Category: Reflections

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Resilience

The rise in the use of the term “resilience” seems to mirror the sense of an accelerating pace of change. So, what does it mean? And is the meaning evolving over time?

One sense of the meaning implies a physical ability to handle stresses and shocks without breaking or failing. Flexible, robust and strong are synonyms; and opposites are rigid, fragile, and weak.

So, digging a bit deeper we know that strong implies an ability to withstand extreme stress while resilient implies the ability to withstanding variable stress. And the opposite of resilient is brittle because something can be both strong and brittle.

This is called passive resilience because it is an inherent property and cannot easily be changed. A ball is designed to be resilient – it will bounce back – and this inherent in the material and the structure. The implication of this is that to improve passive resilience we would need to remove and to replace with something better suited to the range of expected variation.

The concept of passive resilience applies to processes as well, and a common manifestation of a brittle process is one that has been designed using averages.

Processes imply flows. The flow into a process is called demand, while the flow out of the process is called activity. What goes in must come out, so if the demand exceeds the activity then a backlog will be growing inside the process. This growing queue creates a number of undesirable effects – first it takes up space, and second it increases the time for demand to be converted into activity. This conversion time is called the lead-time.

So, to avoid a growing queue and a growing wait, there must be sufficient flow-capacity at each and every step along the process. The obvious solution is to set the average flow-capacity equal to the average demand; and we do this because we know that more flow-capacity implies more cost – and to stay in business we must keep a lid on costs!

This sounds obvious and easy but does it actually work in practice?

The surprising answer is “No”. It doesn’t.

What happens in practice is that the measured average activity is always less than the funded flow-capacity, and so less than the demand. The backlogs will continue to grow; the lead-time will continue to grow; the waits will continue to grow; the internal congestion will continue to grow – until we run out of space. At that point everything can grind to a catastrophic halt. That is what we mean by a brittle process.

This fundamental and unexpected result can easily and quickly be demonstrated in a concrete way on a table top using ordinary dice and tokens. A credible game along these lines was described almost 40 years ago in The Goal by Eli Goldratt, originator of the school of improvement called Theory of Constraints. The emotional impact of gaining this insight can be profound and positive because it opens the door to a way forward which avoids the Flaw of Averages trap. There are countless success stories of using this understanding.

So, when we need to cope with variation and we choose a passive resilience approach then we have to plan to the extremes of the range of variation. Sometimes that is not possible and we are forced to accept the likelihood of failure. Or we can consider a different approach.

Reactive resilience is one that living systems have evolved to use extensively, and is illustrated by the simple reflex loop shown in the diagram.

A reactive system has three components linked together – a sensor (i.e. temperature sensitive nerves endings in the skin), a processor (i.e. the grey matter of the spinal chord) and an effector (i.e. the muscle, ligaments and bones). So, when a pre-defined limit of variation is reached (e.g. the flame) then the protective reaction withdraws the finger before it becomes damaged. The advantage this type of reactive resilience is that it is relatively simple and relatively fast. The disadvantage is that it is not addressing the cause of the problem.

This is called reactive, automatic and agnostic.

The automatic self-regulating systems that we see in biology, and that we have emulated in our machines, are evidence of the effectiveness of a combination of passive and reactive resilience. It is good enough for most scenarios – so long as the context remains stable. The problem comes when the context is evolving, and in that case the automatic/reflex/blind/agnostic approach will fail – at some point.

Survival in an evolving context requires more – it requires proactive resilience.

What that means is that the processor component of the feedback loop gains an extra feature – a memory. The advantage this brings is that past experience can be recalled, reflected upon and used to guide future expectation and future behaviour. We can listen and learn and become proactive. We can look ahead and we can keep up with our evolving context. One might call, this reactive adaptation or co-evolution and it is a widely observed phenomenon in nature.

The usual manifestation is this called competition.

Those who can reactively adapt faster and more effectively than others have a better chance of not failing – i.e. a better chance of survival. The traditional term for this is survival of the fittest but the trendier term for proactive resilience is agile.

And that is what successful organisations are learning to do. They are adding a layer of proactive resilience on top of their reactive resilience and their passive resilience.

All three layers of resilience are required to survive in an evolving context.

One manifestation of this is the concept of design which is where we create things with the required resilience before they are needed. This is illustrated by the design squiggle which has time running left to right and shows the design evolving adaptively until there is sufficient clarity to implement and possibly automate.

And one interesting thing about design is that it can be done without an understanding of how something works – just knowing what works is enough. The elegant and durable medieval cathedrals were designed and built by Master builders who had no formal education. They learned the heuristics as apprentices and through experience.

And if we project the word game forwards we might anticipate a form of resilience called proactive adaptation. However, we sense that is a novel thing because there is no proadaptive word in the dictionary.

PS. We might also use the term Anti-Fragile, which is the name of a thought-provoking book that explores this very topic.

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Sting in the Tail

Monday 19th July 2021 was the official end of COVID-19 restrictions in England – yet the number of positive tests, hospital admissions and deaths is rising. “How can that make any sense!” wail the doom mongers. Is it irresponsible? Are we destined for a deadly third wave? Is a nasty sting in the tail on the way?

To address these questions we need to step back and look at the bigger picture.

As we have seen, the evolution of the COVID-19 pandemic has been tricky to predict because the virus and the host have been co-evolving. The host has implemented social distancing and developed vaccines to attenuate the viral spread and illness severity. The virus has mutated and more contagious variants have emerged as the dominant players.

And trying to work out how all these factors combine together is beyond the computational ability of the 1.4 kg of chimpware between our ears. Our intuition is confounded by the counter-intuitive complexity. We need help.

Here is the published data … the orange line is the daily reported positive COVID tests and the red dotted line is the daily reported COVID deaths. There is a clear temporal association but the size of the peaks don’t seem to make sense – even when we note that the test and death lines are plotted on very different scales.

One problem here is that the number of positive tests reported is very dependent on the testing process. In the first wave only hospital admissions were tested; in the second wave there was much more community-based testing of symptomatic people; and now many people are self-testing regularly to provide evidence of wellness.

The only way to unravel this Gordian Knot of interacting influences is to use the data to build and calibrate a causal structure model (CSM). Conventional statistical analysis is not up to the job because it conflates association and causation. We need something which is able to provide a diagnosis and a prognosis. Something that can use the past to help predict the future.

The blue line in the chart below is the output of a CSM that has been designed using proven principles of epidemic dynamics, and calibrated using historical data. And it predicts that there is indeed a third wave underway and that it is minor in comparison with the first two in terms of the predicted mortality.

The emergence of a third wave is the combined effect of three things:
a) The relaxing of social distancing rules.
b) The emergence and spread of more contagious variants of the virus.
c) The known fact that the vaccine is not 100% effective.
d) The known fact that immunity after illness or vaccination will wane with time.

One use of a CSM is to conduct counterfactual analysis which helps us to deepen our understanding of how complex systems behave. These are called “What would have happened if?” experiments.

One such experiment is “What would have happened if the vaccine was completely effective?“

Here is the CSM prediction for a 100% effective vaccine: The first and second waves were the same because the vaccination programme did not start until the peak of the second wave – and there is no third wave even with complete relaxation of social distancing.

But the actual data disproves this causal hypothesis because there is a third wave developing.

So, here is the CSM prediction for a 0% effective vaccine: The first and second waves are largely unchanged and now we have a third wave as bad as the second. A nasty sting in the tail.

But then the epidemic fizzles out because all the host “fuel” of susceptible people has been used up.

Setting the aggregate effectiveness of the vaccine to 75% gives us the best fit to the historical data; and that value is consistent with the pilot studies of vaccine effectiveness.

And what is the most useful evidence that suggests this latest prediction is reliable? It is that the infection rate is predicted to be falling already, despite distancing rules being relaxed, and that is what the data is showing.

And with this re-calibrated CSM we can estimate the impact of the vaccination programme in terms of lives saved … at it comes out at about 40,000 people! That is a lot.

So what next?

Well, we know that immunity will wane with time, and we know that new viral variants will emerge, and we know that coronavirus will be with us for the foreseeable future at a background level.

And we have seen how this pandemic has exposed the vulnerabilities of our current socioeconomic systems – health and social care, education, transport, communication, commerce and so on. Every part of the system has been affected because everything is interconnected.

We cannot just go back to business as usual. The world has been changed. And our immediate challenge is to redesign and rebuild a health care system that is safer, more efficient and more agile and that will serve us better in the future.

Another lesson learned is just how useful systems engineering theory, tools and techniques has been – the CSM demonstrated above is a standard systems engineering technique.

So, we will need some more health care systems engineers. A lot more. And they will need to be embedded at all levels in the NHS as an integral part of the system.

A self-healing health care system.

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The Crystal Ball

A crystal ball or orbuculum is a crystal or glass ball and is associated with the performance of clairvoyance and the ability to predict future events.

Before the modern era, those who claimed to be able to see the future were treated with suspicion and branded as alchemists, magicians and heretics.

Nowadays we take it for granted that the weather can be predicted with surprising accuracy for a few days at least – certainly long enough to influence our decisions.

And weather forecasting is a notoriously tricky challenge because small causes can have big effects – and big causes can have no effects.  The reason for this is that weather forecasting is called a nonlinear problem and to solve it we have had to resort to using sophisticated computer simulations run on powerful computers.

In contrast, predicting the course of the COVID-19 epidemic is a walk in the park.   It too is a nonlinear problem but much a less complicated one that can be solved using a simple computer simulation on a basic laptop.

The way it is done is to use the equations that describe how epidemics work (which have been known for nearly 100 years) and then use the emerging data to calibrate the model, so over time it gets more accurate.

Here’s what it looks like for COVID-19 associated mortality in the UK.  The red dotted line is the reported data and the oscillation is caused by the reporting process with weekend delays.  The solid red line is the same data with the 7-day oscillation filtered out to reveal the true pattern.  The blue line is the prediction made my the model.

And we can see how accurate the prediction is, especially since the peak of the third wave.

What this chart does not show is the restrictions being gradually lifted and completely removed by April 2020.

