One of the most effective ways to inspire others is to demonstrate what is possible, and then to explain how it is possible.

And one way to do that is to use a simulation game.

There are many different forms of simulation game from the imagination playground games we remember as children, to sophisticated and highly realistic computer simulations.

The purpose is the same: to have the experience without the risk and cost of doing it for real; to learn from the experience; and to increase our chance of success in the real world.


Simulations are very effective educational tools because we can simplify, focus, practice, pause, rewind, and reflect.

They are also very effective exploration tools for developing our understanding of hows things work.  We need to know that before we can make things work better.


And anyone who has tried it will confirm: creating an effective simulation game is not easy. It takes passion, persistence and practice and many iterations to get it right.

And that in itself is a powerful learning experience.


This week the topic of simulations has cropped up several times.

Firstly, the hands-on simulations at the Flow Design Practical Skills Workshop and how they generated insight and inspiration.  The experience certainly fired imaginations and will hopefully lead to innovations. For more click here …

Secondly, the computer simulation called the “Save The NHS Game” which is designed to illustrate the complex and counter-intuitive behaviour of real systems.  The rookie crew “crashed” the simulated healthcare system, but that was OK, it was just a simulation.  In the process they learned a lot about how not to improve NHS productivity. For more click here …

And later the same day being a crash-test dummy for an innovative table-top simulation game using different sizes and shapes of pasta and an ice tray to illustrate the confusing concept of carve-out!  For more click here …

And finally, a fantastic conversation with Dr Bryn Baxendale from the Trent Simulation Centre about how simulation training has become a growing part of how we train individuals and teams, especially in clinical skills, safety and human factors.


In health care systems engineering we use simulation tools in the diagnosis, design and delivery phases of complex improvement-by-design projects. So learning how to design, build and verify the simulation tools we need is a core part advanced HCSE training.  For more click here …

Lots of simulation stimulation. What a great week!

One of the questions we all ask ourselves, perhaps unconsciously, when we are considering change is: “What is in it for me?

And if we do not get a convincing enough answer, quickly enough, we move on.

Effective sales people know this, and anyone needing to engage and influence others needs to as well.


One approach is to ask the same questions as the person we seek to influence are asking themselves, perhaps unconsciously.

So if you have an interest in healthcare improvement … see if these questions resonate with you.

The Elephant in the Room is an English-language metaphorical idiom for an obvious problem or risk no one wants to discuss.

An undiscussable topic.

And the undiscussability is also undiscussable.

So the problem or risk persists.

And people come to harm as a result.

Which is not the intended outcome.

So why do we behave this way?

Perhaps it is because the problem looks too big and too complicated to solve in one intuitive leap, and we give up and label it a “wicked problem”.


The well known quote “When eating an elephant take one bite at a time” is attributed to Creighton Abrams, a US Chief of Staff.


It says that even seemingly “impossible” problems can be solved so long as we proceed slowly and carefully, in small steps, learning as we go.

And the continued decline of the NHS UK Unscheduled Care performance seems to be an Elephant-in-the-Room problem, as shown by the monthly A&E 4-hour performance over the last 10 years and the fact that this chart is not published by the NHS.

Red = England, Brown=Wales, Grey=N.Ireland, Purple=Scotland.


This week I experienced a bite of this Elephant being taken and chewed on.

The context was a Flow Design – Practical Skills – One Day Workshop and the design challenge posed to the eager delegates was to improve the quality and efficiency of a one stop clinic.

A seemingly impossible task because the delegates reported that the queues, delays and chaos that they experienced in the simulated clinic felt very realistic.

Which means that this experience is accepted as inevitable, and is impossible to improve without more resources, but financial cuts prevent that, so we have to accept the waits.


At the end of the day their belief had been shattered.

The queues, delays and chaos had evaporated and the cost to run the new one stop clinic design was actually less than the old one.

And when we combined the quality metrics with the cost metrics and calculated the measured improvement in productivity; the answer was over 70%!

The delegates experienced it all first-hand. They did the diagnosis, design, and delivery using no more than squared-paper and squeaky-pen.

And at the end they were looking at a glaring mismatch between their rhetoric and the reality.

The “impossible to improve without more money” hypothesis lay in tatters – it had been rationally, empirically and scientifically disproved.

I’d call that quite a big bite out of the Elephant-in-the-Room.


So if you have a healthy appetite for Elephant-in-the-Room challenges, and are not afraid to try something different, then there is a whole menu of nutritious food-for-thought at a FISH&CHIPs® practical skills workshop.

This is the now-infamous statement that Donald Rumsfeld made at a Pentagon Press Conference which triggered some good-natured jesting from the assembled journalists.

But there is a problem with it.

There is a fourth combination that he does not mention: the Unknown-Knowns.

Which is a shame because they are actually the most important because they cause the most problems.  Avoidable problems.


Suppose there is a piece of knowledge that someone knows but that someone else does not; then we have an unknown-known.

None of us know everything and we do not need to, because knowledge that is of no value to us is irrelevant for us.

But what happens when the unknown-known is of value to us, and more than that; what happens when it would be reasonable for someone else to expect us to know it; because it is our job to know.


A surgeon would be not expected to know a lot about astronomy, but they would be expected to know a lot about anatomy.


So, what happens if we become aware that we are missing an important piece of knowledge that is actually already known?  What is our normal human reaction to that discovery?

Typically, our first reaction is fear-driven and we express defensive behaviour.  This is because we fear the potential loss-of-face from being exposed as inept.

From this sudden shock we then enter a characteristic emotional pattern which is called the Nerve Curve.

After the shock of discovery we quickly flip into denial and, if that does not work then to anger (i.e. blame).  We ignore the message and if that does not work we shoot the messenger.


And when in this emotionally charged state, our rationality tends to take a back seat.  So, if we want to benefit from the discovery of an unknown-known, then we have to learn to bite-our-lip, wait, let the red mist dissipate, and then re-examine the available evidence with a cool, curious, open mind.  A state of mind that is receptive and open to learning.


Recently, I was reminded of this.


The context is health care improvement, and I was using a systems engineering framework to conduct some diagnostic data analysis.

My first task was to run a data-completeness-verification-test … and the data I had been sent did not pass the test.  There was some missing.  It was an error of omission (EOO) and they are the hardest ones to spot.  Hence the need for the verification test.

The cause of the EOO was an unknown-known in the department that holds the keys to the data warehouse.  And I have come across this EOO before, so I was not surprised.

Hence the need for the verification test.

I was not annoyed either.  I just fed back the results of the test, explained what the issue was, explained the cause, and they listened and learned.


The implication of this specific EOO is quite profound though because it appears to be ubiquitous across the NHS.

To be specific it relates to the precise details of how raw data on demand, activity, length of stay and bed occupancy is extracted from the NHS data warehouses.

So it is rather relevant to just about everything the NHS does!

And the error-of-omission leads to confusion at best; and at worst … to the following sequence … incomplete data =>  invalid analysis => incorrect conclusion => poor decision => counter-productive action => unintended outcome.

Does that sound at all familiar?


So, if would you like to learn about this valuable unknown-known is then I recommend the narrative by Dr Kate Silvester, an internationally recognised expert in healthcare improvement.  In it, Kate re-tells the story of her emotional roller-coaster ride when she discovered she was making the same error.


Here is the link to the full abstract and where you can download and read the full text of Kate’s excellent essay, and help to make it a known-known.

That is what system-wide improvement requires – sharing the knowledge.

Only a few parts of the NHS were adversely affected by the RansomWare cyber-attack on Friday 12th May 2017.

This well-known malware was designed to exploit a security loop-hole in out-of-date and poorly maintained computers still using the Windows XP operating system.

And just like virulent organisms and malignant cells … the loop-holes in our IT immune systems were exploited to cause infectious diseases and cancer!


The diagnosis and treatment of these acquired IT diseases is painful, expensive and it comes with no guarantee of a happy outcome.

Lesson: Proactive prevention is better than reactive cure!

And all it requires to achieve it is … a Checklist.


Prevention requires pre-emptive design, and to do this the system needs to be studied, and understood well enough for an early warning system (EWS) to be designed, tested and implemented.

Having an effective EWS also requires that the measured response to an EWS alert has been designed, tested and implemented as well.

The sensor and the effector are linked by something called a processor.

And the processor can be implemented using an easy-to-use, low-cost, effective tool called a Checklist.


The NHS was not cyber-attacked.  Parts of the NHS were more vulnerable than others to a well-known, endemic cyber-threat, and they were more vulnerable because they did not use an effective cyber-security checklist.  An error of omission.


Checklists are not recipes of how or why to do something.  They are primarily there to remind us to do what is required, and to not do what is not required.

But we need to refer to them … we need to befriend them … we need to create them and maintain them. They are our friends and they will protect us from harm.

And if we do that the we will reap the benefits of time and energy that are released in the future – to do with as we choose.

There is a Catch-22 in health care improvement and it goes a bit like this:

Most people are too busy fire-fighting the chronic chaos to have time to learn how to prevent the chaos, so they are stuck.

There is a deeper Catch-22 as well though:

The first step in preventing chaos is to diagnose the root cause and doing that requires experience, and we don’t have that experience available, and we are too busy fire-fighting to develop it.


Health care is improvement science in action – improving the physical and psychological health of those who seek our help. Patients.

And we have a tried-and-tested process for doing it.

First we study the problem to arrive at a diagnosis; then we design alternative plans to achieve our intended outcome and we decide which plan to go with; and then we deliver the plan.

Study ==> Plan ==> Do.

Diagnose  ==> Design & Decide ==> Deliver.

But here is the catch. The most difficult step is the first one, diagnosis, because there are many different illnesses and they often present with very similar patterns of symptoms and signs. It is not easy.

And if we make a poor diagnosis then all the action plans that follow will be flawed and may lead to disappointment and even harm.

Complaints and litigation follow in the wake of poor diagnostic ability.

So what do we do?

We defer reassuring our patients, we play safe, we request more tests and we refer for second opinions from specialists. Just to be on the safe side.

These understandable tactics take time, cost money and are not 100% reliable.  Diagnostic tests are usually precisely focused to answer specific questions but can have false positive and false negative results.

To request a broad batch of tests in the hope that the answer will appear like a rabbit out of a magician’s hat is … mediocre medicine.


This diagnostic dilemma arises everywhere: in primary care and in secondary care, and in non-urgent and urgent pathways.

And it generates extra demand, more work, bigger queues, longer delays, growing chaos, and mounting frustration, disappointment, anxiety and cost.

The solution is obvious but seemingly impossible: to ensure the most experienced diagnostician is available to be consulted at the start of the process.

But that must be impossible because if the consultants were seeing the patients first, what would everyone else do?  How would they learn to become more expert diagnosticians? And would we have enough consultants?


When I was a junior surgeon I had the great privilege to have the opportunity to learn from wise and experienced senior surgeons, who had seen it, and done it and could teach it.

Mike Thompson is one of these.  He is a general surgeon with a special interest in the diagnosis and treatment of bowel cancer.  And he has a particular passion for improving the speed and accuracy of the diagnosis step; because it can be a life-saver.

Mike is also a disruptive innovator and an early pioneer of the use of endoscopy in the outpatient clinic.  It is called point-of-care testing nowadays, but in the 1980’s it was a radically innovative thing to do.

He also pioneered collecting the symptoms and signs from every patient he saw, in a standard way using a multi-part printed proforma. And he invested many hours entering the raw data into a computer database.

He also did something that even now most clinicians do not do; when he knew the outcome for each patient he entered that into his database too – so that he could link first presentation with final diagnosis.


Mike knew that I had an interest in computer-aided diagnosis, which was a hot topic in the early 1980’s, and also that I did not warm to the Bayesian statistical models that underpinned it.  To me they made too many simplifying assumptions.

