Posts by Simon

This week, it was my great pleasure to award the first Health Care Systems Engineering (HCSE) Level 2 Medal to Dr Kate Silvester, MBA, FRCOphth.

Kate is internationally recognised as an expert in health care improvement and over more than two decades has championed the adoption of improvement methods such as Lean and Quality Improvement in her national roles in the Modernisation Agency and then the NHS Institute for Innovation and Improvement.

Kate originally trained as a doctor and then left the NHS to learn manufacturing systems engineering with Lucas and Airbus.  Kate then brought these very valuable skills back with her into the NHS when she joined the Cancer Services Collaborative.

Kate is co-founder of the Journal of Improvement Science and over the last five years has been highly influential in the development of the Health Care Systems Engineering Programme – the first of its kind in the world that is designed by clinicians for clinicians.

The HCSE Programme is built on the pragmatic See One-Do Some-Teach Many principle of developing competence and confidence through being trained and coached by a more experienced practitioner while doing projects of increasing complexity and training and coaching others who are less experienced.

Competence is based on evidence-of-effectiveness, and Kate has achieved HCSE Level 2 by demonstrating that she can do HCSE and that she can teach and coach others how to do HCSE as well.

To illustrate, here is a recent FHJ paper that Kate has authored which illustrates the HCSE principles applied in practice in a real hospital.  This work was done as part of the Health Foundation’s Flow, Cost and Quality project that Kate led and recent evidence proves that the improvements have sustained and spread.  South Warwickshire NHS Foundation Trust is now one of the top-performing Trusts in the NHS.

More recently, Kate has trained and coached new practitioners in Exeter and North Devon who have delivered improvements and earned their HCSE 1 wings.

Congratulations Kate!

One of the most frequent niggles that I hear from patients is the difficultly they have getting an appointment with their general practitioner.  I too have personal experience of the distress caused by the ubiquitous “Phone at 8AM for an Appointment” policy, so in June 2018 when I was approached to help a group of local practices redesign their appointment booking system I said “Yes, please!


What has emerged is a fascinating, enjoyable and rewarding journey of co-evolution of learning and co-production of an improved design.  The multi-skilled design team (MDT) we pulled together included general practitioners, receptionists and practice managers and my job was to show them how to use the health care systems engineering (HCSE) framework to diagnose, design, decide and deliver what they wanted: A safe, calm, efficient, high quality, value-4-money appointment booking service for their combined list of 50,000 patients.


This week they reached the start of the ‘decide and deliver‘ phase.  We have established the diagnosis of why the current booking system is not delivering what we all want (i.e. patients and practices), and we have assembled and verified the essential elements of an improved design.

And the most important outcome for me is that the Primary Care MDT now feel confident and capable to decide what and how to deliver it themselves.   That is what I call embedded capability and achieving it is always an emotional roller coaster ride that we call The Nerve Curve.

What we are dealing with here is called a complex adaptive system (CAS) which has two main components: Processes and People.  Both are complicated and behave in complex ways.  Both will adapt and co-evolve over time.  The processes are the result of the policies that the people produce.  The policies are the result of the experiences that the people have and the explanations that they create to make intuitive sense of them.

But, complex systems often behave in counter-intuitive ways, so our intuition can actually lead us to make unwise decisions that unintentionally perpetuate the problem we are trying to solve.  The name given to this is a wicked problem.

A health care systems engineer needs to be able to demonstrate where these hidden intuitive traps lurk, and to explain what causes them and how to avoid them.  That is the reason the diagnosis and design phase is always a bit of a bumpy ride – emotionally – our Inner Chimp does not like to be challenged!  We all resist change.  Fear of the unknown is hard-wired into us by millions of years of evolution.

But we know when we are making progress because the “ah ha” moments signal a slight shift of perception and a sudden new clarity of insight.  The cognitive fog clears a bit and a some more of the unfamiliar terrain ahead comes into view.  We are learning.

The Primary Care MDT have experienced many of these penny-drop moments over the last six months and unfortunately there is not space here to describe them all, but I can share one pivotal example.


A common symptom of a poorly designed process is a chronically chaotic queue.

[NB. In medicine the term chronic means “long standing”.  The opposite term is acute which means “recent onset”].

Many assume, intuitively, that the cause of a chronically chaotic queue is lack of capacity; hence the incessant calls for ‘more capacity’.  And it appears that we have learned this reflex response by observing the effect of adding capacity – which is that the queue and chaos abate (for a while).  So that proves that lack of capacity was the cause. Yes?

Well actually it doesn’t.  Proving causality requires a bit more work.  And to illustrate this “temporal association does not prove causality trap” I invite you to consider this scenario.

I have a headache => I take a paracetamol => my headache goes away => so the cause of my headache was lack of paracetamol. Yes?

Errr .. No!

There are many contributory causes of chronically chaotic queues and lack of capacity is not one of them because the queue is chronic.  What actually happens is that something else triggers the onset of chaos which then consumes the very resource we require to avoid the chaos.  And once we slip into this trap we cannot escape!  The chaos-perpretuating behaviour we observe is called fire-fighting and the necessary resource it consumes is called resilience.


Six months ago, the Primary Care MDT believed that the cause of their chronic appointment booking chaos was a mismatch between demand and capacity – i.e. too much patient demand for the appointment capacity available.  So, there was a very reasonable resistance to the idea of making the appointment booking process easier for patients – they justifiably feared being overwhelmed by a tsunami of unmet need!

Six months on, the Primary Care MDT understand what actually causes chronic queues and that awareness has been achieved by a step-by-step process of explanation and experimentation in the relative safety of the weekly design sessions.

We played simulation games – lots of them.

One particularly memorable “Ah Ha!” moment happened when we played the Carveout Game which is done using dice, tiddly-winks, paper and coloured-pens.  No computers.  No statistics.  No queue theory gobbledygook.  No smoke-and-mirrors.  No magic.

What the Carveout Game demonstrates, practically and visually, is that an easy way to trigger the transition from calm-efficiency to chaotic-ineffectiveness is … to impose a carveout policy on a system that has been designed to achieve optimum efficiency by using averages.  Boom!  We slip on the twin banana skins of the Flaw-of-Averages and Sub-Optimisation, slide off the performance cliff, and career down the rocky slope of Chronic Chaos into the Depths of Despair – from which we cannot then escape.

This visual demonstration was a cognitive turning point for the MDT.  They now believed that there is a rational science to improvement and from there we were on the step-by-step climb to building the necessary embedded capability.


It now felt like the team were pulling what they needed to know.  I was no longer pushing.  We had flipped from push-to-pull.  That is called the tipping point.

And that is how health care systems engineering (HCSE) works.


Health care is a complex adaptive system, and what a health care systems engineer actually “designs” is a context-sensitive  incubator that nurtures the seeds of innovation that already exist in the system and encourages them to germinate, grow and become strong enough to establish themselves.

That is called “embedded improvement-by-design capability“.

And each incubator need to be different – because each system is different.  One-solution-fits-all-problems does not work here just as it does not in medicine.  Each patient is both similar and unique.


Just as in medicine, first we need to diagnose the actual cause;  second we need to design some effective solutions; third we need to decide which design to implement and fourth we need to deliver it.

But the how-to-do-it feels a bit counter-intuitive, and if it were not we would already be doing it. But the good news is that anyone can learn how to do HCSE.

As we approach the end of 2018 it is a good time to look back and reflect on what has happened this year.

It has been my delight to have had the opportunity to work with front-line teams at University Hospital of North Midlands (UHNM) and to introduce them to the opportunity that health care systems engineering (HCSE) offers.

This was all part of a coordinated, cooperative strategy commissioned by the Staffordshire Clinical Commissioning Groups, and one area we were asked to look at was unscheduled care.

It was not my brief to fix problems.  I was commissioned to demonstrate how a systems engineer might approach them.  The first step was to raise awareness, then develop some belief and then grow some embedded capability – in the system itself.

The rest was up to the teams who stepped up to the challenge.  So what happened?

Winter is always a tough time for the NHS and especially for unscheduled care so let us have a look  and compare UHNM with NHS England as a whole – using the 4 hour A&E target yield – and over a longer time period of 7 years (so that we can see some annual cycles and longer term trends).

The A&E performance for the NHS in England as whole has been deteriorating at an accelerating pace over the 7 years.  This is a system-wide effect and there are a multitude of plausible causes.

