Restoring Pride-in-Work

In 1986, Dr Don Berwick from Boston attended a 4-day seminar run by Dr W. Edwards Deming in Washington.  Dr Berwick was a 40 year old paediatrician who was also interested in health care management and improving quality and productivity.  Dr Deming was an 86 year old engineer and statistician who, when he was in his 40’s, helped the US to improve the quality and productivity of the industrial processes supporting the US and Allies in WWII.

Don Berwick describes attending the seminar as an emotionally challenging life-changing experience when he realised that his well-intended attempts to improve quality by inspection-and-correction was a counterproductive, abusive approach that led to fear, demotivation and erosion of pride-in-work.  His blinding new clarity of insight led directly to the Institute of Healthcare Improvement in the USA in the early 1990’s.

One of the tenets of Dr Deming’s theories is that the ingrained beliefs and behaviours that erode pride-in-work also lead to the very outcomes that management do not want – namely conflict between managers and workers and economic failure.

So, an explicit focus on improving pride-in-work as an early objective in any improvement exercise makes very good economic sense, and is a sign of wise leadership and competent management.


Last week a case study was published that illustrates exactly that principle in action.  The important message in the title is “restore the calm”.

One of the most demotivating aspects of health care that many complain about is the stress caused a chaotic environment, chronic crisis and perpetual firefighting.  So, anything that can restore calm will, in principle, improve motivation – and that is good for staff, patients and organisations.

The case study describes, in detail, how calm was restored in a chronically chaotic chemotherapy day unit … on Weds, June 19th 2019 … in one day and at no cost!

To say that the chemotherapy nurses were surprised and delighted is an understatement.  They were amazed to see that they could treat the same number of patients, with the same number of staff, in the same space and without the stress and chaos.  And they had time to keep up with the paperwork; and they had time for lunch; and they finished work 2 hours earlier than previously!

Such a thing was not possible surely? But here they were experiencing it.  And their patients noticed the flip from chaos-to-strangely-calm too.

The impact of the one-day-test was so profound that the nurses voted to adopt the design change the following week.  And they did.  And the restored calm has been sustained.


What happened next?

The chemotherapy nurses were able to catch up with their time-owing that had accumulated from the historical late finishes.  And the problem of high staff turnover and difficultly in recruitment evaporated.  Highly-trained chemotherapy nurses who had left because of the stressful chaos now want to come back.  Pride-in-work has been re-established.  There are no losers.  It is a win-win-win result for staff, patients and organisations.


So, how was this “miracle” achieved?

Well, first of all it was not a miracle.  The flip from chaos-to-calm was predicted to happen.  In fact, that was the primary objective of the design change.

So, how what this design change achieved?

By establishing the diagnosis first – the primary cause of the chaos – and it was not what the team believed it was.  And that is the reason they did not believe the design change would work; and that is the reason they were so surprised when it did.

So, how was the diagnosis achieved?

By using an advanced systems engineering technique called Complex Physical System (CPS) modelling.  That was the game changer!  All the basic quality improvement techniques had been tried and had not worked – process mapping, direct observation, control charts, respectful conversations, brainstorming, and so on.  The system structure was too complicated. The system behaviour was too complex (i.e. chaotic).

What CPS revealed was that the primary cause of the chaotic behaviour was the work scheduling policy.  And with that clarity of focus, the team were able to re-design the policy themselves using a simple paper-and-pen technique.  That is why it cost nothing to change.

So, why hadn’t they been able to do this before?

Because systems engineering is not a taught component of the traditional quality improvement offerings.  Healthcare is rather different to manufacturing! As the complexity of the health care system increases we need to learn the more advanced tools that are designed for this purpose.

What is the same is the principle of restoring pride-in-work and that is what Dr Berwick learned from Dr Deming in 1986, and what we saw happen on June 19th, 2019.

To read the story of how it was done click here.

From Push to Pull

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


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


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

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

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

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

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

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

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


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

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

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

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

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

Errr .. No!

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


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

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

We played simulation games – lots of them.

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

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

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


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

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


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

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

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


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

This how-to-do-it framework feels counter-intuitive.  If it was obvious we would already be doing it.  But the good news is that the evidence proves that it works and that anyone can learn how to do HCSE.

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 health care systems engineer might approach them.  The first step was to raise awareness, then develop some belief and then grow some embedded capability – in the health care 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!  UHNM dipped badly too.

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.

Filter-Pull versus Push-Carveout

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

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

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

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

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

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

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

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

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

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

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

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


What do they mean by filters?

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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

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


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

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

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

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

The 85% Optimum Bed Occupancy Myth

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

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

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

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

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


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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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


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

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

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

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


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

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


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

From the same heat-map perhaps?

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

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

H’mm.  “Houston, we have a problem“.


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

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


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

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

 

The Pathology of Variation II

It is that time of year – again.

Winter.

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

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


And the evidence seems plausible.

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

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

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


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


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


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

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

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

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

Health care is a victim of its own success.

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

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

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

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


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

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


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

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

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

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

So the first gap to fill is the Awareness gap.

H.R.O.

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

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

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

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

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

So what are HROs?


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

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

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

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


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

And that was no accident.

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


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

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

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

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


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

Monklands A&E Improvement

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


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

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

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

The Disbelief to Belief Transition

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

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

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

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

It also requires skills, training, practice and experience.

The same is true in healthcare improvement.


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

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

Logically, rationally, enjoyably and surprisingly quickly.

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

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

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


How do we do that?

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

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

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


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

The Rise And Fall of Quality Improvement

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

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

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

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

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

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

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

So what happened?

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

It certainly looks that way.

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


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

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

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


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

Will that happen in healthcare too?

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

Could it be that flow and productivity are also important?

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

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

Is it too politically embarrassing?

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


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

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

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

OMG!

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

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

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

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

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

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

What opportunity does systems engineering offer health care?

That question is being actively explored … here.

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.

The Storyboard

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

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

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

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

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

Continue reading “The Storyboard”

Miracle on Tavanagh Avenue

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

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


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

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

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


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

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

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

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


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

Here is the back story …

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

The approach the NPDT chose was:

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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


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


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

Remember the essential ingredients?

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

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

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

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


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

You have demonstrated what was always possible.

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

To read the fuller story click here.

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


How Do We Know We Have Improved?

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

<Pete> Yes. That is exactly it.

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

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

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

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

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

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

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

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

<Phil> What are the other three pieces then?

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

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

<Pete> The size of the sample.

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

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

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

<Pete> Yes. Exactly the same thing.

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

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

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

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

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

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

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

<Pete> Yes.

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

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

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

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

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

<Pete> Yes. It is called a simulator.

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

<Pete> Exactly like that.

<Phil> Do you have one?

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

<Phil> Can you show me?

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

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

“Houston, we have a problem!”

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

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

uk_ae_runchart

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


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

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

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

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

A risk RAG chart is an obsolete performance management tool.

Here is another snippet …

nhse_ae_nov_2016

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

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

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

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


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

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

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

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


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

A: System behaviour charts.

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

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

Click here for more on how and why.


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

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

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

Learning is the only option.

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

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


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

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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Crash Test Dummy

CrashTestDummyThere are two complementary approaches to safety and quality improvement: desire and design.

In the improvement-by-desire world we use a suck-it-and-see approach to fix a problem.

 It is called PDSA. Plan-Do-Study-Act.

Sometimes this works and we pat ourselves on the back, and remember the learning for future use.

Sometimes it works for us but has a side effect: it creates a problem for someone else.  And we may not be aware of the unintended consequence unless someone shouts “Oi!” It may be too late by then of course.

Sometimes it doesn’t work.  And we have to just suck it up, remind ourselves  to “learn to fail or fail to learn”, and get back on the horse.


The more parts in a system, and the more interconnected they are, the more likely it is that a well-intended suck-it-and-see change will fail completely or create an unintended negative impact.

And after we have experienced that disappointment a few times our learned behaviour is to … do nothing … and to put up with the problems.  It seems the safest option.


In the improvement-by-design world we choose to study first, and to find the causal roots of the system behaviour we are seeing.  Our first objective is a causal diagnosis.

With that we can propose rational design changes that we anticipate will deliver the improvement we seek without creating adverse side effects.

And we have learned the hard way that our intuition can trick us … so we need a way to test our proposed designs … in a safe, and controlled, and measured way.

We need a crash test dummy!


What they do is to deliberately experience our design in a controlled experiment, and what they generate for us is constructive, objective and subjective feedback. What did work, and what did not.

A crash test dummy is both tough and sensitive at the same time.  They do not break easily and yet they feel the pain and gain too.  They are robust and resilient.


And with their feedback we can re-visit our design and improve it further, or we can use it to offer evidence-based assurance that our design is fit-for-purpose.

Safety and Quality Assurance is improvement-by-design.

Safety and Quality Control is improvement-by-desire.

If you were a passenger or a patient … which option would you prefer?

PS. It is possible to have both.

Precious Life Time

stick_figure_help_button_150_wht_9911Imagine this scenario:

You develop some non-specific symptoms.

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

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


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

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

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

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


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

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

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

It doesn’t.

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

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

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

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


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

It can be better.  A lot better!

And they demonstrate that this better way can be designed.

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

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

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

CDU_Waiting_Room

If life time is so precious, why waste it?

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

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!

FrailSafe Design

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

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

So we want our health care system to be a FrailSafe Design.

But is it? How would we know? And what could we do to improve it?


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

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

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


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

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

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

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

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

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

That was the flow diagnosis and design done.

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

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

And that was basically it.

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

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

We had created a FrailSafe Design … which implied that what was happening before was clearly not safe for these frail patients!


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

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

It cost nothing to achieve and the message was clear, as this quote is from the 2011 paper illustrates …

Injury_2011_Message

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


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

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?

The Five-day versus Seven-day Bun-Fight

Dr_Bob_ThumbnailThere is a big bun-fight kicking off on the topic of 7-day working in the NHS.

The evidence is that there is a statistical association between mortality in hospital of emergency admissions and day of the week: and weekends are more dangerous.

There are fewer staff working at weekends in hospitals than during the week … and delays and avoidable errors increase … so risk of harm increases.

The evidence also shows that significantly fewer patients are discharged at weekends.


So the ‘obvious’ solution is to have more staff on duty at weekends … which will cost more money.


Simple, obvious, linear and wrong.  Our intuition has tricked us … again!


Let us unravel this Gordian Knot with a bit of flow science and a thought experiment.

1. The evidence shows that there are fewer discharges at weekends … and so demonstrates lack of discharge flow-capacity. A discharge process is not a single step, there are many things that must flow in sync for a discharge to happen … and if any one of them is missing or delayed then the discharge does not happen or is delayed.  The weakest link effect.

2. The evidence shows that the number of unplanned admissions varies rather less across the week; which makes sense because they are unplanned.

3. So add those two together and at weekends we see hospitals filling up with unplanned admissions – not because the sick ones are arriving faster – but because the well ones are leaving slower.

4. The effect of this is that at weekends the queue of people in beds gets bigger … and they need looking after … which requires people and time and money.

5. So the number of staffed beds in a hospital must be enough to hold the biggest queue – not the average or some fudged version of the average like a 95th percentile.

6. So a hospital running a 5-day model needs more beds because there will be more variation in bed use and we do not want to run out of beds and delay the admission of the newest and sickest patients. The ones at most risk.

7. People do not get sicker because there is better availability of healthcare services – but saying we need to add more unplanned care flow capacity at weekends implies that it does.  What is actually required is that the same amount of flow-resource that is currently available Mon-Fri is spread out Mon-Sun. The flow-capacity is designed to match the customer demand – not the convenience of the supplier.  And that means for all parts of the system required for unplanned patients to flow.  What, where and when. It costs the same.

8. Then what happens is that the variation in the maximum size of the queue of patients in the hospital will fall and empty beds will appear – as if by magic.  Empty beds that ensure there is always one for a new, sick, unplanned admission on any day of the week.

9. And empty beds that are never used … do not need to be staffed … so there is a quick way to reduce expensive agency staff costs.

So with a comprehensive 7-day flow-capacity model the system actually gets safer, less chaotic, higher quality and less expensive. All at the same time. Safety-Flow-Quality-Productivity.

Yield

Dr_Bob_ThumbnailA recurring theme this week has been the concept of ‘quality’.

And it became quickly apparent that a clear definition of quality is often elusive.

Which seems to have led to a belief that quality is difficult to measure because it is subjective and has no precise definition.

The science of quality improvement is nearly 100 years old … and it was shown a long time ago, in 1924 in fact, that it is rather easy to measure quality – objectively and scientifically.

The objective measure of quality is called “yield”.

To measure yield we simply ask all our customers this question:

Did your experience meet your expectation?” 

If the answer is ‘Yes’ then we count this as OK; if it is ‘No’ then we count it as Not OK.

Yield is the ratio of the OKs divided by the number of customers who answered.


But this tried-and-tested way of measuring quality has a design flaw:

Where does a customer get their expectation from?

Because if a customer has an unrealistically high expectation then whatever we do will be perceived by them as Not OK.

So to consistently deliver a high quality service (i.e. high yield) we need to be able to influence both the customer experience and the customer expectation.


If we set our sights on a worthwhile and realistic expectation and we broadcast that to our customers, then we also need a way of avoiding their disappointment … that our objective quality outcome audit may reveal.

One way to defuse disappointment is to set a low enough expectation … which is, sadly, the approach adopted by naysayers,  complainers, cynics and doom-mongers. The inept.

That is not the path to either improvement or to excellence. It is the path to apathy.

A better approach is to set ourselves some internal standards of expectation and to check at each step if our work meets our own standard … and if it fails then we know we need have some more work to do.

This commonly used approach to maintaining quality is called a check-and-correct design.

So let us explore the ramifications of this check-and-correct approach to quality.


Suppose the quality of the product or service that we deliver is influenced by many apparently random factors. And when we actually measure our yield we discover that the chance of getting a right-first-time outcome is about 50%.  This amounts to little more than a quality lottery and we could simulate that ‘random’ process by tossing a coin.

So to set a realistic expectation for future customers there are two further questions we need to answer:
1. How long can an typical customer expect to wait for our product or service?
2. How much can an typical customer expect to pay for our product or service?

It is not immediately and intuitively obvious what the answers to these questions are … so we need to perform an experiment to find out.

Suppose we have five customers who require our product or service … we could represent them as Post It Notes; and suppose we have a clock … we could measure how long the process is taking; and suppose we have our coin … we can simulate the yield of the step; … and suppose we do not start the lead time clock until we start the work for each customer.

We now have the necessary and sufficient components to assemble a simple simulation model of our system … a model that will give us realistic answers to our questions.

So let us see what happens … just click the ‘Start Game’ button.

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It is worth running this exercise about a dozen times and recording the data for each run … then plotting the results on a time-series chart.

The data to plot is the make-time (which is the time displayed on the top left) and the cost (which is display top middle).

The make-time is the time from starting the first game to completing the last task.

The cost is the number of coin tosses we needed to do to deliver all work to the required standard.

And here are the charts from my dozen runs (yours will be different).

PostItNote_MakeTimeChart

PostItNote_CostChart

The variation from run to run is obvious; as is the correlation between a make-time and a high cost.

The charts also answer our two questions … a make time up to 90 would not be exceptional and an average cost of 10 implies that is the minimum price we need to charge in order to stay in business.

Our customers are waiting while we check-and-correct our own errors and we are expecting them to pay for the extra work!

In the NHS we have a name for this low-quality high-cost design: Payment By Results.


The charts also show us what is possible … a make time of 20 and a cost of 5.

That happened when, purely by chance, we tossed five heads in a row in the Quality Lottery.

So with this insight we could consider how we might increase the probability of ‘throwing a head’ i.e. doing the work right-first-time … because we can see from our charts what would happen.

The improved quality and cost of changing ourselves and our system to remove the root causes of our errors.

Quality Improvement-by-Design.

That something worth learning how to do.

And can we honestly justify not doing it?

What is Productivity?

It was the time for Bob and Leslie’s regular coaching session. Dr_Bob_ThumbnailBob was already on line when Leslie dialed in to the teleconference.

<Leslie> Hi Bob, sorry I am a bit late.

<Bob> No problem Leslie. What aspect of improvement science shall we explore today?

<Leslie> Well, I’ve been working through the Safety-Flow-Quality-Productivity cycle in my project and everything is going really well.  The team are really starting to put the bits of the jigsaw together and can see how the synergy works.

<Bob> Excellent. And I assume they can see the sources of antagonism too.

<Leslie> Yes, indeed! I am now up to the point of considering productivity and I know it was introduced at the end of the Foundation course but only very briefly.

<Bob> Yes,  productivity was described as a system metric. A ratio of a steam metric and a stage metric … what we get out of the streams divided by what we put into the stages.  That is a very generic definition.

<Leslie> Yes, and that I think is my problem. It is too generic and I get it confused with concepts like efficiency.  Are they the same thing?

<Bob> A very good question and the short answer is “No”, but we need to explore that in more depth.  Many people confuse efficiency and productivity and I believe that is because we learn the meaning of words from the context that we see them used in. If  others use the words imprecisely then it generates discussion, antagonism and confusion and we are left with the impression of that it is a ‘difficult’ subject.  The reality is that it is not difficult when we use the words in a valid way.

<Leslie> OK. That reassures me a bit … so what is the definition of efficiency?

<Bob> Efficiency is measure of wasted resource – it is the ratio of the minimum cost of the resources required to complete one task divided by the actual cost of the resources used to complete one task.

<Leslie> Um.  OK … so how does time come into that?

<Bob> Cost is a generic concept … it can refer to time, money and lots of other things.  If we stick to time and money then we know that if we have to employ ‘people’ then time will cost money because people need money to buy essential stuff that the need for survival. Water, food, clothes, shelter and so on.

<Leslie> So, we could use efficiency in terms of resource-time required to complete a task?

<Bob> Yes. That is a very useful way of looking at it.

<Leslie> So, how is productivity different? Completed tasks out divided by the cash in to pay for resource time would be a productivity metric. It looks the same.

<Bob> Does it?  The definition of efficiency is possible cost divided by actual cost. It is not the same as our definition of system productivity.

<Leslie> Ah yes, I see. So do others define productivity the same way?

<Bob> Let us try looking it up on Wikipedia …

<Leslie> OK … here we go …

Productivity is an average measure of the efficiency of production. It can be expressed as the ratio of output to inputs used in the production process, i.e. output per unit of input”.

Now that is really confusing!  It looks like efficiency and productivity are the same. Let me see what the Wikipedia definition of efficiency is …

“Efficiency is the (often measurable) ability to avoid wasting materials, energy, efforts, money, and time in doing something or in producing a desired result”.

But that is closer to your definition of efficiency – the actual cost is the minimum cost plus the cost of waste.

<Bob> Yes.  I think you are starting to see where the confusion arises.  And this is because there is a critical piece of the jigsaw missing.

<Leslie> Oh …. and what is that?

<Bob> Worth.

<Leslie> Eh?

<Bob> Efficiency has nothing to do with whether the output of the stream has any worth.  I can produce a worthless product very efficiently.  And what if we have the situation where the output of my process is actually harmful.  The more efficiently I use my resources the more harm I will cause from a fixed amount of resource … and in that situation it is actually safer to have an inefficient process!

<Leslie> Wow!  That really hits the nail on the head … and the implications are … um … profound.  Efficiency is objective and relates only to flow … and between flow and productivity we have to cross the Safety-Quality line.  Productivity also includes the subjective concept of worth or value. That all makes complete sense now. A productive system is a subjectively and objectively win-win-win design.

<Bob> Yup.  Get the safety, flow and quality perspectives of the design in synergy and productivity will sky-rocket. It is called a Fit-4-Purpose design that creates a Value-4-Money product or service

Excellent or Mediocre?

smack_head_in_disappointment_150_wht_16653Many organisations proclaim that their vision, goal, objective and mission is to achieve excellence but then proceed to deliver mediocre performance.

