Archive for the ‘SFQP’ Category

A story was shared this week.

A story of hope for the hard-pressed NHS, its patients, its staff and its managers and its leaders.

A story that says “We can learn how to fix the NHS ourselves“.

And the story comes with evidence; hard, objective, scientific, statistically significant evidence.


The story starts almost exactly three years ago when a Clinical Commissioning Group (CCG) in England made a bold strategic decision to invest in improvement, or as they termed it “Achieving Clinical Excellence” (ACE).

They invited proposals from their local practices with the “carrot” of enough funding to allow GPs to carve-out protected time to do the work.  And a handful of proposals were selected and financially supported.

This is the story of one of those proposals which came from three practices in Sutton who chose to work together on a common problem – the unplanned hospital admissions in their over 70’s.

Their objective was clear and measurable: “To reduce the cost of unplanned admissions in the 70+ age group by working with hospital to reduce length of stay.

Did they achieve their objective?

Yes, they did.  But there is more to this story than that.  Much more.


One innovative step they took was to invest in learning how to diagnose why the current ‘system’ was costing what it was; then learning how to design an improvement; and then learning how to deliver that improvement.

They invested in developing their own improvement science skills first.

They did not assume they already knew how to do this and they engaged an experienced health care systems engineer (HCSE) to show them how to do it (i.e. not to do it for them).

Another innovative step was to create a blog to make it easier to share what they were learning with their colleagues; and to invite feedback and suggestions; and to provide a journal that captured the story as it unfolded.

And they measured stuff before they made any changes and afterwards so they could measure the impact, and so that they could assess the evidence scientifically.

And that was actually quite easy because the CCG was already measuring what they needed to know: admissions, length of stay, cost, and outcomes.

All they needed to learn was how to present and interpret that data in a meaningful way.  And as part of their IS training,  they learned how to use system behaviour charts, or SBCs.


By Jan 2015 they had learned enough of the HCSE techniques and tools to establish the diagnosis and start to making changes to the parts of the system that they could influence.


Two years later they subjected their before-and-after data to robust statistical analysis and they had a surprise. A big one!

Reducing hospital mortality was not a stated objective of their ACE project, and they only checked the mortality data to be sure that it had not changed.

But it had, and the “p=0.014” part of the statement above means that the probability that this 20.0% reduction in hospital mortality was due to random chance … is less than 1.4%.  [This is well below the 5% threshold that we usually accept as “statistically significant” in a clinical trial.]

But …

This was not a randomised controlled trial.  This was an intervention in a complicated, ever-changing system; so they needed to check that the hospital mortality for comparable patients who were not their patients had not changed as well.

And the statistical analysis of the hospital mortality for the ‘other’ practices for the same patient group, and the same period of time confirmed that there had been no statistically significant change in their hospital mortality.

So, it appears that what the Sutton ACE Team did to reduce length of stay (and cost) had also, unintentionally, reduced hospital mortality. A lot!


And this unexpected outcome raises a whole raft of questions …


If you would like to read their full story then you can do so … here.

It is a story of hunger for improvement, of humility to learn, of hard work and of hope for the future.

bull_by_the_horns_anim_150_wht_9609This week I witnessed an act of courage by someone prepared to take the health care bull by the horns.

On 25th October 2016 a landmark review was published about the integrated health and social care system in Northern Ireland.

It is not a comfortable read.

And the act of courage was the simultaneous publication of the document “Health and Well-being 2026” by Michelle O’Neill, the new Minister of Health.

The full document can be downloaded here.


It is courageous because it says, bluntly, that there is a burning platform, the level of service is not acceptable, doing nothing is not an option, and nothing short of a system-wide redesign will be required.

It is courageous because it sets a clear vision, a burning ambition, and is very clear that this will not be a quick fix. It is a ten year plan.

That implies a constancy of purpose will need to be maintained for at least a decade.

science_of_improvement

And it is courageous because it says that:

we will have to learn how to do this

Here is one paragraph that says that:

Developing the science of improvement can be done at the same time as making improvements

and

We need an infrastructure that makes this possible.”


The good news is that this science of improvement in health care is already well advanced, and it will advance further: a whole health and social care system transformation-by-design is a challenge of some magnitude.

A health and social care system engineering (HSCSE) challenge.


One component of the ten year plan is to develop this capability through a process called co-production.

co-productionNotice that the focus is on pro-actively preventing illness, not just re-actively managing it.