The human body is a complex adaptive system. It defies simplification.

Mike and I took a different approach.  We  just counted how many of each diagnostic group were associated with each pattern of presenting symptoms and signs.

The problem was that even his database of 8000+ patients was not big enough! This is why others had resorted to using statistical simplifications.

So we used the approach that an experienced diagnostician uses.  We used the information we had already gleaned from a patient to decide which question to ask next, and then the next one and so on.


And we always have three pieces of information at the start – the patient’s age, gender and presenting symptom.

What surprised and delighted us was how easy it was to use the database to help us do this for the new patients presenting to his clinic; the ones who were worried that they might have bowel cancer.

And what surprised us even more was how few questions we needed to ask arrive at a statistically robust decision to reassure-or-refer for further tests.

So one weekend, I wrote a little computer program that used the data from Mike’s database and our simple bean-counting algorithm to automate this process.  And the results were amazing.  Suddenly we had a simple and reliable way of using past experience to support our present decisions – without any statistical smoke-and-mirror simplifications getting in the way.

The computer program did not make the diagnosis, we were still responsible for that; all it did was provide us with reliable access to a clear and comprehensive digital memory of past experience.


What it then enabled us to do was to learn more quickly by exploring the complex patterns of symptoms, signs and outcomes and to develop our own diagnostic “rules of thumb”.

We learned in hours what it would take decades of experience to uncover. This was hot stuff, and when I presented our findings at the Royal Society of Medicine the audience was also surprised and delighted (and it was awarded the John of Arderne Medal).

So, we called it the Hot Learning System, and years later I updated it with Mike’s much bigger database (29,000+ records) and created a basic web-based version of the first step – age, gender and presenting symptom.  You can have a play if you like … just click HERE.


So what are the lessons here?

  1. We need to have the most experienced diagnosticians at the start of the improvement process.
  2. The first diagnostic assessment can be very quick so long as we have developed evidence-based heuristics.
  3. We can accelerate the training in diagnostic skills using simple information technology and basic analysis techniques.

And exactly the same is true in the health care system improvement.

We need to have an experienced health care improvement practitioner involved at the start, because if we skip this critical study step and move to plan without a correct diagnosis, then we will make errors, poor decisions, and counter-productive actions.  And then generate more work, more queues, more delays, more chaos, more distress and increased costs.

Exactly the opposite of what we want.

Q1: So, how do we develop experienced improvement practitioners more quickly?

Q2: Is there a hot learning system for improvement science?

A: Yes, there is. It can be found here.

Have you heard the phrase “you either love it or you hate it“?  It is called the Marmite Effect.

Improvement science has Marmite-like effect on some people, or more specifically, the theory part does.

Both evidence and experience show that most people prefer to learn-by-doing first; and then consolidate their learning with the minimum, necessary amount of supporting theory.

But that is not how we usually share what we know with others.  We usually attempt to teach the theory first, perhaps in the belief that it will speed up the process of learning.

Sadly, it usually has the opposite effect. Too much theory too soon often creates a barrier to engagement. It actually slows learning down! Which was not the impact we were intending.


The implications of this is that teachers of the science of improvement need to provide a range of different ways to engage with the subject.  Complementary ways.  And leave the choice of which suits whom … to the learner.

And the way to tell if it is working is … the sound of laughter.

Why is that?


Laughing is a complex behaviour that leaves us feeling happier. Which is good.

Comedians make a living from being able to trigger this behaviour in their audiences, and we will gladly part with hard cash when we know something will make us feel better.

And laughing is one of the healthiest ways to feel better!

So why do we laugh when we are learning?

It is believed that one trigger for the laughter reaction is the sudden shift from one perspective to another.  More specifically, a mental shift that relieves a growing emotional tension.  The punch line of a really good joke for example.

And later-in-life learning is often more a process of unlearning.

When we challenge a learned assumption with evidence and if we disprove it … we are unlearning.  And doing that generates emotional tension. We are often very attached to our unconscious assumptions and will usually resist them being challenged.

The way to unlearn effectively is to use the evidence of our own eyes to raise doubts about our unconscious assumptions.  We need to actively generate a bit of confusion.

Then, we resolve the apparent paradox by creatively shifting perspective, often with a real example, a practical explanation or a hands-on demonstration.

And when we experience the “Ah ha! Now I see!” reaction, and we emerge from the fog of confusion, we will relieve the emotional tension and our involuntary reaction is to laugh.

But if our teacher unintentionally triggers a Marmite effect; a “Yeuk, I am NOT enjoying this!” feeling, then we need to respect that, and step back, and adopt a different tack.


Over the last few months I have been experimenting with different approaches to introducing the principles of improvement-by-design.

And the results are clear.

A minority prefer to start with the abstract theory, and then apply it in practice.

The majority have various degrees of Marmite reaction to the theory, and some are so put off that they actively disengage.  But when they have an opportunity to see the same principles demonstrated in a concrete, practical way; they learn and laugh.

Unlearning-by-doing seems to work better for the majority.

So, if you want to have fun and learn how to deliver significant and sustained improvements … then the evidence points to this as the starting point …

… the Flow Design Practical Skills One Day Workshop.

And if you also want to dip into a bit of the tried-and-tested theory that underpins improvement-by-design then you can do that as well, either before or later (when it becomes necessary), or both.


So, to have lots of fun and learn some valuable improvement-by-design practical skills at the same time …  click here.

This week about thirty managers and clinicians in South Wales conducted two experiments to test the design of the Flow Design Practical Skills One Day Workshop.

Their collective challenge was to diagnose and treat a “chronically sick” clinic and the majority had no prior exposure to health care systems engineering (HCSE) theory, techniques, tools or training.

Two of the group, Chris and Jat, had been delegates at a previous ODWS, and had then completed their Level-1 HCSE training and real-world projects.

They had seen it and done it, so this experiment was to test if they could now teach it.

Could they replicate the “OMG effect” that they had experienced and that fired up their passion for learning and using the science of improvement?

Read on »

Chickens make interesting pets. They have personalities – no two are the same – and they produce something useful and valuable. Eggs. Yum yum!

But chickens are yummy too … especially to foxes. So we have a problem. We need to keep our ‘chucks’ safe and that means a fox-proof coop.

Here’s a picture of a chicken coop … looks great doesn’t it? You can just hear the happy clucks and taste the fresh eggs.

Have you any idea how complicated, difficult and expensive this would be to build from scratch?

Better not even try … just reach for the laptop and credit card and order a prefabricated one.  Just assembling the courier-delivered-flat-packed-made-in-China-from-renewable-forest-softwood coop will be enough of a DIY challenge!


We have had chickens for years and we have learned that they are very funny-feathered-characters-who-lay-eggs.

And we started with an old Wendy house, some softwood battening, some rolls of weld-mesh, a bag of screws and staples and a big dollop of suck-it-and-see.

The first attempt was Heath-Robinson but it worked OK.  The old Wendy house was transformed into a cosy coop and a safe-from-foxes chuck run.

And the eggs were delicious and nutritious.


But the arrow of time is relentless, and as with all organic things, the “rot had set in”.

The time had come for an update. Doing nothing was not an option.

Q: Start from scratch with a blank piece of paper and design and build a new coop and run (i.e. scrap the old one)? Or re-purpose what we have (i.e. cut out the rot, keep the good stuff and re-fashion something that is fit-for-purpose for years to come?

Oh, and we also need to keep-the-ship-afloat in the process – i.e. the keep the chucks safe-from-foxes and happily laying eggs.  That meant doing the project in one day.


What was interesting about this mini-transformation project was that I could apply exactly the same improvement framework as I would to a health care systems engineering one.

I had a clear problem (unsafe, semi-rotten chicken coop) and a clear purpose (fit-for-purpose and affordable coop and run).

Next I needed a diagnosis.  What was rotten and what was not?  And that required a bit of poking with a probe … and what I found was that most of the rot was hidden!

First I needed to study the problem (symptoms) and the purpose (required outcome) and the problem again (disease).

This was going to require some radical surgery!

With a clear destination and diagnosis it was now time to plan. For this I needed a robust design framework for exploring “radical” options – particularly those that open new opportunities that the old design prevented!  This is called “future-proofing”.

And the capital cost is always a factor – building a shiny, high-tech version of an old design that is no longer fit-for-purpose is a waste of capital investment and locks us into the past.


And remember, the innovative, fit-for-purpose, elegant, affordable design is just a dream when it is still only a plan.  Someone has to do the building work.  And it has to be feasible with the time, tools and skills available.  And all that needs to be considered at the design stage too!

With the benefit of hindsight, I have come to appreciate that the most valuable long-term investment is the new theory, new techniques, new tools and the new skills to use them. This is called “innovation”.


So with a diagnosis, a design, a sunny day, a sharpened-pencil-behind-the-ear, a just-in-time delivery of the bulkier building materials, a freshly charged power drill, and a hot cuppa … the work started.

It was going to be like performing a major operation.

The chucks were more than happy to be let out to scratch around in the garden; and groundwork always generates the opportunity for a creepy-crawly feast!  But safety comes first – foxes mainly hunt at night so in one daylight period I had to surgically excise the rot and then transform what was left into a safe space for the chucks to sleep.

When the study and plan work has been done diligently – the do phase is enjoyable.

If we skip the study phase and leap straight to plan with all the old assumptions (some rotten some not) still in place … the do phase is usually miserable! (No wonder many people have developed a high level of aversion to change!).


And the outcome?

Happy chucks, safely tucked up in their transformed, rot-free, safe-from-harm, coop and run.

The work is not quite finished – a new roof is awaiting installation but that is a quality issue not a safety one.

Safety always comes first.

And just look at how much rot had to be chopped out.

Any surgeon will tell you … “for the fastest recovery you have to cut out all the rot first“.

And that requires careful planning, courage, skill, a sharp blade, focus and … team work!

In medical training we have to learn about lots of things. That is one reason why it takes a long time to train a competent and confident clinician.

First, we learn the anatomy (structure) and physiology (function) of the normal, healthy human.

Then we learn about how this amazingly complex system can go wrong.  We learn pathology.  And we do that so that we understand the relationship between the cause (disease) and the effect (symptoms and signs).

Then we learn diagnostics – which is how to work backwards from the effects to the most likely cause(s).

And only then can we learn therapeutics – the design and delivery of a treatment plan that we are confident will relieve the symptoms by curing the disease.


The NHS is an amazingly complex system, and it too can go wrong.  It can exhibit a wide spectrum of symptoms and signs; medical errors, long delays, unhappy patients, burned-out staff, and overspent budgets.

But, there is no equivalent training in how to diagnose and treat a sick health care system.  And this is not acceptable, especially given that the knowledge of how to do this is already available.

It is called complex adaptive systems engineering (CASE).


Before the Renaissance, the understanding of how the body works was primitive and it was believed that illness was “God’s Will” so we had to just grin-and-bear (and pray).

The Scientific Revolution brought us profound theories, innovative techniques and capability extending tools.  And the impact has been dramatic.  Those who have access to this knowledge live better and longer than ever.  Those who don’t … don’t.

Our current understanding of how health care systems work is, to be blunt, medieval.  The current approaches amount to little more than rune reading, incantations and the prescription of purgatives and leeches.  And the impact is about as effective.

So we need to study the anatomy, physiology and pathology of complex adaptive systems like healthcare.

And just this week a prototype complex system pathology training system was tested … and it employed cutting-edge 21st Century technology: Pasta Twizzles.

The specific topic under scrutiny was variation.  A brain-bending concept that is usually relegated to the mystical smoke-and-mirrors world called “Sadistics”.

But no longer!

The Mists-of-Jargon and Fog-of-Formulae were blown away as we switched on the Light-of-Simulation and went exploring.

Empirically. Pragmatically.


And what we discovered was jaw-dropping.