The COVID Crystal Ball says it will be OK so long as nothing unexpected happens – like a new variation that evades our immune systems, or even a new bug completely.

It has been a tough year.  We have learned a lot through hardship and heroism and that a random act of nature can swat us like an annoying fly.

So, perhaps our sense of hope should be tempered with some humility because the chart above did not need to look like that.  We have the knowledge, tools and skills to to better.  We have lots of Crystal Balls.

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Second Wave

The summer holidays are over and schools are open again – sort of.

Restaurants, pubs and nightclubs are open again – sort of.

Gyms and leisure facilities are open again – sort of.

And after two months of gradual easing of social restrictions and massive expansion of test-and-trace we now have the spectre of a Second Wave looming.  It has happened in Australia, Italy, Spain and France so it can happen here.

As usual, the UK media are hyping up the general hysteria and we now also have rioting disbelievers claiming it is all a conspiracy and that re-applying local restrictions is an infringement of their liberty.

So, what is all the fuss about?

We need to side-step the gossip and get some hard data from a reliable source (i.e. not a newspaper). Here is what worldometer is sharing …

OMG!  It looks like The Second Wave is here already!  There are already as many cases now as in March and we still have the mantra “Stay At Home – Protect the NHS – Save Lives” ringing in our ears.  But something is not quite right.  No one is shouting that hospitals are bursting at the seams.  No one is reporting that the mortuaries are filling up.  Something is different.  What is going on?  We need more data.That is odd!  We can clearly see that cases and deaths went hand-in-hand in the First Wave with about 1:5 cases not making it.  But this time the deaths are not rising with the cases.

Ah ha!  Maybe that is because the virus has mutated into something much more benign and because we have got much better at diagnosing and treating this illness – the ventilators and steroids saved the day.  Hurrah!  It’s all a big fuss about nothing … we should still be able to have friends round for parties and go on pub crawls again!

But … what if there was a different explanation for the patterns on the charts above?

It is said that “data without context is meaningless” … and I’d go further than that … data without context is dangerous because if it leads to invalid conclusions and inappropriate decisions we can get well-intended actions that cause unintended harm.  Death.

So, we need to check the context of the data.

In the First Wave the availability of the antigen (swab) test was limited so it was only available to hospitals and the “daily new cases” were in patients admitted to hospital – the ones with severe enough symptoms to get through the NHS 111 telephone triage.  Most people with symptoms, even really bad ones, stayed at home to protect the NHS.  They didn’t appear in the statistics.

But did the collective sacrifice of our social lives save actual lives?

The original estimates of the plausible death toll in the UK ranged up to 500,000 from coronavirus alone (and no one knows how many more from the collateral effects of an overwhelmed NHS).  The COVID-19 body count to date is just under 50000, so putting a positive spin on that tragic statistic, 90% of the potential deaths were prevented.  The lock-down worked.  The NHS did not collapse.  The Nightingales stood ready and idle – an expensive insurance policy.  Lives were actually saved.

Why isn’t that being talked about?

And the context changed in another important way.  The antigen testing capacity was scaled up despite being mired in confusing jargon.  Who thought up the idea of calling them “pillars”?

But, if we dig about on the GOV.UK website long enough there is a definition:

So, Pillar 1 = NHS testing capacity Pillar 2 = commercial testing capacity and we don’t actually know how much was in-hospital testing and how much was in-community testing because the definitions seem to reflect budgets rather than patients.  Ever has it been thus in the NHS!

However, we can see from the chart below that testing activity (blue bars) has increased many-fold but the two testing streams (in hospital and outside hospital) are combined in one chart.  Well, it is one big pot of tax-payers cash after all and it is the same test.

To unravel this a bit we have to dig into the website, download the raw data, and plot it ourselves.  Looking at Pillar 2 (commercial) we can see they had a late start, caught the tail of the First Wave, and then ramped up activity as the population testing caught up with the available capacity (because hospital activity has been falling since late April).

Now we can see that the increased number of positive tests could be explained by the fact that we are now testing anyone with possible COVID-19 symptoms who steps up – mainly in the community.  And we were unable to do this before because the testing capacity did not exist.

The important message is that in the First Wave we were not measuring what was happening in the community – it was happening though – it must have been.  We measured the knock on effects: hospital admissions with positive tests and deaths after positive tests.

So, to present the daily positive tests as one time-series chart that conflates both ‘pillars’ is both meaningless and dangerous and it is no surprise that people are confused.

This raises a question: “Can we estimate how many people there would have been in the community in the First Wave so that we can get a sense of what the rising positive test rate means now?“

The way that epidemiologists do this is to build a generic simulation of the system dynamics of an epidemic (a SEIR multi-compartment model) and then use the measured data to calibrate the this model so that it can then be used for specific prediction and planning.

Here is an example of the output of a calibrated multi-compartment system dynamics model of the UK COVID-19 epidemic for a nominal 1.3 million population.  The compartments that are included are Susceptible, Exposed, Infectious, and Recovered (i.e. not infectious) and this model also simulates the severity of the illness i.e. Severe (in hospital), Critical (in ITU) and Died.

The difference in size of the various compartments is so great that the graph below requires two scales – the solid line (Infectious) is plotted on the left hand scale and the others are plotted on the right hand scale which is 10 times smaller.  The green line is today and the reported data up to that point has been used to calibrate the model and to estimate the historical metrics that we did not measure – such as how many people in the community were infectious (and would have tested positive).

At the peak of the First Wave, for this population of 1.3 million, the model estimates there were about 800 patients in hospital (which there were) and 24,000 patients in the community who would have tested positive if we had been able to test them.  24,000/800 = 30 which means the peak of the grey line is 30 x higher than the peak of the orange line – hence the need for the two Y-axes with a 10-fold difference in scale.

Note the very rapid rise in the number of infectious people from the beginning of March when the first UK death was announced, before the global pandemic was declared and before the UK lock-down was enacted in law and implemented.  Coronavirus was already spreading very rapidly.

Note how this rapid rise in the number of infectious people came to an abrupt halt when the UK lock-down was put into place in the third week of March 2020.  Social distancing breaks the chain of transmission from one infectious person to many other susceptible ones.

Note how the peaks of hospital admissions, critical care admissions and deaths lag after the rise in infectious people (because it takes time for the coronavirus to do its damage) and how each peak is smaller (because only about 1:30 get sick enough to need admission, and only 1:5 of hospital admissions do not survive.

Note how the fall in the infectious group was more gradual than the rise (because the lock-down was partial,  because not everyone could stay at home (essential services like the NHS had to continue), and because there was already a big pool of infectious people in the community.

So, by early July 2020 it was possible to start a gradual relaxation of the lock down and from then we can see a gradual rise in infectious people again.  But now we were measuring them because of the growing capacity to perform antigen tests in the community.  The relatively low level and the relatively slow rise are much less dramatic than what was happening in March (because of the higher awareness and the continued social distancing and use of face coverings).  But it is all too easy to become impatient and complacent.

But by early September 2020 it was clear that the number on infectious people was growing faster in the community – and then we saw hospital admissions reach a minimum and start to rise again.  And then the number if deaths reach a minimum and start to rise again.  And this evidence proves that the current level of social distancing is not enough to keep a lid on this disease.  We are in the foothills of a Second Wave.

So what do we do next?

First, we must estimate the effect that the current social distancing policies are having and one way to do that would be to stop doing them and see what happens.  Clearly that is not an ethical experiment to perform given what we already know.  But, we can simulate that experiment using our calibrated SEIR model.  Here is what is predicted to happen if we went back to the pre-lockdown behaviours: There would be a very rapid spread of the virus followed by a Second Wave that would be many times bigger than the first!!  Then it would burn itself out and those who had survived could go back to some semblance of normality.  The human sacrifice would be considerable though.

So, despite the problems that the current social distancing is causing, they pale into insignificance compared to what could happen if they were dropped.

The previous model shows what is predicted would happen if we continue as we are with no further easing of restrictions and assuming people stick to them.  In short, we will have COVID-for-Christmas and it could be a very nasty business indeed as it would come at the same time as other winter-associated infectious diseases such as influenza and norovirus.

The next chart shows what could happen if we squeeze the social distancing brake a bit harder by focusing only on the behaviours that the track-and-trace-and-test system is highlighting as the key drivers of the growth infections, admissions and deaths.

What we see is an arrest of the rise of the number of infectious people (as we saw before), a small and not sustained increase in hospital admissions, then a slow decline back to the levels that were achieved in early July – and at which point it would be reasonable to have a more normal Christmas.

And another potential benefit of a bit more social distancing might be a much less problematic annual flu epidemic because that virus would also find it harder to spread – plus we have a flu vaccination which we can use to reduce that risk further.

It is not going to be easy.  We will have to sacrifice a bit of face-to-face social life for a bit longer.  We will have to measure, monitor, model and tweak the plan as we go.

And one thing we can do immediately is to share the available information in a more informative and less histrionic way than we are seeing at the moment.

Update: Sunday 1st November 2020

Yesterday the Government had to concede that the policy of regional restrictions had failed and bluffing it out and ignoring the scientific advice was, with the clarity of hindsight, an unwise strategy.

In the face of the hard evidence of rapidly rising COVID+ve hospital admissions and deaths, the decision to re-impose a national 4-week lock-down was announced.  This is the only realistic option to prevent overwhelming the NHS at a time of year that it struggles with seasonal influenza causing a peak of admissions and deaths.

Paradoxically, this year the effect of influenza may be less because social distancing will reduce the spread of that as well and also because there is a vaccination for influenza.  Many will have had their flu jab early … I certainly did.

So, what is the predicted effect of a 4 week lock down?  Well, the calibrated model (also used to generate the charts above) estimates that it could indeed suppress the Second Wave and mitigate a nasty COVID-4-Christmas scenario.  But even with it the hospital admissions and associated mortality will continue to increase until the effect kicks in.

Brace yourselves.