The current UHNM system came into being at the end of 2014 with the merger of the Stafford and Stoke Hospital Trusts – and although their combined A&E performance dropped below average for England – the chart above shows that it did not continue to slide.

The NHS across the UK had a very bad time in the winter of 2017/18 – with a double whammy of sequential waves of Flu B and Flu A not helping!

But look at what happened at UHNM since Feb 2018.  Something has changed for the better and this is a macro system effect.  There has been a positive deviation from the expectation with about a 15% improvement in A&E 4-hr yield.  That is outstanding!

Now, I would say that news is worth celebrating and shouting “Well done everyone!” and then asking “How was that achieved?” and “What can we all learn that we can take forward into 2019 and build on?

Merry Christmas.

It is November 2018, the clocks have changed back to GMT, the trick-and-treats are done, the fireworks light the night skies and spook the hounds, and the seasonal aisles in the dwindling number of high street stores are already stocked for Christmas.

I have been a bit quiet on the blog front this year but that is because there has been a lot happening behind the scenes and I have had to focus.

One output of is the recent publication of an article in Future Healthcare Journal on the topic of health care systems engineering (HCSE).  Click here to read the article and the rest of this excellent edition of FHJ that is dedicated to “systems”.

So, as we are back to the winter phase of the annual NHS performance cycle it is a good time to glance at the A&E Performance Radar and see who is doing well, and not-so-well.

Based on past experience, I was expecting Luton to be Top-of-the-Pops and so I was surprised (and delighted) to see that Barnsley have taken the lead.  And the chart shows that Barnsley has turned around a reasonable but sagging performance this year.

So I would be asking “What has happened at Barnsley that we can all learn from? What did you change and how did you know what and how to do that?

To be sure, Luton is still in the top three and it is interesting to explore who else is up there and what their A&E performance charts look like.

The data is all available for anyone with a web-browser to view – here.

For completeness, this is the chart for Luton, and we can see that, although the last point is lower than Barnsley, the performance-over-time is more consistent and less variable. So who is better?

NB. This is a meaningless question and illustrates the unhelpful tactic of two-point comparisons with others, and with oneself. The better question is “Is my design fit-for-purpose?”

The question I have for Luton is different. “How do you achieve this low variation and how do you maintain it? What can we all learn from you?”

And I have some ideas how they do that because in a recent HSJ interview they said “It is all about the filters“.


What do they mean by filters?

A filter is an essential component of any flow design if we want to deliver high safety, high efficiency, high effectiveness, and high productivity.  In other words, a high quality, fit-4-purpose design.

And the most important flow filters are the “upstream” ones.

The design of our upstream flow filters is critical to how the rest of the system works.  Get it wrong and we can get a spiralling decline in system performance because we can unintentionally trigger a positive feedback loop.

Queues cause delays and chaos that consume our limited resources.  So, when we are chasing cost improvement programme (CIP) targets using the “salami slicer” approach, and combine that with poor filter design … we can unintentionally trigger the perfect storm and push ourselves over the catastrophe cliff into perpetual, dangerous and expensive chaos.

If we look at the other end of the NHS A&E league table we can see typical examples that illustrate this pattern.  I have used this one only because it happens to be bottom this month.  It is not unique.

All other NHS trusts fall somewhere between these two extremes … stable, calm and acceptable and unstable, chaotic and unacceptable.

Most display the stable and chaotic combination – the “Zone of Perpetual Performance Pain”.

So what is the fundamental difference between the outliers that we can all learn from? The positive deviants like Barnsley and Luton, and the negative deviants like Blackpool.  I ask this because comparing the extremes is more useful than laboriously exploring the messy, mass-mediocrity in the middle.

An effective upstream flow filter design is a necessary component, but it is not sufficient. Triage (= French for sorting) is OK but it is not enough.  The other necessary component is called “downstream pull” and omitting that element of the design appears to be the primary cause of the chronic chaos that drags trusts and their staff down.

It is not just an error of omission though, the current design is an actually an error of commission. It is anti-pull; otherwise known as “push”.


This year I have been busy on two complicated HCSE projects … one in secondary care and the other in primary care.  In both cases the root cause of the chronic chaos is the same.  They are different systems but have the same diagnosis.  What we have revealed together is a “push-carveout” design which is the exact opposite of the “upstream-filter-plus-downstream-pull” design we need.

And if an engineer wanted to design a system to be chronically chaotic then it is very easy to do. Here is the recipe:

a) Set high average utilisation target of all resources as a proxy for efficiency to ensure everything is heavily loaded. Something between 80% and 100% usually does the trick.

b) Set a one-size-fits-all delivery performance target that is not currently being achieved and enforce it punitively.  Something like “>95% of patients seen and discharged or admitted in less than 4 hours, or else …”.

c) Divvy up the available resources (skills, time, space, cash, etc) into ring-fenced pots.

Chronic chaos is guaranteed.  The Laws of Physics decree it.


Unfortunately, the explanation of why this is the case is counter-intuitive, so it is actually better to experience it first, and then seek the explanation.  Reality first, reasoning second.

And, it is a bittersweet experience, so it needs to be done with care and compassion.

And that’s what I’ve been busy doing this year. Creating the experiences and then providing the explanations.  And if done gradually what then happens is remarkable and rewarding.

The FHJ article outlines one validated path to developing individual and organisational capability in health care systems engineering.

It is always a huge compliment to see an idea improved and implemented by inspired innovators.

Health care systems engineering (HCSE) brings together concepts from the separate domains of systems engineering and health care.  And one idea that emerged from this union is to regard the health care system as a living, evolving, adapting entity.

In medicine we have the concept of ‘vital signs’ … a small number of objective metrics that we can measure easily and quickly.  With these we can quickly assess the physical health of a patient and decide if we need to act, and when.

With a series of such measurements over time we can see the state of a patient changing … for better or worse … and we can use this to monitor the effect of our actions and to maintain the improvements we achieve.

For a patient, the five vital signs are conscious level, respiratory rate, pulse, blood pressure and temperature. To sustain life we must maintain many flows within healthy ranges and the most critically important is the flow of oxygen to every cell in the body.  Oxygen is carried by blood, so blood flow is critical.

So, what are the vital signs for a health care system where the flows are not oxygen and blood?  They are patients, staff, consumables, equipment, estate, data and cash.

The photograph shows a demonstration of a Vitals Dashboard for a part of the cancer care system in the ABMU health board in South Wales.  The inspirational innovators who created it are Imran Rao (left), Andy Jones (right) and Chris Jones (top left), and they are being supported by ABMU to do this as part of their HCSE training programme.

So well done guys … we cannot wait to hear how being better able to seeing the voice of your cancer system translates into improved care for patients, and improved working life for the dedicated NHS staff, and improved use of finite public resources.  Win-win-win.

In medicine we use checklists as aide memoirs because they help us to avoid errors of omission, especially in an emergency when we are stressed and less able to think logically.

One that everyone learns if they do a First Aid course is A.B.C. and it stands for Airway, Breathing, Circulation.  It is designed to remind us what to do first because everything that follows depends on it, and then what to do next, and so on.  Avoiding the errors of omission improves outcomes.


In the world of improvement we are interested in change-for-the-better and there are many models of change that we can use to remind us not to omit necessary steps.

One of these is called the Six Steps model (or trans-theoretical model to use the academic title) and it is usually presented as a cycle starting with a state called pre-contemplation.

This change model arose from an empirical study of people who displayed addictive behaviours (e.g. smoking, drinking, drugs etc) and specifically, those who had overcome them without any professional assistance.

The researchers compared the stories from the successful self-healers with the accepted dogma for the management of addictions, and they found something very interesting.  The dogma advocated action, but the stories showed that there were some essential steps before action; steps that should not be omitted.  Specifically, the contemplation and determination steps.

If corrective actions were started too early then the success rate was low.  When the pre-action steps were added the success rate went up … a lot!


The first step is to raise awareness which facilitates a shift from pre-contemplation to contemplation.  The second step is to provide information that gradually increases the pros for change and at the same time gradually decreases the cons for change.

If those phases are managed skillfully then a tipping point is reached where the individual decides to make the change and moves themselves to the third step, the determination or planning phase.

Patience and persistence is required.  The contemplation phase can last a long time.  It is the phase of exploration, evidence and explanation. It is preparing the ground for change and can be summed up in one word: Study.