Why is this?

It is certainly not from lack of purpose, passion or people.

So, the flaw must lie somewhere in the process.


The clue lies in how we measure performance … and to see the collective mindset behind the design of the performance measurement system we just need to examine the key performance indicators or KPIs.

Do they measure failure or success?


Let us look at some from the NHS …. hospital mortality, hospital acquired infections, never events, 4-hour A&E breaches, cancer wait breaches, 18 week breaches, and so on.

In every case the metric reported is a failure metric. Not a success metric.

And the focus of action is all about  getting away from failure.

Damage mitigation, damage limitation and damage compensation.


So, we have the answer to our question: our performance metrics prove we know we are doing a good job when we are not failing.

But are we?

When we are not failing we are not doing a bad job … is that the same as doing a good job?

Q: Does excellent  = not excrement?

A: No. There is something between the extremes of excrement and excellent.

And the succeed-or-fail dichotomy is a distorting simplification created by applying an arbitrary threshold to a continuous measure of performance.


So, how, specifically, have we designed our current system to avoid failure?

Usually by imposing an arbitrary target connected to a punitive reaction to failure. Performance management by fear.

This tactic generates an expected and predictable punishment-avoidance and back-covering behaviour which is manifest as a lot of repeated checking and correcting of the inevitable errors that we find.  A lot of extra work that requires extra time and that requires extra money.

So, while an arbitrary-target-driven-check-and-correct design may avoid failing on safety, the additional cost may cause us to then fail on financial viability.

Out of the frying pan and into the fire.

No wonder Governance and Finance come into conflict!

And if we do manage to pull off an uneasy compromise … then what level of quality are we achieving?


Studies show that if take a random sample of 100 people from the pool of ‘disappointed by their experience’ and we ask if they are prepared to complain then only 5% will do so.

So, if we use complaints as our improvement feedback loop and we react to that and make changes that eliminate these complaints then what do we get? Excellence?

Nope.

We get what we designed … just good enough to avoid the 5% of complaints but not the 95% of disappointment.

We get mediocrity.


And what do we do then?

We start measuring ‘customer satisfaction’ … which is actually asking the question ‘did your experience meet your expectation?’

And if we find that satisfaction scores are disappointingly low then how do we improve them?

We have two choices: improve the experience or reduce the expectation.

But as we are very busy doing the necessary checking-and-correcting then our path of least resistance to greater satisfaction is … to lower expectations.

And we do that by donning the black hat of the pessimist and we lay out the the risks and dangers.

And by doing that we generate anxiety and fear.  Which was not the intended outcome.


Our mission statement proclaims ‘trusted to achieve excellence’ rather than ‘designed to deliver mediocrity’.

But mediocrity is what the evidence says we are delivering. Just good enough to avoid a smack.

And if we are honest with ourselves then we are forced to conclude that:

A design that uses failure metrics as the primary feedback loop can achieve no better than mediocrity.


So, if we choose  to achieve excellence then we need a better feedback design.

We need a design that uses success metrics as the primary feedback loop and we use failure metrics only in safety critical contexts.

And the ideal people to specify the success metrics are those who feel the benefit directly and immediately … the patients who receive care and the staff who give it.

Ask a patient what they want and they do not say “To be treated in less than 18 weeks”.  In fact I have yet to meet a patient who has even heard of the 18-week target!

A patient will say ‘I want to know what is wrong, what can be done, when it can be done, who will do it, what do I need to do, and what can I expect to be the outcome’.

Do we measure any of that?

Do we measure accuracy of diagnosis? Do we measure use of best evidenced practice? Do we know the possible delivery time (not the actual)? Do we inform patients of what they can expect to happen? Do we know what they can expect to happen? Do we measure the experience and outcome for every patient? Good and not so good? Do we feed that information back continuously and learn from it?

Nope.


So …. if we choose and commit to delivering excellence then we will need to start measuring-4-success and feeding what we see back to those who deliver the care.

Warts and all.

We want to know when we are doing a good job, and we need to know where to focus further improvement effort.

And if we abdicate that commitment and choose to deliver mediocrity-by-default then we are the engineers of our own chaos and despair.

We have the choice.

We are the agents of our own destiny.

Righteous Indignation

NHS_Legal_CostsThis heading in the the newspaper today caught my eye.

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

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

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

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

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

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

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


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

And what is the plan to reduce this cost?

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

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

How do we know?


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

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

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

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

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

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

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

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


The error here is one of a different sort.

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

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

Example:

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

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

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

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


So why was my inner chimp really so unhappy?

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

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

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

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

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

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

A Little Law and Order

teamwork_puzzle_build_PA_150_wht_2341[Bing bong]. The sound heralded Lesley logging on to the weekly Webex coaching session with Bob, an experienced Improvement Science Practitioner.

<Bob> Good afternoon Lesley.  How has your week been and what topic shall we explore today?

<Lesley> Hi Bob. Well in a nutshell, the bit of the system that I have control over feels like a fragile oasis of calm in a perpetual desert of chaos.  It is hard work keeping the oasis clear of the toxic sand that blows in!

<Bob> A compelling metaphor. I can just picture it.  Maintaining order amidst chaos requires energy. So what would you like to talk about?

<Lesley> Well, I have a small shoal of FISHees who I am guiding  through the foundation shallows and they are getting stuck on Little’s Law.  I confess I am not very good at explaining it and that suggests to me that I do not really understand it well enough either.

<Bob> OK. So shall we link those two theme – chaos and Little’s Law?

<Lesley> That sounds like an excellent plan!

<Bob> OK. So let us refresh the foundation knowledge. What is Little’s Law?

<Lesley>It is a fundamental Law of process physics that relates flow, with lead time and work in progress.

<Bob> Good. And specifically?

<Lesley> Average lead time is equal to the average flow multiplied by the average work in progress.

<Bob>Yes. And what are the units of flow in your equation?

<Lesley> Ah yes! That is  a trap for the unwary. We need to be clear how we express flow. The usual way is to state it as number of tasks in a defined period of time, such as patients admitted per day.  In Little’s Law the convention is to use the inverse of that which is the average interval between consecutive flow events. This is an unfamiliar way to present flow to most people.

<Bob> Good. And what is the reason that we use the ‘interval between events’ form?

<Leslie> Because it is easier to compare it with two critically important  flow metrics … the takt time and the cycle time.

<Bob> And what is the takt time?

<Leslie> It is the average interval between new tasks arriving … the average demand interval.

<Bob> And the cycle time?

<Leslie> It is the shortest average interval between tasks departing …. and is determined by the design of the flow constraint step.

<Bob> Excellent. And what is the essence of a stable flow design?

<Lesley> That the cycle time is less than the takt time.

<Bob>Why less than? Why not equal to?

<Leslie> Because all realistic systems need some flow resilience to exhibit stable and predictable-within-limits behaviour.

<Bob> Excellent. Now describe the design requirements for creating chronically chaotic system behaviour?

<Leslie> This is a bit trickier to explain. The essence is that for chronically chaotic behaviour to happen then there must be two feedback loops – a destabilising loop and a stabilising loop.  The destabilising loop creates the chaos, the stabilising loop ensures it is chronic.

<Bob> Good … so can you give me an example of a destabilising feedback loop?

<Leslie> A common one that I see is when there is a long delay between detecting a safety risk and the diagnosis, decision and corrective action.  The risks are often transitory so if the corrective action arrives long after the root cause has gone away then it can actually destabilise the process and paradoxically increase the risk of harm.

<Bob> Can you give me an example?

<Leslie>Yes. Suppose a safety risk is exposed by a near miss.  A delay in communicating the niggle and a root cause analysis means that the specific combination of factors that led to the near miss has gone. The holes in the Swiss cheese are not static … they move about in the chaos.  So the action that follows the accumulation of many undiagnosed near misses is usually the non-specific mantra of adding yet another safety-check to the already burgeoning check-list. The longer check-list takes more time to do, and is often repeated many times, so the whole flow slows down, queues grow bigger, waiting times get longer and as pressure comes from the delivery targets corners start being cut, and new near misses start to occur; on top of the other ones. So more checks are added and so on.

<Bob> An excellent example! And what is the outcome?

<Leslie> Chronic chaos which is more dangerous, more disordered and more expensive. Lose lose lose.

<Bob> And how do the people feel who work in the system?

<Leslie> Chronically naffed off! Angry. Demotivated. Cynical.

<Bob>And those feelings are the key symptoms.  Niggles are not only symptoms of poor process design, they are also symptoms of a much deeper problem: a violation of values.

<Leslie> I get the first bit about poor design; but what is that second bit about values?

<Bob>  We all have a set of values that we learned when we were very young and that have bee shaped by life experience.  They are our source of emotional energy, and our guiding lights in an uncertain world. Our internal unconscious check-list.  So when one of our values is violated we know because we feel angry. How that anger is directed varies from person to person … some internalise it and some externalise it.

<Leslie> OK. That explains the commonest emotion that people report when they feel a niggle … frustration which is the same as anger.

<Bob>Yes.  And we reveal our values by uncovering the specific root causes of our niggles.  For example if I value ‘Hard Work’ then I will be niggled by laziness. If you value ‘Experimentation’ then you may be niggled by ‘Rigid Rules’.  If someone else values ‘Safety’ then they may value ‘Rigid Rules’ and be niggled by ‘Innovation’ which they interpret as risky.

<Leslie> Ahhhh! Yes, I see.  This explains why there is so much impassioned discussion when we do a 4N Chart! But if this behaviour is so innate then it must be impossible to resolve!

<Bob> Understanding  how our values motivate us actually helps a lot because we are naturally attracted to others who share the same values – because we have learned that it reduces conflict and stress and improves our chance of survival. We are tribal and tribes share the same values.

<Leslie> Is that why different  departments appear to have different cultures and behaviours and why they fight each other?

<Bob> It is one factor in the Silo Wars that are a characteristic of some large organisations.  But Silo Wars are not inevitable.

<Leslie> So how are they avoided?

<Bob> By everyone knowing what common purpose of the organisation is and by being clear about what values are aligned with that purpose.

<Leslie> So in the healthcare context one purpose is avoidance of harm … primum non nocere … so ‘safety’ is a core value.  Which implies anything that is felt to be unsafe generates niggles and well-intended but potentially self-destructive negative behaviour.

<Bob> Indeed so, as you described very well.

<Leslie> So how does all this link to Little’s Law?

<Bob>Let us go back to the foundation knowledge. What are the four interdependent dimensions of system improvement?

<Leslie> Safety, Flow, Quality and Productivity.

<Bob> And one measure of  productivity is profit.  So organisations that have only short term profit as their primary goal are at risk of making poor long term safety, flow and quality decisions.

<Leslie> And flow is the key dimension – because profit is just  the difference between two cash flows: income and expenses.

<Bob> Exactly. One way or another it all comes down to flow … and Little’s Law is a fundamental Law of flow physics. So if you want all the other outcomes … without the emotionally painful disorder and chaos … then you cannot avoid learning to use Little’s Law.

<Leslie> Wow!  That is a profound insight.  I will need to lie down in a darkened room and meditate on that!

<Bob> An oasis of calm is the perfect place to pause, rest and reflect.

Reducing Avoidable Harm

patient_stumbling_with_bandages_150_wht_6861Primum non nocere” is Latin for “First do no harm”.

It is a warning mantra that had been repeated by doctors for thousands of years and for good reason.

Doctors  can be bad for your health.

I am not referring to the rare case where the doctor deliberately causes harm.  Such people are criminals and deserve to be in prison.

I am referring to the much more frequent situation where the doctor has no intention to cause harm – but harm is the outcome anyway.

Very often the risk of harm is unavoidable. Healthcare is a high risk business. Seriously unwell patients can be very unstable and very unpredictable.  Heroic efforts to do whatever can be done can result in unintended harm and we have to accept those risks. It is the nature of the work.  Much of the judgement in healthcare is balancing benefit with risk on a patient by patient basis. It is not an exact science. It requires wisdom, judgement, training and experience. It feels more like an art than a science.

The focus of this essay is not the above. It is on unintentionally causing avoidable harm.

Or rather unintentionally not preventing avoidable harm which is not quite the same thing.

Safety means prevention of avoidable harm. A safe system is one that does that. There is no evidence of harm to collect. A safe system does not cause harm. Never events never happen.

Safe systems are designed to be safe.  The root causes of harm are deliberately designed out one way or another.  But it is not always easy because to do that we need to understand the cause-and-effect relationships that lead to unintended harm.  Very often we do not.


In 1847 a doctor called Ignaz Semmelweis made a very important discovery. He discovered that if the doctors and medical students washed their hands in disinfectant when they entered the labour ward, then the number of mothers and babies who died from infection was reduced.

And the number dropped a lot.

It fell from an annual average of 10% to less than 2%!  In really bad months the rate was 30%.

The chart below shows the actual data plotted as a time-series chart. The yellow flag in 1848 is just after Semmelweis enforced a standard practice of hand-washing.

Vienna_Maternal_Mortality_1785-1848

Semmelweis did not know the mechanism though. This was not a carefully designed randomised controlled trial (RCT). He was desperate. And he was desperate because this horrendous waste of young lives was only happening on the doctors ward.  On the nurses ward, which was just across the corridor, the maternal mortality was less than 2%.

The hospital authorities explained it away as ‘bad air’ from outside. That was the prevailing belief at the time. Unavoidable. A risk that had to be just accepted.

Semmeleis could not do a randomized controlled trial because they were not invented until a century later.

And Semmelweis suspected that the difference between the mortality on the nurses and the doctors wards was something to do with the Mortuary. Only the doctors performed the post-mortems and the practice of teaching anatomy to medical students using post-mortem dissection was an innovation pioneered in Vienna in 1823 (the first yellow flag on the chart above). But Semmelweis did not have this data in 1847.  He collated it later and did not publish it until 1861.

What Semmelweis demonstrated was the unintended and avoidable deaths were caused by ignorance of the mechanism of how microorganisms cause disease. We know that now. He did not.

It would be another 20 years before Louis Pasteur demonstrated the mechanism using the famous experiment with the swan neck flask. Pasteur did not discover microorganisms;  he proved that they did not appear spontaneously in decaying matter as was believed. He proved that by killing the bugs by boiling, the broth in the flask  stayed fresh even though it was exposed to the air. That was a big shock but it was a simple and repeatable experiment. He had a mechanism. He was believed. Germ theory was born. A Scottish surgeon called Joseph Lister read of this discovery and surgical antisepsis was born.

Semmelweis suspected that some ‘agent’ may have been unwittingly transported from the dead bodies to the live mothers and babies on the hands of the doctors.  It was a deeply shocking suggestion that the doctors were unwittingly killing their patients.

The other doctors did not take this suggestion well. Not well at all. They went into denial. They discounted the message and they discharged the messenger. Semmelweis never worked in Vienna again. He went back to Hungary and repeated the experiment. It worked.


Even today the message that healthcare practitioners can unwittingly bring avoidable harm to their patients is disturbing. We still seek solace in denial.

Hospital acquired infections (HAI) are a common cause of harm and many are avoidable using simple, cheap and effective measures such as hand-washing.

The harm does not come from what we do. It comes from what we do not do. It happens when we omit to follow the simple safety measures that have be proven to work. Scientifically. Statistically Significantly. Understood and avoidable errors of omission.


So how is this “statistically significant scientific proof” acquired?

By doing experiments. Just like the one Ignaz Semmelweis conducted. But the improvement he showed was so large that it did not need statistical analysis to validate it.  And anyway such analysis tools were not available in 1847. If they had been he might have had more success influencing his peers. And if he had achieved that goal then thousands, if not millions, of deaths from hospital acquired infections may have been prevented.  With the clarity of hindsight we now know this harm was avoidable.

No. The problem we have now is because the improvement that follows a single intervention is not very large. And when the causal mechanisms are multi-factorial we need more than one intervention to achieve the improvement we want. The big reduction in avoidable harm. How do we do that scientifically and safely?


About 20% of hospital acquired infections occur after surgical operations.

We have learned much since 1847 and we have designed much safer surgical systems and processes. Joseph Lister ushered in the era of safe surgery, much has happened since.

We routinely use carefully designed, ultra-clean operating theatres, sterilized surgical instruments, gloves and gowns, and aseptic techniques – all to reduce bacterial contamination from outside.

But surgical site infections (SSIs) are still common place. Studies show that 5% of patients on average will suffer this complication. Some procedures are much higher risk than others, despite the precautions we take.  And many surgeons assume that this risk must just be accepted.

Others have tried to understand the mechanism of SSI and their research shows that the source of the infections is the patients themselves. We all carry a ‘bacterial flora’ and normally that is no problem. Our natural defense – our skin – is enough.  But when that biological barrier is deliberately breached during a surgical operation then we have a problem. The bugs get in and cause mischief. They cause surgical site infections.

So we have done more research to test interventions to prevent this harm. Each intervention has been subject to well-designed, carefully-conducted, statistically-valid and very expensive randomized controlled trials.  And the results are often equivocal. So we repeat the trials – bigger, better controlled trials. But the effects of the individual interventions are small and they easily get lost in the noise. So we pool the results of many RCTs in what is called a ‘meta-analysis’ and the answer from that is very often ‘not proven’ – either way.  So individual surgeons are left to make the judgement call and not surprisingly there is wide variation in practice.  So is this the best that medical science can do?

No. There is another way. What we can do is pool all the learning from all the trials and design a multi-facetted intervention. A bundle of care. And the idea of a bundle is that the  separate small effects will add or even synergise to create one big effect.  We are not so much interested in the mechanism as the outcome. Just like Ignaz Semmelweiss.

And we can now do something else. We can test our bundle of care using statistically robust tools that do not require a RCT.  They are just as statistically valid as a RCT but a different design.

And the appropriate tool for this to measure the time interval between adverse the events  – and then to plot this continuous metric as a time-series chart.

But we must be disciplined. First we must establish the baseline average interval and then we introduce our bundle and then we just keep measuring the intervals.

If our bundle works then the interval between the adverse events gets longer – and we can easily prove that using our time-series chart. The longer the interval the more ‘proof’ we have.  In fact we can even predict how long we need to observe to prove that ‘no events’ is a statistically significant improvement. That is an elegant an efficient design.


Here is a real and recent example.

The time-series chart below shows the interval in days between surgical site infections following routine hernia surgery. These are not life threatening complications. They rarely require re-admission or re-operation. But they are disruptive for patients. They cause pain, require treatment with antibiotics, and the delay recovery and return to normal activities. So we would like to avoid them if possible.

Hernia_SSI_CareBundle

The green and red lines show the baseline period. The  green line says that the average interval between SSIs is 14 days.  The red line says that an interval more than about 60 days would be surprisingly long: valid statistical evidence of an improvement.  The end of the green and red lines indicates when the intervention was made: when the evidence-based designer care bundle was adopted together with the discipline of applying it to every patient. No judgement. No variation.

The chart tells the story. No complicated statistical analysis is required. It shows a statistically significant improvement.  And the SSI rate fell by over 80%. That is a big improvement.

We still do not know how the care bundle works. We do not know which of the seven simultaneous simple and low-cost interventions we chose are the most important or even if they work independently or in synergy.  Knowledge of the mechanism was not our goal.

Our goal was to improve outcomes for our patients – to reduce avoidable harm – and that has been achieved. The evidence is clear.

That is Improvement Science in action.

And to read the full account of this example of the Science of Improvement please go to:

http://www.journalofimprovementscience.org

It is essay number 18.

And avoid another error of omission. If you have read this far please share this message – it is important.

Jiggling

hurry_with_the_SFQP_kit[Dring] Bob’s laptop signaled the arrival of Leslie for their regular ISP remote coaching session.

<Bob> Hi Leslie. Thanks for emailing me with a long list of things to choose from. It looks like you have been having some challenging conversations.