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Coronavirus


The start of a new year, decade, century or millennium is always associated with a sense of renewal and hope.  Little did we know that in January 2020 a global threat had hatched and was growing in the city of Wuhan, Hubei Province, China.  A virus of the family coronaviridae had mutated and jumped from animal to man where it found a new host and a vehicle to spread itself.   Several weeks later the World became aware of the new threat and in the West … we ignored it.  Maybe we still remember the SARS epidemic which was heralded as a potential global catastrophe but was contained in the Far East and fizzled out.  So, maybe we assumed this SARS-like virus would do the same.

It didn’t.  This mutant was different.  It caused a milder illness and unwitting victims were infectious before they were symptomatic.  And most got better on their own, so they spread the mutant to many other people.  Combine that mutant behaviour with the winter (when infectious diseases spread more easily because we spend more time together indoors), Chinese New Year and global air travel … and we have the perfect recipe for cooking up a global pandemic of a new infectious disease.  But we didn’t know that at the time and we carried on as normal, blissfully unaware of the catastrophe that was unfolding.

By February 2020 it became apparent that the mutant had escaped containment in China and was wreaking havoc in other countries – with Italy high on the casualty list.  We watched in horror at the scenes on television of Italian hospitals overwhelmed with severely ill people fighting for breath as the virus attacked their lungs.  The death toll rose sharply but we still went on our ski holidays and assumed that the English Channel and our Quarantine Policy would protect us.

They didn’t.  This mutant was different.  We now know that it had already silently gained access into the UK and was growing and spreading.  The first COVID-19 death reported in the UK was in early March 2020 and only then did we sit up and start to take notice.  This was getting too close to home.

But it was too late.  The mathematics of how epidemics spread was worked out 100 years ago, not long after the 1918 pandemic of Spanish Flu that killed tens of millions of people before it burned itself out.  An epidemic is like cancer.  By the time it is obvious it is already far advanced because the growth is not linear – it is exponential.

As a systems engineer I am used to building simulation models to reveal the complex and counter-intuitive behaviour of nonlinear systems using the methods first developed by Jay W. Forrester in the 1950’s.  And when I looked up the equations that describe epidemics (on Wikipedia) I saw that I could build a system dynamics model of a COVID-19 epidemic using no more than an Excel spreadsheet.

So I did.  And I got a nasty surprise.  Using the data emerging from China on the nature of the spread of the mutant virus, the incidence of severe illness and the mortality rate … my simple Excel model predicted that, if COVID-19 was left to run its natural course in the UK, then it would burn itself out over several months but the human cost would be 500,000 deaths and the NHS would be completely overwhelmed with a “tsunami of sick”.  And I could be one of them!  The fact that there is no treatment and no vaccine for this novel threat excluded those options.  My basic Excel model confirmed that the only effective option to mitigate this imminent catastrophe was to limit the spread of the virus through social engineering i.e. an immediate and drastic lock-down.  Everyone who was not essential to maintaining core services should “Stay at home, Protect the NHS and Save lives“.  That would become the mantra.  And others were already saying this – epidemiologists whose careers are spent planning for this sort of eventuality.  But despite all this there still seemed to be little sense of urgency, perhaps because their super-sophisticated models predicted that the peak of the UK epidemic would be in mid-June so there was time to prepare.  My basic model predicted that the peak would be in mid-April, in about 4 weeks, and that it was already too late to prevent about 50,000 deaths.

It turns out I was right.  That is exactly what happened.  By mid-March 2020 London was already seeing an exponential rise in hospital admissions, intensive care admissions and deaths and suddenly the UK woke up and panicked.  By that time I had enlisted the help of a trusted colleague who is a public health doctor and who had studied epidemiology, and together we wrote up and published the emerging story as we saw it:

An Acute Hospital Demand Surge Planning Model for the COVID-19 Epidemic using Stock-and-Flow Simulation in Excel: Part 1. Journal of Improvement Science 2020: 68; 1-20.  The link to download the full paper is here.

I also shared the draft paper with another trusted friend and colleague who works for my local clinical commissioning group (CCG) and I asked “Has the CCG a sense of the speed and magnitude of what is about to happen and has it prepared for the tsunami of sick that primary care will need to see?“

What then ensued was an almost miraculous emergence of a coordinated and committed team of health care professionals and NHS managers with a single, crystal clear goal:  To design, build and deliver a high-flow, drive-through community-based facility to safely see-and-assess hundreds of patients per day with suspected COVID-19 who were too sick/worried to be managed on the phone, but not sick enough to go to A&E.  This was not a Nightingale Ward – that was a parallel, more public and much more expensive endeavour designed as a spillover for overwhelmed acute hospitals.  Our purpose was to help to prevent that and the time scale was short.  We had three weeks to do it because Easter weekend was the predicted peak of the COVID-19 surge if the national lock-down policy worked as hoped.  No one really had an accurate estimate how effective the lock-down would be and how big the peak of the tsunami of sick would rise as it crashed into the NHS.  So, we planned for the worst and hoped for the best.  The Covid Referral Centre (CRC) was an insurance policy and we deliberately over-engineered it use to every scrap of space we had been offered in a small car park on the south side of the NEC site.

The CRC needed to open by Sunday 12th April 2020 and we were ready, but the actual opening was delayed by NHS bureaucracy and politics.  It did eventually open on 22nd April 2020, just four weeks after we started, and it worked exactly as designed.  The demand was, fortunately, less than our worst case scenario; partly because we had missed the peak by 10 days and we opened the gates to a falling tide; and partly because the social distancing policy had been more effective than hoped; and partly because it takes time for risk-averse doctors to develop trust and to change their ingrained patterns of working.  A drive-thru COVID-19 see-and-treat facility? That was innovative and untested!!

The CRC expected to see a falling demand as the first wave of COVID-19 washed over, and that exactly is what happened.  So, as soon as that prediction was confirmed, the CRC was progressively repurposed to provide other much needed services such as drive-thru blood tests, drive-thru urgent care, and even outpatient clinics in the indoor part of the facility.

The CRC closed its gates to suspected COVID-19 patients on 31st July 2020, as planned and as guided by the simple Excel computer model.

This is health care systems engineering in action.

And the simple Excel model has been continuously re-calibrated as fresh evidence has emerged.  The latest version predicts that a second peak of COVID-19 (that is potentially worse than the first) will happen in late summer or autumn if social distancing is relaxed too far (see below).

But we don’t know what “too far” looks like in practical terms.  Oh, and a second wave could kick off just just when we expect the annual wave of seasonal influenza to arrive.  Or will it?  Maybe the effect of social distancing for COVID-19 in other countries will suppress the spread of seasonal flu as well?  We don’t know that either but the data of the incidence of flu from Australia certainly supports that hypothesis.

We may need a bit more health care systems engineering in the coming months. We shall see.

Oh, and if we are complacent enough to think a second wave could never happen in the UK … here is what is happening in Australia.

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A New Decade of Hope

At the end of the decade it is the time to reflect on what has happened in the past before planning for the future.  As always, the hottest topic in health care is the status of the emergency care services, and we have the data – it is public.

This shows the last 9 years of aggregate, monthly data for Scotland (red), England (blue), Wales (teal) and N.Ireland (orange).  It does not take a data scientist and a supercomputer to interpret – there is a progressive system-wide progressive deterioration year-on-year.  The winter dips are obvious and the worst of these affect all four countries indicating a systemic cause … the severity of the winter weather/illness cycle -i.e. the Flu Season.

What this chart also says is that all the effort and money being expended in winter planning is not working well enough – and the nagging question is “Why not?”

Many claim that it is the predicted demographic “time bomb” … but if it is predicted then how come it has not been mitigated?

Many claim that it is a growing funding gap … but most NHS funding is spent on staff and  and training nurses, doctors and allied health professionals (AHPs) takes time.  Again, a predicted eventuality that has not been mitigated.

This looming crisis in a lack of heath care workers is a global health challenge … and is described by Mark Britnell in “Human – Solving the global workforce crisis in healthcare“.

Mark was the CEO of University Hospitals Birmingham from 2000 and has worked for KPMG since 2009 in a global health role so is well placed to present a strategic overview.

But, health care workers deliver care to patients – one at a time.  They are not responsible for designing the system of health care delivery; or ensuring all the pieces of that vast jigsaw link up and work in a synchronised way; or for the long term planning needed to mitigate the predictable effects of demographic drift and technology advances.

Who is responsible for that challenge and are they adequately trained to do it?

The evidence would appear to suggest that there is a gap that either no one has noticed or that no one is prepared to discuss.  An Undiscussable?

The global gap in the healthcare workforce is predicted to be about 20% by 2030.  That is a big gap to fill because with the NHS workforce of 1.3 million people – that implies training 260,000 new staff of all types in the next 10 years, in addition to replacing those that leave.

Assuming the processes and productivity stay as they are now.

So, perhaps there is a parallel approach, one that works more quickly and a lower cost.

When current health care processes are examined through a flow engineering lens they are found to be poorly designed. They are both ineffective (do not reliably deliver the intended outcome) and inefficient (waste a lot of resources in delivering any outcome).  Further examination reveals that the processes have never been designed … they have evolved.

And just because something is described as current practice does not prove that it is good design.

An expected symptom of a poorly designed process is a combination of chronic queues, delays, chaos, reactive fire-fighting and burnout.  And the assumed cause is often lack of resources because when extra resource is added the queues and chaos subsides, for a while.

But, if the unintentional poor design of the process is addressed then a sequence of surprising things can happen. The chaos evaporates immediately without any extra resources. A feeling of calm is restored and the disruptive fire-fighting stops. The health care workers are able to focus on what they do best and pride-in-work is restored. Patient experience improves and staff feel that feedback and become more motivated. The complaining abates, sickness and absence falls, funded-but-hard-to-recruit-to posts are refilled and there are more hands on the handle of a more efficient/effective/productive pump.  The chronic queues and delays start to melt away – as if by magic.

And if that all sounds totally impossible then here are a couple of recent, real-world case studies written by different teams in different cities in different parts of the UK.  One from cancer care and one from complex diabetic care.

They confirm that this chaos-to-calm transformation is possible.

So, is there a common thread that links these two examples?

