Archive for the ‘Examples’ Category

stick_figure_help_button_150_wht_9911Imagine this scenario:

You develop some non-specific symptoms.

You see your GP who refers you urgently to a 2 week clinic.

You are seen, assessed, investigated and informed that … you have cancer!


The shock, denial, anger, blame, bargaining, depression, acceptance sequence kicks off … it is sometimes called the Kübler-Ross grief reaction … and it is a normal part of the human psyche.

But there is better news. You also learn that your condition is probably treatable, but that it will require chemotherapy, and that there are no guarantees of success.

You know that time is of the essence … the cancer is growing.

And time has a new relevance for you … it is called life time … and you know that you may not have as much left as you had hoped.  Every hour is precious.


So now imagine your reaction when you attend your local chemotherapy day unit (CDU) for your first dose of chemotherapy and have to wait four hours for the toxic but potentially life-saving drugs.

They are very expensive and they have a short shelf-life so the NHS cannot afford to waste any.   The Aseptic Unit team wait until all the safety checks are OK before they proceed to prepare your chemotherapy.  That all takes time, about four hours.

Once the team get to know you it will go quicker. Hopefully.

It doesn’t.

The delays are not the result of unfamiliarity … they are the result of the design of the process.

All your fellow patients seem to suffer repeated waiting too, and you learn that they have been doing so for a long time.  That seems to be the way it is.  The waiting room is well used.

Everyone seems resigned to the belief that this is the best it can be.

They are not happy about it but they feel powerless to do anything.


Then one day someone demonstrates that it is not the best it can be.

It can be better.  A lot better!

And they demonstrate that this better way can be designed.

And they demonstrate that they can learn how to design this better way.

And they demonstrate what happens when they apply their new learning …

… by doing it and by sharing their story of “what-we-did-and-how-we-did-it“.

CDU_Waiting_Room

If life time is so precious, why waste it?

And perhaps the most surprising outcome was that their safer, quicker, calmer design was also 20% more productive.

Portsmouth_News_20160609We form emotional attachments to places where we have lived and worked.  And it catches our attention when we see them in the news.

So this headline caught my eye, because I was a surgical SHO in Portsmouth in the closing years of the Second Millennium.  The good old days when we still did 1:2 on call rotas (i.e. up to 104 hours per week) and we were paid 70% LESS for the on call hours than the Mon-Fri 9-5 work.  We also had stable ‘firms’, superhuman senior registrars, a canteen that served hot food and strong coffee around the clock, and doctors mess parties that were … well … messy!  A lot has changed.  And not all for the better.

Here is the link to the fuller story about the emergency failures.

And from it we get the impression that this is a recent problem.  And with a bit of a smack and some name-shame-blame-game feedback from the CQC, then all will be restored to robust health. H’mm. I am not so sure that is the full story.


Portsmouth_A&E_4Hr_YieldHere is the monthly aggregate A&E 4-hour target performance chart for Portsmouth from 2010 to date.

It says “this is not a new problem“.

It also says that the ‘patient’ has been deteriorating spasmodically over six years and is now critically-ill.

And giving a critically-ill hospital a “good telling off” is about as effective as telling a critically-ill patient to “pull themselves together“.  Inept management.

In A&E a critically-ill patient requires competent resuscitation using a tried-and-tested process of ABC.  Airway, Breathing, Circulation.


Also, the A&E 4-hour performance is only a symptom of the sickness in the whole urgent care system.  It is the reading on an emotometer inserted into the A&E orifice of the acute hospital!  Just one piece in a much bigger flow jigsaw.

It only tells us the degree of distress … not the diagnosis … nor the required treatment.


So what level of A&E health can we realistically expect to be able to achieve? What is possible in the current climate of austerity? Just how chilled-out can the A&E cucumber run?

Luton_A&E_4Hr_Yield

This is the corresponding A&E emotometer chart for a different district general hospital somewhere else in NHS England.

Luton & Dunstable Hospital to be specific.

This A&E happiness chart looks a lot healthier and it seems to be getting even healthier over time too.  So this is possible.