A disease called the “Flaw of Averages” and its malignant manifestation “Carveoutosis“.


And with our new knowledge we opened the door to a previously hidden world of opportunity and improvement.

Then we activated the Laser-of-Insight and evaporated the queues and chaos that, before our new understanding, we had accepted as inevitable and beyond our understanding or control.

They were neither. And never had been. We were deluding ourselves.

Welcome to the Resilient Design – Practical Skills – One Day Workshop.

Validation Test: Passed.

A story was shared this week.

A story of hope for the hard-pressed NHS, its patients, its staff and its managers and its leaders.

A story that says “We can learn how to fix the NHS ourselves“.

And the story comes with evidence; hard, objective, scientific, statistically significant evidence.


The story starts almost exactly three years ago when a Clinical Commissioning Group (CCG) in England made a bold strategic decision to invest in improvement, or as they termed it “Achieving Clinical Excellence” (ACE).

They invited proposals from their local practices with the “carrot” of enough funding to allow GPs to carve-out protected time to do the work.  And a handful of proposals were selected and financially supported.

This is the story of one of those proposals which came from three practices in Sutton who chose to work together on a common problem – the unplanned hospital admissions in their over 70’s.

Their objective was clear and measurable: “To reduce the cost of unplanned admissions in the 70+ age group by working with hospital to reduce length of stay.

Did they achieve their objective?

Yes, they did.  But there is more to this story than that.  Much more.


One innovative step they took was to invest in learning how to diagnose why the current ‘system’ was costing what it was; then learning how to design an improvement; and then learning how to deliver that improvement.

They invested in developing their own improvement science skills first.

They did not assume they already knew how to do this and they engaged an experienced health care systems engineer (HCSE) to show them how to do it (i.e. not to do it for them).

Another innovative step was to create a blog to make it easier to share what they were learning with their colleagues; and to invite feedback and suggestions; and to provide a journal that captured the story as it unfolded.

And they measured stuff before they made any changes and afterwards so they could measure the impact, and so that they could assess the evidence scientifically.

And that was actually quite easy because the CCG was already measuring what they needed to know: admissions, length of stay, cost, and outcomes.

All they needed to learn was how to present and interpret that data in a meaningful way.  And as part of their IS training,  they learned how to use system behaviour charts, or SBCs.


By Jan 2015 they had learned enough of the HCSE techniques and tools to establish the diagnosis and start to making changes to the parts of the system that they could influence.


Two years later they subjected their before-and-after data to robust statistical analysis and they had a surprise. A big one!

Reducing hospital mortality was not a stated objective of their ACE project, and they only checked the mortality data to be sure that it had not changed.

But it had, and the “p=0.014” part of the statement above means that the probability that this 20.0% reduction in hospital mortality was due to random chance … is less than 1.4%.  [This is well below the 5% threshold that we usually accept as “statistically significant” in a clinical trial.]

But …

This was not a randomised controlled trial.  This was an intervention in a complicated, ever-changing system; so they needed to check that the hospital mortality for comparable patients who were not their patients had not changed as well.

And the statistical analysis of the hospital mortality for the ‘other’ practices for the same patient group, and the same period of time confirmed that there had been no statistically significant change in their hospital mortality.

So, it appears that what the Sutton ACE Team did to reduce length of stay (and cost) had also, unintentionally, reduced hospital mortality. A lot!


And this unexpected outcome raises a whole raft of questions …


If you would like to read their full story then you can do so … here.

It is a story of hunger for improvement, of humility to learn, of hard work and of hope for the future.

Improvement implies change, but change does not imply improvement.

We have all experienced the pain of disappointment when a change that promised much delivered no improvement, or even worse, a negative impact.

We have learned to become wary and skeptical about change.

We have learned a whole raft of tactics for deflection and diffusion of the enthusiasm of others.  And by doing so we don the black hat of the healthy skeptic and the tell tale mantra of “Yes, but …”.

So here is an onion diagram to use as a reference.  It comes from a recently published essay that compares and contrasts two schools of flow improvement.  Eli Goldratt’s “Theory of Constraints” and a translation of Systems Engineering called 6M Design®.


The first five layers can be described as “denial”, the second four as “grudging acceptance” … and the last one is the sound of the final barrier coming down and revealing the raw emotion underpinning our reluctance to change. Fear.


The good news is that this diagram helps us to shape and steer change in a way that improves its chances of success, because if we can learn to peel back these layers by sharing information that soothes the fear of the unknown, then we can align and engage.  And that is essential for emotional momentum to build.

So when we meet resistance do we push or not?

Ask yourself. How would prefer to be engaged? Pushed or not?

Bob Jekyll was already sitting at a table, sipping a pint of Black Sheep and nibbling on a bowl of peanuts when Hugh and Louise arrived.

<Hugh> Hello, are you Bob?

<Bob> Yes, indeed! You must be Hugh and Louise. Can I get you a thirst quencher?

<Louise> Lime and soda for me please.

<Hugh> I’ll have the same as you, a Black Sheep.

<Bob> On the way.

<Hugh> Hello Louise, I’m Hugh Lewis.  I am the ops manager for acute medicine at St. Elsewhere’s Hospital. It is good to meet you at last. I have seen your name on emails and performance reports.

<Louise> Good to meet you too Hugh. I am senior data analyst for St. Elsewhere’s and I think we may have met before, but I’m not sure when.  Do you know what this is about? Your invitation was a bit mysterious.

<Hugh> Yes. Sorry about that. I was chatting to a friend of mine at the golf club last week, Dr Bill Hyde who is one of our local GPs.  As you might expect, we got to talking about the chronic pressure we are all under in both primary and secondary care.  He said he has recently crossed paths with an old chum of his from university days who he’d had a very interesting conversation with in this very pub, and he recommended I email him. So I did. And that led to a phone conversation with Bob Jekyll. I have to say he asked some very interesting questions that left me feeling a mixture of curiosity and discomfort. After we talked Bob suggested that we meet for a longer chat and that I invite my senior data analyst along. So here we are.

<Louise> I have to say my curiosity was pricked by your invitation, specifically the phrase ‘system behaviour charts’. That is a new one on me and I have been working in the NHS for some time now. It is too many years to mention since I started as junior data analyst, fresh from university!

<Hugh> That is the term Bob used, and I confess it was new to me too.

<Bob> Here we are, Black Sheep, lime soda and more peanuts.  Thank you both for coming, so shall we talk about the niggle that Hugh raised when we spoke on the phone?

<Hugh> Ah! Louise, please accept my apologies in advance. I think Bob might be referring to when I said that “90% of the performance reports don’t make any sense to me“.

<Louise> There is no need to apologise Hugh. I am actually reassured that you said that. They don’t make any sense to me either! We only produce them that way because that is what we are asked for.  My original degree was geography and I discovered that I loved data analysis! My grandfather was a doctor so I guess that’s how I ended up in doing health care data analysis. But I must confess, some days I do not feel like I am adding much value.

<Hugh> Really? I believe we are in heated agreement! Some days I feel the same way.  Is that why you invited us both Bob?

<Bob> Yes.  It was some of the things that Hugh said when we talked on the phone.  They rang some warning bells for me because, in my line of work, I have seen many people fall into a whole minefield of data analysis traps that leave them feeling confused and frustrated.

<Louise> What exactly is your line of work, Bob?

<Bob> I am a systems engineer.  I design, build, verify, integrate, implement and validate systems. Fit-for-purpose systems.

<Louise> In health care?

<Bob> Not until last week when I bumped into Bill Hyde, my old chum from university.  But so far the health care system looks just like all the other ones I have worked in, so I suspect some of the lessons from other systems are transferable.

<Hugh> That sounds interesting. Can you give us an example?

<Bob> OK.  Hugh, in our first conversation, you often used the words “demand”  and “capacity”. What do you mean by those terms?

<Hugh> Well, demand is what comes through the door, the flow of requests, the workload we are expected to manage.  And capacity is the resources that we have to deliver the work and to meet our performance targets.  Capacity is the staff, the skills, the equipment, the chairs, and the beds. The stuff that costs money to provide.  As a manager, I am required to stay in-budget and that consumes a big part of my day!

<Bob> OK. Speaking as an engineer I would like to know the units of measurement of “demand” and “capacity”?

<Hugh> Oh! Um. Let me think. Er. I have never been asked that question before. Help me out here Louise.  I told you Bob asks tricky questions!

<Louise> I think I see what Bob is getting at.  We use these terms frequently but rather loosely. On reflection they are not precisely defined, especially “capacity”. There are different sorts of capacity all of which will be measured in different ways so have different units. No wonder we spend so much time discussing and debating the question of if we have enough capacity to meet the demand.  We are probably all assuming different things.  Beds cannot be equated to staff, but too often we just seem to lump everything together when we talk about “capacity”.  So by doing that what we are really asking is “do we have enough cash in the budget to pay for the stuff we thing we need?”. And if we are failing one target or another we just assume that the answer is “No” and we shout for “more cash”.

<Bob> Exactly my point. And this was one of the warning bells.  Lack of clarity on these fundamental definitions opens up a minefield of other traps like the “Flaw of Averages” and “Time equals Money“.  And if we are making those errors then they will, unwittingly, become incorporated into our data analysis.

<Louise> But we use averages all the time! What is wrong with an average?

<Bob> I can sense you are feeling a bit defensive Louise.  There is no need to.  An average is perfectly OK and is very useful tool.  The “flaw” is when it is used inappropriately.  Have you heard of Little’s Law?

<Louise> No. What’s that?

<Bob> It is the mathematically proven relationship between flow, work-in-progress and lead time.  It is a fundamental law of flow physics and it uses averages. So averages are OK.

<Hugh> So what is the “Flaw of Averages”?

<Bob> It is easier to demonstrate it than to describe it.  Let us play a game.  I have some dice and we have a big bowl of peanuts.  Let us simulate a simple two step process.  Hugh you are Step One and Louise you are Step Two.  I will be the the source of demand.

I will throw a dice and count that many peanuts out of the bowl and pass them to Hugh.  Hugh, you then throw the dice and move that many peanuts from your heap to Louise, then Louise throws the dice and moves that many from her pile to the final heap which we will call activity.

<Hugh> Sounds easy enough.  If we all use the same dice then the average flow through each step will be the same so after say ten rounds we should have, um …

<Louise> … thirty five peanuts in the activity heap.  On average.

<Bob> OK.  That’s the theory, let’s see what happens in reality.  And no eating the nuts-in-progress please.


They play the game and after a few minutes they have completed the ten rounds.


<Hugh> That’s odd.  There are only 30 nuts in the activity heap and we expected 35.  Nobody nibbled any nuts so its just chance I suppose.  Lets play again. It should average out.

…..  …..

<Louise> Thirty four this time which is better, but is still below the predicted average.  That could still be a chance effect though.  Let us run the ‘nutty’ game this a few more times.

….. …..

<Hugh> We have run the same game six times with the same nuts and the same dice and we delivered activities of 30, 34, 30, 24, 23 and 31 and there are usually nuts stuck in the process at the end of each game, so it is not due to a lack of demand.  We are consistently under-performing compared with our theoretical prediction.  That is weird.  My head says we were just unlucky but I have a niggling doubt that there is more to it.

<Louise> Is this the Flaw of Averages?

<Bob> Yes, it is one of them. If we set our average future flow-capacity to the average historical demand and there is any variation anywhere in the process then we will see this effect.

<Hugh> H’mmm.  But we do this all the time because we assume that the variation will average out over time. Intuitively it must average out over time.  What would happen if we kept going for more cycles?

<Bob> That is a very good question.  And your intuition is correct.  It does average out eventually but there is a catch.

<Hugh> What is the catch?

<Bob>  The number of peanuts in the process and the time it takes for one peanut to get through is very variable.