Yes, there is, and once again the spotlight is shone on the Undiscussable Gap … the fact that the NHS does not appear to have the embedded capability to redesign itself.

There is a hidden workforce gap that none of the existing programmes will address – because it is not a lack of health care workers – it is a lack of appropriately trained health care manager-designers.

The Undiscussable Elephant Is In The Room … the Undiscussable Emperor Has No Clothes.

And if history teaches us anything, Necessity is the Mother of Innovation and the chart at the top of the page shows starkly that there is an Growing Urgent Necessity.

And if two embedded teams can learn this magic trick of flipping chaos into calm at no cost, then perhaps others can too?

Welcome to the New Decade of Hope and Health Care Systems Engineering.

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Measuring Chaos

One of the big hurdles in health care improvement is that most of the low hanging fruit have been harvested.

These are the small improvement projects that can be done quickly because as soon as the issue is made visible to the stakeholders the cause is obvious and the solution is too.

This is where kaizen works well.

The problem is that many health care issues are rather more difficult because the process that needs improving is complicated (i.e. it has lots of interacting parts) and usually exhibits rather complex behaviour (e.g. chaotic).

One good example of this is a one stop multidisciplinary clinic.

These are widely used in healthcare and for good reason.  It is better for a patient with a complex illness, such as diabetes, to be able to access whatever specialist assessment and advice they need when they need it … i.e. in an outpatient clinic.

The multi-disciplinary team (MDT) is more effective and efficient when it can problem-solve collaboratively.

The problem is that the scheduling design of a one stop clinic is rather trickier than a traditional simple-but-slow-and-sequential new-review-refer design.

A one stop clinic that has not been well-designed feels chaotic and stressful for both staff and patients and usually exhibits the paradoxical behaviour of waiting patients and waiting staff.

So what do we need to do?

We need to map and measure the process and diagnose the root cause of the chaos, and then treat it.  A quick kaizen exercise should do the trick. Yes?

But how do we map and measure the chaotic behaviour of lots of specialists buzzing around like blue-***** flies trying to fix the emergent clinical and operational problems on the hoof?  This is not the linear, deterministic, predictable, standardised machine-dominated production line environment where kaizen evolved.

One approach might be to get the staff to audit what they are doing as they do it. But that adds extra work, usually makes the chaos worse, fuels frustration and results in a very patchy set of data.

Another approach is to employ a small army of observers who record what happens, as it happens.  This is possible and it works, but to be able to do this well requires a lot of experience of the process being observed.  And even if that is achieved the next barrier is the onerous task of transcribing and analysing the ocean of harvested data.  And then the challenge of feeding back the results much later … i.e. when the sands have shifted.

So we need a different approach … one that is able to capture the fine detail of a complex process in real-time, with minimal impact on the process itself, and that can process and present the wealth of data in a visual easy-to-assess format, and in real-time too.

This is a really tough design challenge …
… and it has just been solved.

Here are two recent case studies that describe how it was done using a robust systems engineering method.

Abstract

Abstract

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Reflect and Celebrate

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.

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Making NHS Data Count

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

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The Awareness Ability Gap

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 “incompetent” felt too judgemental.

The idea is that when we learn, we all start from a position of being unaware of our inability.  We don’t know what we don’t know.

This state is 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 ask, listen, reflect, learn, 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 Zone of Known Known.

The final phase comes when our ability becomes so habitual that we forget how we achieve our skill – it has become so intuitive and 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.

The multi-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 grief reaction 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 (a) this is a normal and expected process and (b) we can get stuck along the path of transition.  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 emotional 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.

But that sense is, paradoxically, associated with the steepest part of the learning curve.  It is almost as it there is a piece of emotional elastic linking the blue and green lines and how we feel is related to how much it is being stretched and in what direction.

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

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Simulation Stimulation

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!

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Eating the Elephant in the Room

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.

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The Checklist

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.

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Catch-22

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.

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The Chicken Coop

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!

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Diagnose-Design-Deliver

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.

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Levels of Resistance

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?

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Hugh, Louise and Bob

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.

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Streeeeeetch!

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.

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The Power of Pictures

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.

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The Lost Tribe

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 it 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!

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Pride and Joy

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.

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Value, Verify and Validate

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 Krarup 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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Socrates the Improvement Coach

One of the challenges involved in learning the science of improvement, is to be able to examine our own beliefs.

We need to do that to identify the invalid assumptions that lead us to make poor decisions, and to act in ways that push us off the path to our intended outcome.

Over two thousand years ago, a Greek philosopher developed a way of exposing invalid assumptions.  He was called Socrates.

The Socratic method involves a series of questions that are posed to help a person or group to determine their underlying beliefs and the extent of their knowledge.  It is a way to develop better hypotheses by steadily identifying and eliminating those that lead to contradictions.

Socrates designed his method to force one to examine one’s own beliefs and the validity of such beliefs.

That skill is as valuable today as it was then, and is especially valuable when we explore complex subjects,  such as improving the performance of our health and social care system.

Our current approach is called reactive improvement – and we are reacting to failure.

Reactive improvement zealots seem obsessed with getting away from failure, disappointment, frustration, fear, waste, variation, errors, cost etc. in the belief that what remains after the dross has been removed is the good stuff. The golden nuggets.

And there is nothing wrong with that.

It has a couple of downsides though:

  1. Removing dross leaves holes, that all too easily fill up with different dross!
  2. Reactive improvement needs a big enough problem to drive it.  A crisis!

The implication is that reactive improvement grinds to a halt as the pressure is relieved and as it becomes mired in a different form of bureaucratic dross … the Quality Control Inspectorate!

No wonder we feel as if we are trapped in a perpetual state of chronic and chaotic mediocrity.

Creative improvement is, as the name suggests, focused on creating something that we want in the future.  Something like a health and social care system that is safe, calm, fit-4-purpose, and affordable.

Creative improvement does not need a problem to get started. A compelling vision and a choice to make-it-so is enough.

Creative improvement does not fizzle out as soon as we improve… because our future vision is always there to pull us forward.  And the more we practice creative improvement, the better we get, the more progress we make, and the stronger the pull becomes.

The main thing that blocks us from using creative improvement are our invalid, unconscious beliefs and assumptions about what is preventing us achieving our vision now.

So we need a way to examine our beliefs and assumptions in a disciplined and robust way, and that is the legacy that Socrates left us.

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Righteous Indignation

On 5th July 2018, the NHS will be 70 years old, and like many of those it was created to serve, it has become elderly and frail.

We live much longer, on average, than we used to and the growing population of frail elderly are presenting an unprecedented health and social care challenge that the NHS was never designed to manage.

The creases and cracks are showing, and each year feels more pressured than the last.

This week a story that illustrates this challenge was shared with me along with permission to broadcast …

“My mother-in-law is 91, in general she is amazingly self-sufficient, able to arrange most of her life with reasonable care at home via a council tendered care provider.

She has had Parkinson’s for years, needing regular medication to enable her to walk and eat (it affects her jaw and swallowing capability). So the care provision is time critical, to get up, have lunch, have tea and get to bed.

She’s also going deaf, profoundly in one ear, pretty bad in the other. She wears a single ‘in-ear’ aid, which has a micro-switch on/off toggle, far too small for her to see or operate. Most of the carers can’t put it in, and fail to switch it off.

Her care package is well drafted, but rarely adhered to. It should be 45 minutes in the morning, 30, 15, 30 through the day. Each time administering the medications from the dossette box. Despite the register in/out process from the carers, many visits are far less time than designed (and paid for by the council), with some lasting 8 minutes instead of 30!

Most carers don’t ensure she takes her meds, which sometimes leads to dropped pills on the floor, with no hope of picking them up!

While the care is supposedly ‘time critical’ the provider don’t manage it via allocated time slots, they simply provide lists, that imply the order of work, but don’t make it clear. My mother-in-law (Mum) cannot be certain when the visit will occur, which makes going out very difficult.

The carers won’t cook food, but will micro-wave it, thus if a cooked meal is to happen, my Mum will start it, with the view of the carers serving it. If they arrive early, the food is under-cooked (“Just put vinegar on it, it will taste better”) and if they arrive late, either she’ll try to get it out herself, or it will be dried out / cremated.

Her medication pattern should be every 4 to 5 hours in the day, with a 11:40 lunch visit, and a 17:45 tea visit, followed by a 19:30 bed prep visit, she finishes up with too long between meds, followed by far too close together. Her GP has stated that this is making her health and Parkinson’s worse.

Mum also rarely drinks enough through the day, in the hot whether she tends to dehydrate, which we try to persuade her must be avoided. Part of the problem is Parkinson’s related, part the hassle of getting to the toilet more often. Parkinson’s affects swallowing, so she tends to sip, rather than gulp. By sipping often, she deludes herself that she is drinking enough.

She also is stubbornly not adjusting methods to align to issues. She drinks tea and water from her lovely bone china cups. Because her grip is not good and her hand shakes, we can’t fill those cups very high, so her ‘cup of tea’ is only a fraction of what it could be.

As she can walk around most days, there’s no way of telling whether she drinks enough, and she frequently has several different carers in a day.

When Mum gets dehydrated, it affects her memory and her reasoning, similar to the onset of dementia. It also seems to increase her probability of falling, perhaps due to forgetting to be defensive.

When she falls, she cannot get up, thus usually presses her alarm dongle, resulting in me going round to get her up, check for concussion, and check for other injuries, prior to settling her down again. These can be ten weeks apart, through to a few in a week.

When she starts to hallucinate, we do our very best to increase drinking, seeking to re-hydrate.

On Sunday, something exceptional happened, Mum fell out of bed and didn’t press her alarm. The carer found her and immediately called the paramedics and her GP, who later called us in. For the first time ever she was not sufficiently mentally alert to press her alarm switch.

After initial assessment, she was taken to A&E, luckily being early on Sunday morning it was initially quite quiet.

Hospital

The Hospital is on the boundary between two counties, within a large town, a mixture of new build elements, between aging structures. There has been considerable investment within A&E, X-ray etc. due partly to that growth industry and partly due to the closures of cottage hospitals and reducing GP services out of hours.