Notice that the design is centered on both the customer and the supplier, not just on the supplier.

And notice that the population served are also expected to be equal partners in the transformation-by-design process.


Courage, constancy of purpose and capability development  … a very welcome breath of fresh air!


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

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

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

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

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

Then they could build the World’s Best Car.

Or could they?

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


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

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

So the relationships were more important than the parts!


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


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

A: Add more failure-avoidance targets of course!

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

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

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

stick_figure_help_button_150_wht_9911Imagine this scenario:

You develop some non-specific symptoms.

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

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


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

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

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

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


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

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

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

It doesn’t.

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

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

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

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


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

It can be better.  A lot better!

And they demonstrate that this better way can be designed.

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

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

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

CDU_Waiting_Room

If life time is so precious, why waste it?

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

CapstanA capstan is a simple machine for combining the effort of many people and enabling them to achieve more than any of them could do alone.

The word appears to have come into English from the Portuguese and Spanish sailors at around the time of the Crusades.

Each sailor works independently of the others. There is no requirement them to be equally strong because the capstan will combine their efforts.  And the capstan also serves as a feedback loop because everyone can sense when someone else pushes harder or slackens off.  It is an example of simple, efficient, effective, elegant design.


In the world of improvement we also need simple, efficient, effective and elegant ways to combine the efforts of many in achieving a common purpose.  Such as raising the standards of excellence and weighing the anchors of resistance.

In health care improvement we have many simultaneous constraints and we have many stakeholders with specific perspectives and special expertise.

And if we are not careful they will tend to pull only in their preferred direction … like a multi-way tug-o-war.  The result?  No progress and exhausted protagonists.

There are those focused on improving productivity – Team Finance.

There are those focused on improving delivery – Team Operations.

There are those focused on improving safety – Team Governance.

And we are all tasked with improving quality – Team Everyone.

So we need a synergy machine that works like a capstan-of-old, and here is one design.

Engine_Of_ExcellenceIt has four poles and it always turns in a clockwise direction, so the direction of push is clear.

And when all the protagonists push in the same direction, they will get their own ‘win’ and also assist the others to make progress.

This is how the sails of success are hoisted to catch the wind of change; and how the anchors of anxiety are heaved free of the rocks of fear; and how the bureaucratic bilge is pumped overboard to lighten our load and improve our speed and agility.

And the more hands on the capstan the quicker we will achieve our common goal.

Collective excellence.

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

These highlights are a snapshot.

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

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

Which I do not believe is the intention.

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


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

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

NHSE_Purpose

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

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

primum non nocere (first do no harm),

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

What about:

tunc mederi (then do some healing)


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

It seems to be notably absent.

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


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

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

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

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

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

SHMI_Specification

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

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

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

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

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


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

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

So why has LOS been omitted from SHMI?

The reason may be more political than statistical.

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

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

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

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

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

All notably absent!

flag_waving_mountain_150_clr_13781A wise person once said:

Improvement implies change, but change does not imply improvement.

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

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


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

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

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


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

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

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


So let us take health care improvement as the example.

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

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

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

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

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

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


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

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

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

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


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

understanding variation.

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

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

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


This chance combination of apparently independent events got me thinking.

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

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


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

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

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

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


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

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

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


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

Or are there other forces at play as well?

Some deeper undiscussables perhaps?

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

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

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


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

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


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

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

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

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


Two rare and precious pearls did indeed grow.

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

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

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

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


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

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

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


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


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

british_pound_money_three_bundled_stack_400_wht_2425This week I conducted an experiment – on myself.

I set myself the challenge of measuring the cost of chaos, and it was tougher than I anticipated it would be.

It is easy enough to grasp the concept that fire-fighting to maintain patient safety amidst the chaos of healthcare would cost more in terms of tears and time …

… but it is tricky to translate that concept into hard numbers; i.e. cash.


Chaos is an emergent property of a system.  Safety, delivery, quality and cost are also emergent properties of a system. We can measure cost, our finance departments are very good at that. We can measure quality – we just ask “How did your experience match your expectation”.  We can measure delivery – we have created a whole industry of access target monitoring.  And we can measure safety by checking for things we do not want – near misses and never events.

But while we can feel the chaos we do not have an easy way to measure it. And it is hard to improve something that we cannot measure.