<Louise> Is there any pattern to the variation? Is it predictable?

<Bob> Another excellent question.  Yes, there is a pattern.  It is called “chaos”.  Predictable chaos if you like.

<Hugh> So is that the reason you said on the phone that we should present our metrics as time-series charts?

<Bob> Yes, one of them.  The appearance of chaotic system behaviour is very characteristic on a time-series chart.

<Louise> And if we see the chaos pattern on our charts then we could conclude that we have made the Flaw of Averages error?

<Bob> That would be a reasonable hypothesis.

<Hugh> I think I understand the reason you invited us to a face-to-face demonstration.  It would not have worked if you had just described it.  You have to experience it because it feels so counter-intuitive.  And this is starting to feel horribly familiar; perpetual chaos about sums up my working week!

<Louise> You also mentioned something you referred to as the “time equals money” trap.  Is that somehow linked to this?

<Bob> Yes.  We often equate time and money but they do not behave the same way.  If have five pounds today and I only spend four pounds then I can save the remaining one pound for tomorrow and spend it then – so the Law of Averages works.  But if I have five minutes today and I only use four minutes then the other minute cannot be saved and used tomorrow, it is lost forever.  That is why the Law of Averages does not work for time.

<Hugh> But that means if we set our budgets based on the average demand and the cost of people’s time then not only will we have queues, delays and chaos, we will also consistently overspend the budget too.  This is sounding more and more familiar by the minute!  This is nuts, if you will excuse the pun.

<Louise> So what is the solution?  I hope you would not have invited us here if there was no solution.

<Bob> Part of the solution is to develop our knowledge of system behaviour and how we need to present it in a visual format. With that we develop a deeper understanding of what the system behaviour charts are saying to us.  With that we can develop our ability to make wiser decisions that will lead to effective actions which will eliminate the queues, delays, chaos and cost-pressures.

<Hugh> This is possible?

<Bob> Yes. It is called systems engineering. That’s what I do.

<Louise> When do we start?

<Bob> We have started.

Dr Bill Hyde was already at the bar when Bob Jekyll arrived.

Bill and  Bob had first met at university and had become firm friends, but their careers had diverged and it was only by pure chance that their paths had crossed again recently.

They had arranged to meet up for a beer and to catch up on what had happened in the 25 years since they had enjoyed the “good old times” in the university bar.

<Dr Bill> Hi Bob, what can I get you? If I remember correctly it was anything resembling real ale. Will this “Black Sheep” do?

<Bob> Hi Bill, Perfect! I’ll get the nibbles. Plain nuts OK for you?

<Dr Bill> My favourite! So what are you up to now? What doors did your engineering degree open?

<Bob> Lots!  I’ve done all sorts – mechanical, electrical, software, hardware, process, all except civil engineering. And I love it. What I do now is a sort of synthesis of all of them.  And you? Where did your medical degree lead?

<Dr Bill> To my hearts desire, the wonderful Mrs Hyde, and of course to primary care. I am a GP. I always wanted to be a GP since I was knee-high to a grasshopper.

<Bob> Yes, you always had that “I’m going to save the world one patient at a time!” passion. That must be so rewarding! Helping people who are scared witless by the health horror stories that the media pump out.  I had a fright last year when I found a lump.  My GP was great, she confidently diagnosed a “hernia” and I was all sorted in a matter of weeks with a bit of nifty day case surgery. I was convinced my time had come. It just shows how damaging the fear of the unknown can be!

<Dr Bill> Being a GP is amazingly rewarding. I love my job. But …

<Bob> But what? Are you alright Bill? You suddenly look really depressed.

<Dr Bill> Sorry Bob. I don’t want to be a damp squib. It is good to see you again, and chat about the old days when we were teased about our names.  And it is great to hear that you are enjoying your work so much. I admit I am feeling low, and frankly I welcome the opportunity to talk to someone I know and trust who is not part of the health care system. If you know what I mean?

<Bob> I know exactly what you mean.  Well, I can certainly offer an ear, “a problem shared is a problem halved” as they say. I can’t promise to do any more than that, but feel free to tell me the story, from the beginning. No blood-and-guts gory details though please!

<Dr Bill> Ha! “Tell me the story from the beginning” is what I say to my patients. OK, here goes. I feel increasingly overwhelmed and I feel like I am drowning under a deluge of patients who are banging on the practice door for appointments to see me. My intuition tells me that the problem is not the people, it is the process, but I can’t seem to see through the fog of frustration and chaos to a clear way forward.

<Bob> OK. I confess I know nothing about how your system works, so can you give me a bit more context.

<Dr Bill> Sorry. Yes, of course. I am what is called a single-handed GP and I have a list of about 1500 registered patients and I am contracted to provide primary care for them. I don’t have to do that 24 x 7, the urgent stuff that happens in the evenings and weekends is diverted to services that are designed for that. I work Monday to Friday from 9 AM to 5 PM, and I am contracted to provide what is needed for my patients, and that means face-to-face appointments.

<Bob> OK. When you say “contracted” what does that mean exactly?

<Dr Bill> Basically, the St. Elsewhere’s® Practice is like a small business. It’s annual income is a fixed amount per year for each patient on the registration list, and I have to provide the primary care service for them from that pot of cash. And that includes all the costs, including my income, our practice nurse, and the amazing Mrs H. She is the practice receptionist, manager, administrator and all-round fixer-of-anything.

<Bob> Wow! What a great design. No need to spend money on marketing, research, new product development, or advertising! Just 100% pure service delivery of tried-and-tested medical know-how to a captive audience for a guaranteed income. I have commercial customers who would cut off their right arms for an offer like that!

<Dr Bill> Really? It doesn’t feel like that to me. It feels like the more I offer, the more the patients expect. The demand is a bottomless well of wants, but the income is capped and my time is finite!

<Bob> H’mm. Tell me more about the details of how the process works.

<Dr Bill> Basically, I am a problem-solving engine. Patients phone for an appointment, Mrs H books one, the patient comes at the appointed time, I see them, and I diagnose and treat the problem, or I refer on to a specialist if it’s more complicated. That’s basically it.

<Bob> OK. Sounds a lot simpler than 99% of the processes that I’m usually involved with. So what’s the problem?

<Dr Bill> I don’t have enough capacity! After all the appointments for the day are booked Mrs H has to say “Sorry, please try again tomorrow” to every patient who phones in after that.  The patients who can’t get an appointment are not very happy and some can get quite angry. They are anxious and frustrated and I fully understand how they feel. I feel the same.

<Bob> We will come back to what you mean by “capacity”. Can you outline for me exactly how a patient is expected to get an appointment?

<Dr Bill> We tell them to phone at 8 AM for an appointment, there is a fixed number of bookable appointments, and it is first-come-first-served.  That is the only way I can protect myself from being swamped and is the fairest solution for patients.  It wasn’t my idea; it is called Advanced Access. Each morning at 8 AM we switch on the phones and brace ourselves for the daily deluge.

<Bob> You must be pulling my leg! This design is a batch-and-queue phone-in appointment booking lottery!  I guess that is one definition of “fair”.  How many patients get an appointment on the first attempt?

<Dr Bill> Not many.  The appointments are usually all gone by 9 AM and a lot are to people who have been trying to get one for several days. When they do eventually get to see me they are usually grumpy and then spring the trump card “And while I’m here doctor I have a few other things that I’ve been saving up to ask you about“. I help if I can but more often than not I have to say, “I’m sorry, you’ll have to book another appointment!“.

<Bob> I’m not surprised you patients are grumpy. I would be too. And my recollection of seeing my GP with my scary lump wasn’t like that at all. I phoned at lunch time and got an appointment the same day. Maybe I was just lucky, or maybe my GP was as worried as me. But it all felt very calm. When I arrived there was only one other patient waiting, and I was in and out in less than ten minutes – and mightily reassured I can tell you! It felt like a high quality service that I could trust if-and-when I needed it, which fortunately is very infrequently.

<Dr Bill> I dream of being able to offer a service like that! I am prepared to bet you are registered with a group practice and you see whoever is available rather than your own GP. Single-handed GPs like me who offer the old fashioned personal service are a rarity, and I can see why. We must be suckers!

<Bob> OK, so I’m starting to get a sense of this now. Has it been like this for a long time?

<Dr Bill> Yes, it has. When I was younger I was more resilient and I did not mind going the extra mile.  But the pressure is relentless and maybe I’m just getting older and grumpier.  My real fear is I end up sounding like the burned-out cynics that I’ve heard at the local GP meetings; the ones who crow about how they are counting down the days to when they can retire and gloat.

<Bob> You’re the same age as me Bill so I don’t think either of us can use retirement as an exit route, and anyway, that’s not your style. You were never a quitter at university. Your motto was always “when the going gets tough the tough get going“.

<Dr Bill> Yeah I know. That’s why it feels so frustrating. I think I lost my mojo a long time back. Maybe I should just cave in and join up with the big group practice down the road, and accept the inevitable loss of the personal service. They said they would welcome me, and my list of 1500 patients, with open arms.

<Bob> OK. That would appear to be an option, or maybe a compromise, but I’m not sure we’ve exhausted all the other options yet.  Tell me, how do you decide how long a patient needs for you to solve their problem?

<Dr Bill> That’s easy. It is ten minutes. That is the time recommended in the Royal College Guidelines.

<Bob> Eh? All patients require exactly ten minutes?

<Dr Bill> No, of course not!  That is the average time that patients need.  The Royal College did a big survey and that was what most GPs said they needed.

<Bob> Please tell me if I have got this right.  You work 9-to-5, and you carve up your day into 10-minute time-slots called “appointments” and, assuming you are allowed time to have lunch and a pee, that would be six per hour for seven hours which is 42 appointments per day that can be booked?

<Dr Bill> No. That wouldn’t work because I have other stuff to do as well as see patients. There are only 25 bookable 10-minute appointments per day.

<Bob> OK, that makes more sense. So where does 25 come from?

<Dr Bill> Ah! That comes from a big national audit. For an average GP with and average  list of 1,500 patients, the average number of patients seeking an appointment per day was found to be 25, and our practice population is typical of the national average in terms of age and deprivation.  So I set the upper limit at 25. The workload is manageable but it seems to generate a lot of unhappy patients and I dare not increase the slots because I’d be overwhelmed with the extra workload and I’m barely coping now.  I feel stuck between a rock and a hard place!

<Bob> So you have set the maximum slot-capacity to the average demand?

<Dr Bill> Yes. That’s OK isn’t it? It will average out over time. That is what average means! But it doesn’t feel like that. The chaos and pressure never seems to go away.


There was a long pause while Bob mulls over what he had heard, sips his pint of Black Sheep and nibbles on the dwindling bowl of peanuts.  Eventually he speaks.


<Bob> Bill, I have some good news and some not-so-good news and then some more good news.

<Dr Bill> Oh dear, you sound just like me when I have to share the results of tests with one of my patients at their follow up appointment. You had better give me the “bad news sandwich”!

<Bob> OK. The first bit of good news is that this is a very common, and easily treatable flow problem.  The not-so-good news is that you will need to change some things.  The second bit of good news is that the changes will not cost anything and will work very quickly.

<Dr Bill> What! You cannot be serious!! Until ten minutes ago you said that you knew nothing about how my practice works and now you are telling me that there is a quick, easy, zero cost solution.  Forgive me for doubting your engineering know-how but I’ll need a bit more convincing than that!

<Bob> And I would too if I were in your position.  The clues to the diagnosis are in the story. You said the process problem was long-standing; you said that you set the maximum slot-capacity to the average demand; and you said that you have a fixed appointment time that was decided by a subjective consensus.  From an engineering perspective, this is a perfect recipe for generating chronic chaos, which is exactly the symptoms you are describing.

<Dr Bill> Is it? OMG. You said this is well understood and resolvable? So what do I do?