It took some persuasion to have Mum put on a drip, as she hadn’t had breakfast or any fluids, and dehydration was a probable primary cause of her visit. They took bloods, an X-ray of her chest (to check for fall related damage) and a CT scan of her head, to see if there were issues.

I called the carers to tell them to suspend visits, but the phone simply rang without be answered (not for the first time.)

After about six hours, during which time she was awake, but not very lucid, she was transferred to the day ward, where after assessment she was given some meds, a sandwich and another drip.

Later that evening we were informed she was to be kept on a drip for 24 hours.

The next day (Bank Holiday Monday) she was transferred to another ward. When we arrived she was not on a drip, so their decisions had been reversed.

I spoke at length with her assigned staff nurse, and was told the following: Mum could come out soon if she had a 24/7 care package, and that as well as the known issues mum now has COPD. When I asked her what COPD was, she clearly didn’t know, but flustered a ‘it is a form of heart failure that affects breathing’. (I looked it up on my phone a few minutes later.)

So, to get mum out, I had to arrange a 24/7 care package, and nowhere was open until the next day.

Trying to escalate care isn’t going to be easy, even in the short term. My emails to ‘usually very good’ social care people achieved nothing to start with on Tuesday, and their phone was on the ‘out of hours’ setting for evenings and weekends, despite being during the day of a normal working week.

Eventually I was told that there would be nothing to achieve until the hospital processed the correct exit papers to Social Care.

When we went in to the hospital (on Tuesday) a more senior nurse was on duty. She explained that mum was now medically fit to leave hospital if care can be re-established. I told her that I was trying to set up 24/7 care as advised. She looked through the notes and said 24/7 care was not needed, the normal 4 x a day was enough. (She was clearly angry).

I then explained that the newly diagnosed COPD may be part of the problem, she said that she’s worked with COPD patients for 16 years, and mum definitely doesn’t have COPD. While she was amending the notes, I noticed that mum’s allergy to aspirin wasn’t there, despite us advising that on entry. The nurse also explained that as the hospital is in one county, but almost half their patients are from another, they are always stymied on ‘joined up working’

While we were talking with mum, her meds came round and she was only given paracetamol for her pain, but NOT her meds for Parkinson’s. I asked that nurse why that was the case, and she said that was not on her meds sheet. So I went back to the more senior nurse, she checked the meds as ordered and Parkinson’s was required 4 x a day, but it was NOT transferred onto the administration sheet. The doctor next to us said she would do it straight away, and I was told, “Thank God you are here to get this right!”

Mum was given her food, it consisted of some soup, which she couldn’t spoon due to lack of meds and a dry tough lump of gammon and some mashed sweet potato, which she couldn’t chew.

When I asked why meds were given at five, after the delivery of food, they said ‘That’s our system!’, when I suggested that administering Parkinson’s meds an hour before food would increase the ability to eat the food they said “that’s a really good idea, we should do that!”

On Wednesday I spoke with Social Care to try to re-start care to enable mum to get out. At that time the social worker could neither get through to the hospital nor the carers. We spoke again after I had arrived in hospital, but before I could do anything.

On arrival at the hospital I was amazed to see the white-board declaring that mum would be discharged for noon on Monday (in five days-time!). I spoke with the assigned staff nurse who said, “That’s the earliest that her carers can re-start, and anyway its nearly the weekend”.

I said that “mum was medically OK for discharge on Tuesday, after only two days in the hospital, and you are complacent to block the bed for another six days, have you spoken with the discharge team?”

She replied, “No they’ll have gone home by now, and I’ve not seen them all day” I told her that they work shifts, and that they will be here, and made it quite clear if she didn’t contact SHEDs that I’d go walkabout to find them. A few minutes later she told me a SHED member would be with me in 20 minutes.

While the hospital had resolved her medical issues, she was stuck in a ward, with no help to walk, the only TV via a complex pay-for system she had no hope of understanding, with no day room, so no entertainment, no exercise, just boredom encouraged to lay in bed, wear a pad because she won’t be taken to the loo in time.

When the SHED worker arrived I explained the staff nurse attitude, she said she would try to improve those thinking processes. She took lots of details, then said that so long as mum can walk with assistance, she could be released after noon, to have NHS carer support, 4 times a day, from the afternoon. She walked around the ward for the first time since being admitted, and while shaky was fine.

Hopefully all will be better now?”

This story is not exceptional … I have heard it many times from many people in many different parts of the UK.  It is the norm rather than the exception.

It is the story of a fragmented and fractured system of health and social care.

It is the story of frustration for everyone – patients, family, carers, NHS staff, commissioners, and tax-payers.  A fractured care system is unsafe, chaotic, frustrating and expensive.

There are no winners here.  It is not a trade off, compromise or best possible.

It is just poor system design.

What we want has a name … it is called a Frail Safe design … and this is not a new idea.  It is achievable. It has been achieved.

http://www.frailsafe.org.uk

So why is this still happening?

The reason is simple – the NHS does not know any other way.  It does not know how to design itself to be safe, calm, efficient, high quality and affordable.

It does not know how to do this because it has never learned that this is possible.

But it is possible to do, and it is possible to learn, and that learning does not take very long or cost very much.

And the return vastly outnumbers the investment.

The title of this blog is Righteous Indignation …

… if your frail elderly parents, relatives or friends were forced to endure a system that is far from frail safe; and you learned that this situation was avoidable and that a safer design would be less expensive; and all you hear is “can’t do” and “too busy” and “not enough money” and “not my job” …  wouldn’t you feel a sense of righteous indignation?

I do.

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Fragmentation Cost

figure_falling_with_arrow_17621The late Russell Ackoff used to tell a great story. It goes like this:

“A team set themselves the stretch goal of building the World’s Best Car.  So the put their heads together and came up with a plan.

First they talked to drivers and drew up a list of all the things that the World’s Best Car would need to have. Safety, speed, low fuel consumption, comfort, good looks, low emissions and so on.

Then they drew up a list of all the components that go into building a car. The engine, the wheels, the bodywork, the seats, and so on.

Then they set out on a quest … to search the world for the best components … and to bring the best one of each back.

Then they could build the World’s Best Car.

Or could they?

No.  All they built was a pile of incompatible parts. The WBC did not work. It was a futile exercise.

Then the penny dropped. The features in their wish-list were not associated with any of the separate parts. Their desired performance emerged from the way the parts worked together. The working relationships between the parts were as necessary as the parts themselves.

And a pile of average parts that work together will deliver a better performance than a pile of best parts that do not.

So the relationships were more important than the parts!

From this they learned that the quickest, easiest and cheapest way to degrade performance is to make working-well-together a bit more difficult.  Irrespective of the quality of the parts.

Q: So how do we reverse this degradation of performance?

A: Add more failure-avoidance targets of course!

But we just discovered that the performance is the effect of how the parts work well together?  Will another failure-metric-fueled performance target help? How will each part know what it needs to do differently – if anything?  How will each part know if the changes they have made are having the intended impact?

Fragmentation has a cost.  Fear, frustration, futility and ultimately financial failure.

So if performance is fading … the quality of the working relationships is a good place to look for opportunities for improvement.

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Notably Absent

KingsFund_Quality_Report_May_2016This week the King’s Fund published their Quality Monitoring Report for the NHS, and it makes depressing reading.

These highlights are a snapshot.

The website has some excellent interactive time-series charts that transform the deluge of data the NHS pumps out into pictures that tell a shameful story.

On almost all reported dimensions, things are getting worse and getting worse faster.

Which I do not believe is the intention.

But it is clearly the impact of the last 20 years of health and social care policy.

What is more worrying is the data that is notably absent from the King’s Fund QMR.

The first omission is outcome: How well did the NHS deliver on its intended purpose?  It is stated at the top of the NHS England web site …

NHSE_Purpose

And lets us be very clear here: dying, waiting, complaining, and over-spending are not measures of what we want: health and quality success metrics.  They are a measures of what we do not want; they are failure metrics.

The fanatical focus on failure is part of the hyper-competitive, risk-averse medical mindset:

primum non nocere (first do no harm),

and as a patient I am reassured to hear that but is no harm all I can expect?

What about:

tunc mederi (then do some healing)

And where is the data on dying in the Kings Fund QMR?

It seems to be notably absent.

And I would say that is a quality issue because it is something that patients are anxious about.  And that may be because they are given so much ‘open information’ about what might go wrong, not what should go right.

And you might think that sharp, objective data on dying would be easy to collect and to share.  After all, it is not conveniently fuzzy and subjective like satisfaction.

It is indeed mandatory to collect hospital mortality data, but sharing it seems to be a bit more of a problem.

The fear-of-failure fanaticism extends there too.  In the wake of humiliating, historical, catastrophic failures like Mid Staffs, all hospitals are monitored, measured and compared. And the negative deviants are named, shamed and blamed … in the hope that improvement might follow.

And to do the bench-marking we need to compare apples with apples; not peaches with lemons.  So we need to process the raw data to make it fair to compare; to ensure that factors known to be associated with higher risk of death are taken into account. Factors like age, urgency, co-morbidity and primary diagnosis.  Factors that are outside the circle-of-control of the hospitals themselves.

And there is an army of academics, statisticians, data processors, and analysts out there to help. The fruit of their hard work and dedication is called SHMI … the Summary Hospital Mortality Index.

SHMI_Specification

Now, the most interesting paragraph is the third one which outlines what raw data is fed in to building the risk-adjusted model.  The first four are objective, the last two are more subjective, especially the diagnosis grouping one.

The importance of this distinction comes down to human nature: if a hospital is failing on its SHMI then it has two options:
(a) to improve its policies and processes to improve outcomes, or
(b) to manipulate the diagnosis group data to reduce the SHMI score.

And the latter is much easier to do, it is called up-coding, and basically it involves camping at the pessimistic end of the diagnostic spectrum. And we are very comfortable with doing that in health care. We favour the Black Hat.

And when our patients do better than our pessimistically-biased prediction, then our SHMI score improves and we look better on the NHS funnel plot.