So the experiment was to see if I could create some chaos, then if I could calm it, and then if I could measure the cost of the two designs – the chaotic one and the calm one.  The difference, I reasoned, would be the cost of the chaos.

And to do that I needed a typical chunk of a healthcare system: like an A&E department where the relationship between safety, flow, quality and productivity is rather important (and has been a hot topic for a long time).

But I could not experiment on a real A&E department … so I experimented on a simplified but realistic model of one. A simulation.

What I discovered came as a BIG surprise, or more accurately a sequence of big surprises!

  1. First I discovered that it is rather easy to create a design that generates chaos and danger.  All I needed to do was to assume I understood how the system worked and then use some averaged historical data to configure my model.  I could do this on paper or I could use a spreadsheet to do the sums for me.
  2. Then I discovered that I could calm the chaos by reactively adding lots of extra capacity in terms of time (i.e. more staff) and space (i.e. more cubicles).  The downside of this approach was that my costs sky-rocketed; but at least I had restored safety and calm and I had eliminated the fire-fighting.  Everyone was happy … except the people expected to foot the bill. The finance director, the commissioners, the government and the tax-payer.
  3. Then I got a really big surprise!  My safe-but-expensive design was horribly inefficient.  All my expensive resources were now running at rather low utilisation.  Was that the cost of the chaos I was seeing? But when I trimmed the capacity and costs the chaos and danger reappeared.  So was I stuck between a rock and a hard place?
  4. Then I got a really, really big surprise!!  I hypothesised that the root cause might be the fact that the parts of my system were designed to work independently, and I was curious to see what happened when they worked interdependently. In synergy. And when I changed my design to work that way the chaos and danger did not reappear and the efficiency improved. A lot.
  5. And the biggest surprise of all was how difficult this was to do in my head; and how easy it was to do when I used the theory, techniques and tools of Improvement-by-Design.

So if you are curious to learn more … I have written up the full account of the experiment with rationale, methods, results, conclusions and references and I have published it here.

custom_meter_15256[Drrrrrrring]

<Leslie> Hi Bob, I hope I am not interrupting you.  Do you have five minutes?

<Bob> Hi Leslie. I have just finished what I was working on and a chat would be a very welcome break.  Fire away.

<Leslie> I really just wanted to say how much I enjoyed the workshop this week, and so did all the delegates.  They have been emailing me to say how much they learned and thanking me for organising it.

<Bob> Thank you Leslie. I really enjoyed it too … and I learned lots … I always do.

<Leslie> As you know I have been doing the ISP programme for some time, and I have come to believe that you could not surprise me any more … but you did!  I never thought that we could make such a dramatic improvement in waiting times.  The queue just melted away and I still cannot really believe it.  Was it a trick?

<Bob> Ahhhh, the siren-call of the battle-hardened sceptic! It was no trick. What you all saw was real enough. There were no computers, statistics or smoke-and-mirrors used … just squared paper and a few coloured pens. You saw it with your own eyes; you drew the charts; you made the diagnosis; and you re-designed the policy.  All I did was provide the context and a few nudges.

<Leslie> I know, and that is why I think seeing the before and after data would help me. The process felt so much better, but I know I will need to show the hard evidence to convince others, and to convince myself as well, to be brutally honest.  I have the before data … do you have the after data?

<Bob> I do. And I was just plotting it as BaseLine charts to send to you.  So you have pre-empted me.  Here you are.

StE_OSC_Before_and_After
This is the waiting time run chart for the one stop clinic improvement exercise that you all did.  The leftmost segment is the before, and the rightmost are the after … your two ‘new’ designs.

As you say, the queue and the waiting has melted away despite doing exactly the same work with exactly the same resources.  Surprising and counter-intuitive but there is the evidence.

<Leslie> Wow! That fits exactly with how it felt.  Quick and calm! But I seem to remember that the waiting room was empty, particularly in the case of the design that Team 1 created. How come the waiting is not closer to zero on the chart?

<Bob> You are correct.  This is not just the time in the waiting room, it also includes the time needed to move between the rooms and the changeover time within the rooms.  It is what I call the ‘tween-time.

<Leslie> OK, that makes sense now.  And what also jumps out of the picture for me is the proof that we converted an unstable process into a stable one.  The chaos was calmed.  So what is the root cause of the difference between the two ‘after’ designs?

