that have been around for a lot time, it cooks up innovative permutations and combinations that reveal new and ever more useful properties.

Very often a breakthrough in understanding comes from a simplification, not from making it more complicated.

Knowledge evolves in just the same way.

Sometimes a well understood simplification in one branch of science is used to solve an ‘impossible’ problem in another.  Cross-fertilisation of learning is a healthy part of the evolution process.


Improvement implies evolution of knowledge and understanding, and then application of that insight in the process of designing innovative ways of doing things better.


And so it is in healthcare.  For many years the emphasis on healthcare improvement has been the Safety-and-Quality dimension, and for very good reasons.  We need to avoid harm and we want to achieve happiness; for everyone.

But many of the issues that plague healthcare systems are not primarily SQ issues … they are flow and productivity issues. FP. The safety and quality problems are secondary – so only focussing on them is treating the symptoms and not the cause.  We need to balance the wheel … we need flow science.


Fortunately the science of flow is well understood … outside healthcare … but apparently not so well understood inside healthcare … given the queues, delays and chaos that seem to have become the expected norm.  So there is a big opportunity for cross fertilisation here.  If we choose to make it happen.


For example, from computer science we can borrow the knowledge of how to schedule tasks to make best use of our finite resources and at the same time avoid excessive waiting.

It is a very well understood science. There is comprehensive theory, a host of techniques, and fit-for-purpose tools that we can pick of the shelf and use. Today if we choose to.

So what are the reasons we do not?

Is it because healthcare is quite introspective?

Is it because we believe that there is something ‘special’ about healthcare?

Is it because there is no evidence … no hard proof … no controlled trials?

Is it because we assume that queues are always caused by lack of resources?

Is it because we do not like change?

Is it because we do not like to admit that we do not know stuff?

Is it because we fear loss of face?


Whatever the reasons the evidence and experience shows that most (if not all) the queues, delays and chaos in healthcare systems are iatrogenic.

This means that they are self-generated. And that implies we can un-self-generate them … at little or no cost … if only we knew how.

The only cost is to our egos of having to accept that there is knowledge out there that we could use to move us in the direction of excellence.

New meat for our old bones?

stick_figure_magic_carpet_150_wht_5040It was the appointed time for Bob and Leslie’s regular coaching session as part of the improvement science practitioner programme.

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

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

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

<Bob> OK. Training to do what?

<Leslie> Outpatient demand and capacity planning!

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

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

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

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

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

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

IST_DandC_Model_01<Bob> OK.  I am guessing that other delegates may have felt less comfortable with this. Was that the case?

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

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

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

IST_DandC_Model_02

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

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

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

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


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

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

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

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

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

<Leslie> Spot on.  What does percentile mean?


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

IST_DandC_Model_03<Leslie> Yes. I recognise that. This chart shows a stable system of demand with an average of around 150 new referrals per week and the variation distributed above and below the average in a symmetrical pattern, falling off to zero around the upper and lower process limits.  I believe that you said that over 99% will fall within the limits.

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

<Leslie> OK.

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

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

IST_DandC_Model_04

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

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

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

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

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


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

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

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

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


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

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

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

<Bob> A reasonable hypothesis.

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

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

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

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


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

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

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

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

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

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

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

 

CAS_DiagramThe theme this week has been emergent learning.

By that I mean the ‘ah ha’ moment that happens when lots of bits of a conceptual jigsaw go ‘click’ and fall into place.

When, what initially appears to be smoky confusion suddenly snaps into sharp clarity.  Eureka!  And now new learning can emerge.


This did not happen by accident.  It was engineered.


The picture above is part of a bigger schematic map of a system – in this case a system related to the global health challenge of escalating obesity.

It is a complicated arrangement of boxes and arrows. There are  dotted lines that outline parts of the system that have leaky boundaries like the borders on a political map.

But it is a static picture of the structure … it tells us almost nothing about the function, the system behaviour.

And our intuition tells us that, because it is a complicated structure, it will exhibit complex and difficult to understand behaviour.  So, guided by our inner voice, we toss it into the pile labelled Wicked Problems and look for something easier to work on.


Our natural assumption that a complicated structure always leads to complex behavior is an invalid simplification, and one that we can disprove in a matter of moments.


Exhibit 1. A system can be complicated and yet still exhibit simple, stable and predictable behavior.

