Archive for the ‘Training’ Category

The thing that gives me the biggest buzz when it comes to improvement is to see a team share their story of what they have learned-by-doing; and what they have delivered that improves their quality of life and the quality of their patients’ experience.

And while the principles that underpin these transformations are generic, each story is unique because no two improvement challenges are exactly the same and no two teams are exactly the same.

The improvement process is not a standardised production line.  It is much more organic and adaptive experience and that requires calm, competent, consistent, compassionate and courageous facilitation.

So when I see a team share their story of what they have done and learned then I know that behind the scenes there will have been someone providing that essential ingredient.

This week a perfect example of a story like this was shared.

It is about what the whole team who run the Diabetic Complex Cases Clinic at Guy’s and St. Thomas’ NHS Trust in London.  Everyone involved was involved.  It tells how they saw what might be possible and how they stepped up to the challenge of learning to apply the same principles in their world.  And it tells the story of what they diagnosed, designed and delivered.

The expert facilitation and support was provided Ellen Pirie who works for the Health Innovation Network (HIN) in South London and who is a Level 2 Health Care Systems Engineer.

The link to the GSTT Diabetic Complex Clinic Team story is here.

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

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

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

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


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

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

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

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

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

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


What happened next?

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


So, how was this “miracle” achieved?

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

So, how what this design change achieved?

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

So, how was the diagnosis achieved?

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

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

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

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

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

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

Innovation means anything new and new ideas spread through groups of people in a characteristic way that was described by Everett Rogers in the 1970’s.

The evidence showed that innovation started with the small minority of innovators (about 2%)  and  diffuses through the population – first to the bigger minority called early adopters.

Later, it became apparent that the diffusion path was not smooth and that there was a chasm into which many promising innovations fell and from which they did not emerge.

If this change chasm can be bridged then a tipping point is achieved when wider adoption by the majority becomes much more likely.

And for innovations that fundamentally change the way we live and work, this whole process can take decades! Generations even.

Take mobile phones and the Internet as good examples. How many can remember life before those innovations?  And we are living the transition to renewable energy, artificial intelligence and electric cars.


So, it is very rewarding to see growing evidence that the innovators who started the health care improvement movement back in the 1990’s, such as Dr Don Berwick in the USA and Dr Kate Silvester in the UK, have grown a generation of early adopters who now appear to have crossed the chasm.

The evidence for that can be found on the NHS Improvement website – for example the QSIR site (Quality, Service Improvement and Redesign).

Browsing through the QSIR catalogue of improvement tools I recognised them all from previous incarnations developed and tested by the NHS Modernisation Agency and NHS Institute for Innovation and Improvement.  And although those organisations no longer exist, they served as incubators for the growing community of healthcare improvement practitioners (CHIPs) and their legacy lives on.

This is all good news because we now also have a new NHS Long Term Plan which sets out an ambitious vision for the next 10 years and it is going to need a lot of work from the majority of people who work in the NHS to deliver. That will need capability-at-pace-and-scale.

And this raises some questions:

Q1: Will the legacy of the MA and NHSi scale to meet the more challenging task of designing and delivering the vision of a system of Integrated Care Systems (ICS) that include primary care, secondary care, community care, mental health and social care?

Q2: Will some more innovation be required?

If history is anything to go by, then I suspect the the answers will be “Q1: No” and “Q2: Yes”.

Bring it on!

This is the name given to an endemic, chronic, systemic, design disease that afflicts the whole NHS that very few have heard of, and even fewer understand.

This week marked two milestones in the public exposure of this elusive but eminently treatable health care system design illness that causes queues, delays, overwork, chaos, stress and risk for staff and patients alike.

The first was breaking news from the team in Swansea led by Chris Jones.

They had been grappling with the wicked problem of chronic queues, delays, chaos, stress, high staff turnover, and escalating costs in their Chemotherapy Day Unit (CDU) at the Singleton Hospital.

The breakthrough came earlier in the year when we used the innovative eleGANTT® system to measure and visualise the CDU chaos in real-time.