<Leslie> Hi Bob. Yes indeed! The deepening gloom and the last few blog topics seem to be polarising opinion. Some are claiming it is all hopeless and others, perhaps out of desperation, are trying the FISH stuff for themselves and discovering that it works.  The ‘What Ifs’ are engaged in war of words with the ‘Yes Buts’.

<Bob> I like your metaphor! Where would you like to start on the long list of topics?

<Leslie> That is my problem. I do not know where to start. They all look equally important.

<Bob> So, first we need a way to prioritise the topics to get the horse-before-the-cart.

<Leslie> Sounds like a good plan to me!

<Bob> One of the problems with the traditional improvement approaches is that they seem to start at the most difficult point. They focus on ‘quality’ first – and to be fair that has been the mantra from the gurus like W.E.Deming. ‘Quality Improvement’ is the Holy Grail.

<Leslie>But quality IS important … are you saying they are wrong?

<Bob> Not at all. I am saying that it is not the place to start … it is actually the third step.

<Leslie>So what is the first step?

<Bob> Safety. Eliminating avoidable harm. Primum Non Nocere. The NoNos. The Never Events. The stuff that generates the most fear for everyone. The fear of failure.

<Leslie> You mean having a service that we can trust not to harm us unnecessarily?

<Bob> Yes. It is not a good idea to make an unsafe design more efficient – it will deliver even more cumulative harm!

<Leslie> OK. That makes perfect sense to me. So how do we do that?

<Bob> It does not actually matter.  Well-designed and thoroughly field-tested checklists have been proven to be very effective in the ‘ultra-safe’ industries like aerospace and nuclear.

<Leslie> OK. Something like the WHO Safe Surgery Checklist?

<Bob> Yes, that is a good example – and it is well worth reading Atul Gawande’s book about how that happened – “The Checklist Manifesto“.  Gawande is a surgeon who had published a lot on improvement and even so was quite skeptical that something as simple as a checklist could possibly work in the complex world of surgery. In his book he describes a number of personal ‘Ah Ha!’ moments that illustrate a phenomenon that I call Jiggling.

<Leslie> OK. I have made a note to read Checklist Manifesto and I am curious to learn more about Jiggling – but can we stick to the point? Does quality come after safety?

<Bob> Yes, but not immediately after. As I said, Quality is the third step.

<Leslie> So what is the second one?

<Bob> Flow.

There was a long pause – and just as Bob was about to check that the connection had not been lost – Leslie spoke.

<Leslie> But none of the Improvement Schools teach basic flow science.  They all focus on quality, waste and variation!

<Bob> I know. And attempting to improve quality before improving flow is like papering the walls before doing the plastering.  Quality cannot grow in a chaotic context. The flow must be smooth before that. And the fear of harm must be removed first.

<Leslie> So the ‘Improving Quality through Leadership‘ bandwagon that everyone is jumping on will not work?

<Bob> Well that depends on what the ‘Leaders’ are doing. If they are leading the way to learning how to design-for-safety and then design-for-flow then the bandwagon might be a wise choice. If they are only facilitating collaborative agreement and group-think then they may be making an unsafe and ineffective system more efficient which will steer it over the edge into faster decline.

<Leslie>So, if we can stabilize safety using checklists do we focus on flow next?

<Bob>Yup.

<Leslie> OK. That makes a lot of sense to me. So what is Jiggling?

<Bob> This is Jiggling. This conversation.

<Leslie> Ah, I see. I am jiggling my understanding through a series of ‘nudges’ from you.

<Bob>Yes. And when the learning cogs are a bit rusty, some Improvement Science Oil and a bit of Jiggling is more effective and much safer than whacking the caveman wetware with a big emotional hammer.

<Leslie>Well the conversation has certainly jiggled Safety-Flow-Quality-and-Productivity into a sensible order for me. That has helped a lot. I will sort my to-do list into that order and start at the beginning. Let me see. I have a plan for safety, now I can focus on flow. Here is my top flow niggle. How do I design the resource capacity I need to ensure the flow is smooth and the waiting times are short enough to avoid ‘persecution’ by the Target Time Police?

<Bob> An excellent question! I will send you the first ISP Brainteaser that will nudge us towards an answer to that question.

<Leslie> I am ready and waiting to have my brain-teased and my niggles-nudged!

The Speed of Trust

London_UndergroundSystems are built from intersecting streams of work called processes.

This iconic image of the London Underground shows a system map – a set of intersecting transport streams.

Each stream links a sequence of independent steps – in this case the individual stations.  Each step is a system in itself – it has a set of inner streams.

For a system to exhibit stable and acceptable behaviour the steps must be in synergy – literally ‘together work’. The steps also need to be in synchrony – literally ‘same time’. And to do that they need to be aligned to a common purpose.  In the case of a transport system the design purpose is to get from A to B safety, quickly, in comfort and at an affordable cost.

In large socioeconomic systems called ‘organisations’ the steps represent groups of people with special knowledge and skills that collectively create the desired product or service.  This creates an inevitable need for ‘handoffs’ as partially completed work flows through the system along streams from one step to another. Each step contributes to the output. It is like a series of baton passes in a relay race.

This creates the requirement for a critical design ingredient: trust.

Each step needs to be able to trust the others to do their part:  right-first-time and on-time.  All the steps are directly or indirectly interdependent.  If any one of them is ‘untrustworthy’ then the whole system will suffer to some degree. If too many generate dis-trust then the system may fail and can literally fall apart. Trust is like social glue.

So a critical part of people-system design is the development and the maintenance of trust-bonds.

And it does not happen by accident. It takes active effort. It requires design.

We are social animals. Our default behaviour is to trust. We learn distrust by experiencing repeated disappointments. We are not born cynical – we learn that behaviour.

The default behaviour for inanimate systems is disorder – and it has a fancy name – it is called ‘entropy’. There is a Law of Physics that says that ‘the average entropy of a system will increase over time‘. The critical word is ‘average’.

So, if we are not aware of this and we omit to pay attention to the hand-offs between the steps we will observe increasing disorder which leads to repeated disappointments and erosion of trust. Our natural reaction then is ‘self-protect’ which implies ‘check-and-reject’ and ‘check and correct’. This adds complexity and bureaucracy and may prevent further decline – which is good – but it comes at a cost – quite literally.

Eventually an equilibrium will be achieved where our system performance is limited by the amount of check-and-correct bureaucracy we can afford.  This is called a ‘mediocrity trap’ and it is very resilient – which means resistant to change in any direction.


To escape from the mediocrity trap we need to break into the self-reinforcing check-and-reject loop and we do that by developing a design that challenges ‘trust eroding behaviour’.  The strategy is to develop a skill called  ‘smart trust’.

To appreciate what smart trust is we need to view trust as a spectrum: not as a yes/no option.

At one end is ‘nonspecific distrust’ – otherwise known as ‘cynical behaviour’. At the other end is ‘blind trust’ – otherwise  known and ‘gullible behaviour’.  Neither of these are what we need.

In the middle is the zone of smart trust that spans healthy scepticism  through to healthy optimism.  What we need is to maintain a balance between the two – not to eliminate them. This is because some people are ‘glass-half-empty’ types and some are ‘glass-half-full’. And both views have a value.

The action required to develop smart trust is to respectfully challenge every part of the organisation to demonstrate ‘trustworthiness’ using evidence.  Rhetoric is not enough. Politicians always score very low on ‘most trusted people’ surveys.

The first phase of this smart trust development is for steps to demonstrate trustworthiness to themselves using their own evidence, and then to share this with the steps immediately upstream and downstream of them.

So what evidence is needed?

SFQP1Safety comes first. If a step cannot be trusted to be safe then that is the first priority. Safe systems need to be designed to be safe.

Flow comes second. If the streams do not flow smoothly then we experience turbulence and chaos which increases stress,  the risk of harm and creates disappointment for everyone. Smooth flow is the result of careful  flow design.

Third is Quality which means ‘setting and meeting realistic expectations‘.  This cannot happen in an unsafe, chaotic system.  Quality builds on Flow which builds on Safety. Quality is a design goal – an output – a purpose.

Fourth is Productivity (or profitability) and that does not automatically follow from the other three as some QI Zealots might have us believe. It is possible to have a safe, smooth, high quality design that is unaffordable.  Productivity needs to be designed too.  An unsafe, chaotic, low quality design is always more expensive.  Always. Safe, smooth and reliable can be highly productive and profitable – if designed to be.

So whatever the driver for improvement the sequence of questions is the same for every step in the system: “How can I demonstrate evidence of trustworthiness for Safety, then Flow, then Quality and then Productivity?”

And when that happens improvement will take off like a rocket. That is the Speed of Trust.  That is Improvement Science in Action.

Do Not Give Up Too Soon

clock_hands_spinning_import_150_wht_3149Tangible improvement takes time. Sometimes it takes a long time.

The more fundamental the improvement the more people are affected. The more people involved the greater the psychological inertia. The greater the resistance the longer it takes to show tangible effects.

The advantage of deep-level improvement is that the cumulative benefit is greater – the risk is that the impatient Improvementologist may give up too early – sometimes just before the benefit becomes obvious to all.

The seeds of change need time to germinate and to grow – and not all good ideas will germinate. The green shoots of innovation do not emerge immediately – there is often a long lag and little tangible evidence for a long time.

This inevitable  delay is a source of frustration, and the impatient innovator can unwittingly undo their good work.  By pushing too hard they can drag a failure from the jaws of success.

Q: So how do we avoid this trap?

The trick is to understand the effect of the change on the system.  This means knowing where it falls on our Influence Map that is marked with the Circles of Control, Influence and Concern.

Our Circle of Concern includes all those things that we are aware of that present a threat to our future survival – such as a chunk of high-velocity space rock smashing into the Earth and wiping us all out in a matter of milliseconds. Gulp! Very unlikely but not impossible.

Some concerns are less dramatic – such as global warming – and collectively we may have more influence over changing that. But not individually.

Our Circle of Influence lies between the limit of our individual control and the limit of our collective control. This a broad scope because “collective” can mean two, twenty, two hundred, two thousand, two million, two billion and so on.

Making significant improvements is usually a Circle of Influence challenge and only collectively can we make a difference.  But to deliver improvement at this level we have to influence others to change their knowledge, understanding, attitudes, beliefs and behaviour. That is not easy and that is not quick. It is possible though – with passion, plausibility, persistence, patience – and an effective process.

It is here that we can become impatient and frustrated and are at risk of giving up too soon – and our temperaments influence the risk. Idealists are impatient for fundamental change. Rationals, Guardians and Artisans do not feel the same pain – and it is a rich source of conflict.

So if we need to see tangible results quickly then we have to focus closer to home. We have to work inside our Circle of Individual Influence and inside our Circle of Control.  The scope of individual influence varies from person-to-person but our Circle of Control is the same for all of us: the outer limit is our skin.  We all choose our behaviour and it is that which influences others: for better or for worse.  It is not what we think it is what we do. We cannot read or control each others minds. We can all choose our attitudes and our actions.

So if we want to see tangible improvement quickly then we must limit the scope of our action to our Circle of Individual Influence and get started.  We do what we can and as soon as we can.

Choosing what to do and what not do requires wisdom. That takes time to develop too.


Making an impact outside the limit of our Circle of Individual Influence is more difficult because it requires influencing many other people.

So it is especially rewarding for to see examples of how individual passion, persistence and patience have led to profound collective improvement.  It proves that it is still possible. It provides inspiration and encouragement for others.

One example is the recently published Health Foundation Quality, Cost and Flow Report.

This was a three-year experiment to test if the theory, techniques and tools of Improvement Science work in healthcare: specifically in two large UK acute hospitals – Sheffield and Warwick.

The results showed that Improvement Science does indeed work in healthcare and it worked for tough problems that were believed to be very difficult if not impossible to solve. That is very good news for everyone – patients and practitioners.

But the results have taken some time to appear in published form – so it is really good news to report that the green shoots of improvement are now there for all to see.

The case studies provide hard evidence that win-win-win outcomes are possible and achievable in the NHS.

The Impossibility Hypothesis has been disproved. The cynics can step off the bus. The skeptics have their evidence and can now become adopters.

And the report offers a lot of detail on how to do it including two references that are available here:

  1. A Recipe for Improvement PIE
  2. A Study of Productivity Improvement Tactics using a Two-Stream Production System Model

These references both describe the fundamentals of how to align financial improvement with quality and delivery improvement to achieve the elusive win-win-win outcome.

A previously invisible door has opened to reveal a new Land of Opportunity. A land inhabited by Improvementologists who mark the path to learning and applying this new knowledge and understanding.

There are many who do not know what to do to solve the current crisis in healthcare – they now have a new vista to explore.

Do not give up too soon –  there is a light at the end of the dark tunnel.

And to get there safely and quickly we just need to learn and apply the Foundations of Improvement Science in Healthcare – and we first learn to FISH in our own ponds first.

fish

What is the Temperamenture?

tweet_birdie_flying_between_phones_150_wht_9168Tweet
The sound heralded the arrival of a tweet so Bob looked up from his book and scanned the message. It was from Leslie, one of the Improvement Science apprentices.

It said “If your organisation is feeling poorly then do not forget to measure the Temperamenture. You may have Cultural Change Fever.

Bob was intrigued. This was a novel word and he suspected it was not a spelling error. He know he was being teased. He tapped a reply on his iPad “Interesting word ‘Temperamenture’ – can you expand?” 

Ring Ring
<Bob> Hello, Bob here.

There was laughing on the other end of the line – it was Leslie.

<Leslie> Ho Ho. Hi Bob – I thought that might prick your curiosity if you were on line. I know you like novel words.

<Bob> Ah! You know my weakness – I am at your mercy now!  So, I am consumed with curiosity – as you knew I would be.

<Leslie> OK. No more games. You know that you are always saying that there are three parts to Improvement Science – Processes, People and Systems – and that the three are synergistic so they need to be kept in balance …

<Bob> Yes.

<Leslie> Well, I have discovered a source of antagonism that creates a lot of cultural imbalance and emotional heat in my organisation.

<Bob> OK. So I take from that you mean an imbalance in the People part that then upsets the Process and System parts.

<Leslie> Yes, exactly. In your Improvement Science course you mentioned the theory behind this but did not share any real examples.

<Bob> That is very possible.  Hard evidence and explainable examples are easier for the Process component – the People stuff is more difficult to do that way.  Can you be more specific?  I think I know where you may be going with this.

<Leslie> OK. Where do you feel I am going with it?

<Bob> Ha! The student becomes the teacher. Excellent response! I was thinking something to do with the Four Temperaments.

<Leslie>Yes.  And specifically the conflict that can happen between them.  I am thinking of the tension between the Idealists and the Guardians.

<Bob> Ah!  Yes. The Bile Wars – Yellow and Black. The Cholerics versus the Melancholics. So do you have hard evidence of this happening in reality rather than just my theoretical rhetoric?

<Leslie> Yes!  But the facts do not seem to fit the theory. You know that I work in a hospital. Well one of the most important “engines” of a hospital is the surgical operating suite. Conveniently called the SOS.

<Bob> Yes. It seems to be a frequent source of both Nuggets and Niggles.

<Leslie> Well, I am working with the SOS team at my hospital and I have to say that they are a pretty sceptical bunch.  Everyone seems to have strong opinions.  Strong but different opinions of what should happen and who should do it.  The words someone and should get mentioned a lot.  I have not managed to find this elusive “someone” yet.  The some-one, no-one, every-one, any-one problem.

<Bob> OK. I have heard this before. I hear that surgeons in particular have strong opinions – and they disagree with each other!  I remember watching episodes of “Doctor in the House” many years ago.  What was the name of the irascible chief surgeon played by James Robertson Justice? Sir Lancelot Spratt the archetype consultant surgeon. Are they actually like that?

<Leslie> I have not met any as extreme as Sir Lancelot though some do seem to emulate that role model.  In reality the surgeons, anaesthetists, nurses, ODPs, and managers all seem to believe there is one way that a theatre should be run, their way, and their separate “one ways” do not line up.  Hence the conflict and high emotional temperature.

<Bob> OK, so how does the Temperament dimension relate to this?  Is there a temperament mismatch between the different tribes in the operating suite as the MBTI theory would suggest?

<Leslie> That was my hypothesis and I decided that the only way I could test it was by mapping the temperaments using the Temperament Sorter from the FISH toolbox.

<Bob> Excellent, but you would need quite a big sample to draw any statistically valid conclusions.  How did you achieve that with a group of disparate sceptics?

<Leslie>I know.  So I posed this challenge as a research question – and they were curious enough to give it a try.  Well, the Surgeons and Anaesthetists were anyway.  The Nurses, OPDs and Managers chose to sit on the fence and watch the game.

<Bob>Wow! Now I am really interested. What did you find?

<Leslie>Woah there!  I need to explain how we did it first.  They have a monthly audit meeting where they all get together as separate groups and after I posed the question they decided to do use the Temperament Sorter at one of those meetings.  It was done in a light-hearted way and it was really good fun too.  I brought some cartoons and descriptions of the sixteen MBTI types and they tried to guess who was which type.

<Bob>Excellent.  So what did you find?

<Leslie>We disproved the hypothesis that there was a Temperament mismatch.

<Bob>Really!  What did the data show?

<Leslie> It showed that the Temperament profile for both surgeons and anaesthetists was different from the population average …

<Bob>OK, and …?

<Leslie>… and that there was no statistical difference between surgeons and anaesthetists.

<Bob> Really! So what are they both?

<Leslie> Guardians. The majority of both tribes are SJs.

There was a long pause.  Bob was digesting this juicy new fact.  Leslie knew that if there was one thing that Bob really liked it was having a theory disproved by reality.  Eventually he replied.

<Bob> Clarity of hindsight is a wonderful thing.  It makes complete sense that they are Guardians.  Speaking as a patient, what I want most is Safety and Predictability which is the ideal context for Guardians to deliver their best.  I am sure that neither surgeons nor anaesthetists like “surprises” and I suspect that they both prefer doing things “by the book”.  They are sceptical of new ideas by temperament.

<Leslie> And there is more.

<Bob> Excellent! What?

<Leslie> They are tough-minded Guardians. They are STJs.

<Bob> Of course!  Having the responsibility of “your life in my hands” requires a degree of tough-mindedness and an ability to not get too emotionally hooked.  Sir Lancelot is a classic extrovert tough-minded Guardian!  The Rolls-Royce and the ritual humiliation of ignorant underlings all fits.  Wow!  Well done Leslie.  So what have you done with this new knowledge and deeper understanding?

<Leslie> Ouch! You got me! That is why I sent the Tweet. Now what do I do?

<Bob> Ah! I am not sure.  We are both sailing in uncharted water now so I suggest we explore and learn together.  Let me ponder and do some exploring of the implications of your findings and I will get back to you.  Can you do the same?

<Leslie> Good plan. Shall we share notes in a couple of days?

<Bob> Excellent. I look forward to it.


This is not a completely fictional narrative.

In a recent experiment the Temperament of a group of 66 surgeons and 65 anaesthetists was mapped using a standard Myers-Briggs Type Indicator® tool.  The data showed that the proportion reporting a Guardian (xSxJ) preference was 62% for the surgeons and 59% for the anaesthetists.  The difference was not statistically significant [For the statistically knowledgable the Chi-squared test gave a p-value of 0.84].  The reported proportion of the normal population who have a Guardian temperament is 34% so this is very different from the combined group of operating theatre doctors [Chi-squared test, p<0.0001].  Digging deeper into the data the proportion showing the tough-minded Guardian preference, the xSTJ, was 55% for the Surgeons and 46% for the Anaesthetists which was also not significantly different [p=0.34] but compared with a normal population proportion of 24% there are significantly more tough-minded Guardians in the operating theatre [p<0.0001].