Harrison_H1The picture is of a clock designed and built by John Harrison (1693-1776).  It is called H1 and it is a sea clock.

Masters of sailing ships required very accurate clocks to calculate their longitude, the East-West coordinate on the Earth’s surface. And in the 18th Century this was a BIG problem. Too many ships were getting lost at sea.

Harrison’s sea clock is complicated.  It has many moving parts, but it was the most stable and accurate clock of its time.  And his later ones were smaller, more accurate and even more complicated.


Exhibit 2.  A system can be simple yet still exhibit complex, unstable and unpredictable behavior.

Double-compound-pendulumThe image is of a pendulum made of only two rods joined by a hinge.  The structure is simple yet the behavior is complex, and this can only be appreciated with a dynamic visualisation.

The behaviour is clearly not random. It has structure. It is called chaotic.

So, with these two real examples we have disproved our assumption that a complicated structure always leads to complex behaviour; and we have also disproved its inverse … that complex behavior always comes from a complicated structure.


This deeper insight gives us hope.

We can design complicated systems to exhibit stable and predictable behaviour if, like John Harrison, we know how to.

But John Harrison was a rare, naturally-gifted, mechanical genius, and even with that advantage it took him decades to learn how to design and to build his sea clocks.  He was the first to do so and he was self-educated so his learning was emergent.

And to make it easier, he was working on a purely mechanical system comprised of non-living parts that only obeyed the Laws of Newtonian physics.


Our healthcare system is not quite like that.  The parts are living people whose actions are limited by physical Laws but whose decisions are steered by other policies … learned ones … and ones that can change.  They are called heuristics and they can vary from person-to-person and minute-to-minute.  Heuristics can be learned, unlearned, updated, and evolved.

This is called emergent learning.

And to generate it we only need to ‘engineer’ the context for it … the rest happens as if by magic … but only if we do the engineering well.


This week I personally observed over a dozen healthcare staff simultaneously re-invent a complicated process scheduling technique, at the same time as using it to eliminate the  queues, waiting and chaos in the system they wanted to improve.

Their queues just evaporated … without requiring any extra capacity or money. Eureka!


We did not show them how to do it so they could not have just copied what we did.

We designed and built the context for their learning to emerge … and it did.  On its own.

The ISP One Day Intensive Workshop delivered emergent learning … just as it was designed to do.

This engineering is called complex adaptive system design and this one example proves that CASD is both possible, learnable and therefore teachable.

stick_figure_on_cloud_150_wht_9604Last week Bob and Leslie were exploring the data analysis trap called a two-points-in-time comparison: as illustrated by the headline “This winter has not been as bad as last … which proves that our winter action plan has worked.

Actually it doesn’t.

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


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

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

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

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

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

<Bob> Excellent … what example did you choose?

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

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

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

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

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

MaxMonthTemp1960-2015

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

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

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

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

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

BigFreeze_1963This picture from January 1963 shows copper telephone lines that are so weighed down with ice, and for so long, that they have stretched down to the ground.  In this era of mobile phones we forget this was what telecommunication was like!

 

 

HeatWave_1976

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

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

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

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

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

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

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

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

MaxMonthTemp1960-2015_ByMonth

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

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

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

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

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

MaxTempNov1960-2015_LockedLimits

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

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

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

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

MaxTempDecJanFeb1960-2015-SplitThis showed that the more recent winter months have a higher maximum temperature … on average. The difference is just over one degree Celsius. But it also shows that that the month-to-month and year-to-year variation still dominates the picture.

<Bob> Which implies?

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

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

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

comparing_information_anim_5545[Bzzzzzz] Bob’s phone vibrated to remind him it was time for the regular ISP remote coaching session with Leslie. He flipped the lid of his laptop just as Leslie joined the virtual meeting.

<Leslie> Hi Bob, and Happy New Year!

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

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

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

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

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

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

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

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

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

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

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

<Leslie> Yes, exactly.

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

<Leslie> Yes.

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

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

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

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

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

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

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

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

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

<Leslie> Excellent. I’m all ears.

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

<Leslie> OK.

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

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

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

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

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

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

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

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

<Bob> No. It is increased.

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

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

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

<Bob> Yes.

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

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

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

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


10 minutes later …


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

<Bob> Perfect!