This rich set of data enabled us, for the first time, to apply a powerful systems engineering  technique called counterfactual analysis which revealed the primary cause of the chaos – the elusive and counter-intuitive design disease carvoutosis multiforme fulminans.

And this diagnosis implied that the chaos could be calmed quickly and at no cost.

But that news fell on deaf ears because, not surprisingly, the CDU team were highly sceptical that such a thing was possible.

So, to convince them we needed to demonstrate the adverse effect of carveoutosis in a way that was easy to see.  And to do that we used some advanced technology: dice and tiddly winks.

The reaction of the CDU nurses was amazing.  As soon as they ‘saw’ it they ‘clicked’ and immediately grasped how to apply it in their world.  They designed the change they needed to make in a matter of minutes.


But the proof-of-the-pudding-is-in-the eating and we arranged a one-day-test-of-change of their anti-carveout design.

The appointed day arrived, Wednesday 17th June.  The CDU nurses implemented their new design, which cost nothing to change.  Within an hour of the day starting they reported that the CDU was strangely calm.   And at the end of the day they reported that it had remained strangely calm all day; and that they had time for lunch; and that they had time to do all their admin as they went; and that they finished on time; and that the patients did not wait for their chemotherapy; and that the patients noticed the chaos-to-calm transformation too.

They treated just the same number of patients as usual with the same staff, in the same space and with the same equipment.  It cost nothing to make the change.

To say they they were surprised is an understatement.  They were so surprised and so delighted that they did not want to go back to the old design – but they had to because it was only a one-day-test-of-change.

So, on Thursday and Friday they reverted back to the carveoutosis design.  And the chaos returned.  That nailed it!  There was a riot!!  The CDU nurses refused to wait until later in the year to implement the new design and they voted unanimously to implement it from the following Monday.  And they did.  And calm was restored.


The second milestone happened on Thursday 11th July when we ran a Health Care Systems Engineering (HCSE) Masterclass on the very same topic … chronic systemic carveoutosis multiforme fulminans.

This time we used the dice and tiddly winks to demonstrate the symptoms, signs and the impact of treatment.  Then we explored the known pathophysiology of this elusive and endemic design disease in much more depth.

This is health care systems engineering in action.

It works.

One of the most surprising aspects of systems is how some big changes have no observable effect and how some small changes are game-changers. Why is that?

The technical name for this phenomenon is leverage points.

When a nudge is made at a leverage point in a real system the impact is amplified – so a small cause can have a big effect.

And when a big kick is made where there is no leverage point the effort is dissipated. Like flogging a dead horse.

Other names for leverage points are triggers, buttons, catalysts, fuses etc.


The fact that there is a big effect does not imply it is a good effect.

Poking a leverage point can trigger a catastrophe just as it can trigger a celebration. It depends on how it is poked.

Perhaps that is one reason people stay away from them.

But when our heath care system performance is in decline, if we do nothing or if we act but stay away from leverage points (i.e. flog the dead horse) then we will deny ourselves the opportunity of improvement.

So, we need a way to (a) identify the leverage points and (b) know how to poke them positively and know how to not poke them into delivering a catastrophe.


Here is a couple of real examples.


The time-series chart above shows the A&E performance of a real acute trust.  Notice the pattern as we read left-to-right; baseline performance is OKish and dips in the winters, and the winter dips get deeper but the baseline performance recovers.  In April 2015 (yellow flag) the system behaviour changes, and it goes into a steady decline with added winter dips.  This is the characteristic pattern of poking a leverage point in the wrong way … and the fact it happened at the start of the financial year suggests that Finance was involved.  Possibly triggered by a cost-improvement programme (CIP) action somewhere else in the system.  Save a bit of money here and create a bigger problem over there. That is how systems work. Not my budget so not my problem.

Here is a different example, again from a real hospital and around the same time.  It starts with a similar pattern of deteriorating performance and there is a clear change in system behaviour in Jan 2015.  But in this case the performance improves and stays improved.  Again, the visible sign of a leverage point being poked but this time in a good way.