<Bob> Give me a minute.  You said the average demand is 25 per day. What sort of service would you like your patients to experience? Would “90% can expect a same day appointment on the first call” be good enough as a starter?

<Dr Bill> That would be game changing!  Mrs H would be over the moon to be able to say “Yes” that often. I would feel much less anxious too, because I know the current system is a potentially dangerous lottery. And my patients would be delighted and relieved to be able to see me that easily and quickly.

<Bob> OK. Let me work this out. Based on what you’ve said, some assumptions, and a bit of flow engineering know-how; you would need to offer up to 31 appointments per day.

<Dr Bill> What! That’s impossible!!! I told you it would be impossible! That would be another hour a day of face-to-face appointments. When would I do the other stuff? And how did you work that out anyway?

<Bob> I did not say they would have to all be 10-minute appointments, and I did not say you would expect to fill them all every day. I did however say you would have to change some things.  And I did say this is a well understood flow engineering problem.  It is called “resilience design“. That’s how I was able to work it out on the back of this Black Sheep beer mat.

<Dr Bill> H’mm. That is starting to sound a bit more reasonable. What things would I have to change? Specifically?

<Bob> I’m not sure what specifically yet.  I think in your language we would say “I have taken a history, and I have a differential diagnosis, so next I’ll need to examine the patient, and then maybe do some tests to establish the actual diagnosis and to design and decide the treatment plan“.

<Dr Bill> You are learning the medical lingo fast! What do I need to do first? Brace myself for the forensic rubber-gloved digital examination?

<Bob> Alas, not yet and certainly not here. Shall we start with the vital signs? Height, weight, pulse, blood pressure, and temperature? That’s what my GP did when I went with my scary lump.  The patient here is not you, it is your St. Elsewhere’s® Practice, and we will need to translate the medical-speak into engineering-speak.  So one thing you’ll need to learn is a bit of the lingua-franca of systems engineering.  By the way, that’s what I do now. I am a systems engineer, or maybe now a health care systems engineer?

<Dr Bill> Point me in the direction of the HCSE dictionary! The next round is on me. And the nuts!

<Bob> Excellent. I’ll have another Black Sheep and some of those chilli-coated ones. We have work to do.  Let me start by explaining what “capacity” actually means to an engineer. Buckle up. This ride might get a bit bumpy.


This story is fictional, but the subject matter is factual.

Bob’s diagnosis and recommendations are realistic and reasonable.

Chapter 1 of the HCSE dictionary can be found here.

And if you are a GP who recognises these “symptoms” then this may be of interest.

When education fails to keep pace with technology the result is inequality. Without the skills to stay useful as innovations arrive, workers suffer“. The Economist January 14th 2017, p 11.

The stark reality is that we all have to develop the habit of lifelong learning, especially if we want to avoid mid-career obsolescence.

A terrifying prospect for the family bread-winner.

This risk is especially true in health care because medical and managerial technology is always changing as the health care system evolves and adapts to the shifting sands and tides.

But we cannot keep going back to traditional classroom methods to update our knowledge and skills: it is too disruptive and expensive.  And when organisations are in a financial squeeze, the training budget is usually the first casualty!

So, how can we protect ourselves?  One answer is a MOOC.

The mantra is “learn while you earn” which means that we do not take time out to do this intermittently, we do it in parallel, and continuously.

The MOOC model leverages the power of the Internet and mobile technology, allowing us to have bites of learning where and when it most suits us, at whatever pace we choose to set.

We can have all the benefits of traditional education too: certificates, communities, and coaching.

And when keeping a job, climbing the career ladder, or changing companies all require a bang-up-to-date set of skills – a bit of time, effort and money may be a very wise investment and deliver a healthy return!


And the good news is that there a is a MOOC for Healthcare Improvement.

It is called the …

Foundations of Improvement Science in Healthcare

which is an open door to a growing …

Community of Healthcare Improvement Practitioners.

Click HERE for a free taste …. yum yum!


 

Today was an especially interesting one.

All days are interesting and every day I learn something of great value and today was no different.

But today was in a different league!


My job today was to deliver health care. I am a surgeon. I perform operations that are intended to improve the health of the people who place their trust in me.

Patients.

But I was only able to deliver three operations today. Usually I would do eight. Normally I would use every precious minute of operating theatre time.

But today, half of that (very expensive) time went unused. It was paid for but it was wasted. The whole theatre team were idle. And patients needing operations were waiting too. Lose, lose.

And the reason?

The day surgery unit in my hospital was being used for something that it was not designed for. It was being used by non-surgical patients.

And that was the best of a bad job because the alternative was those non-surgical patients would otherwise have been lying on trolleys in corridors.


But how could frail elderly medical emergency admissions spill over into the day surgery unit?

Because the current design of the health and social care system guarantees that will happen.  That was not the intention, but it is the impact of the policies that dictate how the system behaves.


So, to fill in the idle time while unable to operate (and after deleting all the spam email and processing the non-spam email) I looked at jobs on the NHS jobs website.

This is a behaviour I have observed many times, and to-date I have not indulged in it, but today I was idle, and I was irritated, and I was curious to see what I might find.

And I quite quickly came across a job for a “STP Programme Director” with an eye-watering, five-figure salary!  H’mmm …

STP is shortcut for “Sustainability and Transformation Plans” and, forgive me for appearing skeptical but, that sounds rather familiar.

But, ever wary of the dangers of pre-judgement, I dug deeper into the online information to learn more.


And I downloaded the STP for our local health care economy, all 80-pages of it, and I even had time to read it.

The offered purpose made complete sense to me.

A vision of an integrated health and social care system that converts public cash into public contentment. Fantastic! Sign me up to that!!

What I was less able to make sense of was the process for delivering the dream.

The job of the STP Programme Director seemed to be “to bring all the separate parts of the current system together and to weld them into a synergistic whole“.

That would be the perfect job for someone who sees the whole as greater than the sum of the parts, and someone with the skills and experience to do that. Someone like a systems engineer. A health and social care systems engineer.

My interest was growing!


And it was at that point that I felt the emotional pain of disappointment.

There was nothing new in the JD or the STP that even hinted at “how” this wonderful vision would be achieved. All I found was the well-worn “CIP and QIPP” language.

That, forgive me for saying, does not seem to have delivered so far. Apologies for the reality check.

Oh well! Never mind. My skepticism had prepared me for disappointment.


Ah! Here is the next patient. Time to wield the scalpel and to actually deliver some health care. A much better use of my time than web-surfing, eh?


But the idle time was not completely wasted. I did learn much but from the opportunity to experience the streeeeetch between the NHS reality and the NHS rhetoric.

Every day is an opportunity to learn something. You never know what will turn up tomorrow.

Sometimes change is dramatic. A big improvement appears very quickly. And when that happens we are caught by surprise (and delight).

Our emotional reaction is much faster than our logical response. “Wow! That’s a miracle!


Our logical Tortoise eventually catches up with our emotional Hare and says “Hare, we both know that there is no such thing as miracles and magic. There must be a rational explanation. What is it?

And Hare replies “I have no idea, Tortoise.  If I did then it would not have been such a delightful surprise. You are such a kill-joy! Can’t you just relish the relief without analyzing the life out of it?

Tortoise feels hurt. “But I just want to understand so that I can explain to others. So that they can do it and get the same improvement.  Not everyone has a ‘nothing-ventured-nothing-gained’ attitude like you! Most of us are too fearful of failing to risk trusting the wild claims of improvement evangelists. We have had our fingers burned too often.


The apparent miracle is real and recent … here is a snippet of the feedback:

Notice carefully the last sentence. It took a year of discussion to get an “OK” and a month of planning to prepare the “GO”.

That is not a miracle and some magic … that took a lot of hard work!

The evangelist is the customer. The supplier is an engineer.


The context is the chronic niggle of patients trying to get an appointment with their GP, and the chronic niggle of GPs feeling overwhelmed with work.

Here is the back story …

In the opening weeks of the 21st Century, the National Primary Care Development Team (NPDT) was formed.  Primary care was a high priority and the government had allocated £168m of investment in the NHS Plan, £48m of which was earmarked to improve GP access.

The approach the NPDT chose was:

harvest best practice +
use a panel of experts +
disseminate best practice.

Dr (later Sir) John Oldham was the innovator and figure-head.  The best practice was copied from Dr Mark Murray from Kaiser Permanente in the USA – the Advanced Access model.  The dissemination method was copied from from Dr Don Berwick’s Institute of Healthcare Improvement (IHI) in Boston – the Collaborative Model.

The principle of Advanced Access is “today’s-work-today” which means that all the requests for a GP appointment are handled the same day.  And the proponents of the model outlined the key elements to achieving this:

1. Measure daily demand.
2. Set capacity so that is sufficient to meet the daily demand.
3. Simple booking rule: “phone today for a decision today”.

But that is not what was rolled out. The design was modified somewhere between aspiration and implementation and in two important ways.

First, by adding a policy of “Phone at 08:00 for an appointment”, and second by adding a policy of “carving out” appointment slots into labelled pots such as ‘Dr X’ or ‘see in 2 weeks’ or ‘annual reviews’.

Subsequent studies suggest that the tweaking happened at the GP practice level and was driven by the fear that, by reducing the waiting time, they would attract more work.

In other words: an assumption that demand for health care is supply-led, and without some form of access barrier, the system would be overwhelmed and never be able to cope.


The result of this well-intended tampering with the Advanced Access design was to invalidate it. Oops!

To a systems engineer this is meddling was counter-productive.

The “today’s work today” specification is called a demand-led design and, if implemented competently, will lead to shorter waits for everyone, no need for urgent/routine prioritization and slot carve-out, and a simpler, safer, calmer, more efficient, higher quality, more productive system.

In this context it does not mean “see every patient today” it means “assess and decide a plan for every patient today”.

In reality, the actual demand for GP appointments is not known at the start; which is why the first step is to implement continuous measurement of the daily number and category of requests for appointments.

The second step is to feed back this daily demand information in a visual format called a time-series chart.

The third step is to use this visual tool for planning future flow-capacity, and for monitoring for ‘signals’, such as spikes, shifts, cycles and slopes.

That was not part of the modified design, so the reasonable fear expressed by GPs was (and still is) that by attempting to do today’s-work-today they would unleash a deluge of unmet need … and be swamped/drowned.

So a flood defense barrier was bolted on; the policy of “phone at 08:00 for an appointment today“, and then the policy of  channeling the over spill into pots of “embargoed slots“.

The combined effect of this error of omission (omitting the measured demand visual feedback loop) and these errors of commission (the 08:00 policy and appointment slot carve-out policy) effectively prevented the benefits of the Advanced Access design being achieved.  It was a predictable failure.

But no one seemed to realize that at the time.  Perhaps because of the political haste that was driving the process, and perhaps because there were no systems engineers on the panel-of-experts to point out the risks of diluting the design.

It is also interesting to note that the strategic aim of the NPCT was to develop a self-sustaining culture of quality improvement (QI) in primary care. That didn’t seem to have happened either.


The roll out of Advanced Access was not the success it was hoped. This is the conclusion from the 300+ page research report published in 2007.


The “Miracle on Tavanagh Avenue” that was experienced this week by both patients and staff was the expected effect of this tampering finally being corrected; and the true potential of the original demand-led design being released – for all to experience.

Remember the essential ingredients?

1. Measure daily demand and feed it back as a visual time-series chart.
2. Set capacity so that is sufficient to meet the daily demand.
3. Use a simple booking rule: “phone anytime for a decision today”.

But there is also an extra design ingredient that has been added in this case, one that was not part of the original Advanced Access specification, one that frees up GP time to provide the required “resilience” to sustain a same-day service.

And that “secret” ingredient is how the new design worked so quickly and feels like a miracle – safe, calm, enjoyable and productive.