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

TPIT_SourceData

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

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

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

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

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

<Leslie> So how do we avoid this problem?

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


 

take_a_walk_text_10710One of the barriers to improvement is jumping to judgment too quickly.

Improvement implies innovation and action …

doing something different …

and getting a better outcome.

Before an action is a decision.  Before a decision is a judgment.

And we make most judgments quickly, intuitively and unconsciously.  Our judgments are a reflection of our individual, inner view of the world. Our mental model.

So when we judge intuitively and quickly then we will actually just reinforce our current worldview … and in so doing we create a very effective barrier to learning and improvement.

We guarantee the status quo.


So how do we get around this barrier?

In essence we must train ourselves to become more consciously aware of the judgment step in our thinking process.  And one way to flush it up to the surface is to ask the deceptively powerful question … And?

When someone is thinking through a problem then an effective contribution that we can offer is to listen, reflect, summarize, clarify and to encourage by asking “And?”

This process has a name.  It is called a coaching conversation.

And anyone can learn to how do it. Anyone.

figure_pointing_out_chart_data_150_wht_8005Most of the time we are not aware of what is possible.

We live in a world dominated by blissful ignorance.

And we tolerate poor quality services by lowering our expectation.

And then one day we get a shock …

… we have an experience that disproves our hypothesis of impossibility. Just one. One is enough.


And our usual reaction is then some form of denial.

Usually we say “That is not true, it’s a fluke!” or we say “That is OK for you but I could never do it.”

But not always.  Sometimes we say “Can you show me another example and show me how to do it?”

And then we try ourselves … and then we get another shock.  It actually works! We can do it.


So now we are engaged … and when we try again, we make a right fist of it.  Another shock!  We need to “do some”. Practice makes perfect … if we do enough of it.

So we do … easier examples at  first then progressively more challenging ones.

We improve progressively … we progress … we develop know how … we develop competence … we develop confidence … and we start to grow a reputation.

And soon we are rewarded by being invited to teach others what we know.


And then we get another shock … the biggest of all … we no longer know how we are doing it … we have practiced so much and for so long that it has become second nature.

This is the toughest stage of all and this is where we learn the most.

When we teach others what is second nature to us … we learn more than they do.


10% of learning is in seeing others do it .

20% of learning is in doing it ourselves.

70% of learning is in teaching and coaching others to learn how to do it.

See One – Do Some – Teach Many.

That is how capability diffuses.

idea_bulbs_pop_up_150_wht_16515Quality is subjective. It is in the head and the heart of the beholder.

We feel quality when our experience exceeds our expectation.

And this simple definition of quality has some profound implications.


The first is that to measure quality we need to know both parts of the quality equation … we need to know both the expectation and the experience.

Why is that?

One reason is because we can set expectation much more easily than we can set the experience.

Example:  Suppose I am a hospital and I am interested in the perceived quality of the service that I provide to patients.  So I implement a quality survey and I ask the patients one question as they leave.  I ask “Were you satisfied?” The exit poll data is assiduously collected, processed and presented at the monthly Quality Committee meeting. If our average satisfaction score this month is better than last month we “high five” and if it is less then we “deep dive”.

Q1: What is the relationship between the satisfaction score and the actual experience?
A1: Satisfied means that experience equals expectation. That’s all.

Q2: What is the easier way to improve satisfaction scores? Improve experience or reduce expectation?
A2: Reduce expectation.

And that is what we do. We take the path of low resistance to improving satisfaction. We set low expectations. We talk only about what might go wrong. Never about what will go right.


The message here is that to understand quality we have to measure both expectation and experience.  And when harvesting feedback we need to ask both questions.

Compare these two alternatives:
Q: Were you satisfied with the service you received in outpatients today?
A: Yes.

Q: What did you experience in outpatients today and what was your expectation?
A: I struggled to find a parking place and I was a bit worried that I would be late for my appointment, but I ended up waiting over two hours anyway. I did not know how long the wait would be and I was then worried that I had not put enough time on the parking ticket. But it is what I expected because the appointment letter said that I need to allow up to three hours. My appointment took ten minutes and the doctor was nice.


We assume that because we usually experience queues and delays then it would be much more difficult to improve patient satisfaction by actually improving their experience … in other words … eliminating the avoidable root causes of the queues and delays.