So what then is the difference between Surgeons and Anaesthetists in their preferred modes of thinking?

The data shows that Surgeons are more likely to prefer Extraversion – the ESTJ profile – compared with Anaesthetists – who lean more towards Introversion – the ISTJ profile (p=0.12). This p-value means that with the data available there is a one in eight chance that this difference is due to chance. We would needs a bigger set of data to get greater certainty.

The temperament gradient is enough to create a certain degree of tension because although the Guardian temperament is the same, and the tough-mindedness is the same, the dominant function differs between the ESTJ and the ISTJ types.  As the Surgeons tend to the ESTJ mode, their dominant function is Thinking Judgement. The Anaesthetists tend to perfer ISTJ so their dominant fuction is Sensed Perceiving. This makes a big difference.

And it fits with their chosen roles in the operating theatre. The archetype ESTJ Surgeon is the Supervisor and decides what to do and who does it. The archetype ISTJ Anaesthetist is the Inspector and monitors and maintains safety and stability. This is a sweepig generalisation of course – but a useful one.

The roles are complementary, the minor conflict is inevitable, and the tension is not a “bad” thing – it is healthy – for the patient.  But when external forces threaten the safety, predictability and stability the conflict is amplified.

lightning_strike_150_wht_5809Rather like the weather.

Hot wet air looks clear. Cold dry air looks clear too.  When hot-humid air from the tropics meets cold-crisp air from the poles then a band of of fog will be created.  We call it a weather front and it generates variation.  And if the temperature and humidity difference is excessive then storm clouds will form. The lightning will flash and the thunder will growl as the energy is released.

Clouds obscure clarity of forward vision but clouds also create shade from the sun above; clouds trap warmth beneath; and clouds create rain which is necessary to sustain growth. Clouds are not all bad.  Some cloudiness is necessary.

An Improvement Scientist knows that 100% harmony is not the healthiest ratio. Unchallenged group-think is potentially dangerous.  Zero harmony is also unhealthy.  Open warfare is destructive.  Everyone loses.  A mixture of temperaments, a diversity of perspectives, a bit of fog, and a bit of respectful challenge is healthier than All-or-None.

It is at the complex and dynamic interface between different temperaments that learning and innovation happens so a slight temperamenture gradient is ideal.  The emotometer should not read too cold or too hot.

Understanding this dynamic is a big step towards being able to manage the creative tension.

To explore the Temperamenture Map of your team, department and organisation try the Temperament Sorter tool – one of the Improvement Science cultural diagnostic tests.

Creep-Crack-Crunch

The current crisis of confidence in the NHS has all the hallmarks of a classic system behaviour called creep-crack-crunch.

The first obvious crunch may feel like a sudden shock but it is usually not a complete surprise and it is actually one of a series of cracks that are leading up to a BIG CRUNCH. These cracks are an early warning sign of pressure building up in parts of the system and causing localised failures. These cracks weaken the whole system. The underlying cause is called creep.

SanFrancisco_PostEarthquake

Earthquakes are a perfect example of this phenomemon. Geological time scales are measured in thousands of years and we now know that the surface of the earth is a dynamic structure with vast contient-sized plates of solid rock floating on a liquid core of molten magma. Over millions of years the continents have moved huge distances and the world we see today on our satellite images is just a single frame in a multi-billion year geological video.  That is the geological creep bit. The cracks first appear at the edges of these tectonic plates where they smash into each other, grind past each other or are pulled apart from each other.  The geological hot-spots are marked out on our global map by lofty mountain ranges, fissured earthquake zones, and deep mid-ocean trenches. And we know that when a geological crunch arrives it happens in a blink of the geological eye.

The panorama above shows the devastation of San Francisco caused by the 1906 earthquake. San Francisco is built on the San Andreas Fault – the junction between the Pacific plate and the North American plate. The dramatic volcanic eruption in Iceland in 2010 came and went in a matter of weeks but the irreversible disruption it caused for global air traffic will be felt for years. The undersea earthquakes that caused the devastating tsunamis in 2006 and 2011 lasted only a few minutes; the deadly shock waves crossed an ocean in a matter of hours; and when they arrived the silent killer wiped out whole shoreside communities in seconds. Tens of thousands of lives were lost and the social after-shocks of that geological-crunch will be felt for decades.

These are natural disasters. We have little or no influence over them. Human-engineered disasters are a different matter – and they are just as deadly.

The NHS is an example. We are all painfully aware of the recent crisis of confidence triggered by the Francis Report. Many could see the cracks appearing and tried to blow their warning whistles but with little effect – they were silenced with legal gagging clauses and the opening cracks were papered over. It was only after the crunch that we finally acknowledged what we already knew and we started to search for the creep. Remorse and revenge does not bring back those who have been lost.  We need to focus on the future and not just point at the past.

UK_PopulationPyramid_2013Socio-economic systems evolve at a pace that is measured in years. So when a social crunch happens it is necessary to look back several decades for the tell-tale symptoms of creep and the early signs of cracks appearing.

Two objective measures of a socio-economic system are population and expenditure.

Population is people-in-progress; and national expenditure is the flow of the cash required to keep the people-in-progress watered, fed, clothed, housed, healthy and occupied.

The diagram above is called a population pyramid and it shows the distribution by gender and age of the UK population in 2013. The wobbles tell a story. It does rather look like the profile of a bushy-eyebrowed, big-nosed, pointy-chinned old couple standing back-to-back and maybe there is a hidden message for us there?

The “eyebrow” between ages 67 and 62 is the increase in births that happened 62 to 67 years ago: betwee 1946 and 1951. The post WWII baby boom.  The “nose” of 42-52 year olds are the “children of the 60’s” which was a period of rapid economic growth and new optimism. The “upper lip” at 32-42 correlates with the 1970’s that was a period of stagnant growth,  high inflation, strikes, civil unrest and the dark threat of global thermonuclear war. This “stagflation” is now believed to have been triggered by political meddling in the Middle-East that led to the 1974 OPEC oil crisis and culminated in the “winter of discontent” in 1979.  The “chin” signals there was another population expansion in the 1980s when optimism returned (SALT-II was signed in 1979) and the economy was growing again. Then the “neck” contraction in the 1990’s after the 1987 Black Monday global stock market crash.  Perhaps the new optimism of the Third Millenium led to the “chest” expansion but the financial crisis that followed the sub-prime bubble to burst in 2008 has yet to show its impact on the population chart. This static chart only tells part of the story – the animated chart reveals a significant secondary expansion of the 20-30 year old age group over the last decade. This cannot have been caused by births and is evidence of immigration of a large number of young couples – probably from the expanding Europe Union.

If this “yo-yo” population pattern is repeated then the current economic downturn will be followed by a contraction at the birth end of the spectrum and possibly also net emigration. And that is a big worry because each population wave takes a 100 years to propagate through the system. The most economically productive population – the  20-60 year olds  – are the ones who pay the care bills for the rest. So having a population curve with lots of wobbles in it causes long term socio-economic instability.

Using this big-picture long-timescale perspective; evidence of an NHS safety and quality crunch; silenced voices of cracks being papered-over; let us look for the historical evidence of the creep.

Nowadays the data we need is literally at our fingertips – and there is a vast ocean of it to swim around in – and to drown in if we are not careful.  The Office of National Statistics (ONS) is a rich mine of UK socioeconomic data – it is the source of the histogram above.  The trick is to find the nuggets of knowledge in the haystack of facts and then to convert the tables of numbers into something that is a bit more digestible and meaningful. This is what Russ Ackoff descibes as the difference between Data and Information. The data-to-information conversion needs context.

Rule #1: Data without context is meaningless – and is at best worthless and at worse is dangerous.

boxes_connected_PA_150_wht_2762With respect to the NHS there is a Minotaur’s Labyrinth of data warehouses – it is fragmented but it is out there – in cyberspace. The Department of Health publishes some on public sites but it is a bit thin on context so it can be difficult to extract the meaning.

Relying on our memories to provide the necessary context is fraught with problems. Memories are subject to a whole range of distortions, deletions, denials and delusions.  The NHS has been in existence since 1948 and there are not many people who can personally remember the whole story with objective clarity.  Fortunately cyberspace again provides some of what we need and with a few minutes of surfing we can discover something like a website that chronicles the history of the NHS in decades from its creation in 1948 – http://www.nhshistory.net/ – created and maintained by one person and a goldmine of valuable context. The decade that is of particular interest is 1998-2007 – Chapter 6

With just some data and some context it is possible to pull together the outline of the bigger picture of the decade that led up to the Mid Staffordshire healthcare quality crunch.

We will look at this as a NHS system evolving over time within its broader UK context. Here is the time-series chart of the population of England – the source of the demand on the NHS.

Population_of_England_1984-2010This shows a significant and steady increase in population – 12% overall between 1984 an 2012.

This aggregate hides a 9% increase in the under 65 population and 29% growth in the over 65 age group.

This is hard evidence of demographic creep – a ticking health and social care time bomb. And the curve is getting steeper. The pressure is building.

The next bit of the map we need is a measure of the flow through hospitals – the activity – and this data is available as the annual HES (Hospital Episodes Statistics) reports.  The full reports are hundreds of pages of fine detail but the headline summaries contain enough for our present purpose.

NHS_HES_Admissions_1997-2011

The time- series chart shows a steady increase in hospital admissions. Drilling into the summaries revealed that just over a third are emergency admissions and the rest are planned or maternity.

In the decade from 1998 to 2008 there was a 25% increase in hospital activity. This means more work for someone – but how much more and who for?

But does it imply more NHS beds?

Beds require wards, buildings and infrastructure – but it is the staff that deliver the health care. The bed is just a means of storage.  One measure of capacity and cost is the number of staffed beds available to be filled.  But this like measuring the number of spaces in a car park – it does not say much about flow – it is a just measure of maximum possible work in progress – the available space to hold the queue of patients who are somewhere between admission and discharge.

Here is the time series chart of the number of NHS beds from 1984 to 2006. The was a big fall in the number of beds in the decade after 1984 [Why was that?]

NHS_Beds_1984-2006

Between 1997 and 2007 there was about a 10% fall in the number of beds. The NHS patient warehouse was getting smaller.

But the activity – the flow – grew by 25% over the same time period: so the Laws Of Physics say that the flow must have been faster.

The average length of stay must have been falling.

This insight has another implication – fewer beds must mean smaller hospitals and lower costs – yes?  After all everyone seems to equate beds-to-cost; more-beds-cost-more less-beds-cost-less. It sounds reasonable. But higher flow means more demand and more workload so that would require more staff – and that means higher costs. So which is it? Less, the same or more cost?

NHS_Employees_1996_2007The published data says that staff headcount  went up by 25% – which correlates with the increase in activity. That makes sense.

And it looks like it “jumped” up in 2003 so something must have triggered that. More cash pumped into the system perhaps? Was that the effect of the Wanless Report?

But what type of staff? Doctors? Nurses? Admin and Clerical? Managers?  The European Working Time Directive (EWTD) forced junior doctors hours down and prompted an expansion of consultants to take on the displaced service work. There was also a gradual move towards specialisation and multi-disciplinary teams. What impact would that have on cost? Higher most likely. The system is getting more complex.

Of course not all costs have the same impact on the system. About 4% of staff are classified as “management” and it is this group that are responsible for strategic and tactical planning. Managers plan the work – workers work the plan.  The cost and efficiency of the management component of the system is not as useful a metric as the effectiveness of its collective decision making. Unfortuately there does not appear to be any published data on management decision making qualty and effectiveness. So we cannot estimate cost-effectiveness. Perhaps that is because it is not as easy to measure effectiveness as it is to count admissions, discharges, head counts, costs and deaths. Some things that count cannot easily be counted. The 4% number is also meaningless. The human head represents about 4% of the bodyweight of an adult person – and we all know that it is not the size of our heads that is important it is the effectiveness of the decisions that it makes which really counts!  Effectiveness, efficiency and costs are not the same thing.

Back to the story. The number of beds went down by 10% and number of staff went up by 25% which means that the staff-per-bed ratio went up by nearly 40%.  Does this mean that each bed has become 25% more productive or 40% more productive or less productive? [What exactly do we mean by “productivity”?]

To answer that we need to know what the beds produced – the discharges from hospital and not just the total number, we need the “last discharges” that signal the end of an episode of hospital care.

NHS_LastDischarges_1998-2011The time-series chart of last-discharges shows the same pattern as the admissions: as we would expect.

This output has two components – patients who leave alive and those who do not.

So what happened to the number of deaths per year over this period of time?

That data is also published annually in the Hospital Episode Statistics (HES) summaries.

This is what it shows ….

NHS_Absolute_Deaths_1998-2011The absolute hospital mortality is reducing over time – but not steadily. It went up and down between 2000 and 2005 – and has continued on a downward trend since then.

And to put this into context – the UK annual mortality is about 600,000 per year. That means that only about 40% of deaths happen in hospitals. UK annual mortality is falling and births are rising so the population is growing bigger and older.  [My head is now starting to ache trying to juggle all these numbers and pictures in it].

This is not the whole story though – if the absolute hospital activity is going up and the absolute hospital mortality is going down then this raw mortality number may not be telling the whole picture. To correct for those effects we need the ratio – the Hospital Mortality Ratio (HMR).

NHS_HospitalMortalityRatio_1998-2011This is the result of combining these two metrics – a 40% reduction in the hospital mortality ratio.

Does this mean that NHS hospitals are getting safer over time?

This observed behaviour can be caused by hospitals getting safer – it can also be caused by hospitals doing more low-risk work that creates a dilution effect. We would need to dig deeper to find out which. But that will distract us from telling the story.

Back to productivity.

The other part of the productivity equation is cost.

So what about NHS costs?  A bigger, older population, more activity, more staff, and better outcomes will all cost more taxpayer cash, surely! But how much more?  The activity and head count has gone up by 25% so has cost gone up by the same amount?

NHS_Annual_SpendThis is the time-series chart of the cost per year of the NHS and because buying power changes over time it has been adjusted using the Consumer Price Index using 2009 as the reference year – so the historical cost is roughly comparable with current prices.

The cost has gone up by 100% in one decade!  That is a lot more than 25%.

The published financial data for 2006-2010 shows that the proportion of NHS spending that goes to hospitals is about 50% and this has been relatively stable over that period – so it is reasonable to say that the increase in cash flowing to hospitals has been about 100% too.

So if the cost of hospitals is going up faster than the output then productivity is falling – and in this case it works out as a 37% drop in productivity (25% increase in activity for 100% increase in cost = 37% fall in productivity).

So the available data which anyone with a computer, an internet connection, and some curiosity can get; and with bit of spreadsheet noggin can turn into pictures shows that over the decade of growth that led up to the the Mid Staffs crunch we had:

1. A slightly bigger population; and a
2. significantly older population; and a
3. 25% increase in NHS hospital activity; and a
4. 10% fall in NHS beds; and a
5. 25% increase in NHS staff; which gives a
6. 40% increase in staff-per-bed ratio; an an
7. 8% reduction in absolute hospital mortality; which gives a
8. 40% reduction in relative hospital mortality; and a
9. 100% increase in NHS  hospital cost; which gives a
10. 37% fall drop in “hospital productivity”.

An experienced Improvement Scientist knows that a system that has been left to evolve by creep-crack-and-crunch can be re-designed to deliver higher quality and higher flow at lower total cost.

The safety creep at Mid-Staffs is now there for all to see. A crack has appeared in our confidence in the NHS – and raises a couple of crunch questions:

Where Has All The Extra Money Gone?

 How Will We Avoid The BIG CRUNCH?

The huge increase in NHS funding over the last decade was the recommendation of the Wanless Report but the impact of implementing the recommendations has never been fully explored. Healthcare is a service system that is designed to deliver two intangible products – health and care. So the major cost is staff-time – particularly the clinical staff.  A 25% increase in head count and a 100% increase in cost implies that the heads are getting more expensive.  Either a higher proportion of more expensive clinically trained and registered staff, or more pay for the existing staff or both.  The evidence shows that about 50% of NHS Staff are doctors and nurses and over the last decade there has been a bigger increase in the number of doctors than nurses. Added to that the Agenda for Change programme effectively increased the total wage bill and the new contracts for GPs and Consultants added more upward wage pressure.  This is cost creep and it adds up over time. The Kings Fund looked at the impact in 2006 and suggested that, in that year alone, 72% of the additional money was sucked up by bigger wage bills and other cost-pressures! The previous year they estimated 87% of the “new money” had disappeared hte same way. The extra cash is gushing though the cracks in the bottom of the fiscal bucket that had been clumsily papered-over. And these are recurring revenue costs so they add up over time into a future financial crunch.  The biggest one may be yet to come – the generous final-salary pensions that public-sector employees enjoy!

So it is even more important that the increasingly expensive clinical staff are not being forced to spend their time doing work that has no direct or indirect benefit to patients.

Trying to do a good job in a poorly designed system is both frustrating and demotivating – and the outcome can be a cynical attitude of “I only work here to pay the bills“. But as public sector wages go up and private sector pensions evaporate the cynics are stuck in a miserable job that they cannot afford to give up. And their negative behaviour poisons the whole pool. That is the long term cumulative cultural and financial cost of poor NHS process design. That is the outcome of not investing earlier in developing an Improvement Science capability.

The good news is that the time-series charts illustrate that the NHS is behaving like any other complex, adaptive, human-engineered value system. This means that the theory, techniques and tools of Improvement Science and value system design can be applied to answer these questions. It means that the root causes of the excessive costs can be diagnosed and selectively removed without compromising safety and quality. It means that the savings can be wisely re-invested to improve the resilience of some parts and to provide capacity in other parts to absorb the expected increases in demand that are coming down the population pipe.

This is Improvement Science. It is a learnable skill.

18/03/2013: Update

The question “Where Has The Money Gone?” has now been asked at the Public Accounts Committee

 

The Writing on the Wall – Part II

Who_Is_To_BlameThe retrospectoscope is the favourite instrument of the forensic cynic – the expert in the after-the-event-and-I-told-you-so rhetoric. The rabble-rouser for the lynch-mob.

It feels better to retrospectively nail-to-a-cross the person who committed the Cardinal Error of Omission, and leave them there in emotional and financial pain as a visible lesson to everyone else.

This form of public feedback has been used for centuries.

It is called barbarism, and it has no place in a modern civilised society.


A more constructive question to ask is:

Could the evolving Mid-Staffordshire crisis have been detected earlier … and avoided?”

And this question exposes a tricky problem: it is much more difficult to predict the future than to explain the past.  And if it could have been detected and avoided earlier, then how is that done?  And if the how-is-known then is everyone else in the NHS using this know-how to detect and avoid their own evolving Mid-Staffs crisis?

To illustrate how it is currently done let us use the actual Mid-Staffs data. It is conveniently available in Figure 1 embedded in Figure 5 on Page 360 in Appendix G of Volume 1 of the first Francis Report.  If you do not have it at your fingertips I have put a copy of it below.

MS_RawData

The message does not exactly leap off the page and smack us between the eyes does it? Even with the benefit of hindsight.  So what is the problem here?

The problem is one of ergonomics. Tables of numbers like this are very difficult for most people to interpret, so they create a risk that we ignore the data or that we just jump to the bottom line and miss the real message. And It is very easy to miss the message when we compare the results for the current period with the previous one – a very bad habit that is spread by accountants.

This was a slowly emerging crisis so we need a way of seeing it evolving and the better way to present this data is as a time-series chart.

As we are most interested in safety and outcomes, then we would reasonably look at the outcome we do not want – i.e. mortality.  I think we will all agree that it is an easy enough one to measure.

MS_RawDeathsThis is the raw mortality data from the table above, plotted as a time-series chart.  The green line is the average and the red-lines are a measure of variation-over-time. We can all see that the raw mortality is increasing and the red flags say that this is a statistically significant increase. Oh dear!