This is health care systems engineering (HCSE) in action.


So congratulations to Harry Longman, the whole team at GP Access, and to Dr Philip Lusty and the team at Riverside Practice, Tavangh Avenue, Portadown, NI.

You have demonstrated what was always possible.

The fear of failure prevented it before, just as it prevented you doing this until you were so desperate you had no other choices.

To read the fuller story click here.

PS. Keep a close eye on the demand time-series chart and if it starts to rise then investigate the root cause … immediately.


I am a big fan of pictures that tell a story … and this week I discovered someone who is creating great pictures … Hayley Lewis.

This is one of Hayley’s excellent sketch notes … the one that captures the essence of the Bruce Tuckman model of team development.

The reason that I share this particular sketch-note is because my experience of developing improvement-by-design teams is that it works just like this!

The tricky phase is the STORMING one because not all teams survive it!

About half sink in the storm – and that seems like an awful waste – and I believe it is avoidable.

This means that before starting the team development cycle, the leader needs to be aware of how to navigate themselves and the team through the storm phase … and that requires training, support and practice.

Which is the reason why coaching from a independent, experienced, capable practitioner is a critical element of the improvement process.

Phil and Pete are having a coffee and a chat.  They both work in the NHS and have been friends for years.

They have different jobs. Phil is a commissioner and an accountant by training, Pete is a consultant and a doctor by training.

They are discussing a challenge that affects them both on a daily basis: unscheduled care.

Both Phil and Pete want to see significant and sustained improvements and how to achieve them is often the focus of their coffee chats.


<Phil> We are agreed that we both want improvement, both from my perspective as a commissioner and from your perspective as a clinician. And we agree that what we want to see improvements in patient safety, waiting, outcomes, experience for both patients and staff, and use of our limited NHS resources.

<Pete> Yes. Our common purpose, the “what” and “why”, has never been an issue.  Where we seem to get stuck is the “how”.  We have both tried many things but, despite our good intentions, it feels like things are getting worse!

<Phil> I agree. It may be that what we have implemented has had a positive impact and we would have been even worse off if we had done nothing. But I do not know. We clearly have much to learn and, while I believe we are making progress, we do not appear to be learning fast enough.  And I think this knowledge gap exposes another “how” issue: After we have intervened, how do we know that we have (a) improved, (b) not changed or (c) worsened?

<Pete> That is a very good question.  And all that I have to offer as an answer is to share what we do in medicine when we ask a similar question: “How do I know that treatment A is better than treatment B?”  It is the essence of medical research; the quest to find better treatments that deliver better outcomes and at lower cost.  The similarities are strong.

<Phil> OK. How do you do that? How do you know that “Treatment A is better than Treatment B” in a way that anyone will trust the answer?

 <Pete> We use a science that is actually very recent on the scientific timeline; it was only firmly established in the first half of the 20th century. One reason for that is that it is rather a counter-intuitive science and for that reason it requires using tools that have been designed and demonstrated to work but which most of us do not really understand how they work. They are a bit like magic black boxes.

<Phil> H’mm. Please forgive me for sounding skeptical but that sounds like a big opportunity for making mistakes! If there are lots of these “magic black box” tools then how do you decide which one to use and how do you know you have used it correctly?

<Pete> Those are good questions! Very often we don’t know and in our collective confusion we generate a lot of unproductive discussion.  This is why we are often forced to accept the advice of experts but, I confess, very often we don’t understand what they are saying either! They seem like the medieval Magi.

<Phil> H’mm. So these experts are like ‘magicians’ – they claim to understand the inner workings of the black magic boxes but are unable, or unwilling, to explain in a language that a ‘muggle’ would understand?

<Pete> Very well put. That is just how it feels.

<Phil> So can you explain what you do understand about this magical process? That would be a start.


<Pete> OK, I will do my best.  The first thing we learn in medical research is that we need to be clear about what it is we are looking to improve, and we need to be able to measure it objectively and accurately.

<Phil> That  makes sense. Let us say we want to improve the patient’s subjective quality of the A&E experience and objectively we want to reduce the time they spend in A&E. We measure how long they wait. 

<Pete> The next thing is that we need to decide how much improvement we need. What would be worthwhile? So in the example you have offered we know that reducing the average time patients spend in A&E by just 30 minutes would have a significant effect on the quality of the patient and staff experience, and as a by-product it would also dramatically improve the 4-hour target performance.

<Phil> OK.  From the commissioning perspective there are lots of things we can do, such as commissioning alternative paths for specific groups of patients; in effect diverting some of the unscheduled demand away from A&E to a more appropriate service provider.  But these are the sorts of thing we have been experimenting with for years, and it brings us back to the question: How do we know that any change we implement has had the impact we intended? The system seems, well, complicated.

<Pete> In medical research we are very aware that the system we are changing is very complicated and that we do not have the power of omniscience.  We cannot know everything.  Realistically, all we can do is to focus on objective outcomes and collect small samples of the data ocean and use those in an attempt to draw conclusions can trust. We have to design our experiment with care!

<Phil> That makes sense. Surely we just need to measure the stuff that will tell us if our impact matches our intent. That sounds easy enough. What’s the problem?

<Pete> The problem we encounter is that when we measure “stuff” we observe patient-to-patient variation, and that is before we have made any changes.  Any impact that we may have is obscured by this “noise”.

<Phil> Ah, I see.  So if the our intervention generates a small impact then it will be more difficult to see amidst this background noise. Like trying to see fine detail in a fuzzy picture.

<Pete> Yes, exactly like that.  And it raises the issue of “errors”.  In medical research we talk about two different types of error; we make the first type of error when our actual impact is zero but we conclude from our data that we have made a difference; and we make the second type of error when we have made an impact but we conclude from our data that we have not.

<Phil> OK. So does that imply that the more “noise” we observe in our measure for-improvement before we make the change, the more likely we are to make one or other error?

<Pete> Precisely! So before we do the experiment we need to design it so that we reduce the probability of making both of these errors to an acceptably low level.  So that we can be assured that any conclusion we draw can be trusted.

<Phil> OK. So how exactly do you do that?

<Pete> We know that whenever there is “noise” and whenever we use samples then there will always be some risk of making one or other of the two types of error.  So we need to set a threshold for both. We have to state clearly how much confidence we need in our conclusion. For example, we often use the convention that we are willing to accept a 1 in 20 chance of making the Type I error.

<Phil> Let me check if I have heard you correctly. Suppose that, in reality, our change has no impact and we have set the risk threshold for a Type 1 error at 1 in 20, and suppose we repeat the same experiment 100 times – are you saying that we should expect about five of our experiments to show data that says our change has had the intended impact when in reality it has not?

<Pete> Yes. That is exactly it.

<Phil> OK.  But in practice we cannot repeat the experiment 100 times, so we just have to accept the 1 in 20 chance that we will make a Type 1 error, and we won’t know we have made it if we do. That feels a bit chancy. So why don’t we just set the threshold to 1 in 100 or 1 in 1000?

<Pete> We could, but doing that has a consequence.  If we reduce the risk of making a Type I error by setting our threshold lower, then we will increase the risk of making a Type II error.

<Phil> Ah! I see. The old swings-and-roundabouts problem. By the way, do these two errors have different names that would make it  easier to remember and to explain?

<Pete> Yes. The Type I error is called a False Positive. It is like concluding that a patient has a specific diagnosis when in reality they do not.

<Phil> And the Type II error is called a False Negative?

<Pete> Yes.  And we want to avoid both of them, and to do that we have to specify a separate risk threshold for each error.  The convention is to call the threshold for the false positive the alpha level, and the threshold for the false negative the beta level.

<Phil> OK. So now we have three things we need to be clear on before we can do our experiment: the size of the change that we need, the risk of the false positive that we are willing to accept, and the risk of a false negative that we are willing to accept.  Is that all we need?

<Pete> In medical research we learn that we need six pieces of the experimental design jigsaw before we can proceed. We only have three pieces so far.

<Phil> What are the other three pieces then?

<Pete> We need to know the average value of the metric we are intending to improve, because that is our baseline from which improvement is measured.  Improvements are often framed as a percentage improvement over the baseline.  And we need to know the spread of the data around that average, the “noise” that we referred to earlier.

<Phil> Ah, yes!  I forgot about the noise.  But that is only five pieces of the jigsaw. What is the last piece?

<Pete> The size of the sample.

<Phil> Eh?  Can’t we just go with whatever data we can realistically get?

<Pete> Sadly, no.  The size of the sample is how we control the risk of a false negative error.  The more data we have the lower the risk. This is referred to as the power of the experimental design.

<Phil> OK. That feels familiar. I know that the more experience I have of something the better my judgement gets. Is this the same thing?

<Pete> Yes. Exactly the same thing.

<Phil> OK. So let me see if I have got this. To know if the impact of the intervention matches our intention we need to design our experiment carefully. We need all six pieces of the experimental design jigsaw and they must all fall inside our circle of control. We can measure the baseline average and spread; we can specify the impact we will accept as useful; we can specify the risks we are prepared to accept of making the false positive and false negative errors; and we can collect the required amount of data after we have made the intervention so that we can trust our conclusion.

<Pete> Perfect! That is how we are taught to design research studies so that we can trust our results, and so that others can trust them too.

<Phil> So how do we decide how big the post-implementation data sample needs to be? I can see we need to collect enough data to avoid a false negative but we have to be pragmatic too. There would appear to be little value in collecting more data than we need. It would cost more and could delay knowing the answer to our question.

<Pete> That is precisely the trap than many inexperienced medical researchers fall into. They set their sample size according to what is achievable and affordable, and then they hope for the best!

<Phil> Well, we do the same. We analyse the data we have and we hope for the best.  In the magical metaphor we are asking our data analysts to pull a white rabbit out of the hat.  It sounds rather irrational and unpredictable when described like that! Have medical researchers learned a way to avoid this trap?

<Pete> Yes, it is a tool called a power calculator.

<Phil> Ooooo … a power tool … I like the sound of that … that would be a cool tool to have in our commissioning bag of tricks. It would be like a magic wand. Do you have such a thing?

<Pete> Yes.

<Phil> And do you understand how the power tool magic works well enough to explain to a “muggle”?

<Pete> Not really. To do that means learning some rather unfamiliar language and some rather counter-intuitive concepts.

<Phil> Is that the magical stuff I hear lurks between the covers of a medical statistics textbook?

<Pete> Yes. Scary looking mathematical symbols and unfathomable spells!

<Phil> Oh dear!  Is there another way for to gain a working understanding of this magic? Something a bit more pragmatic? A path that a ‘statistical muggle’ might be able to follow?

<Pete> Yes. It is called a simulator.

<Phil> You mean like a flight simulator that pilots use to learn how to control a jumbo jet before ever taking a real one out for a trip?

<Pete> Exactly like that.

<Phil> Do you have one?

<Pete> Yes. It was how I learned about this “stuff” … pragmatically.

<Phil> Can you show me?

<Pete> Of course.  But to do that we will need a bit more time, another coffee, and maybe a couple of those tasty looking Danish pastries.

<Phil> A wise investment I’d say.  I’ll get the the coffee and pastries, if you fire up the engines of the simulator.

figures_lost_looking_at_map_anim_150_wht_15601

“Jingle Bells, Jingle Bells” announced Bob’s computer as he logged into the Webex meeting with Lesley.

<Bob> Hi Lesley, in case I forget later I’d like to wish you a Happy Christmas and hope that 2017 brings you new opportunity for learning and fun.

<Lesley> Thanks Bob, and I wish you the same. And I believe the blog last week pointed to some.

<Bob> Thank you and I agree;  every niggle is an opportunity for improvement and the “Houston we have a problem!” one is a biggie.

<Lesley> So how do we start on this one? It is massive!