We do not have to do anything at all about actually improving the outcomes of the service we provide, which is handy because we cannot do that. We do not measure it!

And what might be notably absent from the data fed in to the SHMI risk-model?  Data that is objective and easy to measure.  Data such as length of stay (LOS) for example?

Is there a statistical reason that LOS is omitted? Not really. Any relevant metric is a contender for pumping into a risk-adjustment model.  And we all know that the sicker we are, the longer we stay in hospital, and the less likely we are to come out unharmed (or at all).  And avoidable errors create delays and complications that imply more risk, more work and longer length of stay. Irrespective of the illness we arrived with.

So why has LOS been omitted from SHMI?

The reason may be more political than statistical.

We know that the risk of death increases with infirmity and age.

We know that if we put frail elderly patients into a hospital bed for a few days then they will decondition and become more frail, require more time in hospital, are more likely to need a transfer of care to somewhere other than home, are more susceptible to harm, and more likely to die.

So why is LOS not in the risk-of-death SHMI model?

And it is not in the King’s Fund QR report either.

Nor is the amount of cash being pumped in to keep the HMS NHS afloat each month.

All notably absent!

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Burning Ambition

flag_waving_mountain_150_clr_13781A wise person once said:

Improvement implies change, but change does not imply improvement.

To get improvement on any dimension we need to change something: our location, our perspective, our actions, our decisions, our assumptions, our beliefs even.

And we hate doing that because we know from life experience that change does not guarantee improvement.  Even with well-intended, carefully-considered, and collectively-agreed change … things can get worse.  And we fear that.  So the safest thing to do is … nothing!  We sit on the fence.

Until a ‘fire’ breaks out.  Then we are motivated to move by a stronger emotion … fear for our very survival.  That bigger fear gives us the necessary push and we move to somewhere cooler and safer.

But as the temperature drops, the fear goes away, the push goes away too and we lose momentum and return to torpor.  Until the next fire breaks out.

The other problem with a collective fear-based motivator is that we usually jump in different directions so any shred of cohesion we did have, is lost completely.  The system fragments.  Fear is always destructive.

The alternative to fear-driven change is a different type of motivator … a burning ambition.

Ambition may feel just as hot but it is different in that it continues to pull and to motivate us.  We do not slump back into torpor after the first success.  If anything the sense of achievement fuels our fire-of-ambition and that pulls us with greater force.

And when many others share the same burning ambition then we are pulled into alignment on a common purpose and that can become constructive and synergistic … if we work collaboratively.

So let us take health care improvement as the example.

We have a burning platform.  The newspapers are full of doom-and-gloom about escalating waits, failed targets, weekend mortality effects, spiraling costs and political conflict.

But do we have a collective burning ambition?  A common goal? A shared purpose?

A common goal like a health care system that is safe, delivers on time, meets and exceeds expectation and is affordable ?

If we do, then what is the barrier to change? We have push and we have pull … so where is the friction and resistance coming from?

From inside ourselves perhaps?  Maybe we harbour limiting beliefs that it is impossible or we can’t do it?  Beliefs that self-justify our ‘do nothing’ decision.

So only one example that disproves our limiting beliefs is enough to remove them. Just one.  And I shared a video of it last week – the Luton & Dunstable one.

And the animated video by Dr Peter Fuda captures the essence of this push-and-pull Kurt Lewin Force Field concept brilliantly!

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The NHS Cockpit Dashboard

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

The feedback shows that many people resonated with this sentiment.

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

UK_Type_1_ED_Monthly_4hr_Yield

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

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

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

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

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

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

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

It isn’t.

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

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

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

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

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

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

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

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

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

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

England_A&E_DFA_02

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

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

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

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

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

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

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

And there is hope.

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

They have a name. They are called positive deviants.

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

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

Luton and Dunstable

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What is Transformation?

Transformation

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

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

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

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

Roy_Lilley_Transformation

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

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

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

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

So what sort of transformation does the NHS need?

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

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

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

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

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

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

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

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Undiscussables

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

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

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

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

understanding variation.

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

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

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

This chance combination of apparently independent events got me thinking.

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

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

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

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

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

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

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

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

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

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

Or are there other forces at play as well?

Some deeper undiscussables perhaps?

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Health Care System Engineers

engineers_turbine_engine_16758The NHS is falling.

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

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

And many sense the impending crash.

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

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

So what is needed to avoid the crash?

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

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

We need competent and experienced health care system engineers.

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

So why do we not have enough health care system engineers?

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

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

How can this be?

The answer seems to be that the complex flow system engineering science needed to do this is actually quite new … it is called Complex Adaptive Systems Engineering (CASE) … and it has not diffused into healthcare.

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

So what can we do?

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

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

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

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

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

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

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

This vanguard of innovators have shared their feedback.

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

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

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

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Culture – cause or effect?

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

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

 

HBR_April_2016

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

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

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

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

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

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

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

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

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

It was part of the effect, not the cause.

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

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

So what is the next excuse?  Too busy?

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Grit in the Oyster

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

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

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

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

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

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

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

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

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

Two rare and precious pearls did indeed grow.

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

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

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

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

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

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

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

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

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

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Learning How To Manage …

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

by Julian Simcox

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

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

The key ideas in that essay were seven fold:

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

JS_Blog_20160307_Fig1

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

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

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

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

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

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

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

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

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

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

JS_Blog_20160307_Fig2

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

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

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

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

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

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

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

References

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

 

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Does your job title say “Manager” or “Leader”?

by Julian Simcox

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

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

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

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

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

JS_Blog_20160221_Fig1

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

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

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

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

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

JS_Blog_20160221_Fig2

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

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

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

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

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

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

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

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

References

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

FreshMeatOldBonesEvolution is an amazing process.

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

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

Knowledge evolves in just the same way.

Sometimes a well understood simplification in one branch of science is used to solve an ‘impossible’ problem in another.

Cross-fertilisation of learning is a healthy part of the evolution process.

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

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

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

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

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

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

So what are the reasons we do not?

Is it because healthcare is quite introspective?

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

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

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

Is it because we do not like change?

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

Is it because we fear loss of face?

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

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

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

New meat for our old bones?

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And?

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

Improvement implies innovation and action …

doing something different …

and getting a better outcome.

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

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

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

We guarantee the status quo.

So how do we get around this barrier?

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

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

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

And anyone can learn to how do it. Anyone.

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Surprised and Delighted

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

We feel quality when our experience exceeds our expectation.

And this simple definition of quality has some profound implications.

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

Why is that?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Survival of the Fittest

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

This is a limiting belief.

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

So which is correct?

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

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

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

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

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

There are two ways:

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

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

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

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

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

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

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

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

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

Collective improvement is much, much, much more effective.

Cell_StructureEvidence:

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

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

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

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

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

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

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

come_join_the_team_150_wht_10876So survival is about learning and improving.

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

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

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

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Whip or WIP?

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

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

Perfectionists demonstrate a degree of obsessive-compulsive behaviour too.

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

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

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

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

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

For example …

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

The infamous and feared 18-week target.

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

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

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

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

RTT_Data_Snapshot

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

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

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

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

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

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

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

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

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

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

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

RTT_WIP_RunChart

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

Two patterns are obvious:

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

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

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

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

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

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

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

A&E_Breaches_2004-15

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

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

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

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

So what can we do?

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

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

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

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

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

This is all possible. It is called Improvement Science.

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

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

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

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

PostScript

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

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

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Turning the Corner

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

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

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

The Improvement Science coach.

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

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

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

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

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

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

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

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

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

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

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The Cost of Fragmentation

DiamondAs systems become bigger and more complicated they may fragment into a larger number of smaller parts.

There are many reasons for this behaviour but the essence is that the integrity of a system requires the parts to be connected to each other in some way.  Bonds that hold them together – bonds that are stronger than the forces of disruption that are always battering them.

In some systems these bonds are physical and chemical.

A diamond does not fragment, even under extreme pressure, because the chemical bonds between the carbon atoms in the crystal lattice are very strong . A diamond is not alive – the atoms cannot move around – and that is the secret of its extreme strength. So a diamond cannot adapt either … it is durable but it is dead.

Cell_StructureIn biological systems the bonds are informational.

A cell maintains its integrity because the nanoscale component parts are held together physically, chemically and with information.

Inside a cell the atoms and molecules move around – and that is the secret of its survival. It is alive. It senses. It responds. It evolves. It endures. And it is mortal.

So are the organisms made from cells. A lichen, a tree, an animal and a person.

And so are the organisations built by and from people. A couple, a family, a tribe, a nation, the world.

And it is informational bonds that hold people together – it is how they share data with each other.

These bonds manifest in many ways. Our senses – especially sight, sound and touch. Our language – body, verbal and visual. Our learning – individual and collective. And our emotions, beliefs and behaviours that emerge and evolve over time.

We all know we are mortal. We strive to protect our identity; and we yearn for longevity. We do not want to die. We want and need integrity – at all levels from chemical to cultural.

And to achieve that degree of synergy we need to share that which we have in common:

1) Shared purpose.
2) Shared language.
3) Shared pledge of acceptable behaviours.
4) Shared pool of data, information, knowledge, understanding and wisdom.

Everything else is dynamic. What we believe, what we decide, how we learn, what we do. It is that variability and adaptability that is part of our collective strength along with our shared commitment.

And the balance is critical.

Too rigid and we cannot flex quickly enough to a changing environment; too fluid and we fall apart at the first challenge. We need both stability and agility – so our system of information flows must be fit-for-purpose.

And the price we will all pay for not achieving that critical balance is death-by-fragmentation.

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A Case of Chronic A&E Pain: Part 6

Dr_Bob_ThumbnailDr Bob runs a Clinic for Sick Systems and is sharing the Case of St Elsewhere’s® Hospital which is suffering from chronic pain in their A&E department.

The story so far: The history and examination of St.Elsewhere’s® Emergency Flow System have revealed that the underlying disease includes carveoutosis multiforme.  StE has consented to a knowledge transplant but is suffering symptoms of disbelief – the emotional rejection of the new reality. Dr Bob prescribed some loosening up exercises using the Carveoutosis Game.  This is the appointment to review the progress.