But hang on just a minute – using raw mortality data like this is invalid because we all know that the people are getting older, demand on our hospitals is rising, A&Es are busier, older people have more illnesses, and more of them will not survive their visit to our hospital. This rise in mortality may actually just be because we are doing more work.

Good point! Let us plot the activity data and see if there has been an increase.

MS_Activity

Yes – indeed the activity has increased significantly too.

Told you so! And it looks like the activity has gone up more than the mortality. Does that mean we are actually doing a better job at keeping people alive? That sounds like a more positive message for the Board and the Annual Report. But how do we present that message? What about as a ratio of mortality to activity? That will make it easier to compare ourselves with other hospitals.

Good idea! Here is the Raw Mortality Ratio chart.

MS_RawMortality_RatioAh ha. See! The % mortality is falling significantly over time. Told you so.

Careful. There is an unstated assumption here. The assumption that the case mix is staying the same over time. This pattern could also be the impact of us doing a greater proportion of lower complexity and lower risk work.  So we need to correct this raw mortality data for case mix complexity – and we can do that by using data from all NHS hospitals to give us a frame of reference. Dr Foster can help us with that because it is quite a complicated statistical modelling process. What comes out of Dr Fosters black magic box is the Global Hospital Raw Mortality (GHRM) which is the expected number of deaths for our case mix if we were an ‘average’ NHS hospital.

MS_ExpectedMortality_Ratio

What this says is that the NHS-wide raw mortality risk appears to be falling over time (which may be for a wide variety of reasons but that is outside the scope of this conversation). So what we now need to do is compare this global raw mortality risk with our local raw mortality risk  … to give the Hospital Standardised Mortality Ratio.

MS_HSMRThis gives us the Mid Staffordshire Hospital HSMR chart.  The blue line at 100 is the reference average – and what this chart says is that Mid Staffordshire hospital had a consistently higher risk than the average case-mix adjusted mortality risk for the whole NHS. And it says that it got even worse after 2001 and that it stayed consistently 20% higher after 2003.

Ah! Oh dear! That is not such a positive message for the Board and the Annual Report. But how did we miss this evolving safety catastrophe?  We had the Dr Foster data from 2001

This is not a new problem – a similar thing happened in Vienna between 1820 and 1850 with maternal deaths caused by Childbed Fever. The problem was detected by Dr Ignaz Semmelweis who also discovered a simple, pragmatic solution to the problem: hand washing.  He blew the whistle but unfortunately those in power did not like the implication that they had been the cause of thousands of avoidable mother and baby deaths.  Semmelweis was vilified and ignored, and he did not publish his data until 1861. And even then the story was buried in tables of numbers.  Semmelweis went mad trying to convince the World that there was a problem.  Here is the full story.

Also, time-series charts were not invented until 1924 – and it was not in healthcare – it was in manufacturing. These tried-and-tested safety and quality improvement tools are only slowly diffusing into healthcare because the barriers to innovation appear somewhat impervious.

And the pores have been clogged even more by the social poison called “cynicide” – the emotional and political toxin exuded by cynics.

So how could we detect a developing crisis earlier – in time to avoid a catastrophe?

The first step is to estimate the excess-death-equivalent. Dr Foster does this for you.MS_ExcessDeathsHere is the data from the table plotted as a time-series chart that shows that the estimated-excess-death-equivalent per year. It has an average of 100 (that is two per week) and the average should be close to zero. More worryingly the number was increasing steadily over time up to 200 per year in 2006 – that is about four excess deaths per week – on average.  It is important to remember that HSMR is a risk ratio and mortality is a multi-factorial outcome. So the excess-death-equivalent estimate does not imply that a clear causal chain will be evident in specific deaths. That is a complete misunderstanding of the method.

I am sorry – you are losing me with the statistical jargon here. Can you explain in plain English what you mean?

OK. Let us use an example.

Suppose we set up a tombola at the village fete and we sell 50 tickets with the expectation that the winner bags all the money. Each ticket holder has the same 1 in 50 risk of winning the wad-of-wonga and a 49 in 50 risk of losing their small stake. At the appointed time we spin the barrel to mix up the ticket stubs then we blindly draw one ticket out. At that instant the 50 people with an equal risk changes to one winner and 49 losers. It is as if the grey fog of risk instantly condenses into a precise, black-and-white, yes-or-no, winner-or-loser, reality.

Translating this concept back into HSMR and Mid Staffs – the estimated 1200 deaths are the just the “condensed risk of harm equivalent”.  So, to then conduct a retrospective case note analysis of specific deaths looking for the specific cause would be equivalent to trying to retrospectively work out the reason the particular winning ticket in the tombola was picked out. It is a search that is doomed to fail. To then conclude from this fruitless search that HSMR is invalid, is only to compound the delusion further.  The actual problem here is ignorance and misunderstanding of the basic Laws of Physics and Probability, because our brains are not good at solving these sort of problems.

But Mid Staffs is a particularly severe example and  it only shows up after years of data has accumulated. How would a hospital that was not as bad as this know they had a risk problem and know sooner? Waiting for years to accumulate enough data to prove there was a avoidable problem in the past is not much help. 

That is an excellent question. This type of time-series chart is not very sensitive to small changes when the data is noisy and sparse – such as when you plot the data on a month-by-month timescale and avoidable deaths are actually an uncommon outcome. Plotting the annual sum smooths out this variation and makes the trend easier to see, but it delays the diagnosis further. One way to increase the sensitivity is to plot the data as a cusum (cumulative sum) chart – which is conspicuous by its absence from the data table. It is the running total of the estimated excess deaths. Rather like the running total of swings in a game of golf.

MS_ExcessDeaths_CUSUMThis is the cusum chart of excess deaths and you will notice that it is not plotted with control limits. That is because it is invalid to use standard control limits for cumulative data.  The important feature of the cusum chart is the slope and the deviation from zero. What is usually done is an alert threshold is plotted on the cusum chart and if the measured cusum crosses this alert-line then the alarm bell should go off – and the search then focuses on the precursor events: the Near Misses, the Not Agains and the Niggles.

I see. You make it look easy when the data is presented as pictures. But aren’t we still missing the point? Isn’t this still after-the-avoidable-event analysis?

Yes! An avoidable death should be a Never-Event in a designed-to-be-safe healthcare system. It should never happen. There should be no coffins to count. To get to that stage we need to apply exactly the same approach to the Near-Misses, and then the Not-Agains, and eventually the Niggles.

You mean we have to use the SUI data and the IR1 data and the complaint data to do this – and also ask our staff and patients about their Niggles?

Yes. And it is not the number of complaints that is the most useful metric – it is the appearance of the cumulative sum of the complaint severity score. And we need a method for diagnosing and treating the cause of the Niggles too. We need to convert the feedback information into effective action.

Ah ha! Now I understand what the role of the Governance Department is: to apply the tools and techniques of Improvement Science proactively.  But our Governance Department have not been trained to do this!

Then that is one place to start – and their role needs to evolve from Inspectors and Supervisors to Demonstrators and Educators – ultimately everyone in the organisation needs to be a competent Healthcare Improvementologist.

OK – I now now what to do next. But wait a minute. This is going to cost a fortune!

This is just one small first step.  The next step is to redesign the processes so the errors do not happen in the first place. The cumulative cost saving from eliminating the repeated checking, correcting, box-ticking, documenting, investigating, compensating and insuring is much much more than the one-off investment in learning safe system design.

So the Finance Director should be a champion for safety and quality too.

Yup!

Brill. Thanks. And can I ask one more question? I do not want to appear to skeptical but how do we know we can trust that this risk-estimation system has been designed and implemented correctly? How do we know we are not being bamboozled by statisticians? It has happened before!

That is the best question yet.  It is important to remember that HSMR is counting deaths in hospital which means that it is not actually the risk of harm to the patient that is measured – it is the risk to the reputation of hospital! So the answer to your question is that you demonstrate your deep understanding of the rationle and method of risk-of-harm estimation by listing all the ways that such a system could be deliberately “gamed” to make the figures look better for the hospital. And then go out and look for hard evidence of all the “games” that you can invent. It is a sort of creative poacher-becomes-gamekeeper detective exercise.

OK – I sort of get what you mean. Can you give me some examples?

Yes. The HSMR method is based on deaths-in-hospital so discharging a patient from hospital before they die will make the figures look better. Suppose one hospital has more access to end-of-life care in the community than another: their HSMR figures would look better even though exactly the same number of people died. Another is that the HSMR method is weighted towards admissions classified as “emergencies” – so if a hospital admits more patients as “emergencies” who are not actually very sick and discharges them quickly then this will inflated their estimated deaths and make their actual mortality ratio look better – even though the risk-of-harm to patients has not changed.

OMG – so if we have pressure to meet 4 hour A&E targets and we get paid more for an emergency admission than an A&E attendance then admitting to an Assessmen Area and discharging within one day will actually reward the hospital financially, operationally and by apparently reducing their HSMR even though there has been no difference at all to the care that patients actually recieve?

Yes. It is an inevitable outcome of the current system design.

But that means that if I am gaming the system and my HSMR is not getting better then the risk-of-harm to patients is actually increasing and my HSMR system is giving me false reassurance that everything is OK.   Wow! I can see why some people might not want that realisation to be public knowledge. So what do we do?

Design the system so that the rewards are aligned with lower risk of harm to patients and improved outcomes.

Is that possible?

Yes. It is called a Win-Win-Win design.

How do we learn how to do that?

Improvement Science.

Footnote I:

The graphs tell a story but they may not create a useful sense of perspective. It has been said that there is a 1 in 300 chance that if you go to hospital you will not leave alive for avoidable causes. What! It cannot be as high as 1 in 300 surely?

OK – let us use the published Mid-Staffs data to test this hypothesis. Over 12 years there were about 150,000 admissions and an estimated 1,200 excess deaths (if all the risk were concentrated into the excess deaths which is not what actually happens). That means a 1 in 130 odds of an avoidable death for every admission! That is twice as bad as the estimated average.

The Mid Staffordshire statistics are bad enough; but the NHS-as-a-whole statistics are cumulatively worse because there are 100’s of other hospitals that are each generating not-as-obvious avoidable mortality. The data is very ‘noisy’ so it is difficult even for a statistical expert to separate the message from the morass.

And remember – that  the “expected” mortality is estimated from the average for the whole NHS – which means that if this average is higher than it could be then there is a statistical bias and we are being falsely reassured by being ‘not statistically significantly different’ from the pack.

And remember too – for every patient and family that suffers and avoidable death there are many more that have to live with the consequences of avoidable but non-fatal harm.  That is called avoidable morbidity.  This is what the risk really means – everyone has a higher risk of some degree of avoidable harm. Psychological and physical harm.

This challenge is not just about preventing another Mid Staffs – it is about preventing 1000’s of avoidable deaths and 100,000s of patients avoidably harmed every year in ‘average’ NHS trusts.

It is not a mass conspiracy of bad nurses, bad doctors, bad managers or bad policians that is the root cause.

It is poorly designed processes – and they are poorly designed because the nurses, doctors and managers have not learned how to design better ones.  And we do not know how because we were not trained to.  And that education gap was an accident – an unintended error of omission.  

Our urgently-improve-NHS-safety-challenge requires a system-wide safety-by-design educational and cultural transformation.

And that is possible because the knowledge of how to design, test and implement inherently safe processes exists. But it exists outside healthcare.

And that safety-by-design training is a worthwhile investment because safer-by-design processes cost less to run because they require less checking, less documenting, less correcting – and all the valuable nurse, doctor and manager time freed up by that can be reinvested in more care, better care and designing even better processes and systems.

Everyone Wins – except the cynics who have a choice: to eat humble pie or leave.

Footnote II:

In the debate that has followed the publication of the Francis Report a lot of scrutiny has been applied to the method by which an estimated excess mortality number is created and it is necessary to explore this in a bit more detail.

The HSMR is an estimate of relative risk – it does not say that a set of specific patients were the ones who came to harm and the rest were OK. So looking at individual deaths and looking for the specific causes is to completely misunderstand the method. So looking at the actual deaths individually and looking for identifiable cause-and-effect paths is an misuse of the message.  When very few if any are found to conclude that HSMR is flawed is an error of logic and exposes the ignorance of the analyst further.

HSMR is not perfect though – it has weaknesses.  It is a benchmarking process the”standard” of 100 is always moving because the collective goal posts are moving – the reference is always changing . HSMR is estimated using data submitted by hospitals themselves – the clinical coding data.  So the main weakness is that it is dependent on the quality of the clinicial coding – the errors of comission (wrong codes) and the errors of omission (missing codes). Garbage In Garbage Out.

Hospitals use clinically coded data for other reasons – payment. The way hospitals are now paid is based on the volume and complexity of that activity – Payment By Results (PbR) – using what are called Health Resource Groups (HRGs). This is a better and fairer design because hospitals with more complex (i.e. costly to manage) case loads get paid more per patient on average.  The HRG for each patient is determined by their clinical codes – including what are called the comorbidities – the other things that the patient has wrong with them. More comorbidites means more complex and more risky so more money and more risk of death – roughly speaking.  So when PbR came in it becamevery important to code fully in order to get paid “properly”.  The problem was that before PbR the coding errors went largely unnoticed – especially the comorbidity coding. And the errors were biassed – it is more likely to omit a code than to have an incorrect code. Errors of omission are harder to detect. This meant that by more complete coding (to attract more money) the estimated casemix complexity would have gone up compared with the historical reference. So as actual (not estimated) NHS mortality has gone down slightly then the HSMR yardstick becomes even more distorted.  Hospitals that did not keep up with the Coding Game would look worse even though  their actual risk and mortality may be unchanged.  This is the fundamental design flaw in all types of  benchmarking based on self-reported data.

The actual problem here is even more serious. PbR is actually a payment for activity – not a payment for outcomes. It is calculated from what it cost to run the average NHS hospital using a technique called Reference Costing which is the same method that manufacturing companies used to decide what price to charge for their products. It has another name – Absorption Costing.  The highest performers in the manufacturing world no longer use this out-of-date method. The implication of using Reference Costing and PbR in the NHS are profound and dangerous:

If NHS hospitals in general have poorly designed processes that create internal queues and require more bed days than actually necessary then the cost of that “waste” becomes built into the future PbR tariff. This means average length of stay (LOS) is financially rewarded. Above average LOS is financially penalised and below average LOS makes a profit.  There is no financial pressure to improve beyound average. This is called the Regression to the Mean effect.  Also LOS is not a measure of quality – so there is a to shorten length of stay for purely financial reasons – to generate a surplus to use to fund growth and capital investment.  That pressure is non-specific and indiscrimiate.  PbR is necessary but it is not sufficient – it requires an quality of outcome metric to complete it.    

So the PbR system is based on an out-of-date cost-allocation model and therefore leads to the very problems that are contributing to the MidStaffs crisis – financial pressure causing quality failures and increased risk of mortality.  MidStaffs may be a chance victim of a combination of factors coming together like a perfect storm – but those same factors are present throughout the NHS because they are built into the current design.

One solution is to move towards a more up-to-date financial model called stream costing. This uses the similar data to reference costing but it estimates the “ideal” cost of the “necessary” work to achieve the intended outcome. This stream cost becomes the focus for improvement – the streams where there is the biggest gap between the stream cost and the reference cost are the focus of the redesign activity. Very often the root cause is just poor operational policy design; sometimes it is quality and safety design problems. Both are solvable without investment in extra capacity. The result is a higher quality, quicker, lower-cost stream. Win-win-win. And in the short term that  is rewarded by a tariff income that exceeds cost and a lower HSMR.

Radically redesigning the financial model for healthcare is not a quick fix – and it requires a lot of other changes to happen first. So the sooner we start the sooner we will arrive. 

Robert Francis QC

press_on_screen_anim_150_wht_7028Today is an important day.

The Robert Francis QC Report and recommendations from the Mid-Staffordshire Hospital Crisis has been published – and it is a sobering read.  The emotions that just the executive summary evoked in me were sadness, shame and anger.  Sadness for the patients, relatives, and staff who have been irreversibly damaged; shame that the clinical professionals turned a blind-eye; and anger that the root cause has still not been exposed to public scrutiny.

Click here to get a copy of the RFQC Report Executive Summary.

Click here to see the video of RFQC describing his findings. 

The root cause is ignorance at all levels of the NHS.  Not stupidity. Not malevolence. Just ignorance.

Ignorance of what is possible and ignorance of how to achieve it.

RFQC rightly focusses his recommendations on putting patients at the centre of healthcare and on making those paid to deliver care accountable for the outcomes.  Disappointingly, the report is notably thin on the financial dimension other than saying that financial targets took priority over safety and quality.  He is correct. They did. But the report does not say that this is unnecessary – it just says “in future put safety before finance” and in so doing he does not challenge the belief that we are playing a zero-sum-game. The  assumotion that higher-quality-always-costs-more.

This assumption is wrong and can easily be disproved.

A system that has been designed to deliver safety-and-quality-on-time-first-time-and-every-time costs less. And it costs less because the cost of errors, checking, rework, queues, investigation, compensation, inspectors, correctors, fixers, chasers, and all the other expensive-high-level-hot-air-generation-machinery that overburdens the NHS and that RFQC has pointed squarely at is unnecessary.  He says “simplify” which is a step in the right direction. The goal is to render it irrelevent.

The ignorance is ignorance of how to design a healthcare system that works right-first-time. The fact that the Francis Report even exists and is pointing its uncomfortable fingers-of-evidence at every level of the NHS from ward to government is tangible proof of this collective ignorance of system design.

And the good news is that this collective ignorance is also unnecessary … because the knowledge of how to design safe-and-affordable systems already exists. We just have to learn how. I call it 6M Design® – but  the label is irrelevent – the knowledge exists and the evidence that it works exists.

So here are some of the RFQC recommendations viewed though a 6M Design® lens:       

1.131 Compliance with the fundamental standards should be policed by reference to developing the CQC’s outcomes into a specification of indicators and metrics by which it intends to monitor compliance. These indicators should, where possible, be produced by the National Institute for Health and Clinical Excellence (NICE) in the form of evidence-based procedures and practice which provide a practical means of compliance and of measuring compliance with fundamental standards.

This is the safety-and-quality outcome specification for a healthcare system design – the required outcome presented as a relevent metric in time-series format and qualified by context.  Only a stable outcome can be compared with a reference standard to assess the system capability. An unstable outcome metric requires inquiry to understand the root cause and an appropriate action to restore stability. A stable but incapable outcome performance requires redesign to achieve both stability and capability. And if  the terms used above are unfamiliar then that is further evidence of system-design-ignorance.   
 
1.132 The procedures and metrics produced by NICE should include evidence-based tools for establishing the staffing needs of each service. These measures need to be readily understood and accepted by the public and healthcare professionals.

This is the capacity-and-cost specification of any healthcare system design – the financial envelope within which the system must operate. The system capacity design works backwards from this constraint in the manner of “We have this much resource – what design of our system is capable of delivering the required safety and quality outcome with this capacity?”  The essence of this challenge is to identify the components of poor (i.e. wasteful) design in the existing systems and remove or replace them with less wasteful designs that achieve the same or better quality outcomes. This is not impossible but it does require system diagnostic and design capability. If the NHS had enough of those skills then the Francis Report would not exist.

1.133 Adoption of these practices, or at least their equivalent, is likely to help ensure patients’ safety. Where NICE is unable to produce relevant procedures, metrics or guidance, assistance could be sought and commissioned from the Royal Colleges or other third-party organisations, as felt appropriate by the CQC, in establishing these procedures and practices to assist compliance with the fundamental standards.

How to implement evidence-based research in the messy real world is the Elephant in the Room. It is possible but it requires techniques and tools that fall outside the traditional research and audit framework – or rather that sit between research and audit. This is where Improvement Science sits. The fact that the Report only mentions evidence-based practice and audit implies that the NHS is still ignorant of this gap and what fills it – and so it appears is RFQC.   