So we don’t bother trying. We just reinforce the low expectation.


Another reason we need to know both expectation and experience is because it is our expectation that drives our decision.

If we expect a poor experience we are much less likely to choose that option.

Here is how we learn this avoidance behavior:
Step 1. We start with a reasonable expectation and no experience.
Step 2. We have a poor experience, we feel disappointed, and we lower our expectation.
Step 3. If we have a choice then we avoid the experience. If we have no choice then we accept it.
Step 4. We experience what we expected (but at least have avoided further disappointment).

But are we actually satisfied? Or are we just resigned to the fact that is all we can hope to expect.

Have we learned to become helpless, skeptical or even cynical?


Knowing the patient expectation provides a goldmine of opportunity for a healthcare organization that wants to improve the quality of its service.

Engagement in change does not follow from disappointment – it follows from delight – or more specifically delight accompanied by surprise.

We feel surprised and delighted when we experience something that exceeds our expectation.


So recall the story of the satisfied outpatient with the sense of resigned acceptance.

Then read the feedback below that was shared with me this week … feedback from a doctor in training who has just completed the Foundations of Improvement Science in Healthcare (FISH) course … the free offer.

“To be honest, I was very surprised with the content of the course … in a  good way – so much so that I sat and completed it over two days! 

I was fully expecting a generic online management course filled with the usual buzz words and with no real substance or learning point to take away from it (I’m generally very sceptical of such things as I feel many courses are primarily money making exercises with little real value as the developers are well aware that healthcare professionals need to tick off the management box at their appraisals).

What I actually found was a course that presented the problems of a chaotic department (that I’m all too familiar with as a radiologist) and actually broke down the problem into its root causes and fundamental components in a logical way with simple and effective strategies to improve a service. Considering each process in terms of a series of streams and stages and presenting these functions as a Gantt chart is brilliantly simple, demystifies what actually happens in a process, and is a simple way of identifying all the faff that goes on around the real value work that we – something that I was all to aware of prior to the course but didn’t really know how to tackle. What I have learned is definitely a valuable foundation in managing the various processes of a department such as my own and I will certainly make use of these tools in the future.”

Does that sound like a surprised-and-delighted reaction?

business_race__PA_150_wht_3222There is a widely held belief that competition is the only way to achieve improvement.

This is a limiting belief.

But our experience tells us that competition is an essential part of improvement!

So which is correct?


When two athletes compete they both have to train hard to improve their individual performance. The winner of the race is the one who improves the most.  So by competing with each other they are forced to improve.

The goal of improvement is excellence and the test-of-excellence is performed in the present and is done by competing with others. The most excellent is labelled the “best” or “winner”. Everyone else is branded “second best” or “loser”.

This is where we start to see the limiting belief of competition.

It has a crippling effect.  Many competitive people will not even attempt the race if they do not feel they can win.  Their limiting belief makes them too fearful. They fear loss of self-esteem. Their ego is too fragile. They value hubris more than humility. And by not taking part they abdicate any opportunity to improve. They remain arrogantly mediocre and blissfully ignorant of it. They are the real losers.


So how can we keep the positive effect of competition and at the same time escape the limiting belief?

There are two ways:

First we drop the assumption that the only valid test of excellence is a comparison of us with others in the present.  And instead we adopt the assumption that it is equally valid to compare us with ourselves in the past.

We can all improve compared with what we used to be. We can all be winners of that race.

And as improvement happens our perspective shifts.  What becomes normal in the present would have been assumed to be impossible in the past.


This week I sat at my desk in a state of wonder.

I held in my hand a small plastic widget about the size of the end of my thumb.  It was a new USB data stick that had just arrived, courtesy of Amazon, and on one side in small white letters it proudly announced that it could hold 64 Gigabytes of data (that is 64 x 1024 x 1024 x 1024). And it cost less than a take-away curry.

About 30 years ago, when I first started to learn how to design, build and program computer system, a memory chip that was about the same size and same cost could hold 4 kilobytes (4 x 1024).  

So in just 30 years we have seen a 16-million-fold increase in data storage capacity. That is astounding! Our collective knowledge of how to design and build memory chips has improved so much. And yet we take it for granted.


The second way to side-step the limiting belief is even more powerful.

It is to drop the belief that individual improvement is enough.

Collective improvement is much, much, much more effective.


