Archive for the ‘Quality’ Category

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.

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!

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.

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

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 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 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 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 whichwas 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 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. 

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 bit of fog, and a bit of respectful challenge is healthier than All or None.

It is at the chaotic 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 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.

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

 

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. 

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

 

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.

 

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. 

 

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

 

<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>

Hello Bob, what a pleasant surprise! I am very curious to know what you need my advice about.

? Thank you Leslie.  What I would like your counsel on is how to engage in learning the science of improvement.

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?

? 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.

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!

? 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.

Ahhhh! I see what you mean. Yes. Let me think. Would it help if I describe my current mental metaphor?

? That sounds like an excellent plan.

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 necessaryand 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 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!

? Yes. I see the picture in my mind clearly. That is a powerful metaphor. How did it help overcome the natural resistance to change?

Well using the 6M Design method and taking the ‘sturdy tree of understanding’ as the objective 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.

? 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_9860Well 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 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. 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 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 germinateg, too late and they do not have time to become strong enough to survive in the real world.    

? Yes. I see. The deeper you dig into your seeds-to-trees metaphor the more insightful it becomes.

Bob, you just said something really profound then that has unlocked something for me.

? Did I? What was it?

RainForestYou 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.

? That is a profound observation Leslie. I had not consciously made that distinction.

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.

? Yes. You are a good teacher. I believe others will resonate with your seeds-to-trees metaphor just as I have.

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.

? Thank you Leslie. I look forward to learning with you.


Defusing Trust Eroders – Part I

Defusing Trust Eroders – Part II


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 mentees.

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>

?Hello Leslie. How can I help?

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.

?I will do my best. Can you outline the context or give me an example?

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.

?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.

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 ‘ It is your duty – you have made a commitment’ and another part of me says ‘Stop – you are being suckered.’  What is happening?

?Do you remember that the Improvement Science framework has three parts – Processes, People and Systems?

Yes.

?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, 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.

Unlearned? That is not a concept that I am familiar with. Can you explain? Is it the same as forgetting?

?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?

Yes I have. I used to smoke which is definitely a bad habit and I managed to give up but it was really tough.

?What you did was to unlearn the smoking habit.  You did not forget about smoking.  You could not because you are repeatedly reminded by other people who still indulge in the habit.

Ah ha! I see what you mean. Yes – after I kicked the habit I became a bit of a Stop-Smoking evangelist. I even had a tee shirt. It did not seem to make much impact on the still-smokers though.  If anything it seemed to make them more determined to keep doing it – just to spite me!

?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 behaviour feels good briefly but it is toxic to trust which is why we call it the Trust Eroding Behaviour.

What is the habit? I do not recognise the behaviour that you are referring to.

?The habit is called discounting.  The reason we are not aware of it is we do it unconsciously. 

What is it that we do?

?It is easier to 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_faceOuch! 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, 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 fail I feel sad because my reputation, credibility and self-confidence is damaged.

?OK. Do not 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 fear, anger and sadness. 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 learned disrepectful behaviour.

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 do we learn this discounting habit?

?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 practice.

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 andstarted families – just like me. Does that mean we grow out of it? 

?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 organisations.

I definitely resonate with that statement! Is there a way to unlearn this discounting habit?

?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.”

Wow! That sounds really useful.  And not just at work – I can see benefits in other areas of my life too.

?Yes. Improvement science is powerful medicine.

So what do I need to do?

?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 material to read and then we can talk again.

Will it help me resolve the problem that I have with the department that asked for my help who are behaving like Victims?

?Yes.

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.

?OK. The material is on its way. I look forward to our next conversation.


Defusing Trust Eroders – Part I

Defusing Trust Eroders – Part II

Defusing Trust Eroders – Part III


<Ring Ring><Ring Ring>

?Hello, you are through to the Improvement Science Helpline. How can we help?

This is Leslie, one of your FISH apprentices.  Could I speak to Bob – my ISP coach?

?Yes, Bob is free. I will connect you now.

<Ring Ring><Ring Ring>

?Hello Leslie, Bob here. How can I help?

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?

?Of course. Can you outline the context for me?

Yes. 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.

?OK – are you using the 6M Design method?

Yes – of course!

?OK – have you done The 4N Chart for the customer of their service?

Yes – it was their customers who asked me if I could help and that is what I used to get the context.

?OK – have you done The 4N Chart for the department?

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.

?OK. Do not panic. This sounds like a very common and treatable system illness.  It is a stream design problem which may be the reason your Foundation training feels insufficient. Would you like to see how a Practitioner would approach this?

Yes please!

?OK. Have you mapped their internal process?

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.

?OK – do they record their quality measurement data?

Yes – I have their report.

?OK – how is the information presented?

As an average for the previous month which is reported up to the Quality Performance Committee.

?OK – what was the average for last month?

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 are annoyed that this department keeps getting ‘bailed out’.

?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.

I don’t understand. What is the Flaw of Averages trap?

?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?

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.

?OK. I need to ask you a personal question. Do you believe that improving quality always costs more?

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 departments get caught in the cross-fire.

?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?

Yes. We have the data. The quality inspectors are very thorough!

?OK – can you plot the quality scores for the last fifty jobs as a BaseLine chart?

Yes – give me a second. The average is 24 as I said.

?OK – is the process stable?

Yes – there is only one flag for the fifty. I know from my FISH training that is not a cause for alarm.

?OK – what is the process capability?

I am sorry – I don’t know what you mean by that?

?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.

Um – the lower line is at 17 and the upper line is at 31.

?OK – how many points lie below the target of 21.

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.

?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.

?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.

?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.

?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 Foundation training! I will go and ask. Can I call you back?

?Of course. I will email you my direct number.


Click here to read the rest of the story


<Ring Ring><Ring Ring>

?Hello, Bob here.

Bob – it is Leslie. I am  so excited! I have discovered something amazing.

?Hello Leslie. That is good to hear. Can you tell me what you have discovered?

I have discovered that better quality does not always cost more.

?That is a good discovery. Can you prove it with data?

Yes I can!  I am emailing you the chart now.

?OK – I am looking at your chart. Can you explain to me what you have discovered?

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.

?OK. Can you describe what you did?

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 Foundation 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 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 FISH school are so powerful and now I understand better the reason we learned them first.

?Well done Leslie. You have taken an important step to becoming a fully fledged Improvement Science Practitioner. There are many more but 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 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 output – but at a higher cost.  We are treating the symptoms and ignoring the disease.

The internal design of the process is unchanged – and 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 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 the people further into cross-fire between Governance and Finance.

The other negative consequence of the Inspection-and-Correction 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 reality check 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.

The Six Dice Game is a simpler version of the famous Red Bead Game that W Edwards Deming used to explain why the arbitrary-target-driven-stick-and-carrot style of management creates more problems than it solves.

The illusion of short-term gain but the reality 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.