1.136 Information needs to be used effectively by regulators and other stakeholders in the system wherever possible by use of shared databases. Regulators should ensure that they use the valuable information contained in complaints and many other sources. The CQC’s quality risk profile is a valuable tool, but it is not a substitute for active regulatory oversight by inspectors, and is not intended to be.

Databases store data. Sharing databases will share data. Data is not information. Information requires data and the context for that data.  Furthermore having been informed does not imply either knowledge or understanding. So in addition to sharing information, the capability to convert information-into-decision is also required. And the decisions we want are called “wise decisions” which are those that result in actions and inactions that lead inevitably to the intended outcome.  The knowledge of how to do this exists but the NHS seems ignorant of it. So the challenge is one of education not of yet more investigation.

1.137 Inspection should remain the central method for monitoring compliance with fundamental standards. A specialist cadre of hospital inspectors should be established, and consideration needs to be given to collaborative inspections with other agencies and a greater exploitation of peer review techniques.

This is audit. This is the sixth stage of a 6M Design® – the Maintain step.  Inspectors need to know what they are looking for, the errors of commission and the errors of omission;  and to know what those errors imply and what to do to identify and correct the root cause of these errors when discovered. The first cadre of inspectors will need to be fully trained in healthcare systems design and healthcare systems improvement – in short – they need to be Healthcare Improvementologists. And they too will need to be subject to the same framework of accreditation, and accountability as those who work in the system they are inspecting.  This will be one of the greatest of the challenges. The fact that the Francis report exists implies that we do not have such a cadre. Who will train, accredit and inspect the inspectors? Who has proven themselves competent in reality (not rhetorically)?

1.163 Responsibility for driving improvement in the quality of service should therefore rest with the commissioners through their commissioning arrangements. Commissioners should promote improvement by requiring compliance with enhanced standards that demand more of the provider than the fundamental standards.

This means that commissioners will need to understand what improvement requires and to include that expectation in their commissioning contracts. This challenge is even geater that the creation of a “cadre of inspectors”. What is required is a “generation of competent commissioners” who are also experienced and who have demonstrated competence in healthcare system design. The Commissioners-of-the-Future will need to be experienced healthcare improvementologists.

The NHS is sick – very sick. The medicine it needs to restore its health and vitality does exist – and it will not taste very nice – but to withold an effective treatment for an serious illness on that basis is clinical negligence.

It is time for the NHS to look in the mirror and take the strong medicine. The effect is quick – it will start to feel better almost immediately. 

To deliver safety and quality and quickly and affordably is possible – and if you do not believe that then you will need to muster the humility to ask to have the how demonstrated.

6MDesign

 

Kicking the Habit

no_smoking_400_wht_6805It is not easy to kick a habit. We all know that. And for some reason the ‘bad’ habits are harder to kick than the ‘good’ ones. So what is bad about a ‘bad habit’ and why is it harder to give up? Surely if it was really bad it would be easier to give up?

Improvement is all about giving up old ‘bad’ habits and replacing them with new ‘good’ habits – ones that will sustain the improvement. But there is an invisible barrier that resists us changing any habit – good or bad. And it is that barrier to habit-breaking that we need to understand to succeed. Luck is not a reliable ally.

What does that habit-breaking barrier look like?

The problem is that it is invisible – or rather it is emotional – or to be precise it is chemical.

Our emotions are the output of a fantastically complex chemical system – our brains. And influencing the chemical balance of our brains can have a profound effect on our emotions.  That is how anti-depressants work – they very slightly adjust the chemical balance of every part of our brains. The cumulative effect is that we feel happier.  Nicotine has a similar effect.

And we can achieve the same effect without resorting to drugs or fags – and we can do that by consciously practising some new mental habits until they become ingrained and unconscious. We literally overwrite the old mental habit.

So how do we do this?

First we need to make the mental barrier visible – and then we can focus our attention on eroding it. To do that we need to remove the psychological filter that we all use to exclude our emotions. It is rather like taking off our psychological sunglasses.

When we do that the invisible barrier jumps into view: illuminated by the glare of three negative emotions.  Sadness, fear, and anxiety.  So whenever we feel any of these we know there is a barrier to improvement hiding  the emotional smoke. This is the first stage: tune in to our emotions.

The next step is counter-intuitive. Instead of running away from the negative feeling we consciously flip into a different way of thinking.  We actively engage with our negative feelings – and in a very specific way. We engage in a detached, unemotional, logical, rational, analytical  ‘What caused that negative feeling?’ way.

We then focus on the causes of the negative emotions. And when we have the root causes of our Niggles we design around them, under them, and over them.  We literally design them out of our heads.

The effect is like magic.

And this week I witnessed a real example of this principle in action.

figure_pressing_power_button_150_wht_10080One team I am working with experienced the Power of Improvementology. They saw the effect with their own eyes.  There were no computers in the way, no delays, no distortion and no deletion of data to cloud the issue. They saw the performance of their process jump dramatically – from a success rate of 60% to 96%!  And not just the first day, the second day too.  “Surprised and delighted” sums up their reaction.

So how did we achieve this miracle?

We just looked at the process through a different lens – one not clouded and misshapen by old assumptions and blackened by ignorance of what is possible.  We used the 6M Design® lens – and with the clarity of insight it brings the barriers to improvement became obvious. And they were dissolved. In seconds.

Success then flowed as the Dam of Disbelief crumbled and was washed away.

figure_check_mark_celebrate_anim_150_wht_3617The chaos has gone. The interruptions have gone. The expediting has gone. The firefighting has gone. The complaining has gone.  These chronic Niggles have have been replaced by the Nuggets of calm efficiency, new hope and visible excitement.

And we know that others have noticed the knock-on effect because we got an email from our senior executive that said simply “No one has moaned about TTOs for two days … something has changed.”    

That is Improvementology-in-Action.

 

Curing Chronic Carveoutosis

pin_marker_lighting_up_150_wht_6683Last week the Ray Of Hope briefly illuminated a very common system design disease called carveoutosis.  This week the RoH will tarry a little longer to illuminate an example that reveals the value of diagnosing and treating this endemic process ailment.

Do you remember the days when we used to have to visit the Central Post Office in our lunch hour to access a quality-of-life-critical service that only a Central Post Office could provide – like getting a new road tax disc for our car?  On walking through the impressive Victorian entrances of these stalwart high street institutions our primary challenge was to decide which queue to join.

In front of each gleaming mahogony, brass and glass counter was a queue of waiting customers. Behind was the Post Office operative. We knew from experience that to be in-and-out before our lunch hour expired required deep understanding of the ways of people and processes – and a savvy selection.  Some queues were longer than others. Was that because there was a particularly slow operative behind that counter? Or was it because there was a particularly complex postal problem being processed? Or was it because the customers who had been waiting longer had identified that queue was fast flowing and had defected to it from their more torpid streams? We know that size is not a reliable indicator of speed or quality.figure_juggling_time_150_wht_4437

The social pressure is now mounting … we must choose … dithering is a sign of weakness … and swapping queues later is another abhorrent behaviour. So we employ our most trusted heuristic – we join the end of the shortest queue. Sometimes it is a good choice, sometimes not so good!  But intuitively it feels like the best option.

Of course  if we choose wisely and we succeed in leap-frogging our fellow customers then we can swagger (just a bit) on the way out. And if not we can scowl and mutter oaths at others who (by sheer luck) leap frog us. The Post Office Game is fertile soil for the Aint’ It Awful game which we play when we arrive back at work.

single_file_line_PA_150_wht_3113But those days are past and now we are more likely to encounter a single-queue when we are forced by necessity to embark on a midday shopping sortie. As we enter we see the path of the snake thoughtfully marked out with rope barriers or with shelves hopefully stacked with just-what-we-need bargains to stock up on as we drift past.  We are processed FIFO (first-in-first-out) which is fairer-for-all and avoids the challenge of the dreaded choice-of-queue. But the single-queue snake brings a new challenge: when we reach the head of the snake we must identify which operative has become available first – and quickly!

Because if we falter then we will incur the shame of the finger-wagging or the flashing red neon arrow that is easily visible to the whole snake; and a painful jab in the ribs from the impatient snaker behind us; and a chorus of tuts from the tail of the snake. So as we frantically scan left and right along the line of bullet-proof glass cells looking for clues of imminent availability we run the risk of developing acute vertigo or a painful repetitive-strain neck injury!

stick_figure_sitting_confused_150_wht_2587So is the single-queue design better?  Do we actually wait less time, the same time or more time? Do we pay a fair price for fair-for-all queue design? The answer is not intuitively obvious because when we are forced to join a lone and long queue it goes against our gut instinct. We feel the urge to push.

The short answer is “Yes”.  A single-queue feeding tasks to parallel-servers is actually a better design. And if we ask the Queue Theorists then they will dazzle us with complex equations that prove it is a better design – in theory.  But the scary-maths does not help us to understand how it is a better design. Most of us are not able to convert equations into experience; academic rhetoric into pragmatic reality. We need to see it with our own eyes to know it and understand it. Because we know that reality is messier than theory.    

And if it is a better design then just how much better is it?

To illustrate the potential advantage of a single-queue design we need to push the competing candiates to their performance limits and then measure the difference. We need a real example and some real data. We are Improvementologists! 

First we need to map our Post Office process – and that reveals that we have a single step process – just the counter. That is about as simple as a process gets. Our map also shows that we have a row of counters of which five are manned by fully trained Post Office service operatives.

stick_figure_run_clock_150_wht_7094Now we can measure our process and when we do that we find that we get an average of 30 customers per hour walking in the entrance and and average of 30 cusomers an hour walking out. Flow-out equals flow-in. Activity equals demand. And the average flow is one every 2 minutes. So far so good. We then observe our five operatives and we find that the average time from starting to serve one customer to starting to serve the next is 10 minutes. We know from our IS training that this is the cycle time. Good.

So we do a quick napkin calculation to check and that the numbers make sense: our system of five operatives working in parallel, each with an average cycle time of 10 minutes can collectively process a customer on average every 2 minutes – that is 30 per hour on average. So it appears we have just enough capacity to keep up with the flow of work  – we are at the limit of efficiency.  Good.

CarveOut_00We also notice that there is variation in the cycle time from customer to customer – so we plot our individual measurements asa time-series chart. There does not seem to be an obvious pattern – it looks random – and BaseLine says that it is statistically stable. Our chart tells us that a range of 5 to 15 minutes is a reasonable expectation to set.

We also observe that there is always a queue of waiting customers somewhere – and although the queues fluctuate in size and location they are always there.

 So there is always a wait for some customers. A variable wait; an unpredictable wait. And that is a concern for us because when the queues are too numerous and too long then we see customers get agitated, look at their watches, shrug their shoulders and leave – taking their custom and our income with them and no doubt telling all their friends of their poor experience. Long queues and long waits are bad for business.

And we do not want zero queues either because if there is no queue and our operatives run out of work then they become under-utilised and our system efficiency and productivity falls.  That means we are incurring a cost but not generating an income. No queues and idle resources are bad for business too.

And we do not want a mixture of quick queues and slow queues because that causes complaints and conflict.  A high-conflict customer complaint experience is bad for business too! 

What we want is a design that creates small and stable queues; ones that are just big enough to keep our operatives busy and our customers not waiting too long.

So which is the better design and how much better is it? Five-queues or a single-queue? Carve-out or no-carve-out?

To find the answer we decide to conduct a week-long series of experiments on our system and use real data to reveal the answer. We choose the time from a customer arriving to the same customer leaving as our measure of quality and performance – and we know that the best we can expect is somewhere between 5 and 15 minutes.  We know from our IS training that is called the Lead Time.

time_moving_fast_150_wht_10108On day #1 we arrange our Post Office with five queues – clearly roped out – one for each manned counter.  We know from our mapping and measuring that customers do not arrive in a steady stream and we fear that may confound our experiment so we arrange to admit only one of our loyal and willing customers every 2 minutes. We also advise our loyal and willing customers which queue they must join before they enter to avoid the customer choice challenges.  We decide which queue using a random number generator – we toss a dice until we get a number between 1 and 5.  We record the time the customer enters on a slip of paper and we ask the customer to give it to the operative and we instruct our service operatives to record the time they completed their work on the same slip and keep it for us to analyse later. We run the experiment for only 1 hour so that we have a sample of 30 slips and then we collect the slips,  calculate the difference between the arrival and departure times and plot them on a time-series chart in the order of arrival.

CarveOut_01This is what we found.  Given that the time at the counter is an average of 10 minutes then some of these lead times seem quite long. Some customers spend more time waiting than being served. And we sense that the performance is getting worse over time.

So for the next experiment we decide to open a sixth counter and to rope off a sixth queue. We expect that increasing capacity will reduce waiting time and we confidently expect the performance to improve.

On day #2 we run our experiment again, letting customers in one every 2 minutes as before and this time we use all the numbers on the dice to decide which queue to direct each customer to.  At the end of the hour we collect the slips, calculate the lead times and plot the data – on the same chart.

CarveOut_02This is what we see.

It does not look much better and that is big surprise!

The wide variation from customer to customer looks about the same but with the Eye of Optimism we get a sense that the overall performance looks a bit more stable.

So we conclude that adding capacity (and cost) may make a small difference.

But then we remember that we still only served 30 customers – which means that our income stayed the same while our cost increased by 20%. That is definitely NOT good for business: it is not goiug to look good in a business case “possible marginally better quality and 20% increase in cost and therefore price!”

So on day #3 we change the layout. This time we go back to five counters but we re-arrange the ropes to create a single-queue so the customer at the front can be ‘pulled’ to the first available counter. Everything else stays the same – one customer arriving every 2 minutes, the dice, the slips of paper, everything.  At the end of the hour we collect the slips, do our sums and plot our chart.

CarveOut_03And this is what we get! The improvement is dramatic. Both the average and the variation has fallen – especially the variation. But surely this cannot be right. The improvement is too good to be true. We check our data again. Yes, our customers arrived and departed on average one every 2 minutes as before; and all our operatives did the work in an average of 10 minutes just as before. And we had the exactly the same capacity as we had on day #1. And we finished on time. It is correct. We are gobsmaked. It is like a magic wand has been waved over our process. We never would have predicted  that just moving the ropes around to could have such a big impact.  The Queue Theorists were correct after all!

But wait a minute! We are delivering a much better customer experience in terms of waiting time and at the same cost. So could we do even better with six counters open? What will happen if we keep the single-queue design and open the sixth desk?  Before it made little difference but now we doubt our ability to guess what will happen. Our intuition seems to keep tricking us. We are losing our confidence in predicting what the impact will be. We are in counter-intuitive land! We need to run the experiment for real.

So on day #4 we keep the single-queue and we open six desks. We await the data eagerly.

CarveOut_04And this is what happened. Increasing the capacity by 20% has made virtually no difference – again. So we now have two pieces of evidence that say – adding extra capacity did not make a difference to waiting times. The variation looks a bit less though but it is marginal.

It was changing the Queue Design that made the difference! And that change cost nothing. Rien. Nada. Zippo!

That will look much better in our report but now we have to face the emotional discomfort of having to re-evaluate one of our deepest held assumptions.

Reality is telling us that we are delivering a better quality experience using exactly the same resources and it cost nothing to achieve. Higher quality did NOT cost more. In fact we can see that with a carve-out design when we added capacity we just increased the cost we did NOT improve quality. Wow!  That is a shock. Everything we have been led to believe seems to be flawed.

Our senior managers are not going to like this message at all! We will be challening their dogma directly. And they do not like that. Oh dear! 

Now we can see how much better a no-carveout single-queue pull-design can work; and now we can explain why single-queue designs  are used; and now we can show others our experiment and our data and if they do not believe us they can repeat the experiment themselves.  And we can see that it does not need a real Post Office – a pad of Post It® Notes, a few stopwatches and some willing helpers is all we need.

And even though we have seen it with our own eyes we still struggle to explain how the single-queue design works better. What actually happens? And we still have that niggling feeling that the performance on day #1 was unstable.  We need to do some more exploring.

So we run the day#1 experiment again – the five queues – but this time we run it for a whole day, not just an hour.

CarveOut_06

Ah ha!   Our hunch was right.  It is an unstable design. Over time the variation gets bigger and bigger.

But how can that happen?

Then we remember. We told the customers that they could not choose the shortest queue or change queue after they had joined it.  In effect we said “do not look at the other queues“.

And that happens all the time on our systems when we jealously hide performance data from each other! If we are seen to have a smaller queue we get given extra work by the management or told to slow down by the union rep!  

So what do we do now?  All we are doing is trying to improve the service and all we seem to be achieving is annoying more and more people.

What if we apply a maximum waiting time target, say of 1 hour, and allow customers to jump to the front of their queue if they are at risk if breaching the target? That will smooth out spikes and give everyone a fair chance. Customers will understand. It is intuitively obvious and common sense. But our intuition has tricked us before … 

So we run the experiment again and this time we tell our customers that if they wait 50 minutes then they can jump to the front of their queue. They appreciate this because they now have a upper limit on the time they will wait.  

CarveOut_07And this is what we observe. It looks better than before, at least initially, and then it goes pear-shaped.

All we have done with our ‘carve-out and-expedite-the-long-waiters’ design is to defer the inevitable – the crunch. We cannot keep our promise. By the end everyone is pushing to the frontof the queue. It is a riot!  

And there is more. Look at the lead time for the last few customers – two hours. Not only have they waited a long time, but we have had to stay open for two hours longer. That is a BIG cost pessure in overtime payments.

So, whatever way we look at it: a single-queue design is better.  And no one loses out! The customers have a short and predictable waiting time; the operatives are kept occupied and go home on time; and the executives bask in the reflected glory of the excellent customer feedback.  It is a Three Wins® design.

Seeing is believing – and we now know that it is worth diagnosing and treating carveoutosis.

And the only thing left to do is to explain is how a single-queue design works better. It is not obvious is it? 

puzzle_lightbulb_build_PA_150_wht_4587And the best way to do that is to play the Post Office Game and see what actually happens. 

A big light-bulb moment awaits!

 

 

Update: My little Sylvanian friends have tried the Post Office Game and kindly sent me this video of the before  Sylvanian Post Office Before and the after Sylvanian Post Office After. They say they now know how the single-queue design works better. 

 

Quality First or Time First?

Before we explore this question we need to establish something. If the issue is Safety then that always goes First – and by safety we mean “a risk of harm that everyone agrees is unacceptable”.


figure_running_hamster_wheel_150_wht_4308Many Improvement Zealots state dogmatically that the only way reach the Nirvanah of “Right Thing – On Time – On Budget” is to focus on Quality First.

This is incorrect.  And what makes it incorrect is the word only.

Experience teaches us that it is impossible to divert people to focus on quality when everyone is too busy just keeping afloat. If they stop to do something else then they will drown. And they know it.

The critical word here is busy.

‘Busy’ means that everyone is spending all their time doing stuff – important stuff – the work, the checking, the correcting, the expediting, the problem solving, and the fire-fighting. They are all busy all of the time.

So when a Quality Zealot breezes in and proclaims ‘You should always focus on quality first … that will solve all the problems’ then the reaction they get is predictable. The weary workers listen with their arms-crossed, roll-their eyes, exchange knowing glances, sigh, shrug, shake their heads, grit their teeth, and trudge back to fire-fighting. Their scepticism and cynicism has been cut a notch deeper. And the weary workers get labelled as ‘Not Interested In Quality’ and ‘Resisting Change’  and ‘Laggards’ by the Quality Zealot who has spent more time studying and regurgitating rhetoric than investing time in observing and understanding reality.

The problem here is the seemingly innocuous word ‘always’. It is too absolute. Too black-and-white. Too dogmatic. Too simple.