Cell_StructureEvidence:

The human body is made up of about 50 trillion (50 x 1000 x 1000 x 1000 x 1000) cells – about the same as the number of bytes could store on 1000 of my wonderful new 64 Gigabyte data sticks!

And each cell is a microscopic living individual. A nano-engineered adaptive system of wondrous complexity and elegance.

Each cell breathes, eats, grows, moves, reproduces, senses, learns and remembers. These cells are really smart too! And they talk to each other, and they learn from each other.

And what makes the human possible is that its community of 50 trillion smart cells are a collaborative community … not a competitive community.

If all our cells started to compete with each other we would be very quickly reduced to soup (which is what the Earth was bathed in for about 2.7 billions years).

The first multi-celled organisms gained a massive survival advantage when they learned how to collaborate.

The rest is the Story of Evolution.  Even Charles Darwin missed the point – evolution is more about collaboration than competition – and we are only now beginning to learn that lesson. The hard way.  


come_join_the_team_150_wht_10876So survival is about learning and improving.

And survival of the fittest does not mean the fittest individual … it means the fittest group.

Collaborative improvement is the process through which we can all achieve win-win-win excellence.

And the understanding of how to do this collaborative improvement has a name … it is called Improvement Science.

smack_head_in_disappointment_150_wht_16653The NHS appears to be suffering from some form of obsessive-compulsive disorder.

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

Perfectionists demonstrate a degree of obsessive-compulsive behaviour too.


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

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


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

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

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


For example …

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

The infamous and feared 18-week target.

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

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

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


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

RTT_Data_Snapshot

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

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


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

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

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

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


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


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

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

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

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

RTT_WIP_RunChart

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

Two patterns are obvious:

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

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

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

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

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


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

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

A&E_Breaches_2004-15

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

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

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


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


So what can we do?

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

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

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

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

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

This is all possible. It is called Improvement Science.


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

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

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

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


PostScript

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

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

 

RIA_graphicA question that is often asked by doctors in particular is “What is the difference between Research, Audit and Improvement Science?“.

It is a very good question and the diagram captures the essence of the answer.

Improvement science is like a bridge between research and audit.

To understand why that is we first need to ask a different question “What are the purposes of research, improvement science and audit? What do they do?

In a nutshell:

Research provides us with new knowledge and tells us what the right stuff is.
Improvement Science provides us with a way to design our system to do the right stuff.
Audit provides us with feedback and tells us if we are doing the right stuff right.


Research requires a suggestion and an experiment to test it.   A suggestion might be “Drug X is better than drug Y at treating disease Z”, and the experiment might be a randomised controlled trial (RCT).  The way this is done is that subjects with disease Z are randomly allocated to two groups, the control group and the study group.  A measure of ‘better’ is devised and used in both groups. Then the study group is given drug X and the control group is given drug Y and the outcomes are compared.  The randomisation is needed because there are always many sources of variation that we cannot control, and it also almost guarantees that there will be some difference between our two groups. So then we have to use sophisticated statistical data analysis to answer the question “Is there a statistically significant difference between the two groups? Is drug X actually better than drug Y?”

And research is often a complicated and expensive process because to do it well requires careful study design, a lot of discipline, and usually large study and control groups. It is an effective way to help us to know what the right stuff is but only in a generic sense.


Audit requires a standard to compare with and to know if what we are doing is acceptable, or not. There is no randomisation between groups but we still need a metric and we still need to measure what is happening in our local reality.  We then compare our local experience with the global standard and, because variation is inevitable, we have to use statistical tools to help us perform that comparison.

And very often audit focuses on avoiding failure; in other words the standard is a ‘minimum acceptable standard‘ and as long as we are not failing it then that is regarded as OK. If we are shown to be failing then we are in trouble!

And very often the most sophisticated statistical tool used for audit is called an average.  We measure our performance, we average it over a period of time (to remove the troublesome variation), and we compare our measured average with the minimum standard. And if it is below then we are in trouble and if it is above then we are not.  We have no idea how reliable that conclusion is though because we discounted any variation.


A perfect example of this target-driven audit approach is the A&E 95% 4-hour performance target.

The 4-hours defines the metric we are using; the time interval between a patient arriving in A&E and them leaving. It is called a lead time metric. And it is easy to measure.