Often the trigger for determination (i.e. Plan) and then action (i.e. Do) is relatively small because when we are close to the tipping point it does not take much to nudge us to step across the line.


And there is an aide memoir we can use for this change cycle … one that is a bit easier to remember:

A = Awareness
B = Belief
C = Capability
D = Delivery
E = Excellence (+enjoyment, +evidence, +excitement, +engagement)

First we raise awareness of the issue.
Then we learn a solution is possible and that we can learn the know-how.
Then we plan the work.
Then we work the plan.
Then we celebrate what worked and learn from what did and what did not.

Experience shows that the process is not discrete and sequential and it cannot be project managed into defined time boxes.  Instead, it is a continuum and the phases overlap and blend from one to the next in a more fluid and adaptive way.


Raising awareness requires both empathy and courage because this issue is often treated as undiscussable, and even the idea of discussing it is undiscussable too. Taboo.

But for effective change we need to grasp the nettle, explore the current reality, and start the conversation.

The debate about how to sensibly report NHS metrics has been raging for decades.

So I am delighted to share the news that NHS Improvement have finally come out and openly challenged the dogma that two-point comparisons and red-amber-green (RAG) charts are valid methods for presenting NHS performance data.

Their rather good 147-page guide can be downloaded: HERE


The subject is something called a statistical process control (SPC) chart which sounds a bit scary!  The principle is actually quite simple:

Plot data that emerges over time as a picture that tells a story – #plotthedots

The  main trust of the guide is learning the ropes of how to interpret these pictures in a meaningful way and to avoid two traps (i.e. errors).

Trap #1 = Over-reacting to random variation.
Trap #2 = Under-reacting to non-random variation.

Both of these errors cause problems, but in different ways.


Over-reacting to random variation

Random variation is a fact of life.  No two days in any part of the NHS are the same.  Some days are busier/quieter than others.

Plotting the daily-arrivals-in-A&E dots for a trust somewhere in England gives us this picture.  (The blue line is the average and the purple histogram shows the distribution of the points around this average.)

Suppose we were to pick any two days at random and compare the number of arrivals on those two days? We could get an answer anywhere between an increase of 80% (250 to 450) or a decrease of 44% (450 to 250).

But if we look at the while picture above we get the impression that, over time:

  1. There is an expected range of random-looking variation between about 270 and 380 that accounts for the vast majority of days.
  2. There are some occasional, exceptional days.
  3. There is the impression that average activity fell by about 10% in around August 2017.

So, our two-point comparison method seriously misleads us – and if we react to the distorted message that a two-point comparison generates then we run the risk of increasing the variation and making the problem worse.

Lesson: #plotthedots


One of the downsides of SPC is the arcane and unfamiliar language that is associated with it … terms like ‘common cause variation‘ and ‘special cause variation‘.  Sadly, the authors at NHS Improvement have fallen into this ‘special language’ trap and therefore run the risk of creating a new clique.

The lesson here is that SPC is a specific, simplified application of a more generic method called a system behaviour chart (SBC).

The first SPC chart was designed by Walter Shewhart in 1924 for one purpose and one purpose only – for monitoring the output quality of a manufacturing process in terms of how well the product conformed to the required specification.

In other words: SPC is an output quality audit tool for a manufacturing process.

This has a number of important implications for the design of the SPC tool:

  1. The average is not expected to change over time.
  2. The distribution of the random variation is expected to be bell-shaped.
  3. We need to be alerted to sudden shifts.

Shewhart’s chart was designed to detect early signs of deviation of a well-performing manufacturing process.  To detect possible causes that were worth investigating and minimise the adverse effects of over-reacting or under-reacting.


However,  for many reasons, the tool we need for measuring the behaviour of healthcare processes needs to be more sophisticated than the venerable SPC chart.  Here are three of them:

  1. The average is expected to change over time.
  2. The distribution of the random variation is not expected to be bell-shaped.
  3. We need to be alerted to slow drifts.

Under-Reacting to Non-Random Variation

Small shifts and slow drifts can have big cumulative effects.

Suppose I am a NHS service manager and I have a quarterly performance target to meet, so I have asked my data analyst to prepare a RAG chart to review my weekly data.

The quarterly target I need to stay below is 120 and my weekly RAG chart is set to show green when less than 108 (10% below target) and red when more than 132 (10% above target) because I know there is quite a lot of random week-to-week variation.

On the left is my weekly RAG chart for the first two quarters and I am in-the-green for both quarters (i.e. under target).

Q: Do I need to do anything?

A: The first quarter just showed “greens” and “ambers” so I relaxed and did nothing. There are a few “reds” in the second quarter, but about the same number as the “greens” and lots of “ambers” so it looks like I am about on target. I decide to do nothing again.

At the end of Q3 I’m in big trouble!

The quarterly RAG chart has flipped from Green to Red and I am way over target for the whole quarter. I missed the bus and I’m looking for a new job!

So, would a SPC chart have helped me here?

Here it is for Q1 and Q2.  The blue line is the target and the green line is the average … so below target for both quarters, as the RAG chart said.

The was a dip in Q1 for a few weeks but it was not sustained and the rest of the chart looks stable (all the points inside the process limits).  So, “do nothing” seemed like a perfectly reasonable strategy. Now I feel even more of a victim of fortune!

So, let us look at the full set of weekly date for the financial year and apply our  retrospectoscope.

This is just a plain weekly performance run chart with the target limit plotted as the blue line.

It is clear from this that there is a slow upward drift and we can see why our retrospective quarterly RAG chart flipped from green to red, and why neither our weekly RAG chart nor our weekly SPC chart alerted us in time to avoid it!

This problem is often called ‘leading by looking in the rear view mirror‘.

The variation we needed to see was not random, it was a slowly rising average, but it was hidden in the random variation and we missed it.  So we under-reacted and we paid the price.


This example illustrates another limitation of both RAG charts and SPC charts … they are both insensitive to small shifts and slow drifts when there is lots of random variation around, which there usually is.

So, is there a way to avoid this trap?

Yes. We need to learn to use the more powerful system behaviour charts and the systems engineering techniques and tools that accompany them.


But that aside, the rather good 147-page guide from NHS Improvement is a good first step for those still using two-point comparisons and RAG charts and it can be downloaded: HERE

A few years ago I had a rant about the dangers of the widely promoted mantra that 85% is the optimum average measured bed-occupancy target to aim for.

But ranting is annoying, ineffective and often counter-productive.

So, let us revisit this with some calm objectivity and disprove this Myth a step at a time.

The diagram shows the system of interest (SoI) where the blue box represents the beds, the coloured arrows are the patient flows, the white diamond is a decision and the dotted arrow is information about how full the hospital is (i.e. full/not full).

A new emergency arrives (red arrow) and needs to be admitted. If the hospital is not full the patient is moved to an empty bed (orange arrow), the medical magic happens, and some time later the patient is discharged (green arrow).  If there is no bed for the emergency request then we get “spillover” which is the grey arrow, i.e. the patient is diverted elsewhere (n.b. these are critically ill patients …. they cannot sit and wait).


This same diagram could represent patients trying to phone their GP practice for an appointment.  The blue box is the telephone exchange and if all the lines are busy then the call is dropped (grey arrow).  If there is a line free then the call is connected (orange arrow) and joins a queue (blue box) to be answered some time later (green arrow).

In 1917, a Danish mathematician/engineer called Agner Krarup Erlang was working for the Copenhagen Telephone Company and was grappling with this very problem: “How many telephone lines do we need to ensure that dropped calls are infrequent AND the switchboard operators are well utilised?

This is the perennial quality-versus-cost conundrum. The Value-4-Money challenge. Too few lines and the quality of the service falls; too many lines and the cost of the service rises.

Q: Is there a V4M ‘sweet spot” and if so, how do we find it? Trial and error?

The good news is that Erlang solved the problem … mathematically … and the not-so good news is that his equations are very scary to a non mathematician/engineer!  So this solution is not much help to anyone else.


Fortunately, we have a tool for turning scary-equations into easy-2-see-pictures; our trusty Excel spreadsheet. So, here is a picture called a heat-map, and it was generated from one of Erlang’s equations using Excel.

The Erlang equation is lurking in the background, safely out of sight.  It takes two inputs and gives one output.

The first input is the Capacity, which is shown across the top, and it represents the number of beds available each day (known as the space-capacity).