<Bob> The middle one, the slightly better of the two, is the one where all patients followed the newly designed process.  The rightmost one was where we deliberately threw a spanner in the works by assuming an unpredictable case mix.

<Leslie> Which made very little difference!  The new design was still much, much better than before.

<Bob> Yes. What you are seeing here is the footprint of resilient design. Do you believe it is possible now?

<Leslie> You bet I do!

FreshMeatOldBonesEvolution is an amazing process.

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

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

Knowledge evolves in just the same way.

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

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


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


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

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


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


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

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

So what are the reasons we do not?

Is it because healthcare is quite introspective?

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

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

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

Is it because we do not like change?

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

Is it because we fear loss of face?


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

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

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

New meat for our old bones?

CAS_DiagramThe theme this week has been emergent learning.

By that I mean the ‘ah ha’ moment that happens when lots of bits of a conceptual jigsaw go ‘click’ and fall into place.

When, what initially appears to be smoky confusion suddenly snaps into sharp clarity.  Eureka!  And now new learning can emerge.


This did not happen by accident.  It was engineered.


The picture above is part of a bigger schematic map of a system – in this case a system related to the global health challenge of escalating obesity.

It is a complicated arrangement of boxes and arrows. There are  dotted lines that outline parts of the system that have leaky boundaries like the borders on a political map.

But it is a static picture of the structure … it tells us almost nothing about the function, the system behaviour.

And our intuition tells us that, because it is a complicated structure, it will exhibit complex and difficult to understand behaviour.  So, guided by our inner voice, we toss it into the pile labelled Wicked Problems and look for something easier to work on.


Our natural assumption that a complicated structure always leads to complex behavior is an invalid simplification, and one that we can disprove in a matter of moments.


Exhibit 1. A system can be complicated and yet still exhibit simple, stable and predictable behavior.

Harrison_H1The picture is of a clock designed and built by John Harrison (1693-1776).  It is called H1 and it is a sea clock.

Masters of sailing ships required very accurate clocks to calculate their longitude, the East-West coordinate on the Earth’s surface. And in the 18th Century this was a BIG problem. Too many ships were getting lost at sea.

Harrison’s sea clock is complicated.  It has many moving parts, but it was the most stable and accurate clock of its time.  And his later ones were smaller, more accurate and even more complicated.


Exhibit 2.  A system can be simple yet still exhibit complex, unstable and unpredictable behavior.

Double-compound-pendulumThe image is of a pendulum made of only two rods joined by a hinge.  The structure is simple yet the behavior is complex, and this can only be appreciated with a dynamic visualisation.

The behaviour is clearly not random. It has structure. It is called chaotic.

So, with these two real examples we have disproved our assumption that a complicated structure always leads to complex behaviour; and we have also disproved its inverse … that complex behavior always comes from a complicated structure.


This deeper insight gives us hope.

We can design complicated systems to exhibit stable and predictable behaviour if, like John Harrison, we know how to.

But John Harrison was a rare, naturally-gifted, mechanical genius, and even with that advantage it took him decades to learn how to design and to build his sea clocks.  He was the first to do so and he was self-educated so his learning was emergent.

And to make it easier, he was working on a purely mechanical system comprised of non-living parts that only obeyed the Laws of Newtonian physics.


Our healthcare system is not quite like that.  The parts are living people whose actions are limited by physical Laws but whose decisions are steered by other policies … learned ones … and ones that can change.  They are called heuristics and they can vary from person-to-person and minute-to-minute.  Heuristics can be learned, unlearned, updated, and evolved.

This is called emergent learning.

And to generate it we only need to ‘engineer’ the context for it … the rest happens as if by magic … but only if we do the engineering well.


This week I personally observed over a dozen healthcare staff simultaneously re-invent a complicated process scheduling technique, at the same time as using it to eliminate the  queues, waiting and chaos in the system they wanted to improve.

Their queues just evaporated … without requiring any extra capacity or money. Eureka!


We did not show them how to do it so they could not have just copied what we did.

We designed and built the context for their learning to emerge … and it did.  On its own.

The ISP One Day Intensive Workshop delivered emergent learning … just as it was designed to do.

This engineering is called complex adaptive system design and this one example proves that CASD is both possible, learnable and therefore teachable.

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

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

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

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


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


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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

<Bob> Efficiency is a stream metric – it is the ratio of the minimum cost of the resources required to complete one task divided by the actual cost of the resources used to complete one task.