In this case I do know what happened.  A contributory cause of the deteriorating performance was correctly diagnosed, the leverage point was identified, a change was designed and piloted, and then implemented and validated.  And it worked as predicted.  It was not a fluke.  It was engineered.


So what is the reason that the first example much more commonly seen than the second?

That is a very good question … and to answer it we need to explore the decision making process that leads up to these actions because I refuse to believe that anyone intentionally makes decisions that lead to actions that lead to deterioration in health care performance.

And perhaps we can all learn how to poke leverage points in a positive way?

This recent tweet represents a significant milestone.  It formally recognises and celebrates in public the impact that developing health care systems engineering (HCSE) capability has had on the culture of the organisation.

What is also important is that the HCSE training was not sought and funded by the Trust, it was discovered by chance and funded by their commissioners, the local clinical commissioning group (CCG).


The story starts back in the autumn of 2017 and, by chance, I was chatting with Rob, a friend-of-a-friend, about work. As you do. It turned out that Rob was the CCG Lead for Unscheduled Care and I was describing how HCSE can be applied in any part of any health care system; primary care, secondary care, scheduled, unscheduled, clinical, operational or whatever.  They are all parts of the same system and the techniques and tools of improvement-by-design are generic.  And I described lots of real examples of doing just that and the sustained improvements that had followed.

So he asked “If you were to apply this approach to unscheduled care in a large acute trust how would you do it?“.  My immediate reply was “I would start by training the front line teams in the HCSE Level 1 stuff, and the first step is to raise awareness of what is possible.  We do that by demonstrating it in practice because you have to see it and experience it to believe it.

And so that is what we did.

The CCG commissioned a one-year HCSE Level 1 programme for four teams at University Hospitals of North Midlands (UHNM) and we started in January 2018 with some One Day Flow Workshops.

The intended emotional effect of a Flow Workshop is surprise and delight.  The challenge for the day is to start with a simulated, but very realistic, one-stop outpatient clinic which is chaotic and stressful for everyone.  And with no prior training the delegates transform it into a calm and enjoyable experience using the HCSE approach.  It is called emergent learning.  We have run dozens of these workshops and it has never failed.

After directly experiencing HCSE working in practice the teams that stepped up to the challenge were from ED, Transformation, Ambulatory Emergency Care and Outpatients.


The key to growing HCSE capability is to assemble small teams, called micro-system design teams (MSDTs) and to focus on causes that fall inside their circle of control.

The MSDT sessions need to be regular, short, and facilitated by an experienced HCSE who has seen it, done it and can teach it.

In UHNM, the Transformation team divided themselves between the front-line teams and they learned HCSE together.  Here’s a picture of the ED team … left to right we have Alex, Mark and Julie (ED consultants) then Steve and Janina (Transformation).  The essential tools are a big table, paper, pens, notebooks, coffee and a laptop/projector.

The purpose of each session is empirical learning-by-doing i.e. using a real improvement challenge to learn and practice the method so that before the end of the programme the team can confidently “fly” solo.

That is the key to continued growth and sustained improvement.  The HCSE capability needs to become embedded.

It is good fun and immensely rewarding to see the “ah ha” moments and improvements happen as the needle on the emotometer moves from “Can’t Do” to “Can Do”.

Metamorphosis is re-arranging what you already have in a way that works better.


The tweet is objective evidence that demonstrates the HCSE programme delivers as designed.  It is fit-for-purpose.  It is called validation.

The other objective evidence of effectiveness comes from the learning-by-doing projects themselves.  And for an individual to gain a coveted HCSE Level 1 Certificate of Competency requires writing up to a publishable quality and sharing the story. Warts-and-all.

To read the full story of just click here

And what started this was the CCG who had the strategic vision, looked outside themselves for innovative approaches, and demonstrated the courage to take a risk.

Commissioned Improvement.

On Thursday we had a very enjoyable and educational day.  I say “we” because there were eleven of us learning together.

There was Declan, Chris, Lesley, Imran, Phil, Pete, Mike, Kate, Samar and Ellen and me (behind the camera).  Some are holding their long-overdue HCSE Level-1 Certificates and Badges that were awarded just before the photo was taken.