The second input is the Load (or offered load to use the precise term) which is down the left side, and is the number of bed-days required per day (e.g. if we have an average of 10 referrals per day each of whom would require an average 2-day stay then we have an average of 10 x 2 = 20 bed-days of offered load per day).

The output of the Erlang model is the probability that a new arrival finds all the beds are full and the request for a bed fails (i.e. like a dropped telephone call).  This average probability is displayed in the cell.  The colour varies between red (100% failure) and green (0% failure), with an infinite number of shades of red-yellow-green in between.

We can now use our visual heat-map in a number of ways.

a) We can use it to predict the average likelihood of rejection given any combination of bed-capacity and average offered load.

Suppose the average offered load is 20 bed-days per day and we have 20 beds then the heat-map says that we will reject 16% of requests … on average (bottom left cell).  But how can that be? Why do we reject any? We have enough beds on average! It is because of variation. Requests do not arrive in a constant stream equal to the average; there is random variation around that average.  Critically ill patients do not arrive at hospital in a constant stream; so our system needs some resilience and if it does not have it then failures are inevitable and mathematically predictable.

b) We can use it to predict how many beds we need to keep the average rejection rate below an arbitrary but acceptable threshold (i.e. the quality specification).

Suppose the average offered load is 20 bed-days per day, and we want to have a bed available more than 95% of the time (less than 5% failures) then we will need at least 25 beds (bottom right cell).

c) We can use it to estimate the maximum average offered load for a given bed-capacity and required minimum service quality.

Suppose we have 22 beds and we want a quality of >=95% (failure <5%) then we would need to keep the average offered load below 17 bed-days per day (i.e. by modifying the demand and the length of stay because average load = average demand * average length of stay).


There is a further complication we need to be mindful of though … the measured utilisation of the beds is related to the successful admissions (orange arrow in the first diagram) not to the demand (red arrow).  We can illustrate this with a complementary heat map generated in Excel.

For scenario (a) above we have an offered load of 20 bed-days per day, and we have 20 beds but we will reject 16% of requests so the accepted bed load is only 16.8 bed days per day  (i.e. (100%-16%) * 20) which is the reason that the average  utilisation is only 16.8/20 = 84% (bottom left cell).

For scenario (b) we have an offered load of 20 bed-days per day, and 25 beds and will only reject 5% of requests but the average measured utilisation is not 95%, it is only 76% because we have more beds (the accepted bed load is 95% * 20 = 19 bed-days per day and 19/25 = 76%).

For scenario (c) the average measured utilisation would be about 74%.


So, now we see the problem more clearly … if we blindly aim for an average, measured, bed-utilisation of 85% with the untested belief that it is always the optimum … this heat-map says it is impossible to achieve and at the same time offer an acceptable quality (>95%).

We are trading safety for money and that is not an acceptable solution in a health care system.


So where did this “magic” value of 85% come from?

From the same heat-map perhaps?

If we search for the combination of >95% success (<5% fail) and 85% average bed-utilisation then we find it at the point where the offered load reaches 50 bed-days per day and we have a bed-capacity of 56 beds.

And if we search for the combination of >99% success (<1% fail) and 85% average utilisation then we find it with an average offered load of just over 100 bed-days per day and a bed-capacity around 130 beds.

H’mm.  “Houston, we have a problem“.


So, even in this simplified scenario the hypothesis that an 85% average bed-occupancy is a global optimum is disproved.

The reality is that the average bed-occupancy associated with delivering the required quality for a given offered load with a specific number of beds is almost never 85%.  It can range anywhere between 50% and 100%.  Erlang knew that in 1917.


So, if a one-size-fits-all optimum measured average bed-occupancy assumption is not valid then how might we work out how many beds we need and predict what the expected average occupancy will be?

We would design the fit-4-purpose solution for each specific context …
… and to do that we need to learn the skills of complex adaptive system design …
… and that is part of the health care systems engineering (HCSE) skill-set.

 

One of the really cool things about the 1.3 kg of ChimpWare between our ears is the way it learns.

We have evolved the ability to predict the likely near-future based on a small number of past experiences.

And we do that by creating stored mental models.

Not even the most powerful computers can do it as well as we do – and we doing it without thinking. Literally. It is an unconscious process.

This ability to pro-gnose (‘know before’) gave our ancestors a major survival advantage when we were wandering about on the savanna over 10 million years ago, and we have used this amazing capability to build societies, mega-cities and spaceships.


But this ability is not perfect – it has a flaw – our ChimpOS does not store a picture of reality like a digital camera, it stores a patchy and distorted perception of reality – and then fills in the gaps with guesses (i.e. gaffes).  And we do not notice – consciously.

The cognitive trap is set and sits waiting to be sprung and to trip us up.


Here is an example:

“Improvement implies change”

Yes. That is a valid statement because we can show that whenever improvement has been the effect, then some time before that a change happened.  And we can show that when there are no changes, the system continues to behave as it always has.  Status quo.

The cognitive trap is that our ChimpOS is very good at remembering temporal associations – for example an association between “improvement” and “change” because we remember in the present. So if two concepts are presented at the same time, and we spice the pie with some emotion, then we are more likely to associate them.

The problem comes when we play back the memory … it can come back as …

“change implies improvement” which is not valid.  And we do not notice.

To prove it is not valid we just need to find one example where a change led to a deterioration; an unintended negative consequence, a surprising, confusing and disappointing failure to achieve our intended improvement.

An embarrassing gap between our intent and our impact.

And finding that evidence is not hard. Failures and disappointments in the world of improvement are all too common.


And then we fall into the same cognitive trap because we generalise from a single, bad experience and the lesson our ChimpOS stores for future reference is “change is bad”.

And forever afterwards we feel anxious whenever the idea of change is suggested.

And it is a very effective survival tactic – for a hominid living on the African savanna 10 million years ago, and at risk of falling prey to sharp-fanged, hungry predators.  It is a less useful tactic in the modern world where the risk of being eaten-for-lunch is minimal, and where the pace of change is accelerating.  We must learn to innovate and improve to survive in the social jungle … and we are not well equipped!


Here is another common cognitive trap:

Excellence implies no failures.

Yes. If we are delivering a consistently excellent service then the absence of failures will be a noticeable feature.

No failures implies excellence.

Sadly, this is not a valid inference.  If quality-of-service is measured on a continuum from Excrement-to-Excellent, then we can be delivering a consistently mediocre service, one that is barely adequate, and also have no failures.


The design flaw here is that our ChimpWare/ChimpOS memory system is lossy.

We do not remember all the information required to reconstruct an accurate memory of reality – there is too much information – so we distort, we delete and we generalise.  And we do that because when we evolved it was a good enough solution, and it enabled us to survive as a species, so the ChimpWare/ChimpOS genes were passed on.

We cannot reverse millions of years of evolution.  We cannot get a hardware or software upgrade.  We need to learn to manage with the limitations of what we have between our ears.

And to avoid the cognitive traps we need to practice the discipline of bringing our unconscious assumptions into conscious awareness … and we do that by asking carefully framed questions.

Here is another example to practice with:

A high-efficiency design implies high-utilisation of resources.

Yes, that is valid. Idle resources means wasted resources which means lower efficiency.

Q1: Is the converse also valid?
Q2: Is there any evidence that disproves the converse is valid?

If high-utilisation does not imply high-efficiency, what are the implications of falling into this cognitive trap?  What is the value of measuring utilisation? Does it have a value?

These are useful questions.

When a system reaches the limit of its resilience, it does not fail gradually; it fails catastrophically.  Up until the point of collapse the appearance of stability is reassuring … but it is an illusion.

A drowning person kicks frantically until they are exhausted … then they sink very quickly.

Below is the time series chart that shows the health of the UK Emergency Health Care System from 2011 to the present.

The seasonal cycle is made obvious by the regular winter dips. The progressive decline in England, Wales and NI is also clear, but we can see that Scotland did something different in 2015 and reversed the downward trend and sustained that improvement.

Until, the whole system failed in the winter of 2017/18. Catastrophically.

The NHS is a very complicated system so what hope do we have of understanding what is going on?


The human body is also a complicated system.

In the 19th Century, a profound insight into how the human body works was proposed by the French physiologist, Claude Bernard.