Yes, but … if our hospital deteriorates enough to be put on the ‘critical list’ then we need to call in an Emergency Care Intensive Support Team (ECIST) to resuscitate us.

Kettering_A&E_4Hr_YieldA very good idea.

And how do their critically-ill patients fare?

Here is the chart of one of them. The significant improvement following the ‘resuscitation’ is impressive to be sure!

But, disappointingly, it was not sustained and the patient ‘crashed’ again. Perhaps they were just too poorly? Perhaps the first resuscitation call was sent out too late? But at least they tried their best.

An experienced clinician might comment: Those are indeed a plausible explanations, but before we conclude that is the actual cause, can I check that we did not just treat the symptoms and miss the disease?


Q: So is it actually possible to resuscitate and repair a sick hospital?  Is it possible to restore it to sustained health, by diagnosing and treating the cause, and not just the symptoms?


Monklands_A&E_4Hr_YieldHere is the corresponding A&E emotometer chart of yet another hospital.

It shows the same pattern of deteriorating health. And it shows a dramatic improvement.  It appears to have responded to some form of intervention.

And this time the significant improvement has sustained. The patient did not crash-and-burn again.

So what has happened here that explains this different picture?

This hospital had enough insight and humility to seek the assistance of someone who knew what to do and who had a proven track record of doing it.  Dr Kate Silvester to be specific.  A dual-trained doctor and manufacturing systems engineer.

Dr Kate is now a health care systems engineer (HCSE), and an experienced ‘hospital doctor’.

Dr Kate helped them to learn how to diagnose the root causes of their A&E 4-hr fever, and then she showed them how to design an effective treatment plan.

They did the re-design; they tested it; and they delivered their new design. Because they owned it, they understood it, and they trusted their own diagnosis-and-design competence.

And the evidence of their impact matching their intent speaks for itself.

growing_workload_anim_6858There is a very easy and quick-to-cook recipe for chaos.

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

Eh?

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


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

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

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

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


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

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

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

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

Simple.

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

BUT ….

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

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

It doesn’t.

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

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

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

Try it.


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


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

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

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

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

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

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


So what is the solution?

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

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

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

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

A few weeks ago I raised the undiscussable issue that the NHS feels like it is on a downward trajectory … and that what might be needed are some better engines … and to design, test, build and install them we will need some health care system engineers (HCSEs) … and that we do not have appear to have enough of those. None in fact.

The feedback shows that many people resonated with this sentiment.


This week I had the opportunity to peek inside the NHS Cockpit and look at the Dashboard … and this is what I saw on the A&E Performance panel.

UK_Type_1_ED_Monthly_4hr_Yield

This is the monthly aggregate A&E 4-hour performance for England (red), Scotland (purple), Wales (brown) and Northern Ireland (grey) for the last six years.

The trajectory looked alarmingly obvious to me – the NHS is on a predictable path to destruction – a controlled flight into terrain (CFIT).

The repeating up-and-down pattern is the annual cycle of seasons; better in the summer and worse in the winter.  This signal is driven by the celestial clock … the movement of the planets … which is beyond our power to influence.

The downward trajectory is the cumulative effect of our current design … which is the emergent effect of our collective beliefs, behaviours, policies and politics … which are completely within our gift to change.

If we chose to and if we knew how to – which we do not appear to.

Our collective ineptitude is not a topic for discussion. It is a taboo subject.


And I know that because if it were for discussion then this dashboard would be on public view on a website hosted by the NHS.

It isn’t.


George_DonaldIt was created by George Donald, a member of the public, a disappointed patient, and a retired IT consultant.  And it was shared, free for all to see and use via Twitter (@GMDonald).

The information source is open, public, shared NHS data, but it takes a lot of work to winkle it out and present it like this.  So well done George … keep up the great work!


Now have a closer look at the Dashboard Display … look at the most recent data for England and Scotland.  What do you see?

Does it look like Scotland is pulling out of the dive and England is heading down even faster?

Hard to say for sure; there are lots of signals and noise all mixed up.