<Dr Bob> Hello again. I hope you have done the exercises as we agreed.

<StE> Indeed we have.  Many times in fact because at first we could not believe what we were seeing. We even modified the game to explore the ramifications.  And we have an apology to make. We discounted what you said last week but you were absolutely correct.

<Dr Bob> I am delighted to hear that you have explored further and I applaud you for the curiosity and courage in doing that.  There is no need to apologize. If this flow science was intuitively obvious then we we would not be having this conversation. So, how have you used the new understanding?

<StE> Before we tell the story of what happened next we are curious to know where you learned about this?

<Dr Bob> The pathogenesis of carveoutosis spatialis has been known for about 100 years but in a different context.  The story goes back to the 1870s when Alexander Graham Bell invented the telephone.  He was not an engineer or mathematician by background; he was interested in phonetics and he was a pragmatist and experimented by making things. He invented the telephone and the Bell Telephone Co. was born.  This innovation spread like wildfire, as you can imagine, and by the early 1900’s there were many telephone companies all over the world.  At that time the connections were made manually by telephone operators using patch boards and the growing demand created a new problem.  How many lines and operators were needed to provide a high quality service to bill paying customers? In other words … to achieve an acceptably low chance of hearing the reply “I’m sorry but all lines are busy, please try again later“.  Adding new lines and more operators was a slow and expensive business so they needed a way to predict how many would be needed – and how to do that was not obvious!  In 1917, a Danish mathematician, statistician and engineer called Agner Krarup Erlang published a paper with the solution.  A complicated formula that described the relationship and his Erlang B equation allowed telephone exchanges to be designed, built and staffed and to provide a high quality service at an acceptably low cost.  Mass real-time voice communication by telephone became affordable and has transformed the world.

<StE> Fascinating! We sort of sense there is a link here and certainly the “high quality and low cost” message resonates for us. But how does designing telephone exchanges relate to hospital beds?

<Dr Bob> If we equate an emergency admission needing a bed to a customer making a phone call, and we equate the number of telephone lines to the number of beds, then the two systems are very similar from the flow physics perspective. Erlang’s scary-looking equation can be used to estimate the minimum number of beds needed to achieve any specified level of admission service quality if you know the average rate of demand and average the length of stay.  That is how I made the estimate last week. It is this predictable-within-limits behaviour that you demonstrated to yourself with the Carveoutosis Game.

<StE> And this has been known for nearly 100 years but we have only just learned about it!

<Dr Bob> Yes. That is a bit annoying isn’t it?

<StE> And that explains why when we ‘ring-fence’ our fixed stock of beds the 4-hour performance falls!

<Dr Bob> Yes, that is a valid assertion. By doing that you are reducing your space-capacity resilience and the resulting danger, chaos, disappointment and escalating cost is completely predictable.

<StE> So our pain is iatrogenic as you said! We have unwittingly caused this. That is uncomfortable news to hear.

<Dr Bob> The root cause is actually not what you have done wrong, it is what you have not done right. It is an error of omission. You have not learned to listen to what your system is telling you. You have not learned how that can help you to deepen your understanding of how your system works. It is that information, knowledge, understanding and wisdom that you need to design a safer, calmer, higher quality and more affordable healthcare system.

<StE> And now we can see our omission … before it was like a blind spot … and now we can see the fallacy of our previously deeply held belief: that it was impossible to solve this without more beds, more staff and more money.  The gap is now obvious where before it was invisible. It is like a light has been turned on.  Now we know what to do and we are on the road to recovery. We need to learn how to do this ourselves … but not by guessing and meddling … we need to learn to diagnose and then to design and then to deliver safety, flow, quality and productivity.  All at the same time.

<Dr Bob> Welcome to the world of Improvement Science. And here I must sound a note of caution … there is a lot more to it than just blindly applying Erlang’s B equation. That will get us into the ball-park, which is a big leap forward, but real systems are not just simple, passive games of chance; they are complicated, active and adaptive.  Applying the principles of flow design in that context requires more than just mathematics, statistics and computer models.  But that know-how is available and accessible too … and waiting for when you are ready to take that leap of learning.

OK. I do not think you require any more help from me at this stage. You have what you need and I wish you well.  And please let me know the outcome.

<StE> Thank you and rest assured we will. We have already started writing our story … and we wanted to share the that with you today … but with this new insight we will need to write a few more chapters first.  This is really exciting … thank you so much.

St.Elsewhere’s® is a registered trademark of Kate Silvester Ltd,  and to read more real cases of 4-hour A&E pain download Kate’s: The Christmas Crisis

Part 1 is here. Part 2 is here. Part 3 is here. Part 4 is here. Part 5 is here.

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A Case of Chronic A&E Pain: Part 5

Dr_Bob_ThumbnailDr Bob runs a Clinic for Sick Systems and is sharing the Case of St Elsewhere’s® Hospital which is suffering from chronic pain in their A&E department.

The story so far: The history and examination of St.Elsewhere’s® Emergency Flow System have revealed the footprint of a Horned Gaussian in their raw A&E data. This characteristic sign suggests that the underlying disease includes carveoutosis.  StE has signed up for treatment and has started by installing learning loops. This is the one week follow up appointment.

<Dr Bob> Hi there. How are things? What has changed this week?

<StE> Lots! We shared the eureka moment we had when you described the symptoms, signs and pathogenesis of carvoutosis temporalis using the Friday Afternoon Snail Mail story.  That resonated strongly with lots of people. And as a result that symptom has almost gone – as if by magic!  We are now keeping on top of our emails by doing a few each day and we are seeing decisions and actions happening much more quickly.

<Dr Bob> Excellent. Many find it surprising to see such a large beneficial impact from such an apparently small change. And how are you feeling overall? How is the other pain?

<StE> Still there unfortunately. Our A&E performance has not really improved but we do feel a new sense of purpose, determination and almost optimism.  It is hard to put a finger on it.

<Dr Bob> Does it feel like a paradoxical combination of “feels subjectively better but looks objectively the same”?

<StE> Yes, that’s exactly it. And it is really confusing. Are we just fire-fighting more quickly but still not putting out the fire?

<Dr Bob> Possibly. It depends on your decisions and actions … you may be unwittingly both fighting and fanning the fire at the same time.  It may be that you are suffering from carveoutosis multiforme.

<StE> Is that bad?

<Dr Bob> No. Just trickier to diagnose and treat. It implies that there is more than one type of carveoutosis active at the same time and they tend to amplify each other. The other common type is called carveoutosis spatialis. Shall we explore that hypothesis?

<StE> Um, OK. Does it require more painful poking?

<Dr Bob> A bit. Do you want to proceed? I cannot do so without your consent.

<StE> I suppose so.

<Dr Bob> OK. Can you describe for me what happens to emergency patients after they are admitted. Where do they go to?

<StE> That’s easy.  The medical emergencies go to the medical wards and the others go to the surgical wards. Or rather they should. Very often there is spillover from one to the other because the specialty wards are full. That generates a lot of grumbling from everyone … doctors, nurses and patients. We call them outliers.

<Dr Bob> And when a patient gets to a ward where do they go? Into any available empty bed?

<StE> No.  We have to keep males and females separate, to maintain privacy and dignity.  We get really badly beaten up if we mix them.  Our wards are split up into six-bedded bays and a few single side-rooms, and we are constantly juggling bays and swapping them from male to female and back. Often moving patients around in the process, and often late at night. The patients do not like it and it creates lots of extra work for the nurses.

<Dr Bob> And when did these specialty and gender segregation policies come into force?

<StE> The specialty split goes back decades, the gender split was introduced after StE was built. We were told that it wouldn’t make any difference because we are still admitting the same proportion of males and females so it would average out, but it causes us a lot of headaches!  Maybe we are now having to admit more patients than the hospital was designed to hold!

<Dr Bob> That is possible, but even if you were admitting the same number for the same length of time the symptoms of carveoutosis spatialis are quite predictable. When there is any form of variation in demand, casemix, or gender then if you split your space-capacity into ‘ring-fenced’ areas you will always need more total space-capacity to achieve the same waiting time performance. Always. It is mandated by the Laws of Physics. It is not negotiable. And it does not average out.

<StE> What! So we were mis-informed?  The chaos we are seeing was predictable?

<Dr Bob> The effect of carveoutosis spatialis is predictable. But knowing that does not prove it is the sole cause of the chaos you are experiencing. It may well be a contributory factor though.

<StE> So how big an effect are we talking about here? A few percent?

<Dr Bob> I can estimate it for you.  What are your average number of emergency admissions per day, the split between medical and surgical, the split between gender, and the average length of stay in each group?

<StE> We have an average of sixty emergency admissions per day, the split between medicine and surgery is 50:50 on average;  the gender split is 50:50 on average and the average LoS in each of those 4 groups is 8 days.  We worked out using these number that we should need 480 beds but even now we have about 540 and even that doesn’t seem to be enough!

<Dr Bob> OK, let me work this out … with those parameters and assuming that the LoS does not change then the Laws of Flow Physics predict that you would need about 25% more beds than 480 – nearer six hundred – to be confident that there will always be a free bed for the next emergency admission in all four categories of  patient.

<StE> What! Our Director of Finance has just fallen off his chair! That can’t be correct!

[pause]

But that is exactly what we are seeing.

[pause]

If we we were able to treated this carvoutosis spatialis … if, just for the sake of argument, we could put any patient into any available bed … what effect would that have?  Would we then only need 480 beds?

<Dr Bob> You would if there was absolutely zero variation of any sort … but that is impossible. If nothing else changed the Laws of Physics predict that you would need about 520 beds.

<StE> What! But we have 540 beds now. Are you saying our whole A&E headache would evaporate just by doing that … and we would still have beds to spare?

<Dr Bob> That would be my prognosis, assuming there are no other factors at play that we have not explored yet.

<StE> Now the Head of Governance has just exploded! This is getting messy! We cannot just abandon the privacy and dignity policy.  But there isn’t much privacy or dignity lying on a trolley in the A&E corridor for hours!  We’re really sorry Dr Bob but we cannot believe you. We need proof.