The 95% defined the minimum  acceptable average number of people who are in A&E for less than 4-hours and it is usually aggregated over three months. And it is easy to measure.

So, if about 200 people arrive in a hospital A&E each day and we aggregate for 90 days that is about 18,000 people in total so the 95% 4-hour A&E target implies that we accept as OK for about 900 of them to be there for more than 4-hours.

Do the 900 agree? Do the other 17,100?  Has anyone actually asked the patients what they would like?


The problem with this “avoiding failure” mindset is that it can never lead to excellence. It can only deliver just above the minimum acceptable. That is called mediocrity.  It is perfectly possible for a hospital to deliver 100% on its A&E 4 hour target by designing its process to ensure every one of the 18,000 patients is there for exactly 3 hours and 59 minutes. It is called a time-trap design.

We can hit the target and miss the point.

And what is more the “4-hours” and the “95%” are completely arbitrary numbers … there is not a shred of research evidence to support them.

So just this one example illustrates the many problems created by having a gap between research and audit.


And that is why we need Improvement Science to help us to link them together.

We need improvement science to translate the global knowledge and apply it to deliver local improvement in whatever metrics we feel are most important. Safety metrics, flow metrics, quality metrics and productivity metrics. Simultaneously. To achieve system-wide excellence. For everyone, everywhere.

When we learn Improvement Science we learn to measure how well we are doing … we learn the power of measurement of success … and we learn to avoid averaging because we want to see the variation. And we still need a minimum acceptable standard because we want to exceed it 100% of the time. And we want continuous feedback on just how far above the minimum acceptable standard we are. We want to see how excellent we are, and we want to share that evidence and our confidence with our patients.

We want to agree a realistic expectation rather than paint a picture of the worst case scenario.

And when we learn Improvement Science we will see very clearly where to focus our improvement efforts.


Improvement Science is the bit in the middle.


Stop Press:  There is currently an offer of free on-line foundation training in improvement science for up to 1000 doctors-in-training … here  … and do not dally because places are being snapped up fast!

Nerve_CurveThe emotional journey of change feels like a roller-coaster ride and if we draw as an emotion versus time chart it looks like the diagram above.

The toughest part is getting past the low point called the Well of Despair and doing that requires a combination of inner strength and external support.

The external support comes from an experienced practitioner who has been through it … and survived … and has the benefit of experience and hindsight.

The Improvement Science coach.


What happens as we  apply the IS principles, techniques and tools that we have diligently practiced and rehearsed? We discover that … they work!  And all the fence-sitters and the skeptics see it too.

We start to turn the corner and what we feel next is that the back pressure of resistance falls a bit. It does not go away, it just gets less.

And that means that the next test of change is a bit easier and we start to add more evidence that the science of improvement does indeed work and moreover it is a skill we can learn, demonstrate and teach.

We have now turned the corner of disbelief and have started the long, slow, tough climb through mediocrity to excellence.


This is also a time of risks and there are several to be aware of:

  1. The objective evidence that dramatic improvements in safety, flow, quality and productivity are indeed possible and that the skills can be learned will trigger those most threatened by the change to fight harder to defend their disproved rhetoric. And do not underestimate how angry and nasty they can get!
  2. We can too easily become complacent and believe that the rest will follow easily. It doesn’t.  We may have nailed some of the easier niggles to be sure … but there are much more challenging ones ahead.  The climb to excellence is a steep learning curve … all the way. But the rewards get bigger and bigger as we progress so it is worth it.
  3. We risk over-estimating our capability and then attempting to take on the tougher improvement assignments without the necessary training, practice, rehearsal and support. If we do that we will crash and burn.  It is like a game of snakes and ladders.  Our IS coach is there to help us up the ladders and to point out where the slippery snakes are lurking.

So before embarking on this journey be sure to find a competent IS coach.

They are easy to identify because they will have a portfolio of case studies that they have done themselves. They have the evidence of successful outcomes and that they can walk-the-talk.

And avoid anyone who talks-the-walk but does not have a portfolio of evidence of their own competence. Their Siren song will lure you towards the submerged Rocks of Disappointment and they will disappear like morning mist when you need them most – when it comes to the toughest part – turning the corner. You will be abandoned and fall into the Well of Despair.

So ask your IS coach for credentials, case studies and testimonials and check them out.