So we need to use some Systems Engineering tools to help us separate the signals from the noise; and for this a statistical process control (SPC) chart is useless.  We need a system behaviour chart (SBC) and its handy helper the deviation from aim (DFA) chart.

I will not bore you with the technical details but, suffice it to say, it is a tried-and-tested technique called the Method of Residuals.

Scotland_A&E_DFA_02 Exhibit #1 is the DFA chart for Scotland.  The middle 4 years (2011-2014) are used to create a ‘predictive model’;  the model projection is then compared with measured performance; and the difference is plotted as the DFA chart.

What this “says” is that the 2015/16 performance in Scotland is significantly better than projected, and the change of direction seemed to start in the first half of 2015.

This evidence seems to support the results of our Mark I Eyeball test.

England_A&E_DFA_02

Exhibit #2 – the DFA for England suggests the 2015/16 performance is significantly worse than projected, and this deterioration appears to have started later in 2015.

Oh dear! I do not believe that was the intention, but it appears to be the impact.


So what are England and Scotland doing differently?
What can we all learn from this?
What can we all do differently in the future?

Isn’t that a question that more people like you, me and George could reasonably ask of those whom we entrust to design, build and fly our NHS?

Isn’t that a reasonable question that could be asked by the 65 million people in the UK who might, at any time, be unlucky enough to require a trip to their local A&E department.

So, let us all grasp the nettle and get the Elephant in the Room into plain view and say in unison “The Emperor Has No Clothes!”

We are suffering from mass ineptitude and hubris, to use Dr Atul Gawande’s language, and we need a better collective strategy.


And there is hope.

Some innovative hospitals have had the courage to grasp the nettle. They have seen what is coming; they have fully accepted the responsibility for their own fate; they have stepped up to the challenge; they have looked-listened-and-learned from others, and they are proving what is possible.

They have a name. They are called positive deviants.

Have a look at this short video … it is jaw-dropping … it is humbling … it is inspiring … and it is challenging … because it shows what has been achieved already.

It shows what is possible. Now, and here in the UK.

Luton and Dunstable

 

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.

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 health care 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 frail elderly patients 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 was poor.  Many do not survive, and many of the survivors never returned 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’ one because we had been inspired by similar work done at Bolton some years before and Lean was the flavour of the month.

The actual change was small: it was a flow design tweak that cost nothing to implement.

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 mobilise 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 much easier for the multi-disciplinary teams to provide timely care and to avoid potentially harmful delays.

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

That was the flow diagnosis and 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 (PECT), the trauma and orthopaedic team, the physiotherapists, and so on.

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

And that was basically it.

And the impact 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 clearly not safe for these frail patients!


And there was an improved outcome for the patients who survived: 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, as this quote is from the 2011 paper illustrates …

Injury_2011_Message

What was a bit disappointing was the gap of four years between delivering this dramatic and highly significant patient safety and quality improvement and the sharing of the story.


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

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!

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: freeze, flee or fight.

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

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

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

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


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

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

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

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

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

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

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

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

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


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

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

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


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 clear and understandable; and we say ‘Ah ha!’


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


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

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

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

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; i.e. cash.


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

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


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

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

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

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

  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.

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

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

Can we disprove this hypothesis?

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

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


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

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

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


But there is another critical factor to take into account.

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

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

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

And that changes everything!


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


Which brings us back to our hypothesis.

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

What if we are unintentionally and unconsciously generating it?

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

Everywhere?


And for more about complexity science see Santa Fe Institute

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!

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 85th percentile of demand which is from about 150 slots per week to 170 slots per week. An increase of 7% which implies a 7% increase in costs.

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

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

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


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

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

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

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

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

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

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

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.


 

 

Dr_Bob_Thumbnail

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

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

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

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

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


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

Science_Sequence

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

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

The scientific process starts with study.

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

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


<Dr Bob> What is the presenting symptom?

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

<Dr Bob> Are there any other associated symptoms?

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

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

<StE> “As long as we can remember.”

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

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

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

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

<Dr Bob> And how effective have these been?

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


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

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

So first we need to focus on establishing the diagnosis.