He talked about the stability of the milieu intérieur and his concept came to be called homeostasis: The principle that a self-regulating system can maintain its own stability over a wide range.  In other words, it demonstrates resilience to variation.

The essence of a homeostatic system is that the output is maintained using a compensatory feedback loop, one that is assembled by connecting sensors to processors to effectors. Input-Process-Output (IPO).

And to assess how much stress the whole homeostatic system is under, we do not measure the output (because that is maintained steady by the homeostatic feedback design), instead we measure how hard the stabilising feedback loop is working!


And, when the feedback loop reaches the limit of its ability to compensate, the whole system will fail.  Quickly. Catastrophically.  And when this happens in the human body we call this a “critical illness”.

Doctors know this.  Engineers know this.  But do those who decide and deliver health care policy know this?  The uncomfortable evidence above suggests that they might not.

The homeostatic feedback loop is the “inner voice” of the system.  In the NHS it is the collective voices of those at the point of care who sense the pressure and who are paddling increasingly frantically to minimize risk and to maintain patient safety.

And being deaf to that inner voice is a very dangerous flaw in the system design!


Once a complicated system has collapsed, then it is both difficult and expensive to resuscitate and recover, especially if the underpinning system design flaws are not addressed.

And, if we learn how to diagnose and treat these system design errors, then it is possible to “flip” the system back into stable and acceptable performance.

Surprisingly quickly.


Read on »

It is that time of year – again.

Winter.

The NHS is struggling, front-line staff are having to use heroic measures just to keep the ship afloat, and less urgent work has been suspended to free up space and time to help man the emergency pumps.

And the finger-of-blame is being waggled by the army of armchair experts whose diagnosis is unanimous: “lack of cash caused by an austerity triggered budget constraint”.


And the evidence seems plausible.

The A&E performance data says that each year since 2009, the proportion of patients waiting more than 4 hours in A&Es has been increasing.  And the increase is accelerating. This is a progressive quality failure.

And health care spending since the NHS was born in 1948 shows a very similar accelerating pattern.    

So which is the chicken and which is the egg?  Or are they both symptoms of something else? Something deeper?


Both of these charts are characteristic of a particular type of system behaviour called a positive feedback loop.  And the cost chart shows what happens when someone attempts to control the cash by capping the budget:  It appears to work for a while … but the “pressure” is building up inside the system … and eventually the cash-limiter fails. Usually catastrophically. Bang!


The quality chart shows an associated effect of the “pressure” building inside the acute hospitals, and it is a very well understood phenomenon called an Erlang-Kingman queue.  It is caused by the inevitable natural variation in demand meeting a cash-constrained, high-resistance, high-pressure, service provider.  The effect is to amplify the natural variation and to create something much more dangerous and expensive: chaos.


The simple line-charts above show the long-term, aggregated  effects and they hide the extremely complicated internal structure and the highly complex internal behaviour of the actual system.

One technique that system engineers use to represent this complexity is a causal loop diagram or CLD.

The arrows are of two types; green indicates a positive effect, and red indicates a negative effect.

This simplified CLD is dominated by green arrows all converging on “Cost of Care”.  They are the positive drivers of the relentless upward cost pressure.

Health care is a victim of its own success.

So, if the cash is limited then the naturally varying demand will generate the queues, delays and chaos that have such a damaging effect on patients, providers and purses.

Safety and quality are adversely affected. Disappointment, frustration and anxiety are rife. Expectation is lowered.  Confidence and trust are eroded.  But costs continue to escalate because chaos is expensive to manage.

This system behaviour is what we are seeing in the press.

The cost-constraint has, paradoxically, had exactly the opposite effect, because it is treating the effect (the symptom) and ignoring the cause (the disease).


The CLD has one negative feedback loop that is linked to “Efficiency of Processes”.  It is the only one that counteracts all of the other positive drivers.  And it is the consequence of the “System Design”.

What this means is: To achieve all the other benefits without the pressures on people and purses, all the complicated interdependent processes required to deliver the evolving health care needs of the population must be proactively designed to be as efficient as technically possible.


And that is not easy or obvious.  Efficient design does not happen naturally.  It is hard work!  It requires knowledge of the Anatomy and Physiology of Systems and of the Pathology of Variation.  It requires understanding how to achieve effectiveness and efficiency at the same time as avoiding queues and chaos.  It requires that the whole system is continually and proactively re-designed to remain reliable and resilient.

And that implies it has to be done by the system itself; and that means the NHS needs embedded health care systems engineering know-how.

And when we go looking for that we discover sequence of gaps.

An Awareness gap, a Belief gap and a Capability gap. ABC.

So the first gap to fill is the Awareness gap.

The New Year of 2018 has brought some unexpected challenges. Or were they?

We have belligerent bullies with their fingers on their nuclear buttons.

We have an NHS in crisis, with corridor-queues of urgent frail, elderly, unwell and a month of cancelled elective operations.

And we have winter storms, fallen trees, fractured power-lines, and threatened floods – all being handled rather well by people who are trained to manage the unexpected.

Which is the title of this rather interesting book that talks a lot about HROs.

So what are HROs?


“H” stands for High.  “O” stands for Organisation.

What does R stand for?  Rhetoric? Rigidity? Resistance?

Watching the news might lead one to suggest these words would fit … but they are not the answer.

“R” stands for Reliability and “R” stands for Resilience … and they are linked.


Think of a global system that is so reliable that we all depend on it, everyday.  The Global Positioning System or the Internet perhaps.  We rely on them because they serve a need and because they work. Reliably and resiliently.

And that was no accident.

Both the Internet and the GPS were designed and built to meet the needs of billions and to be reliable and resilient.  They were both created by an army of unsung heroes called systems engineers – who were just doing their job. The job they were trained to do.


The NHS serves a need – and often an urgent one, so it must also be reliable. But it is not.

The NHS needs to be resilient. It must cope with the ebb and flow of seasonal illness. But it does not.

And that is because the NHS has not been designed to be either reliable or resilient. And that is because the NHS has not been designed.  And that is because the NHS does not appear to have enough health care systems engineers trained to do that job.

But systems engineering is a mature discipline, and it works just as well inside health care as it does outside.


And to support that statement, here is evidence of what happened after a team of NHS clinicians and managers were trained in the basics of HCSE.

Monklands A&E Improvement

So the gap seems to be just an awareness/ability gap … which is a bridgeable one.


Who would like to train to be a Health Case Systems Engineer and to join the growing community of HCSE practitioners who have the potential to be the future unsung heroes of the NHS?

Click here if you are interested: http://www.ihcse.uk

PS. “Managing the Unexpected” is an excellent introduction to SE.

It had been some time since Bob and Leslie had chatted so an email from the blue was a welcome distraction from a complex data analysis task.

<Bob> Hi Leslie, great to hear from you. I was beginning to think you had lost interest in health care improvement-by-design.

<Leslie> Hi Bob, not at all.  Rather the opposite.  I’ve been very busy using everything that I’ve learned so far.  It’s applications are endless, but I have hit a problem that I have been unable to solve, and it is driving me nuts!

<Bob> OK. That sounds encouraging and interesting.  Would you be able to outline this thorny problem and I will help if I can.

<Leslie> Thanks Bob.  It relates to a big issue that my organisation is stuck with – managing urgent admissions.  The problem is that very often there is no bed available, but there is no predictability to that.  It feels like a lottery; a quality and safety lottery.  The clinicians are clamoring for “more beds” but the commissioners are saying “there is no more money“.  So the focus has turned to reducing length of stay.

<Bob> OK.  A focus on length of stay sounds reasonable.  Reducing that can free up enough beds to provide the necessary space-capacity resilience to dramatically improve the service quality.  So long as you don’t then close all the “empty” beds to save money, or fall into the trap of believing that 85% average bed occupancy is the “optimum”.

<Leslie> Yes, I know.  We have explored all of these topics before.  That is not the problem.

<Bob> OK. What is the problem?

<Leslie> The problem is demonstrating objectively that the length-of-stay reduction experiments are having a beneficial impact.  The data seems to say they they are, and the senior managers are trumpeting the success, but the people on the ground say they are not. We have hit a stalemate.


<Bob> Ah ha!  That old chestnut.  So, can I first ask what happens to the patients who cannot get a bed urgently?