<Bob> The same way we do on all niggles; we diagnose the root cause first. What do you feel they might be?

<Lesley> Well, following it backwards from your niggle, the board reports are created by the data analysts, and they will produce whatever they are asked to. It must be really irritating for them to have their work rubbished!

<Bob> Are you suggesting that they understand the flaws in what they are asked to do but keep quiet?

<Lesley> I am not sure they do, but there is clearly a gap between their intent and their impact. Where would they gain the insight? Do they have access to the sort of training I have am getting?

<Bob> That is a very good question, and until this week I would not have been able to answer, but an interesting report by the Health Foundation was recently published on that very topic. It is entitled “Understanding Analytical Capability In Health Care” and what I says is that there is a lost tribe of data analysts in the NHS.

<Lesley> How interesting! That certainly resonates with my experience.  All the data analysts I know seem to be hidden away behind their computers, caught in the cross-fire between between the boards and the wards, and very sensibly keeping their heads down and doing what they are asked to.

<Bob> That would certainly help to explain what we are seeing! And the good news is that Martin Bardsley, the author of the paper, has interviewed many people across the system, gathered their feedback, and offered some helpful recommendations.  Here is a snippet.

analysiscapability

<Lesley> I like these recommendations, especially the “in-work training programmes” and inclusion “in general management and leadership training“. But isn’t that one of the purposes of the CHIPs training?

<Bob> It is indeed, which is why it is good to see that Martin has specifically recommended it.

saasoftrecommended

<Lesley> Excellent! That means that my own investment in the CHIPs training has just gained in street value and that’s good for my CV. An unexpected early Xmas present. Thank you!

The immortal words from Apollo 13 that alerted us to an evolving catastrophe …

… and that is what we are seeing in the UK health and social care system … using the thermometer of A&E 4-hour performance. England is the red line.

uk_ae_runchart

The chart shows that this is not a sudden change, it has been developing over quite a long period of time … so why does it feel like an unpleasant surprise?


One reason may be that NHS England is using performance management techniques that were out of date in the 1980’s and are obsolete in the 2010’s!

Let me show you what I mean. This is a snapshot from the NHS England Board Minutes for November 2016.

nhse_rag_nov_2016
RAG stands for Red-Amber-Green and what we want to see on a Risk Assessment is Green for the most important stuff like safety, flow, quality and affordability.

We are not seeing that.  We are seeing Red/Amber for all of them. It is an evolving catastrophe.

A risk RAG chart is an obsolete performance management tool.

Here is another snippet …

nhse_ae_nov_2016

This demonstrates the usual mix of single point aggregates for the most recent month (October 2016); an arbitrary target (4 hours) used as a threshold to decide failure/not failure; two-point comparisons (October 2016 versus October 2015); and a sprinkling of ratios. Not a single time-series chart in sight. No pictures that tell a story.

Click here for the full document (which does also include some very sensible plans to maintain hospital flow through the bank holiday period).

The risk of this way of presenting system performance data is that it is a minefield of intuitive traps for the unwary.  Invisible pitfalls that can lead to invalid conclusions, unwise decisions, potentially ineffective and/or counter-productive actions, and failure to improve. These methods are risky and that is why they should be obsolete.

And if NHSE is using obsolete tools than what hope do CCGs and Trusts have?


Much better tools have been designed.  Tools that are used by organisations that are innovative, resilient, commercially successful and that deliver safety, on-time delivery, quality and value for money. At the same time.

And they are obsolete outside the NHS because in the competitive context of the dog-eat-dog real world, organisations do not survive if they do not innovate, improve and learn as fast as their competitors.  They do not have the luxury of being shielded from reality by having a central tax-funded monopoly!

And please do not misinterpret my message here; I am a 100% raving fan of the NHS ethos of “available to all and free at the point of delivery” and an NHS that is funded centrally and fairly. That is not my issue.

My issue is the continued use of obsolete performance management tools in the NHS.


Q: So what are the alternatives? What do the successful commercial organisations use instead?

A: System behaviour charts.

SBCs are pictures of how the system is behaving over time – pictures that tell a story – pictures that have meaning – pictures that we can use to diagnose, design and deliver a better outcome than the one we are heading towards.

Pictures like the A&E performance-over-time chart above.

Click here for more on how and why.


Therefore, if the DoH, NHSE, NHSI, STPs, CCGs and Trust Boards want to achieve their stated visions and missions then the writing-on-the-wall says that they will need to muster some humility and learn how successful organisations do this.

This is not a comfortable message to hear and it is easier to be defensive than receptive.

The NHS has to change if it wants to survive and continue serve the people who pay the salaries. And time is running out. Continuing as we are is not an option. Complaining and blaming are not options. Doing nothing is not an option.

Learning is the only option.

Anyone can learn to use system behaviour charts.  No one needs to rely on averages, two-point comparisons, ratios, targets, and the combination of failure-metrics and us-versus-them-benchmarking that leads to the chronic mediocrity trap.

And there is hope for those with enough hunger, humility and who are prepared to do the hard-work of developing their personal, team, department and organisational capability to use better management methods.


Apollo 13 is a true story.  The catastrophe was averted.  The astronauts were brought home safely.  The film retells the story of how that miracle was achieved. Perhaps watching the whole film would be somewhere to start, because it holds many valuable lessons for us all – lessons on how effective teams behave.

lencioni_ideal_team_playerThis week I read a new book by one of my favourite authors – Patrick Lencioni.

The book is The Ideal Team Player.

Patrick’s books are written as stories which makes them very accessible and easily memorable.  And each one captures a priceless pearl of wisdom.

Improving a complex adaptive system such as health care can only be done by the people in the system working together and sharing expectations, experiences, knowledge, understanding and wisdom.

So each person needs to understand what it is to be able to contribute effectively to a team – because teams are how complex systems are designed and how they are improved.


Patrick identifies three “virtues” – and he uses that term appropriately.

Hungry … which means a having a burning ambition.  Something needed and wanted. An unsatisfied longing. A vision. A mission. A goal. A pull. A purpose.

Hardworking … which means a willingness to do what is needed to satisfy the hunger. Going that extra mile. Reading that extra book. Solving that extra problem. Giving that extra bit of feedback. Doing that extra job that no one else wants to do. Investing in the future.

Humble … which means that Ego is not running the show.  Confidence is linked to competence. Impact and intent are aligned. The mind is open to learning. The eyes are open to seeing. The ears are open to listening. And the mouth is only open for asking questions and telling stories.


The three virtues are necessary and sufficient, they are effective and efficient.

So if any one is missing the outcome is not achievable.

Time to pick up the mirror and look deeply into it … and ask:

“Am I hungry enough?”
“Am I prepared to commit my lifetime?”
“Am I open to learning from reality and from others?”

Our tangible record of past behaviour provides us with our answers.

 It is the time to dig deep and ask the question: am  hungry, hardworking and humble?

stick_figure_superhero_anim_150_wht_1857Have you heard the phrase “Pride comes before a fall“?

What does this mean? That the feeling of pride is the reason for the subsequent fall?

So by following that causal logic, if we do not allow ourselves to feel proud then we can avoid the fall?

And none of us like the feeling of falling and failing. We are fearful of that negative feeling, so with this simple trick we can avoid feeling bad. Yes?

But we all know the positive feeling of achievement – we feel pride when we have done good work, when our impact matches our intent.  Pride in our work.

Is that bad too?

Should we accept under-achievement and unexceptional mediocrity as the inevitable cost of avoiding the pain of possible failure?  Is that what we are being told to do here?


The phrase comes from the Bible, from the Book of Proverbs 16:18 to be precise.

proverb

And the problem here is that the phrase “pride comes before a fall” is not the whole proverb.

It has been simplified. Some bits have been omitted. And those omissions lead to ambiguity and the opportunity for obfuscation and re-interpretation.

pride_goes_before_a_fall
In the fuller New International Version we see a missing bit … the “haughty spirit” bit.  That is another way of saying “over-confident” or “arrogant”.


But even this “authorised” version is still ambiguous and more questions spring to mind:

Q1. What sort of pride are we referring to? Just the confidence version? What about the pride that follows achievement?

Q2. How would we know if our feeling of confidence is actually justified?

Q3. Does a feeling of confidence always precede a fall? Is that how we diagnose over-confidence? Retrospectively? Are there instances when we feel confident but we do not fail? Are there instances when we do not feel confident and then fail?

Q4. Does confidence cause the fall or it is just a temporal association? Is there something more fundamental that causes both high-confidence and low-competence?


There is a well known model called the Conscious-Competence model of learning which generates a sequence of four stages to achieving a new skill. Such as one we need to achieve our intended outcomes.

We all start in the “blissful ignorance” zone of unconscious incompetence.  Our unknowns are unknown to us.  They are blind spots.  So we feel unjustifiably confident.

hierarchy_of_competence

In this model the first barrier to progress is “wrong intuition” which means that we actually have unconscious assumptions that are distorting our perception of reality.

What we perceive makes sense to us. It is clear and obvious. We feel confident. We believe our own rhetoric.

But our unconscious assumptions can trick us into interpreting information incorrectly.  And if we derive decisions from unverified assumptions and invalid analysis then we may do the wrong thing and not achieve our intended outcome.  We may unintentionally cause ourselves to fail and not be aware of it.  But we are proud and confident.

Then the gap between our intent and our impact becomes visible to all and painful to us. So we are tempted to avoid the social pain of public failure by retreating behind the “Yes, But” smokescreen of defensive reasoning. The “doom loop” as it is sometimes called. The Victim Vortex. “Don’t name, shame and blame me, I was doing my best. I did not intent that to happen. To err is human”.


The good news is that this learning model also signposts a possible way out; a door in the black curtain of ignorance.  It suggests that we can learn how to correct our analysis by using feedback from reality to verify our rhetorical assumptions.  Those assumptions which pass the “reality check” we keep, those which fail the “reality check” we redesign and retest until they pass.  Bit by bit our inner rhetoric comes to more closely match reality and the wisdom of our decisions will improve.

And what we then see is improvement.  Our impact moves closer towards our intent. And we can justifiably feel proud of that achievement. We do not need to be best-compared-with-the-rest; just being better-than-we-were-before is OK. That is learning.

the_learning_curve

And this is how it feels … this is the Learning Curve … or the Nerve Curve as we call it.

What it says is that to be able to assess confidence we must also measure competence. Outcomes. Impact.

And to achieve excellence we have to be prepared to actively look for any gap between intent and impact.  And we have to be prepared to see it as an opportunity rather than as a threat. And we will need to be able to seek feedback and other people’s perspectives. And we need to be to open to asking for examples and explanations from those who have demonstrated competence.

It says that confidence is not a trustworthy surrogate for competence.

It says that we want the confidence that flows from competence because that is the foundation of trust.

Improvement flows at the speed of trust and seeing competence, confidence and trust growing is a joyous thing.

Pride and Joy are OK.

Arrogance and incompetence comes before a fall would be a better proverb.

focus_on_sfqpThe theme of the week has been “focus” and by that I mean the amazing ability of the human mind to concentrate on one thing to the exclusion of almost all else.

To illustrate what I mean, just reflect on what happens when we watch a television program.  We do not see the TV screen, controls, or the “stuff” around it.  Or to be more precise … we do see it but we do not perceive it.

Even our Mark I Eyeballs have evolved to “focus” and I do not mean just the clear bits that create a sharp image on the light-sensitive layer at the back (the retina).

Our retinas are not like a video camera … not at all … they have a very high resolution bit at the center which is quite small, and a rather low resolution bit that surrounds it and that is much bigger.

But we do not perceive that … because we have some very advanced data processing wetware … and the process actually starts in the retina.


And our eyes are always moving … just observe someone else’s eyes when they are looking at a picture or reading a book.  If the cameras in a TV studio did that we would complain!