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

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

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

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

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

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

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

<Bob> Try looking it up on Wikipedia …

<Leslie> OK … here we go …

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

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

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

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

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

<Leslie> Oh …. and what is that?

<Bob> Worth.

<Leslie> Eh?

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

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

<Bob> Yup.  Get the safety. flow and quality perspectives of the design in synergy and productivity will sky-rocket. It is called a Fit-4-Purpose design.

stick_figure_balance_mind_heart_150_wht_9344Improvement implies learning.  And to learn we need feedback from reality because without it we will continue to believe our own rhetoric.

So reality feedback requires both sensation and consideration.

There are many things we might sense, measure and study … so we need to be selective … we need to choose those things that will help us to make the wise decisions.


Wise decisions lead to effective actions which lead to intended outcomes.


Many measures generate objective data that we can plot and share as time-series charts.  Pictures that tell an evolving story.

There are some measures that matter – our intended outcomes for example. Our safety, flow, quality and productivity charts.

There are some measures that do not matter – the measures of compliance for example – the back-covering blame-avoiding management-by-fear bureaucracy.


And there are some things that matter but are hard to measure … objectively at least.

We can sense them subjectively though.  We can feel them. If we choose to.

And to do that we only need to go to where the people are and the action happens and just watch, listen, feel and learn.  We do not need to do or say anything else.

And it is amazing what we learn in a very short period of time. If we choose to.


If we enter a place where a team is working well we will see smiles and hear laughs. It feels magical.  They will be busy and focused and they will show synergism. The team will be efficient, effective and productive.

If we enter place where is team is not working well we will see grimaces and hear gripes. It feels miserable. They will be busy and focused but they will display antagonism. The team will be inefficient, ineffective and unproductive.


So what makes the difference between magical and miserable?

The difference is the assumptions, attitudes, prejudices, beliefs and behaviours of those that they report to. Their leaders and managers.

If the culture is management-by-fear (a.k.a bullying) then the outcome is unproductive and miserable.

If the culture is management-by-fearlessness (a.k.a. inspiring) then the outcome is productive and magical.

It really is that simple.

smack_head_in_disappointment_150_wht_16653Many organisations proclaim that their mission is to achieve excellence but then proceed to deliver mediocre performance.

Why is this?

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

So the flaw must lie somewhere in the process.


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

Do they measure failure or success?


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

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

And the focus of action is getting away from failure.

Damage mitigation, damage limitation and damage compensation.


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

But are we?

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

Q: Does excellence  = not excrement?

A: No. There is something between these extremes.

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


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

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

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

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

Out of the frying pan and into the fire.

No wonder Governance and Finance come into conflict!

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


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

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

Nope.

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

We get mediocrity.


And what do we do then?

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

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

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

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

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

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


Our mission statement proclaims ‘trusted to achieve excellence’ not ‘designed to deliver mediocrity’.

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

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

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


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

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

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

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

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

Do we measure any of that?

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

Nope.


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

Warts and all.

So that we know when we are doing a good job, and we know where to focus further improvement effort.

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

We have the choice.

We just need to make it.

top_surgeon_400_wht_7589All healthcare organisations strive for excellence, which is good, and most achieve mediocrity, which is not so good.

Why is that?

One cause is the design of their model for improvement … the one that is driven by targets, complaints, near misses, serious untoward incidents (SUIs) and never events (which are not never).

A model for improvement that is driven by failure feedback loops can only ever achieve mediocrity, not excellence.

Whaaaaaat?!* That’s rubbish”  I hear you cry … so let us see.


Try this simple test …. just ask any employee in your organisation this question (and start with yourself):

How do you know you are doing a good job?

If the first answer heard is “When no one is complaining” then you have a Mediocrity Design.


When customers have a disappointing experience most do not pen a letter or write an email to complain.  Most just sigh and lower their expectations to avoid future disappointment; many will grumble to family and friends; and only a few (about 5%) will actually complain. They are the really angry extreme.  So they can easily be fobbed off with platitudes … just being earnestly listened to and unreservedly apologised to is usually enough to take the wind out of their sails.  It will escort them back to the silent but disappointed majority whose expectation is being gradually eroded by relentless disappointment. Nothing fundamental needs to change because eventually the complaints dry up, apathy is re-established and chronic mediocrity is assured.