The theme for the day was System Dynamics which is a tried-and-tested approach for developing a deep understanding of how a complex adaptive system (CAS) actually works.  A health care system is a complex adaptive system.

The originator of system dynamics is Jay Wright Forrester who developed it around the end of WW2 (i.e. about 80 years ago) and who later moved to MIT.  Peter Senge, author of The Fifth Discipline was part of the same group as was Donella Meadows who wrote Limits to Growth.  Their dream was much bigger – global health – i.e. the whole planet not just the human passengers!  It is still a hot topic [pun intended].


The purpose of the day was to introduce the team of apprentice health care system engineers (HCSEs) to the principles of system dynamics and to some of its amazing visualisation and prediction techniques and tools.

The tangible output we wanted was an Excel-based simulation model that we could use to solve a notoriously persistent health care service management problem …

How to plan the number of new and review appointment slots needed to deliver a safe, efficient, effective and affordable chronic disease service?

So, with our purpose in mind, the problem clearly stated, and a blank design canvas we got stuck in; and we used the HCSE improvement-by-design framework that everyone was already familiar with.

We made lots of progress, learned lots of cool stuff, and had lots of fun.

We didn’t quite get to the final product but that was OK because it was a very tough design assignment.  We got 80% of the way there though which is pretty good in one day from a standing start.  The last 20% can now be done by the HCSEs themselves.

We were all exhausted at the end.  We had worked hard.  It was a good day.


And I am already looking forward to the next HCSE Masterclass that will be in about six weeks time.  This one will address another chronic, endemic, systemic health care system “disease” called carveoutosis multiforme fulminans.

This week saw the publication of a landmark paper – one that will bring hope to many.  A paper that describes the first step of a path forward out of the mess that healthcare seems to be in.  A rational, sensible, practical, learnable and enjoyable path.


This week I also came across an idea that triggered an “ah ha” for me.  The idea is that the most rapid learning happens when we are making mistakes about half of the time.

And when I say ‘making a mistake’ I mean not achieving what we predicted we would achieve because that implies that our understanding of the world is incomplete.  In other words, when the world does not behave as we expect, we have an opportunity to learn and to improve our ability to make more reliable predictions.

And that ability is called wisdom.


When we get what we expect about half the time, and do not get what we expect about the other half of the time, then we have the maximum amount of information that we can use to compare and find the differences.

Was it what we did? Was it what we did not do? What are the acts and errors of commission and omission? What can we learn from those? What might we do differently next time? What would we expect to happen if we do?


And to explore this terrain we need to see the world as it is … warts and all … and that is the subject of the landmark paper that was published this week.


The context of the paper is improvement of cancer service delivery, and specifically of reducing waiting time from referral to first appointment.  This waiting is a time of extreme anxiety for patients who have suspected cancer.

It is important to remember that most people with suspected cancer do not have it, so most of the work of an urgent suspected cancer (USC) clinic is to reassure and to relieve the fear that the spectre of cancer creates.

So, the sooner that reassurance can happen the better, and for the unlucky minority who are diagnosed with cancer, the sooner they can move on to treatment the better.

The more important paragraph in the abstract is the second one … which states that seeing the system behaviour as it is, warts-and-all,  in near-real-time, allows us to learn to make better decisions of what to do to achieve our intended outcomes. Wiser decisions.

And the reason this is the more important paragraph is because if we can do that for an urgent suspected cancer pathway then we can do that for any pathway.


The paper re-tells the first chapter of an emerging story of hope.  A story of how an innovative and forward-thinking organisation is investing in building embedded capability in health care systems engineering (HCSE), and is now delivering a growing dividend.  Much bigger than the investment on every dimension … better safety, faster delivery, higher quality and more affordability. Win-win-win-win.

The only losers are the “warts” – the naysayers and the cynics who claim it is impossible, or too “wicked”, or too difficult, or too expensive.

Innovative reality trumps cynical rhetoric … and the full abstract and paper can be accessed here.

So, well done to Chris Jones and the whole team in ABMU.