So what is happening here?

The answer is that our advanced data processing wetware is scanning, but not in the way that a radar scans … in a mindless cycle.  Our eye scanning has purpose … it is driven by the mental model inside our heads that is looking for information, and the search is based on what we already believe and perceive.


Psychologists have studied this using cool technology that tracks the eye position and works out what the person is looking at.  And what they found was surprising.

facescanIf we are presented with a picture of a face we will scan it in a very consistent way.  We look at the nose first and then we look at eyes, mouth and we pattern-match to answer the question “Do I recognize this person?

If we do then we can draw on past memories of them to help inform our interpretation of what we see.  If we do not then we need to keep watching and learning.  We need an answer to the question “Is this person an opportunity or a threat?


And it is a very fast process, and it happens out of awareness, and it is hard-wired and it is automatic.

After initial recognition we will focus on the eyes and mouth because, as the Greeks said, “the eyes are the window to the soul“.  We need to infer what the other person is thinking … unconsciously.


And the good news is that this amazing ability to focus is not completely automatic … it can be directed … rather like a radio can be tuned to specific frequency.

And when we learn how to do that as individuals the effect is surprising.

And when we learn how to do that as a group, in synergy, the effect is amazing!

monkey_on_back_anim_150_wht_11200

About 25 years ago a paper was published in the Harvard Business Review with the interesting title of “Teaching Smart People How To Learn

The uncomfortable message was that many people who are top of the intellectual rankings are actually very poor learners.

This sounds like a paradox.  How can people be high-achievers and yet be unable to learn?


Health care systems are stuffed full of super-smart, high-achieving professionals. The cream of educational crop. The top 2%. They are called “doctors”.

And we have a problem with improvement in health care … a big problem … the safety, delivery, quality and affordability of the NHS is getting worse. Not better.

Improvement implies change and change implies learning, so if smart people struggle to learn then could that explain why health care systems find self-improvement so difficult?

This paragraph from the 1991 HBR paper feels uncomfortably familiar:

defensive_reasoning_2

The author, Chris Argyris, refers to something called “single-loop learning” and if we translate this management-speak into the language of medicine it would come out as “treating the symptom and ignoring the disease“.  That is poor medicine.

Chris also suggests an antidote to this problem and gave it the label “double-loop learning” which if translated into medical speak becomes “diagnosis“.  And that is something that doctors can relate to because without a diagnosis, a justifiable treatment is difficult to formulate.


We need to diagnose the root cause(s) of the NHS disease.


The 1991 HBR paper refers back to an earlier 1977 HBR paper called Double Loop Learning in Organisations where we find the theory that underpins it.

The proposed hypothesis is that we all have cognitive models that we use to decide our actions (and in-actions), what I have referred to before as ChimpWare.  In it is a reference to a table published in a 1974 book and the message is that Single-Loop learning is a manifestation of a Model 1 theory-in-action.

defensive_reasoning_models


And if we consider the task that doctors are expected to do then we can empathize with their dominant Model 1 approach.  Health care is a dangerous business.  Doctors can cause a lot of unintentional harm – both physical and psychological.  Doctors are dealing with a very, very complex system – a human body – that they only partially understand.  No two patients are exactly the same and illness is a dynamic process.  Everyone’s expectations are high. We have come a long way since the days of blood-letting and leeches!  Failure is not tolerated.

Doctors are intelligent and competitive … they had to be to win the education race.

Doctors must make tough decisions and have to have tough conversations … many, many times … and yet not be consumed in the process.  They often have to suppress emotions to be effective.

Doctors feel the need to protect patients from harm – both physical and emotional.

And collectively they do a very good job.  Doctors are respected and trusted professionals.


But …  to quote Chris Argyris …

“Model I blinds people to their weaknesses. For instance, the six corporate presidents were unable to realize how incapable they were of questioning their assumptions and breaking through to fresh understanding. They were under the illusion that they could learn, when in reality they just kept running around the same track.”

This blindness is self-reinforcing because …

“All parties withheld information that was potentially threatening to themselves or to others, and the act of cover-up itself was closed to discussion.”


How many times have we seen this in the NHS?

The Mid-Staffordshire Hospital debacle that led to the Francis Report is all the evidence we need.


So what is the way out of this double-bind?

Chris gives us some hints with his Model II theory-in-use.

  1. Valid information – Study.
  2. Free and informed choice – Plan.
  3. Constant monitoring of the implementation – Do.

The skill required is to question assumptions and break through to fresh understanding and we can do that with design-led approach because that is what designers do.

They bring their unconscious assumptions up to awareness and ask “Is that valid?” and “What if” questions.

It is called Improvement-by-Design.

And the good news is that this Model II approach works in health care, and we know that because the evidence is accumulating.

 

thinker_figure_unsolve_puzzle_150_wht_18309Many of the challenges that we face in delivering effective and affordable health care do not have well understood and generally accepted solutions.

If they did there would be no discussion or debate about what to do and the results would speak for themselves.

This lack of understanding is leading us to try to solve a complicated system design challenge in our heads.  Intuitively.

And trying to do it this way is fraught with frustration and risk because our intuition tricks us. It was this sort of challenge that led Professor Rubik to invent his famous 3D Magic Cube puzzle.

It is difficult enough to learn how to solve the Magic Cube puzzle by trial and error; it is even more difficult to attempt to do it inside our heads! Intuitively.


And we know the Rubik Cube puzzle is solvable, so all we need are some techniques, tools and training to improve our Rubik Cube solving capability.  We can all learn how to do it.


Returning to the challenge of safe and affordable health care, and to the specific problem of unscheduled care, A&E targets, delayed transfers of care (DTOC), finance, fragmentation and chronic frustration.

This is a systems engineering challenge so we need some systems engineering techniques, tools and training before attempting it.  Not after failing repeatedly.

se_vee_diagram

One technique that a systems engineer will use is called a Vee Diagram such as the one shown above.  It shows the sequence of steps in the generic problem solving process and it has the same sequence that we use in medicine for solving problems that patients present to us …

Diagnose, Design and Deliver

which is also known as …

Study, Plan, Do.


Notice that there are three words in the diagram that start with the letter V … value, verify and validate.  These are probably the three most important words in the vocabulary of a systems engineer.


One tool that a systems engineer always uses is a model of the system under consideration.

Models come in many forms from conceptual to physical and are used in two main ways:

  1. To assist the understanding of the past (diagnosis)
  2. To predict the behaviour in the future (prognosis)

And the process of creating a system model, the sequence of steps, is shown in the Vee Diagram.  The systems engineer’s objective is a validated model that can be trusted to make good-enough predictions; ones that support making wiser decisions of which design options to implement, and which not to.


So if a systems engineer presented us with a conceptual model that is intended to assist our understanding, then we will require some evidence that all stages of the Vee Diagram process have been completed.  Evidence that provides assurance that the model predictions can be trusted.  And the scope over which they can be trusted.


Last month a report was published by the Nuffield Trust that is entitled “Understanding patient flow in hospitals”  and it asserts that traffic flow on a motorway is a valid conceptual model of patient flow through a hospital.  Here is a direct quote from the second paragraph in the Executive Summary:

nuffield_report_01
Unfortunately, no evidence is provided in the report to support the validity of the statement and that omission should ring an alarm bell.

The observation that “the hospitals with the least free space struggle the most” is not a validation of the conceptual model.  Validation requires a concrete experiment.


To illustrate why observation is not validation let us consider a scenario where I have a headache and I take a paracetamol and my headache goes away.  I now have some evidence that shows a temporal association between what I did (take paracetamol) and what I got (a reduction in head pain).

But this is not a valid experiment because I have not considered the other seven possible combinations of headache before (Y/N), paracetamol (Y/N) and headache after (Y/N).

An association cannot be used to prove causation; not even a temporal association.

When I do not understand the cause, and I am without evidence from a well-designed experiment, then I might be tempted to intuitively jump to the (invalid) conclusion that “headaches are caused by lack of paracetamol!” and if untested this invalid judgement may persist and even become a belief.


Understanding causality requires an approach called counterfactual analysis; otherwise known as “What if?” And we can start that process with a thought experiment using our rhetorical model.  But we must remember that we must always validate the outcome with a real experiment. That is how good science works.

A famous thought experiment was conducted by Albert Einstein when he asked the question “If I were sitting on a light beam and moving at the speed of light what would I see?” This question led him to the Theory of Relativity which completely changed the way we now think about space and time.  Einstein’s model has been repeatedly validated by careful experiment, and has allowed engineers to design and deliver valuable tools such as the Global Positioning System which uses relativity theory to achieve high positional precision and accuracy.


So let us conduct a thought experiment to explore the ‘faster movement requires more space‘ statement in the case of patient flow in a hospital.

First, we need to define what we mean by the words we are using.

The phrase ‘faster movement’ is ambiguous.  Does it mean higher flow (more patients per day being admitted and discharged) or does it mean shorter length of stage (the interval between the admission and discharge events for individual patients)?

The phrase ‘more space’ is also ambiguous. In a hospital that implies physical space i.e. floor-space that may be occupied by corridors, chairs, cubicles, trolleys, and beds.  So are we actually referring to flow-space or storage-space?

What we have in this over-simplified statement is the conflation of two concepts: flow-capacity and space-capacity. They are different things. They have different units. And the result of conflating them is meaningless and confusing.


However, our stated goal is to improve understanding so let us consider one combination, and let us be careful to be more precise with our terminology, “higher flow always requires more beds“. Does it? Can we disprove this assertion with an example where higher flow required less beds (i.e. space-capacity)?

The relationship between flow and space-capacity is well understood.

The starting point is Little’s Law which was proven mathematically in 1961 by J.D.C. Little and it states:

Average work in progress = Average lead time  X  Average flow.

In the hospital context, work in progress is the number of occupied beds, lead time is the length of stay and flow is admissions or discharges per time interval (which must be the same on average over a long period of time).

(NB. Engineers are rather pedantic about units so let us check that this makes sense: the unit of WIP is ‘patients’, the unit of lead time is ‘days’, and the unit of flow is ‘patients per day’ so ‘patients’ = ‘days’ * ‘patients / day’. Correct. Verified. Tick.)

So, is there a situation where flow can increase and WIP can decrease? Yes. When lead time decreases. Little’s Law says that is possible. We have disproved the assertion.


Let us take the other interpretation of higher flow as shorter length of stay: i.e. shorter length of stay always requires more beds.  Is this correct? No. If flow remains the same then Little’s Law states that we will require fewer beds. This assertion is disproved as well.

And we need to remember that Little’s Law is proven to be valid for averages, does that shed any light on the source of our confusion? Could the assertion about flow and beds actually be about the variation in flow over time and not about the average flow?


And this is also well understood. The original work on it was done almost exactly 100 years ago by Agner Arup Erlang and the problem he looked at was the quality of customer service of the early telephone exchanges. Specifically, how likely was the caller to get the “all lines are busy, please try later” response.

What Erlang showed was there there is a mathematical relationship between the number of calls being made (the demand), the probability of a call being connected first time (the service quality) and the number of telephone circuits and switchboard operators available (the service cost).


So it appears that we already have a validated mathematical model that links flow, quality and cost that we might use if we substitute ‘patients’ for ‘calls’, ‘beds’ for ‘telephone circuits’, and ‘being connected’ for ‘being admitted’.

And this topic of patient flow, A&E performance and Erlang queues has been explored already … here.

So a telephone exchange is a more valid model of a hospital than a motorway.

We are now making progress in deepening our understanding.


The use of an invalid, untested, conceptual model is sloppy systems engineering.

So if the engineering is sloppy we would be unwise to fully trust the conclusions.

And I share this feedback in the spirit of black box thinking because I believe that there are some valuable lessons to be learned here – by us all.


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