To achieve excellence we need a better answer to the “How do you know you are doing a good job?” question.

We need to be able to say “I know I am doing a good job because this is what a good outcome looks like; this is my essential contribution to achieving that outcome; and here are the measures of the intended outcomes that we are achieving.

In short we need a clear purpose, a defined part in the process that delivers that purpose, and we need an objective feedback loop that tells us that the purpose has been achieved and that our work is worthwhile.

And if  any of those components are missing then we know we have some improvement work to do.

The first step is usually answering the question “What is our purpose?

The second step is using the purpose to design and install the how-are-we-doing feedback loop.

And the  third step is to learn to use the success feedback loop to ensure that we are always working to have a necessary-and-sufficient process that delivers the intended outcome and that we are playing a part in that.

And when we are reliably achieving our purpose, we set ourselves an even better outcome – an even safer, calmer, higher quality and more productive one … and doing that will generate more improvement work to do.  We will not be idle.


That is the essence of Excellence-by-Design.

FISH_ISP_eggs_jumpingResistance-to-change is an oft quoted excuse for improvement torpor. The implied sub-message is more like “We would love to change but They are resisting“.

Notice the Us-and-Them language.  This is the observable evidence of an “We‘re OK and They’re Not OK” belief.  And in reality it is this unstated belief and the resulting self-justifying behaviour that is an effective barrier to systemic improvement.

This Us-and-Them language generates cultural friction, erodes trust and erects silos that are effective barriers to the flow of information, of innovation and of learning.  And the inevitable reactive solutions to this Us-versus-Them friction create self-amplifying positive feedback loops that ensure the counter-productive behaviour is sustained.

One tangible manifestation are DRATs: Delusional Ratios and Arbitrary Targets.


So when a plausible, rational and well-evidenced candidate for an alternative approach is discovered then it is a reasonable reaction to grab it and to desperately spray the ‘magic pixie dust’ at everything.

This a recipe for disappointment: because there is no such thing as ‘improvement magic pixie dust’.

The more uncomfortable reality is that the ‘magic’ is the result of a long period of investment in learning and the associated hard work in practising and polishing the techniques and tools.

It may look like magic but is isn’t. That is an illusion.

And some self-styled ‘magicians’ choose to keep their hard-won skills secret … because by sharing them know that they will lose their ‘magic powers’ in a flash of ‘blindingly obvious in hindsight’.

And so the chronic cycle of despair-hope-anger-and-disappointment continues.


System-wide improvement in safety, flow, quality and productivity requires that the benefits of synergism overcome the benefits of antagonism.  This requires two changes to the current hope-and-despair paradigm.  Both are necessary and neither are sufficient alone.

1) The ‘wizards’ (i.e. magic folk) share their secrets.
2) The ‘muggles’ (i.e. non-magic folk) invest the time and effort in learning ‘how-to-do-it’.


The transition to this awareness is uncomfortable so it needs to be managed pro-actively … by being open about the risk … and how to mitigate it.

That is what experienced Practitioners of Improvement Science (and ISP) will do. Be open about the challenged ahead.

And those who desperately want the significant and sustained SFQP improvements; and an end to the chronic chaos; and an end to the gaming; and an end to the hope-and-despair cycle …. just need to choose. Choose to invest and learn the ‘how to’ and be part of the future … or choose to be part of the past.


Improvement science is simple … but it is not intuitively obvious … and so it is not easy to learn.

If it were we would be all doing it.

And it is the behaviour of a wise leader of change to set realistic and mature expectations of the challenges that come with a transition to system-wide improvement.

That is demonstrating the OK-OK behaviour needed for synergy to grow.

SFQP_enter_circle_middle_15576For a system to be both effective and efficient the parts need to work in synergy. This requires both alignment and collaboration.

Systems that involve people and processes can exhibit complex behaviour. The rules of engagement also change as individuals learn and evolve their beliefs and their behaviours.

The values and the vision should be more fixed. If the goalposts are obscure or oscillate then confusion and chaos is inevitable.


So why is collaborative alignment so difficult to achieve?

One factor has been mentioned. Lack of a common vision and a constant purpose.

Another factor is distrust of others. Our fear of exploitation, bullying, blame, and ridicule.