<Leslie> Good question.  We have mapped and measured that.  What happens is the most urgent admission failures spill over to commercial service providers, who charge a fee-per-case and we have no choice but to pay it.  The Director of Finance is going mental!  The less urgent admission failures just wait on queue-in-the-community until a bed becomes available.  They are the ones who are complaining the most, so the Director of Governance is also going mental.  The Director of Operations is caught in the cross-fire and the Chief Executive and Chair are doing their best to calm frayed tempers and to referee the increasingly toxic arguments.

<Bob> OK.  I can see why a “Reduce Length of Stay Initiative” would tick everyone’s Nice If box.  So, the data analysts are saying “the length of stay has come down since the Initiative was launched” but the teams on the ground are saying “it feels the same to us … the beds are still full and we still cannot admit patients“.

<Leslie> Yes, that is exactly it.  And everyone has come to the conclusion that demand must have increased so it is pointless to attempt to reduce length of stay because when we do that it just sucks in more work.  They are feeling increasingly helpless and hopeless.

<Bob> OK.  Well, the “chronic backlog of unmet need” issue is certainly possible, but your data will show if admissions have gone up.

<Leslie> I know, and as far as I can see they have not.

<Bob> OK.  So I’m guessing that the next explanation is that “the data is wonky“.

<Leslie> Yup.  Spot on.  So, to counter that the Information Department has embarked on a massive push on data collection and quality control and they are adamant that the data is complete and clean.

<Bob> OK.  So what is your diagnosis?

<Leslie> I don’t have one, that’s why I emailed you.  I’m stuck.


<Bob> OK.  We need a diagnosis, and that means we need to take a “history” and “examine” the process.  Can you tell me the outline of the RLoS Initiative.

<Leslie> We knew that we would need a baseline to measure from so we got the historical admission and discharge data and plotted a Diagnostic Vitals Chart®.  I have learned something from my HCSE training!  Then we planned the implementation of a visual feedback tool that would show ward staff which patients were delayed so that they could focus on “unblocking” the bottlenecks.  We then planned to measure the impact of the intervention for three months, and then we planned to compare the average length of stay before and after the RLoS Intervention with a big enough data set to give us an accurate estimate of the averages.  The data showed a very obvious improvement, a highly statistically significant one.

<Bob> OK.  It sounds like you have avoided the usual trap of just relying on subjective feedback, and now have a different problem because your objective and subjective feedback are in disagreement.

<Leslie> Yes.  And I have to say, getting stuck like this has rather dented my confidence.

<Bob> Fear not Leslie.  I said this is an “old chestnut” and I can say with 100% confidence that you already have what you need in your T4 kit bag?

<Leslie>Tee-Four?

<Bob> Sorry, a new abbreviation. It stands for “theory, techniques, tools and training“.

<Leslie> Phew!  That is very reassuring to hear, but it does not tell me what to do next.

<Bob> You are an engineer now Leslie, so you need to don the hard-hat of Improvement-by-Design.  Start with your Needs Analysis.


<Leslie> OK.  I need a trustworthy tool that will tell me if the planned intervention has has a significant impact on length of stay, for better or worse or not at all.  And I need it to tell me that quickly so I can decide what to do next.

<Bob> Good.  Now list all the things that you currently have that you feel you can trust.

<Leslie> I do actually trust that the Information team collect, store, verify and clean the raw data – they are really passionate about it.  And I do trust that the front line teams are giving accurate subjective feedback – I work with them and they are just as passionate.  And I do trust the systems engineering “T4” kit bag – it has proven itself again-and-again.

<Bob> Good, and I say that because you have everything you need to solve this, and it sounds like the data analysis part of the process is a good place to focus.

<Leslie> That was my conclusion too.  And I have looked at the process, and I can’t see a flaw. It is driving me nuts!

<Bob> OK.  Let us take a different tack.  Have you thought about designing the tool you need from scratch?

<Leslie> No. I’ve been using the ones I already have, and assume that I must be using them incorrectly, but I can’t see where I’m going wrong.

<Bob> Ah!  Then, I think it would be a good idea to run each of your tools through a verification test and check that they are fit-4-purpose in this specific context.

<Leslie> OK. That sounds like something I haven’t covered before.

<Bob> I know.  Designing verification test-rigs is part of the Level 2 training.  I think you have demonstrated that you are ready to take the next step up the HCSE learning curve.

<Leslie> Do you mean I can learn how to design and build my own tools?  Special tools for specific tasks?

<Bob> Yup.  All the techniques and tools that you are using now had to be specified, designed, built, verified, and validated. That is why you can trust them to be fit-4-purpose.

<Leslie> Wooohooo! I knew it was a good idea to give you a call.  Let’s get started.


[Postscript] And Leslie, together with the other stakeholders, went on to design the tool that they needed and to use the available data to dissolve the stalemate.  And once everyone was on the same page again they were able to work collaboratively to resolve the flow problems, and to improve the safety, flow, quality and affordability of their service.  Oh, and to know for sure that they had improved it.

One of the quickest and easiest ways to kill an improvement initiative stone dead is to label it as a “cost improvement program” or C.I.P.

Everyone knows that the biggest single contributor to cost is salaries.

So cost reduction means head count reduction which mean people lose their jobs and their livelihood.

Who is going to sign up to that?

It would be like turkeys voting for Xmas.

There must be a better approach?

Yes. There is.


Over the last few weeks, groups of curious skeptics have experienced the immediate impact of systems engineering theory, techniques and tools in a health care context.

They experienced queues, delays and chaos evaporate in front of their eyes … and it cost nothing to achieve. No extra resources. No extra capacity. No extra cash.

Their reaction was “surprise and delight”.

But … it also exposed a problem.  An undiscussable problem.


Queues and chaos require expensive resources to manage.

We call them triagers, progress-chasers, and fire-fighters.  And when the queues and chaos evaporate then their jobs do too.

The problem is that the very people who are needed to make the change happen are the ones who become surplus-to-requirement as a result of the change.

So change does not happen.

It would like turkeys voting for Xmas.


The way around this impasse is to anticipate the effect and to proactively plan to re-invest the resource that is released.  And to re-invest it doing a more interesting and more worthwhile jobs than queue-and-chaos management.

One opportunity for re-investment is called time-buffering which is an effective way to improve resilience to variation, especially in an unscheduled care context.

Another opportunity for re-investment is tail-gunning the chronic backlogs until they are down to a safe and sensible size.

And many complain that they do not have time to learn about improvement because they are too busy managing the current chaos.

So, another opportunity for re-investment is training – oneself first and then others.


R.I.P.    C.I.P.

The NHS appears to be descending in a frenzy of fear as the winter looms and everyone says it will be worse than last and the one before that.

And with that we-are-going-to-fail mindset, it almost certainly will.

Athletes do not start a race believing that they are doomed to fail … they hold a belief that they can win the race and that they will learn and improve even if they do not. It is a win-win mindset.

But to succeed in sport requires more than just a positive attitude.

It also requires skills, training, practice and experience.

The same is true in healthcare improvement.


That is not the barrier though … the barrier is disbelief.

And that comes from not having experienced what it is like to take a system that is failing and transform it into one that is succeeding.

Logically, rationally, enjoyably and surprisingly quickly.

And, the widespread disbelief that it is possible is paradoxical because there are plenty of examples where others have done exactly that.

The disbelief seems to be “I do not believe that will work in my world and in my hands!

And the only way to dismantle that barrier-of-disbelief is … by doing it.


How do we do that?

The emotionally safest way is in a context that is carefully designed to enable us to surface the unconscious assumptions that are the bricks in our individual Barriers of Disbelief.

And to discard the ones that do not pass a Reality Check, and keep the ones that are OK.

This Disbelief-Busting design has been proven to be effective, as evidenced by the growing number of individuals who are learning how to do it themselves, and how to inspire, teach and coach others to as well.


So, if you would like to flip disbelief-and-hopeless into belief-and-hope … then the door is here.

It is always rewarding when separate but related ideas come together and go “click”.

And this week I had one of those “ah ha” moments while attempting to explain how the process of engagement works.

Many years ago I was introduced to the conscious-competence model of learning which I found really insightful.  Sometime later I renamed it as the awareness-ability model because the term competence felt too judgmental.

The idea is that when we learn we all start from a position of being unaware of our inability.

A state called blissful ignorance.

And it is only when we try to do something that we become aware of what we cannot do; which can lead to temper tantrums!

As we concentrate and practice our ability improves and we enter the zone of know how.  We become able to demonstrate what we can do, and explain how we are doing it.