Sometimes focussing on Quality First is a wise decision. And that situation is when there is low-quality and idle-time. There is some spare capacity to re-invest in understanding the root causes of the quality issues,  in designing them out of the process, and in implementing the design changes.

But when everyone is busy – when there is no idle-time – then focussing on quality first is not a wise decision because it can actually make the problem worse!

[The Quality Zealots will now be turning a strange red colour, steam will be erupting from their ears and sparks will be coming from their finger-tips as they reach for their keyboards to silence the heretical anti-quality lunatic. “Burn, burn, burn” they rant]. 

When everyone is busy then the first thing to focus on is Time.

And because everyone is busy then the person doing the Focus-on-Time stuff must be someone else. Someone like an Improvementologist.  The Quality Zealot is a liability at this stage – but they become an asset later when the chaos has calmed.

And what our Improvementologist is looking for are queues – also known as Work-in-Progress or WIP.

Why WIP?  Why not where the work is happening? Why not focus on resource utilisation? Isn’t that a time metric?

Yes, resource utilisation is a time-related metric but because everyone is busy then resource utilisation will be high. So looking at utilisation will only confirm what we already know.  And everyone is busy doing important stuff – they are not stupid – they are busy and they are doing their best given the constraints of their process design.        

The queue is where an Improvementologist will direct attention first.  And the specific focus of their attention is the cause of the queue.

This is because there is only one cause of a queue: a mismatch-over-time between demand and activity.

So, the critical first step to diagnosing the cause of a queue is to make the flow visible – to plot the time-series charts of demand, activity and WIP.  Until that is done then no progress will be made with understanding what is happening and it wil be impossible to decide what to do. We need a diagnosis before we can treat. And to get a diagnosis we need data from an examination of our process; and we need data on the history of how it has developed. And we need to know how to convert that data into information, and then into understanding, and then into design options, and then into a wise decision, and then into action, and then into improvement.

And we now know how to spot an experienced Improvementologist because the first thing they will look for are the Queues not the Quality.

But why bother with the flow and the queues at all? Customers are not interested in them! If time is the focus then surely it is turnaround times and waiting times that we need to measure! Then we can compare our performance with our ‘target’ and if it is out of range we can then apply the necessary ‘pressure’!

This is indeed what we observe. So let us explore the pros and cons of this approach with an example.

We are the manager of a support department that receives requests, processes them and delivers the output back to the sender. We could be one of many support departments in an organisation:  human resources, procurement, supplies, finance, IT, estates and so on. We are the Backroom Brigade. We are the unsung heros and heroines.

The requests for our service come in different flavours – some are easy to deal with, others are more complex.  They also come with different priorities – urgent, soon and routine. And they arrive as a mixture of dribbles and deluges.  Our job is to deliver high quality work (i.e. no errors) within the delivery time expected by the originator of the request (i.e. on time). If  we do that then we do not get complaints (but we do not get compliments either).

From the outside things look mostly OK.  We deliver mostly on quality and mostly on time. But on the inside our department is in chaos! Every day brings a new fire to fight. Everyone is busy and the pressure and chaos are relentless. We are keeping our head above water – but only just.  We do not enjoy our work-life. It is not fun. Our people are miserable too. Some leave – others complain – others just come to work, do stuff, take the money and go home – like Zombies. They comply.

three_wins_agreementOnce in the past we were were seduced by the sweet talk of a Quality Zealot. We were promised Nirvanah. We were advised to look at the quality of the requests that we get. And this suggestion resonated with us because we were very aware that the requests were of variable quality. Our people had to spend time checking-and-correcting them before we could process them.  The extra checking had improved the quality of what we deliver – but it had increased our costs too. So the Quality Zealot told us we should work more closely with our customers and to ‘swim upstream’ to prevent the quality problems getting to us in the first place. So we sent some of our most experienced and most expensive Inspectors to paddle upstream. But our customers were also very busy and, much as they would have liked, they did not have time to focus on quality either. So our Inspectors started doing the checking-and-correcting for our customers. Our people are now working for our customers but we still pay their wages. And we do not have enough Inspectors to check-and-correct all the requests at source so we still need to keep a skeleton crew of Inspectors in the department. And these stay-at-home Inspectors  are stretched too thin and their job is too pressured and too stressful. So no one wants to do it.And given the choice they would all rather paddle out to the customers first thing in the morning to give them as much time as possible to check-and-correct the requests so the days work can be completed on time.  It all sounds perfectly logical and rational – but it does not seem to have worked as promised. The stay-at-home Inspectors can only keep up with the more urgent work,  delivery of the less urgent work suffers and the chronic chaos and fire-fighting are now aggravated by a stream of interruptions from customers asking when their ‘non-urgent’ requests will be completed.

figure_talk_giant_phone_anim_150_wht_6767The Quality Zealot insisted we should always answer the phone to our customers – so we take the calls – we expedite the requests – we solve the problems – and we fight-the-fire.  Day, after day, after day.

We now know what Purgatory means. Retirement with a pension or voluntary redundancy with a package are looking more attractive – if only we can keep going long enough.

And the last thing we need is more external inspection, more targets, and more expensive Quality Zealots telling us what to do! 

And when we go and look we see a workplace that appears just as chaotic and stressful and angry as we feel. There are heaps of work in progress everywhere – the phone is always ringing – and our people are running around like headless chickens, expediting, fire-fighting and getting burned-out: physically and emotionally. And we feel powerless to stop it. So we hide.

Does this fictional fiasco feel familiar? It is called the Miserable Job Purgatory Vortex.

Now we know the characteristic pattern of symptoms and signs:  constant pressure of work, ever present threat of quality failure, everyone busy, just managing to cope, target-stick-and-carrot management, a miserable job, and demotivated people.

The issue here is that the queues are causing some of the low quality. It is not always low quality that causes all of the queues.

figure_juggling_time_150_wht_4437Queues create delays, which generate interruptions, which force investigation, which generates expediting, which takes time from doing the work, which consumes required capacity, which reduces activity, which increases the demand-activity mismatch, which increases the queue, which increases the delay – and so on. It is a vicious circle. And interruptions are a fertile source of internally generated errors which generates even more checking and correcting which uses up even more required capacity which makes the queues grow even faster and longer. Round and round.  The cries for ‘we need more capacity’ get louder. It is all hands to the pump – but even then eventually there is a crisis. A big mistake happens. Then Senior Management get named-blamed-and shamed,  money magically appears and is thrown at the problem, capacity increases,  the symptoms settle, the cries for more capacity go quiet – but productivity has dropped another notch. Eventually the financial crunch arrives.    

One symptom of this ‘reactive fire-fight design’ is that people get used to working late to catch up at the end of the day so that the next day they can start the whole rollercoaster ride again. And again. And again. At least that is a form of stability. We can expect tomorrow to be just a s miserable as today and yesterday and the day before that. But TOIL (Time Off In Lieu) costs money.

The way out of the Miserable Job Purgatory Vortex is to diagnose what is causing the queue – and to treat that first.

And that means focussing on Time first – and that means Focussing on Flow first.  And by doing that we will improve delivery, improve quality and improve cost because chaotic systems generate errors which need checking and correcting which costs more. Time first is a win-win-win strategy too.

And we already have everything we need to start. We can easily count what comes in and when and what goes out and when.

The first step is to plot the inflow over time (the demand), the outflow over time (the activity), and from that we work out and plot the Work-in-Progress over time. With these three charts we can start the diagnostic process and by that path we can calm the chaos.

And then we can set to work on the Quality Improvement.  


13/01/2013Newspapers report that 17 hospitals are “dangerously understaffed”  Sound familiar?

Next week we will explore how to diagnose the root cause of a queue using Time charts.

For an example to explore please play the SystemFlow Game by clicking here

 

Defusing Trust Eroders – Part III

<Bing Bong>

laptop_mail_PA_150_wht_2109Leslie’s computer heralded the arrival of yet another email!  They were coming in faster and faster – now that the word had got out on the grapevine about Improvementology.

Leslie glanced at the sender.

It was from Bob.  That was a surprise.  Bob had never emailed out-of-the-blue before.  Leslie was too impatient to wait until later to read the email.

<Dear Leslie, could I trouble you to ask your advice on something.  It is not urgent.  A ten minute chat on the phone would be all I need.  If that is OK please let me know a good time is and I will ring you. Bob>

Leslie was consumed with curiosity.  What could Bob possibly want advice on?  It was Leslie who sought advice from Bob – not the other way around.

Leslie could not wait and emailed back immediately that it was OK to talk now.

<Ring Ring>

L: Hello Bob, what a pleasant surprise!  I am very curious to know what you need my advice about.

B: Thank you Leslie.  What I would like your counsel on is how to engage in learning the science of improvement.

L: Wow!  That is a surprising question. I am really confused now. You helped me to learn this new thinking and now you are asking me to teach you?

B: Yes.  On the surface it seems counter-intuitive.  It is a genuine request though.  I need to learn and understand what works for you and what does not.

L: OK.  I think I am getting an idea of what you are asking.  But I am only just getting grips with the basics.  I do not know how to engage others yet and I certainly would not be able to teach anyone!

B: I must apologise.  I was not clear in my request.  I need to understand how you engaged yourself in learning.  I only provided the germ of the idea – it was you who added what was needed for it to develop into something tangible and valuable for you.  I need to understand how that happened.

L: Ahhhh! I see what you mean.  Yes.  Let me think.  Would it help if I describe my current mental metaphor?

B: That sounds like an excellent idea.

L: OK.  Well your phrase ‘germ of an idea’ was a trigger.  I see the science of improvement as a seed of information that grows into a sturdy tree of understanding.  Just like the ‘tiny acorn into the mighty oak’ concept.  Using that seed-to-tree metaphor helped me to appreciate that the seed is necessary but it is not sufficient.  There are other things that are needed too.  Soil, water, air, sunlight, and protection from hazards and predators.

I then realised that the seed-to-tree metaphor goes deeper.  One insight that I had was when I realised that the first few leaves are critical to success – because they provide the ongoing energy and food to support the growth of more leaves, and the twigs, branches, trunk, and roots that support the leaves and supply them with water and nutrients.  I see the tree as synergistic system that has a common purpose: to become big enough and stable enough to be able to survive the inevitable ups-and-downs of reality.  To weather the winter storms and survive the summer droughts.

plant_metaphor_240x135It seemed to me that the first leaf needed to be labelled ‘safety’ because in our industry if we damage our customers or our staff we do not get a second chance!  The next leaf to grow is labelled ‘quality’ and that means quality-by-design.  Doing the right thing and doing it right first time without needing inspection-and-correction. The safety and quality leaves provide the resources needed to grow the next leaf which I labelled ‘delivery’.  Getting the work done in time, on time, every time.  Together these three leaves support the growth of the fourth – ‘economy’ which means using only what is necessary and also having just enough reserve to ride over the inevitable rocks and ruts in the road of reality.

I then reflected on what the water and the sunshine would represent when applying improvement science in the real world.

It occurred to me that the water in the tree is like money in a real system.  It is required for both growth and health; it must flow to where it is needed, when it is needed and as much as needed. Too little will prevent growth, and too much water at the wrong time and wrong place is just as unhealthy.  I did some reading about the biology of trees and I learned that the water is pulled up the tree!  The ‘suck’ is created by the water evaporating from the leaves.  The plant does not have a committee that decides where the available water should go!  It is a simple self-adjusting, auto-regulating system.

The sunshine for the tree is like feedback for people.  In a plant the suns energy provides the motive force for the whole system.  In our organisations we call it motivation and the feedback loop is critical to success.  Keeping people in the dark about what is required and how they are doing is demotivating.  Healthy organisations are feedback-fuelled!

B: I see the picture in my mind clearly.  That is a powerful metaphor.  How did it help overcome the natural resistance to change?

L: Well using the 6M Design method and taking the desire to create a ‘sturdy tree of understanding’ as the goal of the seed-to-tree process, I then considered what the possible ways it could fail – the failure modes and effects analysis method that you taught me.

B: OK. Yes I see how that approach would help – approaching the problem from the far side of the invisible barrier. What insights did that lead to?

poison_faucet_150_wht_9860L: Well it highlighted that just having enough water and enough sunshine was not sufficient – it had to be clean water and the right sort of sunshine.  The quality is as critical as the quantity.  A toxic environment will kill tender new shoots of improvement long before they can get established.  Cynicism is like cyanide!  Non-specific cost cutting is like blindly wielding a pair of sharp secateurs.  Ignoring the competition from wasteful weeds and political predators is a guaranteed recipe-for-failure too.

This seed-to-tree metaphor really helped because it allowed me to draw up a checklist of necessary conditions for successful growth of knowledge and understanding.  Rather like the shopping list that a gardener might have.  Viable seeds, fertile soil, clean water, enough sunlight, and protection from threats and hazards, especially in the early stages.  And patience and perseverance.  Growing from seed takes time.  Not all seeds will germinate.  Not all seeds can thrive in the context our gardener is able to create.  And the harsher the elements the fewer the types of seed that have any chance of survival.  The conditions select the successful seeds.  Deserts select plants that hoard water so the desert remains a desert.  If money is too tight the miserly will thrive at the expense of the charitable – and money remains hoarded and fought over as the rest of the organisation withers.  And the timing is crucial – the seeds need to be planted at the right time in the cycle of change.  Too early and they cannot germinate, too late and they do not have time to become strong enough to survive in the real world winter storms.

B: Yes.  I see. The deeper you dig into your seeds-to-trees metaphor, the more insightful it becomes.

L: Bob, you just said something really profound then that has unlocked something for me.

B: Did I?  What was it?

RainForestL: You said ‘seeds-to-trees’.  Up until you said that I was unconsciously limiting myself to one-seed-to-one-tree.  Of course!  If it works for the individual it can work for the collective.  Woods and forests are collectives.  The best example I can think of is a tropical rainforest.  With ample water and sunshine the plant-collective creates a synergistic system that has endured millions of years of global climate change.  And one of the striking features of the tropical rain forest is the diversity of species.  It is as if that diversity is an important part of the design.  Competition is ever present though – all the trees compete for sunlight – but it is healthy competition.  Trees do not succeed individually by hunting each other down.  And the diversity seems to be an important component of healthy competition too.  It is as if they are in a shared race to the sun and their differences are an asset rather than a liability. If all the trees were the same the forest would be at greater risk of all making the same biological blunder and suddenly becoming extinct if their environment changes unpredictably.  Uniformity only seems to work in harsh conditions.

B: That is a profound observation Leslie.  I had not consciously made that distinction.

L: So have I answered your question?  Have I helped you?  It has certainly helped me by being asked to putting my thoughts into words.  I see it clearer too now.

B: Yes.  You are a good teacher.  I believe others will resonate with your seeds-to-trees metaphor just as I have.

L: Thank you Bob.  I believe I am beginning to understand something you said in a previous conversation – “the teacher is the person who learns the most”.  I am going to test our seeds-to-trees metaphor on the real world!  And I will feedback what I learn – because in doing that I will amplify and clarify my own learning.

B: Thank you Leslie. I look forward to learning with you.


Defusing Trust Eroders – Part II

line_figure_phone_400_wht_9858<Ring Ring><Ring Ring>

B: Hello Leslie. How are you today?

L: Hi Bob – I am OK.  Thank you for your time today.  Is 15 minutes going to be enough?

B: Yes. There is evidence that the ideal chunk of time for effective learning is around 15 minutes.

L: OK.  I said I would read the material you sent me and reflect on it.

B: Yes.  Can you retell your Nerve Curve experience as a storyboard and highlight your ‘ah ha’ moments?

L: OK.  And that was the first ‘ah ha’.  I found the storyboard format a really effective way to capture my sequence of emotional states.

campfire_burning_150_wht_174B: Yes.  There are close links between stories, communication, learning and improvement.  Before we learned to write we used campfire stories to pass collective knowledge from generation to generation.   It is an ancient, in-built skill we all have and we all enjoy a good story.

L: Yes.  My first reaction was to the way you described the Victim role.  It really resonated with how I was feeling and how I was part of the dynamic.  You were spot on with the feelings that dominated my thinking – anxiety and fear. The big ‘ah ha’ for me was to understand the discount that I was making.  Not of others – of myself.

B: OK.  What was the image that you sketched on your storyboard?

L: I am embarrased to say – you will think I am silly.

B: I will not think you are silly.

employee_diciplined_400_wht_5635I know.  And I knew that as soon as I said it.  I think I was actually saying it to myself – or part of myself.  Like I was trying to appease part of myself.  Anyway, the picture I sketched was me as a small child at school standing with my head down, hands by my sides, and being told off in front of the whole class for getting a sum wrong.  I was crying.  I was not very good at maths and even now my mind sort of freezes and I get tears in my eyes and feel scared whenever someone tries to explain something using equations!  I can feel the terror starting to well up just talking about it.

B: OK. No need to panic. Take a long breath and exhale slowly.  The story you have told is very common.  Many of our fears of failure originate from early memories of experiencing ‘education by humiliation’.  It is a blunt and ineffective motivational tool that causes untold and long lasting damage.  It is a symptom of a low quality education system design. Education is an exercise in improvement of knowledge, understanding, capability and confidence.  The unintended outcome of this clumsy teaching tactic is a belief that we cannot solve problems ourselves and it is that invalid belief that creates the self-fulfilling prophecy of repeated failure.

L: Yes! And I know I can solve maths problems – I do it all the time – and I help my children with their maths homework.  So, it is not the maths that is triggering my fear.  What is it?

B: The answer to your question will become clear.  What is the next picture on your storyboard?

emotion_head_mad_400_wht_7632The next picture was of the teacher who was telling me off.  Or rather the face of the teacher.  It was a face of frustration and anger.  I drew a thought bubble and wrote in it “This small, irritating child cannot solve even a simple maths problem and is slowing down the whole lesson by bursting into tears everytime they get stuck.  I blame the parents who are clearly too soft.  They all need to learn some discipline – the hard way.

L: Does this shed any light on your question?

B: Wow!  Yes!  It is not the maths that I am reacting to – it is the behaviour of the teacher.  I am scared of the behaviour.  I feel powerless.  They are the teacher, I am just a small, incompetent, stupid, blubbing child.  They do not care that I do not understand the question, and that I am in distress, and that I am scared that I will be embarassed in front of the whole class, and that I am scared that my parents will see a bad mark on my school report.  And I feel trapped.  I need to rationalise this.  To make sense of it.  Maybe I am stupid?  That would explain why I cannot solve the mths problem.  Maybe I should just give in and accept that I am a failure and too stupid to do maths?

There was a pause.  Then Leslie continued in a different tone.  A more determined tone.

L: But I am not a failure.  This is just my knee jerk habitual reaction to an authority figure displaying anger towards me.  I can decide how I react.  I have complete control over that.  I can disconnect the behaviour I experience and my reaction to it.  I can choose.  Wow!

B: OK. How are you feeling right now?  Can you describe it using a visual metaphor?

ready_to_launch_PA_150_wht_5052L: Um – weird.  Mixed feelings.  I am picturing myself sitting on a giant catapault.  The ends of the huge elastic bands are anchored in the present and I am sitting in the loop but it is stretched way back into the past.  There is something formless in the past that has been holding me back and the tension has been slowly building over time.  And it feels that I have just cut that tie to the past, and I am free, and I am now being accelerated into the future.  I did that.  I am in control of my own destiny and it suddenly feels fun and exciting.

B: OK. How do you feel right now about the memory of the authority figure from the past?

L: OK actually.  That is really weird.  I thought that I would feel angry but I do not.  I just feel free.  It was not them that was the problem.  Their behaviour was not my fault – and it was my reaction to their behaviour that was the issue.  My habitual behaviour.  No, wait a second. Our habitual behaviour.  It is a dynamic.  It takes both people to play the game.

There was a pause.  Leslie sensed that Bob knew that some time was needed to let the emotions settle a bit.