Distrust is a learned behaviour. Our natural inclination is trust. We have to learn distrust. We do this by copying trust-eroding behaviours that are displayed by our role models. So when leaders display these behaviours then we assume it is OK to behave that way too.  And we dutifully emulate.

The most common trust eroding behaviour is called discounting.  It is a passive-aggressive habit characterised by repeated acts of omission:  Such as not replying to emails, not sharing information, not offering constructive feedback, not asking for other perspectives, and not challenging disrespectful behaviour.


There are many causal factors that lead to distrust … so there is no one-size-fits-all solution to dissolving it.

One factor is ineptitude.

This is the unwillingness to learn and to use available knowledge for improvement.

It is one of the many manifestations of incompetence.  And it is an error of omission.


Whenever we are unable to solve a problem then we must always consider the possibility that we are inept.  We do not tend to do that.  Instead we prefer to jump to the conclusion that there is no solution or that the solution requires someone else doing something different. Not us.

The impossibility hypothesis is easy to disprove.  If anyone has solved the problem, or a very similar one, and if they can provide evidence of what and how then the problem cannot be impossible to solve.

The someone-else’s-fault hypothesis is trickier because proving it requires us to influence others effectively.  And that is not easy.  So we tend to resort to easier but less effective methods … manipulation, blame, bullying and so on.


A useful way to view this dynamic is as a set of four concentric circles – with us at the centre.

The outermost circle is called the ‘Circle of Ignorance‘. The collection of all the things that we do not know we do not know.

Just inside that is the ‘Circle of Concern‘.  These are things we know about but feel completely powerless to change. Such as the fact that the world turns and the sun rises and falls with predictable regularity.

Inside that is the ‘Circle of Influence‘ and it is a broad and continuous band – the further away the less influence we have; the nearer in the more we can do. This is the zone where most of the conflict and chaos arises.

The innermost is the ‘Circle of Control‘.  This is where we can make changes if we so choose to. And this is where change starts and from where it spreads.


SFQP_enter_circle_middle_15576So if we want system-level improvements in safety, flow, quality and productivity (or cost) then we need to align these four circles. Or rather the gaps in them.

We start with the gaps in our circle of control. The things that we believe we cannot do … but when we try … we discover that we can (and always could).

With this new foundation of conscious competence we can start to build new relationships, develop trust and to better influence others in a win-win-win conversation.

And then we can collaborate to address our common concerns – the ones that require coherent effort. We can agree and achieve our common purpose, vision and goals.

And from there we will be able to explore the unknown opportunities that lie beyond. The ones we cannot see yet.

Troublemaker_vs_RebelSystem-wide, significant, and sustained improvement implies system-wide change.

And system-wide change implies more than 20% of the people commit to action. This is the cultural tipping point.

These critical 20% have a badge … they call themselves rebels … and they are perceived as troublemakers by those who profit most from the status quo.

But troublemakers and rebels are radically different … as shown in the summary by Lois Kelly.


Rebels share a common, future-focussed purpose.  A mission.  They are passionate, optimistic and creative.  They understand synergy and how to release and align the stored emotional energy of both themselves and others.  And most importantly they are value-led and that makes them attractive.  Values such as honesty, integrity and industry are what make leaders together-effective.

SHCR_logoAnd as we speak there is school for rebels in healthcare gaining momentum …  and their programme is current, open to all and free to access. And the change agent development materials are excellent!

Click here to download their study guide.


Converting possibilities into realities is the essence of design … so our merry band of rebels will also need to learn how to convert their positive rhetoric into practical reality. And that is more physics than psychology.

Streams flow because of physics not because of passion.SFQP_Compass

And this is why the science of improvement is important because it is the synthesis of the people dimension and the process dimension – into a system that delivers significant and sustained improvement.

On all dimensions. Safety, Flow, Quality and Productivity.

The lighthouse is our purpose; the whale represents the magnitude of our challenge; the blue sky is the creative thinking we need … to avoid trying to boil the ocean.

And the noisy, greedy, s****y seagulls are the troublemakers who always will plague us.

[Image by Malaika Art].


SFQPThe flavour of the week has been “chaos”. Again!

Chaos dissipates energy faster than calm so chaotic behaviour is a symptom of an inefficient design.

And we would like to improve our design to restore a state of ‘calm efficiency’.

Chaos is a flow phenomenon … but that is not where the improvement by design process starts. There is a step before that … Safety.