The final phase comes when it becomes so habitual that we forget how we learned our skill – it has become second nature.


Some years later I was introduced to the Nerve Curve which is the emotional roller-coaster ride that accompanies change.  Any form of change.

A five-step model was described in the context of bereavement by psychiatrist Elisabeth Kübler-Ross in her 1969 book “On Death & Dying: What the Dying Have to Teach Doctors, Nurses, Clergy and their Families.

More recently this has been extended and applied by authors such as William Bridges and John Fisher in the less emotionally traumatic contexts called transitions.

The characteristic sequence of emotions are triggered by external events are:

  • shock
  • denial
  • frustration
  • blame
  • guilt
  • depression
  • acceptance
  • engagement
  • excitement.

The important messages in both of these models is that we can get stuck along the path of transition, and we can disengage at several points, signalling to others that we have come off the track.  When we do that we exhibit behaviours such as denial, disillusionment and hostility.


More recently I was introduced to the work of the late Chris Argyris and specifically the concept of “defensive reasoning“.

The essence of the concept:  As we start to become aware of a gap between our intentions and our impact, then we feel threatened and our natural reaction is defensive.  This is the essence of the behaviour called “resistance to change”, and it is interesting to note that “smart” people are particularly adept at it.


These three concepts are clearly related in some way … but how?


As a systems engineer I am used to cyclical processes and the concepts of wavelength, amplitude, phase and offset, and I found myself looking at the Awareness-Ability cycle and asking:

“How could that cycle generate the characteristic shape of the transition curve?”

Then the Argyris idea of the gap between intent and impact popped up and triggered another question:

“What if we look at the gap between our ability and our awareness?”

So, I conducted a thought experiment and imagined myself going around the cycle – and charting my ability, awareness and emotional state along the way … and this sketch emerged. Ah ha!

When my awareness exceeded my ability I felt disheartened. That is the defensive reasoning that Chris Argyris talks about, the emotional barrier to self-improvement.


Ability – Awareness = Engagement


This suggested to me that the process of building self-engagement requires opening the ability-versus-awareness gap a little-bit-at-a-time, sensing the emotional discomfort, and then actively releasing the tension by learning a new concept, principle, technique or tool (and usually all four).

Eureka!

I wonder if the same strategy would work elsewhere?

The first step in a design conversation is to understand the needs of the customer.

It does not matter if you are designing a new kitchen, bathroom, garden, house, widget, process, or system.  It is called a “needs analysis”.

Notice that it is not called a “wants analysis”.  They are not the same thing because there is often a gap between what we want (and do not want) and what we need (and do not need).

The same is true when we are looking to use a design-based approach to improve something that we already have.


This is especially true when we are improving services because the the needs and wants of a service tend to drift and shift continuously, and we are in a continual state of improvement.

For design to work the “customers” and the “suppliers” need work collaboratively to ensure that they both get what they need.

Frustration and fragmentation are the symptoms of a combative approach where a “win” for one is a “lose” for the other (NB. In absolute terms both will end up worse off than they started so both lose in the long term.)


And there is a tried and tested process to collaborative improvement-by-design.

One version is called “experience based co-design” (EBCD) and it was cooked up in a health care context about 20 years ago and shown to work in a few small pilot studies.

The “experience” that triggered the projects was almost always a negative one and was associated with feelings of frustration, anxiety and disappointment. So, the EBCD case studies were more focused on helping the protagonists to share their perspectives, in the belief that will be enough to solve the problem.  And it is indeed a big step forwards.

It has a limitation though.  It assumes that the staff and patients know how to design processes so that they are fit-4-purpose, and the evidence to support that assumption is scanty.

In one pilot in mental health, the initial improvement (a fall in patient and carer complaints) was not sustained.  The reason given was that the staff who were involved in the pilot inevitably moved on, and as they did the old attitudes, beliefs and behaviours returned.


So, an improved version of EBCD is needed.  One that is based on hard evidence of what works and what does not.  One that is also focused on moving towards a future-purpose rather than just moving away from past-problems.

Let us call this improved version “Evidence-Based Co-Design“.

And we already know that by a different name:

Health Care Systems Engineering (HCSE).

OODA is something we all do thousands of times a day without noticing.

Observe – Orient – Decide – Act.

The term is attributed to Colonel John Boyd, a real world “Top Gun” who studied economics and engineering, then flew and designed fighter planes, then became a well-respected military strategist.

OODA is a continuous process of updating our mental model based on sensed evidence.

And it is a fast process because happens largely out of awareness.

This was Boyd’s point: In military terms, the protagonist that can make wiser and faster decisions are more likely to survive in combat.


And notice that it is not a simple linear sequence … it is a system … there are parallel paths and both feed-forward and feed-backward loops … there are multiple information flow paths.

And notice that the Implicit Guidance & Control links do not go through Decision – this means they operate out of awareness and are much faster.

And notice the Feed Forward links link the OODA steps – this is the conscious, sequential, future looking process that we know by another name:

Study-Adjust-Plan-Do.


We use the same process in medicine: first we study the patient and the problem they are presenting (history, examination, investigation), then we adjust our generic mental model of how the body works to the specific patient (diagnosis), then we plan and decide a course of action to achieve the intended outcome, and then we act, we do it (treatment).

But at any point we can jump back to an earlier step and we can jump forwards to a later one.  The observe, orient, decide, act modes are running in parallel.

And the more experience we have of similar problems the faster we can complete the OODA (or SAPD) work because we learn what is the most useful information to attend to, and we learn how to interpret it.

We learn the patterns and what to look for – and that speeds up the process – a lot!


This emergent learning is then re-inforced if the impact of our action matches our intent and prediction and our conscious learning is then internalised as unconscious “rules of thumb” called heuristics.


We start by thinking our way consciously and slowly … and … we finish by feeling our way unconsciously and quickly.


Until … we  encounter a novel problem that does not fit any of our learned pattern matching neural templates. When that happens, our unconscious, parallel processing, pattern-matching system alerts us with a feeling of confusion and bewilderment – and we freeze (often with fright!)

Now we have a choice: We can retreat to using familiar, learned, reactive, knee-jerk patterns of behaviour (presumably in the hope that they will work) or we can switch into a conscious learning loop and start experimenting with novel ideas.

If we start at Hypothesis then we have the Plan-Do-Study-Act cycle; where we generate novel hypotheses to explain the unexpected, and we then plan experiments to test our hypotheses; and we then study the outcome of the experiments and we then we act on our conclusions.

This mindful mode of thinking is well described in the book “Managing the Unexpected” by Weick and Sutcliffe and is the behaviour that underpins the success of HROs – High Reliability Organisations.

The image is of the latest (3rd edition) but the previous (2nd edition) is also worth reading.

So we have two interdependent problem solving modes – the parallel OODA system and the sequential SAPD process.

And we can switch between them depending on the context.


Which is an effective long-term survival strategy because the more we embrace the unexpected, the more opportunities we will have to switch into exploration mode and learn new patterns; and the more patterns we recognise the more efficient and effective our unconscious decision-making process will become.

This complex adaptive system behaviour has another name … Resilience.

“Those who cannot remember the past are condemned to repeat it”.

Aphorism by George Santayana, philosopher (1863-1952).

And the history of quality improvement (QI) is worth reflecting on, because there is massive pressure to grow QI capability in health care as a way of solving some chronic problems.

The chart below is a Google Ngram, it was generated using some phrases from the history of Quality Improvement:

TQM = the total quality management movement that grew from the work of Walter Shewhart in the 1920’s and 30’s and was “incubated” in Japan after being transplanted there by Shewhart’s student W. Edwards Deming in the 1950’s.
ISO 9001 = an international quality standard first published in 2000 that developed from the British Standards Institute (BSI) in the 1970’s via ISO 9000 that was first published in 1987.
Six Sigma = a highly statistical quality improvement / variation reduction methodology that originated in the rapidly expanding semiconductor industry in the 1980’s.

The rise-and-fall pattern is characteristic of how innovations spread; there is a long lag phase, then a short accelerating growth phase, then a variable plateau phase and then a long, decelerating decline phase.

It is called a life-cycle. It is how complex adaptive systems behave. It is how innovations spread. It is expected.

So what happened?

Did the rise of TQM lead to the rise of ISO 9000 which triggered the development of the Six Sigma methodology?