B: Are you OK to continue with your storyboard?

emotion_head_sad_frown_400_wht_7644L: Yes.  The next picture is of the faces of my parents.  They are looking at my school report.  They look sad and are saying “We always dreamed that Leslie would be a doctor or something like that.  I suppose we will have to settle for something less ambitious.  Do not worry Leslie, it is not your fault, it will be OK, we will help you.”  I felt like I had let them down and I had shattered their dream.  I felt so ashamed.  They had given me everything I had ever asked for.  I also felt angry with myself and with them.  And that is when I started beating myself up.  I no longer needed anyone else to do that!  I could persecute myself.  I could play both parts of the game in my own head.  That is what I did just now when it felt like I was talking to myself.

B: OK.  You have now outlined the three roles that together create the dynamic for a stable system of learned behaviour.  A system that is very resistant to change.  It is like a triangular role-playing-game.  We pass the role-hats as we swap places in the triangle and we do it in collusion with others and ourselves and we do it unconsciously.  The purpose of the game is to create opportunities for social interaction – which we need and crave – the process has a clear purpose.  The unintended outcome of this design is that it generates bad feelings, it erodes trust and it blocks personal and organisational development and improvement.  We get stuck in it – rather like a small boat in a whirlpool.  And we cannot see that we are stuck in it.  We just feel bad as we spin around in an emotional maelstrom.  And we feel cheated out of something better but we do not know what it is and how to get it.

There was a long pause.  Leslie’s mind was racing.  The world had just changed.  The pieces had been blown apart and were now re-assembling in a different configuration.  A simpler, clearer and more elegant design.

L: So, tell me if I have this right.  Each of the three roles involves a different discount?

B: Yes.

And each discount requires a different – um – tactic to defuse?

B: Yes.

So, the way to break out of this trust eroding behavioural hamster-wheel is to learn to recognise which role we are in and to consciously deploy the discount defusing tactic.

B: Yes.

And by doing that enough times we learn how to spot the traps that other people are creating and avoid getting sucked into them.

B: Yes. And we also avoid starting them ourselves.

L: Of course! And by doing that we develop growing respect for ourselves and for each other and a growing level of trust in ourselves and in others?  We have started to defuse the trust eroding behaviour and that lowers the barrier to personal and organisational development and improvement.

B: Yes.

L: So what are the three discount defusing tactics?

There was a pause.  Leslie knew what was coming next.  It would be a question.

B: What role are you in now?

L: Oh!  Yes.  I see.  I am still feeling like that small school child at school but now I am asking for the answer and I am discounting myself by assuming that I cannot solve this problem myself.  I am assuming that I need you to rescue me by telling me the answer.  I am still in the trust eroding game, I do not trust myself and I am inviting you to play too, and to reinforce my belief that I cannot solve the problem.

B: And do you need me to tell you the answer?

L: No.  I can probably work this out myself.  And if I do get stuck then I can ask for hints or nudges – not for the answer.  I need to do the learning work and I want to do it.

B: OK.  I will commit to hinting and nudging if asked, and if I do not know the answer I will say so.

L: Phew!  That was definitely an emotional rollercoaster ride on the Nerve Curve.  Looking back it all makes complete sense and I now know what to do – but at the start it felt like I was heading into the Dark Unknown.  You are right.  It is liberating and exhilarating!

B: That feeling of clarity-of-hindsight and exhilaration from learning is what we always strive for.  Both as teachers and students.

L: You mean it is the same for you?  You are still riding the Nerve Curve?  Still feeling surprised, confused, scared, resolved, enlightened then delighted?

B: Ha ha!  Yes.  Every day.  It is fun.  I believe that there is No Limit to Learning so there is an inexhaustible Font of Fun.

L: Wow! I am off to have more Fun from Learning. Thank you so much yet again.

two_stickmen_shaking_hands_puzzle_150_wht_5229B: Thank you Leslie.


Defusing Trust Eroders – Part I

texting_a_friend_back_n_forth_150_wht_5352<Beep><Beep>

Bob heard the beep and looked at his phone. There was a text message from Leslie, one of his Improvementology coachees.

It said:

“Hi Bob, Do you have time to help me with a behaviour barrier that I keep hitting and cannot see a way around?”

Bob thumbed his reply:

“Yes. I am free at the moment – please feel free to call.”

<Ring><Ring>

B: Hello Leslie. What’s on your mind?

L: Hi Bob.  I really hope  you can help me with this recurring Niggle.  I have looked through my Foundation notes and I cannot see where it is described and it does not seem to be a Nerve Curve problem.

B: I will do my best. Can you outline the context or give me an example?

L: It is easier to give you an example.  This week I was working with a team in my organisation who approached me to help them with recurring niggles in their process.  I went to see for myself and I mapped their process and identified where their Niggles were and what was driving them.  That was the easy bit.  But when I started to make suggestions of what they could do to resolve their problems they started to give me a hard time and kept saying ‘Yes, but …”.  It was as if they were asking for help but did not really want it.  They kept emphasising that all their problems were caused by other people outside their department and kept asking me what I could do about it.  I felt as if they were pushing the problem onto me and I was also feeling guilty for not being able to sort it out for them.

There was a pause. Then Bob said.

B: You are correct Leslie.  This is not a Nerve Curve issue.   It is a different people-related system issue.  It is ubiquitous and it is a potentially deadly organisational disease.  We call it Trust Eroding Behaviour.

L: That sounds exactly how it felt for me.  I went to help in good faith and quickly started to feel distrustful of their motives.  It was not a good feeling and I do not know if I want to go back.  One part of me says “Keep going – you have made a commitment” and another part of me says “Stop – you are being suckered”.  What is happening?

B: Do you remember that the Improvement Science framework has three parts – Processes, People and Systems?

L: Yes.

B: OK.  This is part of the People component and it is similar to but different from the Nerve Curve.   The Nerve Curve is a hard-wired emotional response to any change.  The Fright, Freeze, Fight, Flight response.  It is just the way we are and it is not ‘correctable’.  This is different.  This is a learned behaviour.   Which means it can be unlearned.

L: Unlearned?  That is not a concept that I am familiar with.  Can you explain?  Is it the same as forgetting?

B: Forgetting means that you cannot bring something to conscious awareness.   Unlearning is different – it operates at a deeper psychological and emotional level.  Have you ever tried to change a bad habit?

L: Yes, I have!  I used to smoke which is definitely a bad habit and I managed to give up but it was really tough.

B: What you did was to unlearn the smoking habit and replaced it with a healthier one.  You did not forget about smoking.  You could not because you are repeatedly reminded by other people who still indulge in the habit.

L:  Ah ha! I see what you mean.  Yes – after I kicked the habit I became a bit of a Stop-Smoking evangelist.  It did not seem to make much impact on the still-smokers though.  If anything my behaviour seemed to make them more determined to keep doing it – just to spite me!

B: Yes.  What you describe is what many people report.  It is part if the same learned behaviour patterns.  The habit that is causing the issue is rather like smoking because it causes short-term pleasure and long-term pain.  It is both attractive and destructive.  The reactive behaviour generates a positive feeling briefly but it is toxic to trust over the longer term, which is why we call it a Trust Eroding Behaviour.

L: What is the bad habit? I do not recognise the behaviour that you are referring to.

B: The habit is called discounting.  The reason we are not aware of it is because we do it unconsciously.

L: What is it that we do?

B: I will give you some examples.  How do you feel when all the feedback you get is silence? How do you feel when someone complains that their mistake was not their fault? How do you feel when you try to help but you hit invisible barriers that block your progess?

sad_faceL: Ouch!  Those are uncomfortable questions. When I get no feedback I feel anxious and even fearful that I have made a mistake,  and no one is telling me.  There is a conspiracy of silence and a nasty surprise is on its way.  When someone keeps complaining that even though they made the mistake they are not to blame I feel angry.  When I try to help others and I fail to then I feel anxious and sad because my reputation, credibility and self-confidence is damaged.

B: OK. No need to panic. These negative emotional reactions are the normal reaction to discounting behaviour.  Another word for discounting is disrespect.  The three primary emotions we feel are sadness, anger and fear.  Fear is the sense of impending loss; anger is the sense of present loss; and sadness is the sense of past loss.  They are the same emotions that we feel on the Nerve Curve.  What is different is the cause.  Discounting is a disrepectful behaviour that is learned.  So, it can be unlearned.

L: Oooo!  That really resonates with me.  Just reflecting on one day at work I can think of lots of examples of all of those negative feelings.  So, when and how do we learn this discounting habit?

B: It is believed that we learn this behaviour when we are very young – before the age of seven.  And because we learn it so young we internalise it and we become unaware of it.  It then becomes a habit that is reinforced with years of experience and practice.

L: Wow!  That rings true for me – and it may explain why I actively avoided some people at school – they were just toxic.  But they had friends, went to college, got jobs, married and started families – just like me.  Does that mean we grow out of it?

B: Most people unlearn some of these behavioural habits because life-experience teaches them that they are counter-productive.  We all carry some of them though, and they tend to emerge when we are tired and under pressure.  Some people get sort of stuck and carry these behaviours into their adult life.  Their behaviour can be toxic to their relationships and their organisations.

L: I definitely resonate with that statement!  Is there a way to unlearn this discounting habit?

B: Yes – just becoming aware of its existence is the first step.  There are some strategies that we can learn, practice and use to defuse the discounting behaviour and over time our bad habit can be “kicked”.

L: Wow! That sounds really useful.  And not just at work – I can see benefits in other areas of my life too.

B: Yes. Improvement science is powerful medicine.

L: So what do I need to do?

B: You have learned the 6M Design framework for resolving process niggles. There is an equivalent one for dissolving people niggles.  I will send you some links to material to read and then we can talk again.

L: Will it help me resolve the problem that I have with the department that asked for my help who are behaving like Victims?

B: Yes.

L: OK – please send me the material.  I promise to read it, reflect on it and I will arrange another conversation.  I cannot wait to learn how to nail this niggle!  I can see a huge win-win-win opportunity here.

B: OK.  The email is on its way.  I look forward to our next conversation.


The Six Dice Game

<Ring Ring><Ring Ring>

Hello, you are through to the Improvement Science Helpline. How can we help?

This is Leslie, one of your apprentices.  Could I speak to Bob – my Improvement Science coach?

Yes, Bob is free. I will connect you now.

<Ring Ring><Ring Ring>

B: Hello Leslie, Bob here. What is on your mind?

L: Hi Bob, I have a problem that I do not feel my Foundation training has equipped me to solve. Can I talk it through with you?

B: Of course. Can you outline the context for me?

L: OK. The context is a department that is delivering an acceptable quality-of-service and is delivering on-time but is failing financially. As you know we are all being forced to adopt austerity measures and I am concerned that if their budget is cut then they will fail on delivery and may start cutting corners and then fail on quality too.  We need a win-win-win outcome and I do not know where to start with this one.

B: OK – are you using the 6M Design method?

L: Yes – of course!

B: OK – have you done The 4N Chart for the customer of their service?

L: Yes – it was their customers who asked me if I could help and that is what I used to get the context.

B: OK – have you done The 4N Chart for the department?

L: Yes. And that is where my major concerns come from. They feel under extreme pressure; they feel they are working flat out just to maintain the current level of quality and on-time delivery; they feel undervalued and frustrated that their requests for more resources are refused; they feel demoralized; demotivated and scared that their service may be ‘outsourced’. On the positive side they feel that they work well as a team and are willing to learn. I do not know what to do next.

B: OK. Dispair not. This sounds like a very common and treatable system illness.  It is a stream design problem which may be the reason your Foundations training feels insufficient. Would you like to see how a Practitioner would approach this?

L: Yes please!

B: OK. Have you mapped their internal process?

L: Yes. It is a six-step process for each job. Each step has different requirements and are done by different people with different skills. In the past they had a problem with poor service quality so extra safety and quality checks were imposed by the Governance department.  Now the quality of each step is measured on a 1-6 scale and the quality of the whole process is the sum of the individual steps so is measured on a scale of 6 to 36. They now have been given a minimum quality target of 21 to achieve for every job. How they achieve that is not specified – it was left up to them.

B: OK – do they record their quality measurement data?

L: Yes – I have their report.

B: OK – how is the information presented?

L: As an average for the previous month which is reported up to the Quality Performance Committee.

B: OK – what was the average for last month?

L: Their results were 24 – so they do not have an issue delivering the required quality. The problem is the costs they are incurring and they are being labelled by others as ‘inefficient’. Especially the departments who are in budget and they are annoyed that this failing department keeps getting ‘bailed out’.

B: OK. One issue here is the quality reporting process is not alerting you to the real issue. It sounds from what you say that you have fallen into the Flaw of Averages trap.

L: I don’t understand. What is the Flaw of Averages trap?

B: The answer to your question will become clear. The finance issue is a symptom – an effect – it is unlikely to be the cause. When did this finance issue appear?

L: Just after the Safety and Quality Review. They needed to employ more agency staff to do the extra work created by having to meet the new Minimum Quality target.

B: OK. I need to ask you a personal question. Do you believe that improving quality always costs more?

L: I have to say that I am coming to that conclusion. Our Governance and Finance departments are always arguing about it. Governance state ‘a minimum standard of safety and quality is not optional’ and finance say ‘but we are going out of business’. They are at loggerheads. The service departments get caught in the cross-fire.

B: OK. We will need to use reality to demonstrate that this belief is incorrect. Rhetoric alone does not work. If it did then we would not be having this conversation. Do you have the raw data from which the averages are calculated?

L: Yes. We have the data. The quality inspectors are very thorough!

B: OK – can you plot the quality scores for the last fifty jobs as a BaseLine chart?

L: Yes – give me a second. The average is 24 as I said.

B: OK – is the process stable?

L: Yes – there is only one flag for the fifty. I know from my Foundations training that is not a cause for alarm.

B: OK – what is the process capability?

L: I am sorry – I don’t know what you mean by that?

B: My apologies. I forgot that you have not completed the Practitioner training yet. The capability is the range between the red lines on the chart.

L: Um – the lower line is at 17 and the upper line is at 31.

L: OK – how many points lie below the target of 21.

B: None of course. They are meeting their Minimum Quality target. The issue is not quality – it is money.

There was a pause.  Leslie knew from experience that when Bob paused there was a surprise coming.

B: Can you email me your chart?

A cold-shiver went down Leslie’s back. What was the problem here? Bob had never asked to see the data before.

Sure. I will send it now.  The recent fifty is on the right, the data on the left is from after the quality inspectors went in and before the the Minimum Quality target was imposed. This is the chart that Governance has been using as evidence to justify their existence because they are claiming the credit for improving the quality.

B: OK – thanks. I have got it – let me see.  Oh dear.

Leslie was shocked. She had never heard Bob use language like ‘Oh dear’.

There was another pause.

B: Leslie, what is the context for this data? What does the X-axis represent?

Leslie looked at the chart again – more closely this time. Then she saw what Bob was getting at. There were fifty points in the first group, and about the same number in the second group. That was not the interesting part. In the first group the X-axis went up to 50 in regular steps of five; in the second group it went from 50 to just over 149 and was no longer regularly spaced. Eventually she replied.

Bob, that is a really good question. My guess it is that this is the quality of the completed work.

B: It is unwise to guess. It is better to go and see reality.

You are right. I knew that. It is drummed into us during the Foundations training! I will go and ask. Can I call you back?

B: Of course. I will email you my direct number.


<Ring Ring><Ring Ring>

B: Hello, Bob here.

L: Bob – it is Leslie. I am  so excited! I have discovered something amazing.

B: Hello Leslie. That is good to hear. Can you tell me what you have discovered?

L: I have discovered that better quality does not always cost more.

B: That is a good discovery. Can you prove it with data?

L: Yes I can!  I am emailing you the chart now.

B: OK – I am looking at your chart. Can you explain to me what you have discovered?

L: Yes. When I went to see for myself I saw that when a job failed the Minimum Quality check at the end then the whole job had to be re-done because there was no time to investigate and correct the causes of the failure.  The people doing the work said that they were helpless victims of errors that were made upstream of them – and they could not predict from one job to the next what the error would be. They said it felt like quality was a lottery and that they were just firefighting all the time. They knew that just repeating the work was not solving the problem but they had no other choice because they were under enormous pressure to deliver on-time as well. The only solution they could see is was to get more resources but their requests were being refused by Finance on the grounds that there is no more money. They felt completely trapped.

B: OK. Can you describe what you did?

L: Yes. I saw immediately that there were so many sources of errors that it would be impossible for me to tackle them all. So I used the tool that I had learned in the Foundations training: the Niggle-o-Gram. That focussed us and led to a surprisingly simple, quick, zero-cost process design change. We deliberately did not remove the Inspection-and-Correction policy because we needed to know what the impact of the change would be. Oh, and we did one other thing that challenged the current methods. We plotted every attempt, both the successes and the failures, on the BaseLine chart so we could see both the the quality and the work done on one chart.  And we updated the chart every day and posted it chart on the notice board so everyone in the department could see the effect of the change that they had designed. It worked like magic! They have already slashed their agency staff costs, the whole department feels calmer and they are still delivering on-time. And best of all they now feel that they have the energy and time to start looking at the next niggle. Thank you so much! Now I see how the tools and techniques I learned in Foundations are so powerful and now I understand better the reason we learned them first.

B: Well done Leslie. You have taken an important step to becoming a fully fledged Practitioner. You have learned some critical lessons in this challenge.


This scenario is fictional but realistic.

And it has been designed so that it can be replicated easily using a simple game that requires only pencil, paper and some dice.

If you do not have some dice handy then you can use this little program that simulates rolling six dice.

The Six Digital Dice program (for PC only).

Instructions
1. Prepare a piece of A4 squared paper with the Y-axis marked from zero to 40 and the X-axis from 1 to 80.
2. Roll six dice and record the score on each (or roll one die six times) – then calculate the total.
3. Plot the total on your graph. Left-to-right in time order. Link the dots with lines.
4. After 25 dots look at the chart. It should resemble the leftmost data in the charts above.
5. Now draw a horizontal line at 21. This is the Minimum Quality Target.
6. Keep rolling the dice – six per cycle, adding the totals to the right of your previous data.

But this time if the total is less than 21 then repeat the cycle of six dice rolls until the score is 21 or more. Record on your chart the output of all the cycles – not just the acceptable ones.

7. Keep going until you have 25 acceptable outcomes. As long as it takes.

Now count how many cycles you needed to complete in order to get 25 acceptable outcomes.  You should find that it is about twice as many as before you “imposed” the Inspect-and-Correct QI policy.

This illustrates the problem of an Inspection-and-Correction design for quality improvement.  It does improve the quality of the final output – but at a higher cost.

We are treating the symptoms (effects) and ignoring the disease (causes).

The internal design of the process is unchanged so it is still generating mistakes.

How much quality improvement you get and how much it costs you is determined by the design of the underlying process – which has not changed. There is a Law of Diminishing returns here – and a big risk.

The risk is that if quality improves as the result of applying a quality target then it encourages the Governance thumbscrews to be tightened further and forces those delivering the service further into cross-fire between Governance and Finance.

The other negative consequence of the Inspect-and-Correct approach is that it increases both the average and the variation in lead time which also fuels the calls for more targets, more sticks, calls for  more resources and pushes costs up even further.

The lesson from this simple exercise seems clear.

The better strategy for improving quality is to design the root causes of errors out of the processes  because then we will get improved quality and improved delivery and improved productivity and we will discover that we have improved safety as well.  Win-win-win-win.

The Six Dice Game is a simpler version of the famous Red Bead Game that W Edwards Deming used to explain why, in the modern world, the arbitrary-target-driven-command-and-control-stick-and-carrot style of performance management creates more problems than it solves.

The illusion is of short-term gain but the reality is of long-term pain.

And if you would like to see and hear Deming talking about the science of improvement there is a video of him speaking in 1984. He is at the bottom of the page.  Click here.