And thank you for keeping the candle of hope alight in these dark, stormy and uncertain times for the NHS.

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

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

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

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

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

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

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

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

Congratulations Kate!

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


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


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

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

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

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

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

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

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


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

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

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

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

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

Errr .. No!

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


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

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

We played simulation games – lots of them.

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

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

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


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

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


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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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


What do they mean by filters?

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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


Over-reacting to random variation

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

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

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

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

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

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

Lesson: #plotthedots


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

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

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

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

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

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

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


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

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

Under-Reacting to Non-Random Variation

Small shifts and slow drifts can have big cumulative effects.

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

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

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

Q: Do I need to do anything?

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

At the end of Q3 I’m in big trouble!

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

So, would a SPC chart have helped me here?

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

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

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

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

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

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

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


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

So, is there a way to avoid this trap?

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


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

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

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

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

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

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


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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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


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

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

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

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


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

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


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

From the same heat-map perhaps?

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

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

H’mm.  “Houston, we have a problem“.


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

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


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

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

 

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

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

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

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

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

So what are HROs?


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

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

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

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


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

And that was no accident.

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


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

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

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

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


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

Monklands A&E Improvement

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


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

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

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

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

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

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

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

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

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

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

<Bob> OK. What is the problem?

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


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

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

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

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

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

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

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

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

<Bob> OK.  So what is your diagnosis?

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


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

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

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

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

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

<Leslie>Tee-Four?

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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


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

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

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

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

Who is going to sign up to that?

It would be like turkeys voting for Xmas.

There must be a better approach?

Yes. There is.


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

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

Their reaction was “surprise and delight”.

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


Queues and chaos require expensive resources to manage.

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

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

So change does not happen.

It would like turkeys voting for Xmas.


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

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

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

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

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


R.I.P.    C.I.P.

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

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

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

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

It also requires skills, training, practice and experience.

The same is true in healthcare improvement.


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

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

Logically, rationally, enjoyably and surprisingly quickly.

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

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

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


How do we do that?

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

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

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


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

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

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

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

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

A state called blissful ignorance.

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

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

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


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

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

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

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

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

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


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

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


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


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

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

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

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

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

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


Ability – Awareness = Engagement


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

Eureka!

I wonder if the same strategy would work elsewhere?

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

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

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

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


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

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

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


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

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

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

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

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


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

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

And we already know that by a different name:

Health Care Systems Engineering (HCSE).

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

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

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

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

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

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

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

So what happened?

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

It certainly looks that way.

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


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

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

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


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

Will that happen in healthcare too?

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

Could it be that flow and productivity are also important?

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

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

Is it too politically embarrassing?

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


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

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

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

OMG!

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

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

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

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

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

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

What opportunity does systems engineering offer health care?

That question is being actively explored … here.

This week a ground-breaking case study was published.

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

Radiology.

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

Part 1 of their story is shared here.


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

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

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

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


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


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

And visualised just how significant.

Then they shared Part 2 of their story … here.

 

 

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

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

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

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

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

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

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

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


This week I experienced this phenomenon first hand.

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

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

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

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

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


And the outcome was: the participants discovered that …

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

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


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

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

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

… because reality is a much better teacher than rhetoric.

HCSE Practical Skills One Day Workshops

 

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

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

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

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


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

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


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

And that in itself is a powerful learning experience.


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

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

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

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

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


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

Lots of simulation sTimulation. What a great week!

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

An undiscussable topic.

And the undiscussability is also undiscussable.

So the problem or risk persists.

And people come to harm as a result.

Which is not the intended outcome.

So why do we behave this way?

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


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


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

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

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


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

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

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

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


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

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

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

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

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

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

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


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

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

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

There is a deeper Catch-22 as well though:

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


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

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

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

Study ==> Plan ==> Do.

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

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

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

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

So what do we do?

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

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

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


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

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

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

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


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

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

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

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

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


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

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

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

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

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


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

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

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

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

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


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

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

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


So what are the lessons here?

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

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

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

Exactly the opposite of what we want.

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

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

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