It certainly looks that way.

So why is Six Sigma “dying”?  Or is it just being replaced by something else?


This is the corresponding Ngram for “Healthcare Quality Improvement” which seems to sit on the timeline in about the same place as ISO 9001 and that suggests that it was triggered by the TQM movement. 

The Institute of Healthcare Improvement (IHI) was officially founded in 1991 by Dr Don Berwick, some years after he attended one of the Deming 4-day workshops and had an “epiphany”.

Don describes his personal experience in a recent plenary lecture (from time 01:07).  The whole lecture is worth watching because it describes the core concepts and principles that underpin QI.


So given the fact that safety and quality are still very big issues in health care – why does the Ngram above suggest that the use of the term Quality Improvement does not sustain?

Will that happen in healthcare too?

Could it be that there is more to improvement than just a focus on safety (reducing avoidable harm) and quality (improving patient experience)?

Could it be that flow and productivity are also important?

The growing angst that permeates the NHS appears to be more focused on budgets and waiting-time targets (4 hrs in A&E, 63 days for cancer, 18 weeks for scheduled care, etc.).

Mortality and Quality hardly get a mention any more, and the nationally failed waiting time targets are being quietly dropped.

Is it too politically embarrassing?

Has the NHS given up because it firmly believes that pumping in even more money is the only solution, and there isn’t any more in the tax pot?


This week another small band of brave innovators experienced, first-hand, the application of health care systems engineering (HCSE) to a very common safety, flow, quality and productivity problem …

… a chronically chaotic clinic characterized by queues and constant calls for more capacity and cash.

They discovered that the queues, delays and chaos (i.e. a low quality experience) were not caused by lack of resources; they were caused by flow design.  They were iatrogenic.  And when they applied the well-known concepts and principles of scheduling design, they saw the queues and chaos evaporate, and they measured a productivity increase of over 60%.

OMG!

Improvement science is more than just about safety and quality, it is about flow and productivity as well; because we all need all four to improve at the same time.

And yes we need all the elements of Deming’s System of Profound Knowledge (SoPK), but need more than that.  We need to harness the knowledge of the engineers who for centuries have designed and built buildings, bridges, canals, steam engines, factories, generators, telephones, automobiles, aeroplanes, computers, rockets, satellites, space-ships and so on.

We need to revisit the legacy of the engineers like Watt, Brunel, Taylor, Gantt, Erlang, Ford, Forrester and many, many others.

Because it does appear to be possible to improve-by-design as well as to improve-by-desire.

Here is the Ngram with “Systems Engineering” (SE) added and the time line extended back to 1955.  Note the rise of SE in the 1950’s and 1960’s and note that it has sustained.

That pattern of adoption only happens when something is proven to be fit-4-purpose, and is valued and is respected and is promoted and is taught.

What opportunity does systems engineering offer health care?

That question is being actively explored … here.

This week a ground-breaking case study was published.

It describes how a team in South Wales discovered how to make the flows visible in a critical part of their cancer pathway.

Radiology.

And they did that by unintentionally falling into a trap!  A trap that many who set out to improve health care services fall into.  But they did not give up.  They sought guidance and learned some profound lessons.

Part 1 of their story is shared here.


One lesson they learned is that, as they take on more complex improvement challenges, they need to be equipped with the right tools, and they need to be trained to use them, and they need to have practiced using them.

Another lesson they learned is that making the flows in a system visible is necessary before the current behaviour of the system can be understood.

And they learned that they needed a clear diagnosis of how the current system is not performing; before they can attempt to design an intervention to deliver the intended improvement.

They learned how the Study-Plan-Do cycle works, and they learned the reason it starts with “Study”, and not with “Plan”.


They tried, failed, took one step back, asked, listened and learned.


Then with their new knowledge, more advanced tools, and deeper understanding they took two steps forward; diagnosed problem, designed an intervention, and delivered a significant improvement.

And visualised just how significant.

Then they shared Part 2 of their story … here.

 

 

The NHS appears to be getting increasingly desperate in its cost control tactics:


What does this letter say …

  1. The NHS is required to improve productivity by 20%.
  2. The NHS needs to work collaboratively with its suppliers.
  3. The NHS would like to learn the “secrets” from its suppliers.
  4. And then a thinly-veiled threat.

A 20% productivity improvement has never been achieved before using a Cost Improvement Program (CIP) approach … so how will it now?

A 20% productivity improvement requires something a lot more radical than a “zero-inflation policy”.

A 20% productivity improvement requires wholesale system redesign.

And there is good news … that is possible … and the not-so-good news is that the NHS will need to learn how to do it, for itself.


One barrier to doing this is disbelief that it is possible.

Another is ignorance of how to do it.


If the NHS wants to survive, in anything like its current form, then it will need to grasp that nettle/opportunity … and to engage in wholesale raising of awareness of what is possible and how to achieve it.

Denial is not an option.

And there is one way to experience what is possible and how to achieve it … and it can be accessed here.


The seats on the HCSE bus are limited, so only those who are prepared to invest in their own learning and their own future career paths should even consider buying a ticket to ride …

… and follow the footsteps of the courageous innovators.

Here are some of their stories: Journal of Improvement Science

Beliefs drive behaviour. Behaviour drives change. Improvement requires change.

So, improvement requires challenging beliefs; confirming some and disproving others.

And beliefs can only be confirmed or disproved rationally – with evidence and explanation. Rhetoric is too slippery. We can convince ourselves of anything with that!

So it comes as an emotional shock when one of our beliefs is disproved by experiencing reality from a new perspective.

Our natural reaction is surprise, perhaps delight, and then defense. We say “Yes, but ...”.

And that is healthy skepticism and it is a valuable and necessary part of the change and improvement process.

If there are not enough healthy skeptics on a design team it is unbalanced.

If there are too many healthy skeptics on a design team it is unbalanced.


This week I experienced this phenomenon first hand.

The context was a one day practical skills workshop and the topic was:

How to improve the safety, timeliness, quality and affordability of unscheduled care“.

The workshop is designed to approach this challenge from a different perspective.

Instead of asking “What is the problem and how do we solve it?” we took the system engineering approach of asking “What is the purpose and how can we achieve it?”

We used a range of practical exercises to illustrate some core concepts and principles – reality was our teacher. Then we applied those newly acquired insights to the design challenge using a proven methodology that ensured we do not skip steps.


And the outcome was: the participants discovered that …

it is indeed possible to improve the safety, timeliness, quality and affordability of unscheduled health care …

using health care systems engineering concepts, principles, techniques and tools that, until the workshop, they had been unaware even existed.


Their reaction was “OMG” and was shortly followed by “Yes, but …” which is to be expected and is healthy.

The rest of the “Yes, but … ” sentence was “… how will I convince my colleagues?

One way is for them to seek out the same experience …

… because reality is a much better teacher than rhetoric.

HCSE Practical Skills One Day Workshops

 

At some point in the life-cycle of an innovation, there is the possibility of crossing an invisible line called the tipping point.

This happens when enough people have experienced the benefits of the innovation and believe that the innovation is the future.  These lone innovators start to connect and build a new community.

It is an emergent behaviour of a complex adaptive system.


This week I experienced what could be a tipping point.

I attended the Q-Community launch event for the West Midlands that was held at the ICC in Birmingham … and it was excellent.

The invited speakers were both engaging and inspiring – boosting the emotional charge in the old engagement batteries; which have become rather depleted of late by the incessant wailing from the all-too-numerous peddlers of doom-and-gloom.

There was an opportunity to re-connect with fellow radicals who, over nearly two decades, have had the persistent temerity to suggest that improvement is necessary, is possible, have invested in learning how to do it, and have disproved the impossibility hypothesis.

There were new connections with like-minded people who want to both share what they know about the science of improvement and to learn what they do not.

And there were hand-outs, side-shows and break-outs.  Something for everyone.


The voice of the Q-Community will grow louder – and for it to be listened to it will need to be patiently and persistently broadcasting the news stories of what has been achieved, and how it was achieved, and who has demonstrated they can walk-the-talk.  News stories like this one:

Improving safety, flow, quality and affordability of unscheduled care of the elderly.


I sincerely hope that in the future, with the benefit of hindsight, we in the West Midlands will say – the 19th July 2017 was our Q-Community tipping point.

And I pledge to do whatever I can to help make that happen.

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 and enjoyable 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.