<Dr Bob> And so would I were I in your position. Would you like to prove it to yourselves?  I have a game you can play that will demonstrate this unavoidable consequence of the Laws of Physics. Would you like to play it?

<StE> We would indeed!

<Dr Bob> OK. Here are the instructions for the game. This is your homework for this week.  See you next week.

St.Elsewhere’s® is a registered trademark of Kate Silvester Ltd,  and to read more real cases of 4-hour A&E pain download Kate’s: The Christmas Crisis

Part 1 is here. Part 2 is here. Part 3 is here. Part 4 is here.

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A Case of Chronic A&E Pain: Part 4

Dr_Bob_ThumbnailDr Bob runs a Clinic for Sick Systems and is sharing the Case of St Elsewhere’s ® Hospital which is suffering from chronic pain in the A&E department.

Dr Bob is presenting the case study in weekly bite-sized bits that are ample food for thought.

Part 1 is here. Part 2 is here. Part 3 is here.

The story so far:

The history and initial examination of St.Elsewhere’s® Emergency Flow System have revealed the footprint of a Horned Gaussian in their raw A&E data.  That characteristic sign suggests that the underlying disease complex includes one or more forms of carveoutosis.  So that is what Dr Bob and StE will need to explore together.

<Dr Bob> Hello again and how are you feeling since our last conversation?

<StE> Actually, although the A&E pain continues unabated, we feel better. More optimistic. We have followed your advice and have been plotting our daily A&E time-series charts and sharing those with the front-line staff.  And what is interesting to observe is the effect of just doing that.  There are fewer “What you should do!” statements and more “What we could do …” conversations starting to happen – right at the front line.

<Dr Bob> Excellent. That is what usually happens when we switch on the fast feedback loop. I detect that you are already feeling the emotional benefit.  So now we need to explore carveoutosis.  Are you up for that?

<StE> You betcha! 

<Dr Bob> OK. The common pathology in carveoutosis is that we have some form of resource that we, literally, carve up into a larger number of smaller pieces.  It does not matter what the resource is.  It can be time, space, knowledge, skill, cash.  Anything.

<StE> Um, that is a bit abstract.  Can you explain with a real example?

<Dr Bob> OK. I will use the example of temporal carveoutosis.  Do you use email?  And if so what are your frustrations with it … your Niggles?

<StE> Ouch! You poked a tender spot with that question!  Email is one of our biggest sources of frustration.  A relentless influx of dross that needs careful scanning to filter out the important stuff. We waste hours every week on this hamster wheel.  And if we do not clear our Inboxes by close of play on Friday then the following week is even worse!

<Dr Bob> And how many of you put time aside on Friday afternoon to ‘Clear-the-Inbox’?

<StE> We all do. It does at least give us some sense of control amidst the chaos. 

<Dr Bob> OK. This is a perfect example of temporal carveoutosis.  Suppose we consider the extreme case where we only process our emails on a Friday afternoon in a chunk of protected time carved out of our diary.  Now consider the effect of our carved-out-time-policy on the flow of emails. What happens?

<StE> Well, if we all do this then we will only send emails on a Friday afternoon and the person we are sending them to will only read them the following Friday afternoon and if we need a reply we will read that the Friday after.  So the time from sending an email to getting a reply will be two weeks. And it does not make any difference how many emails we send!

<Dr Bob> Yes. That is the effect on the lead-time … but I asked what the effect was on flow?

<StE> Oops! So our answer was correct but that was not the question you asked.  Um, the effect on flow is that it will be very jerky.  Emails will only flow on Friday afternoons … so all the emails for the week will try to flow around in a few hours or minutes.  Ah! That may explain why the email system seems to slow down on Friday afternoons and that only delays the work and adds to our frustration! We naturally assumed it was because the IT department have not invested enough in hardware! Faster computers and bigger mailboxes!

<Dr Bob> What you are seeing is the inevitable and predictable effect of one form of temporal carveoutosis.  The technical name for this is a QBQ time trap and it is an iatrogenic disease. Self-inflicted. (QBQ=queue-batch-queue).

<StE> So if the IT Department actually had the budget, and if they had actually treated the ear-ache we were giving them, and if they had actually invested in faster and bigger computers then the symptom of Friday Snail Mail would go away – but the time trap would remain.  And it might actually reinforce our emails-only-on-a-Friday-afternoon behaviour! Wow! That was not obvious until you forced us to think it through logically.

<Dr Bob> Well. I think that insight is enough to chew over for now. One eureka reaction at a time is enough in my experience. Food for thought requires time to digest.  This week your treatment plan is to share your new insight with the front-line teams.  You can use this example because email Niggles are very common.  And remember … Focus on the Flow.  Repeat that mantra to yourselves until it becomes a little voice in your head that reminds you what to do when you are pricked by the feelings of disappointment, frustration and fear.  Next week …

St.Elsewhere’s® is a registered trademark of Kate Silvester Ltd. And to read more real cases of 4-hour A&E pain download Kate’s: The Christmas Crisis

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The Catastrophe is Coming

Monitor_Summary


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

Summary At A Glance

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

So what has triggered the developing catastrophe?

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

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

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

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

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

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

So what is the solution?  More beds?

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

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

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

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

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

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

Q: Do we have that?
A: Nope.

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

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

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Retrospectoscopy

There is a wonderful invention called the retrospectoscope which is designed to provide clarity of hindsight.

on_top_of_the_books_150_wht_17482And there is an art to retrospectoscopy.

The key to the art is to carefully avoid committing to precise purpose at the start – in the prospectus; then after the actual outcome is demonstrated, to claim that it was predicted and using the ambiguity of the prospectus to hide the sleight-of-hand.

The purpose is to gain a reputation to have foresight and to be able to predict the future … because oracles, sages and soothsayers are much valued in society.

Retrospectoscopy has gained a tarnished reputation but it does have an important role … it provides the ability to learn from experience … but to be effective we have to use the retrospectoscope correctly. It is too easy to abuse it and to fall into the trap of self-justification  by distorting and deleting what we see.

To avoid the trap we need to do several things:

  1. Write down and share our clear diagnosis, plan and prediction at the start … ‘the prospectus’.
  2. Record and share the information that we will need to test our prediction robustly … ‘the evidence’.
  3. Compare our prospective rhetoric with the retrospective reality and share what we find … ‘the learning’.

It is unlikely that our prediction will be 100% accurate … and any deviation from aim is a valuable source of learning … better than predicted, worse than predicted and not predicted are all opportunities for new insights, deeper understanding,  new opportunities, wiser decisions and better outcomes.

If we fail to use the retrospectoscope correctly then we will be caught in a perpetual cycle of self-justifying delusion that is manifest as the name-shame-blame-game.  And if we side-step the expected discomfort of learning we will condemn ourselves to endlessly repeating the painful lessons that history can teach us to avoid.

The common theme in the self-justifying-delusion trap-avoiding recipe is share … if we are not prepared to learn in public then we should accept the inevitable consequences with grace.

Both courage and humility and are leadership assets.

 

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Storytelling

figure_turning_a_custom_page_15415

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

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

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

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

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

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

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

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

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

A bit of something for everyone.

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

A story, tale or fable.

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

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

A Story of Learning Improvement Science

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

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Early Adoption

Rogers_CurveThe early phases of a transformation are where most fall by the wayside.

And the failure rate is horrifying – an estimated 80% of improvement initiatives fail to achieve their goals.

The recent history of the NHS is littered with the rusting wreckage of a series of improvement bandwagons.  Many who survived the crashes are too scarred and too scared to try again.

Transformation and improvement imply change which implies innovation … new ways of thinking, new ways of behaving, new techniques, new tools, and new ways of working.

And it has been known for over 50 years that innovation spreads in a very characteristic way. This process was described by Everett Rogers in a book called ‘Diffusion of Innovations‘ and is described visually in the diagram above.

The horizontal axis is a measure of individual receptiveness to the specific innovation … and the labels are behaviours: ‘I exhibit early adopter behaviour‘ (i.e. not ‘I am an early adopter’).

What Roger’s discovered through empirical observation was that in all cases the innovation diffuses from left-to-right; from innovation through early adoption to the ‘silent’ majority.

Complete diffusion is not guaranteed though … there are barriers between the phases.

One barrier is between innovation and early adoption.

There are many innovations that we never hear about and very often the same innovation appears in many places and often around the same time.

This innovation-adoption barrier is caused by two things:
1) most are not even aware of the problem … they are blissfully ignorant;
2) news of the innovation is not shared widely enough.

Innovators are sensitive people.  They sense there is a problem long before others do. They feel the fear and the excitement of need for innovation. They challenge their own assumptions and they actively seek solutions. They swim against the tide of ignorance, disinterest, skepticism and often toxic cynicism.  So when they do discover a way forward they often feel nervous about sharing it. They have learned (the hard way) that the usual reaction is to be dismissed and discounted.  Most people do not like to learn about unknown problems and hazards; and they like it even less to learn that there are solutions that they neither recognise nor understand.

But not everyone.

There is a group called the early adopters who, like the innovators, are aware of the problem. They just do not share the innovator’s passion to find a solution … irrespective of the risks … so they wait … their antennae tuned for news that a solution has been found.

Then they act.

And they act in one of two ways:

1) Talkers … re-transmit the news of the problem and the discovery of a generic solution … which is essential in building awareness.

2) Walkers … try the innovative approach themselves and in so doing learn a lot about their specific problem and the new ways to solving it.

And it is the early adopters that do both of these actions that are the most effective and the most valuable to everyone else.  Those that talk-the-new-walk and walk-the-new-talk.

And we can identify who they are because they will be able to tell stories of how they have applied the innovation in their world; and the results that they have achieved; and how they achieved them; and what worked well; and what did not; and what they learned; and how they evolved and applied the innovation to meet their specific needs.

They are the leaders, the coaches and the teachers of improvement and transformation.

They See One, Do Some, and Teach Many.

The early adopters are the bridge across the Innovation and Transformation Chasm.