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


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

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

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

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


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

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


StE_4hr_Pain_Chart

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

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

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

StE_4hr_Pain_Chart_RG_DoWSo I have plotted your data in a different way … grouping by day of the week … and this shows there is a weekly pattern to your pain. It looks worse on Mondays and least bad on Fridays.  Is that your experience?

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


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

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

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

Monitor_Summary


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

Summary At A Glance

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


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


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

A&E_Arrivals_2004-15First we have the A&E Arrivals chart … about 3.4 million arrivals per quarter. The annual cycle is obvious … higher in the summer and falling in the winter. And when we compare the first five years with the last six years there has been a small increase of about 5% and that seems to associate with a change of political direction in 2010.

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


A&E_Admissions_2004-15In stark contrast the A&E arrivals that are admitted to hospital has risen relentlessly over the same 11 year period by about 50% … that is about 5% per annum … ten times the increase in arrivals … and with no obvious step in 2010. We can see the annual cycle too.  It is a like a ratchet. Click click click.


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


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

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


A&E_Breaches_2004-15And here is the corresponding A&E 4-hour Target Failure chart.  Up to 2010 the imposed target was 98% success (i.e. 2% acceptable failure) and, after bit of “encouragement” in 2004-5, this was actually achieved in some of the summer months (when the A&E demand was highest remember).

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

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

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

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


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

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

So what has triggered the developing catastrophe?

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

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

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

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

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

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


So what is the solution?  More beds?

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

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

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

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


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


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

Q: Do we have that?
A: Nope.

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

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

figure_turning_a_custom_page_15415

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

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

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


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

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

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

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


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

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

A bit of something for everyone.

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

A story, tale or fable.

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


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

A Story of Learning Improvement Science

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

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

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

Chris_Jones_Poster_and_Certificate

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


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

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

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

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


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


stick_figure_look_point_on_cliff_anim_8156Improvement is like mountain walking.

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

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

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

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

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

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

Improving the whole system will require a much taller one.


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

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

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


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

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

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


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

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

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

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

<Bob> OK. Some context first please.

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

<Bob> OK. Why 10%?

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

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

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

<Bob> So has the design change been made?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[Five minutes later]

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

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

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

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

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

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

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

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

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


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


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

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

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

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

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

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

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

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


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

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

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

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

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

Politicians sell dreams and serve disappointment.


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

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

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


But here we hit a bit of a snag.

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

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

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

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

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

So the ball is squarely in our court.


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


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

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

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

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


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

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

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

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

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

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

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

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

Would you vote for that?

NHS_Legal_CostsThis heading in the the newspaper today caught my eye.

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

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

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

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

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

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

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


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

And what is the plan to reduce this cost?

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

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

How do we know?


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

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

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

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

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

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

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

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


The error here is one of a different sort.

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

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

Example:

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

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

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

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


So why was my inner chimp really so unhappy?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

<Bob> Excellent. How long ago was that?

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

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

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

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

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

<Bob> So what did you focus on?

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

<Bob>So you do not use standard scripts?

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

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

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

<Bob> So doing nothing was not an option.

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

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

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

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

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

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

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

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

<Leslie>Yup. Can you see the diagnosis?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

StE_CallCentre_After

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

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

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

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

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

trapped_in_question_PA_300_wht_3174[Beeeeeep] It was time for the weekly coaching Webex. Bob, a seasoned practitioner of flow science, dialled into the teleconference with Lesley.

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

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

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

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

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

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

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

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

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

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

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

<Lesley> The  post-take ward round.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

<Bob> I will let you answer that.

<Lesley> Um, let me think.

The outcome I want is everyone understands the concept of a time-trap well enough to feel comfortable with trying a different no-trap design because they can see the benefits for them.

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

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

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

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

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

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

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

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

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

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

These are absolute,  eternal and are not negotiable.

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

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

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

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

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

It is the cultural constraint.

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


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

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

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

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

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

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

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


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

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

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

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


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

H’mmmm ….

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

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

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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

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

Chalk and cheese!


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

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


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

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