Safety First
If a design is unsafe it generates harm. So we do not want to improve the smooth efficiency of the harm generator … that will produce more harm!  First we must consider if our system is safe enough.

Despite what many claim our healthcare systems are actually very safe. Sure there are embarrassing exceptions and we can always improve safety further, but we actually have quite a safe design.

It is not a very efficient one though. There is a lot of checking and correcting which uses up time and resources … but safety is good enough for now.

Having done the safety sanity check we can move on to Flow.


Flow Second
Flow comes before quality because it is impossible to deliver a high quality experience in a chaotic system. First we need to calm any chaos.  Or rather we need to diagnose the root causes of the chaotic behaviour and do some flow re-design to restore the calm.

Chaos is funny stuff. It does not behave intuitively. Time is always a factor. The butterflies wing effect is ever present.  Small causes can have big effects, both good and bad. Big causes can have no effect. Causes can be synergistic and they can be antagonistic. The whole is not the sum of the parts. This confusing and counter-intuitive behaviour is called “non linear” and we are all rubbish at getting a mental handle on it.

The good news is that when chaos reigns it is usually possible to calm it with a small number of carefully placed, carefully timed,  carefully designed, synergistic, “tweaks”.

The problem is that when we do what intuitively feels “right” we can easily make poor improvement decisions that lead to ineffective actions. The chaos either does not go away or it gets worse. So we have learned from our ineptitude to just put up with it and accept the inefficiency. The high cost of chaos.

To calm the chaos we have to learn to use the tools designed to do that. And they do exist.


Quality
Safety and Flow represent the “absolute” half of the SFQP cycle.  Harm is an absolute metric. We can devise absolute definitions and count harmful events. Mortality. Mistakes. Hospital  acquired infections.  That sort of stuff.   Flow is absolute too in the sense that the Laws of Physics determine what happens, and they are absolute too. Non negotiable.

Quality is relative.  It is the ratio of experience and expectation.  Both of these are subjective but that is not the point. The point is that it is a ratio. That makes it a relative metric. My expectation influences my perception of quality – so does what I experience.  And this has important implications.  For example we can reduce disappointment by lowering expectation; or we can reduce disappointment by improving experience.  Lowering expectation is the easier option though because to do that we only have to don the “black hat” and paint a grisly picture of a worst case scenario.  Some call it “informed consent”; I call it “abdication of empathy” and “fear-mongering”.

Variable quality can  come from variable experience, variable expectation or both.  So to reduce quality variation we can focus on either part of the ratio; and the easiest is expectation.  Setting a realistic expectation just requires measuring experience retrospectively and sharing it prospectively. Not satisfaction mind you. Experience. Satisfaction surveys are meaningless as an improvement tool because setting a lower expectation will improve satisfaction!

And this is why quality follows flow … because if flow is chaotic then expectation becomes a lottery, and quality does too.  The chaotic behaviour of the St.Elsewhere’s® A&E Department that we saw last week implies that we cannot set any other expectation than “It might be OK or it might be Not OK … we cannot predict. So fingers crossed.” It is a quality lottery!

But with calm and efficient flow we will experience less variation and with that we can set a reasonable expectation. Quality becomes predictable-within-limits.


Productivity
Productivity is also a relative concept. It is the ratio of what we get out of the system divided by what we put in.  Revenue divided by expense for example.

And it does not actually appear last.  As soon as safety, flow or quality improve then they will have an immediate impact on productivity.  Work gets easier.  The cost of harm, chaos and disappointment will fall. And they are surprisingly large costs!

The reason that productivity-by-design comes last is because we are talking about focussed productivity improvement-by-design.  Better value for money for example.  And that requires a specific design focus. It comes last because we need some head-space and some life-time to learn and do good design.

And SFQP is a cycle so after doing the Productivity improvement we go back to Safety and ask “How can we make our design even safer and even simpler?” And so on, round and round the SFQP cycle.

Do no harm, restore the calm, delight for all, and costs will fall.

And if you would like a full-size copy of the SFQP cycle diagram to use and share just click here.

coffee_table_talk_PA_150_wht_6082The Webex icon bounced up and down on Bob’s task bar signalling that Leslie had just joined the weekly ISP coaching session.

<Leslie> Hi Bob. I have been so busy this week that I have not had time to consider a topic to explore.

<Bob> No problem Leslie, I have shelf full of topics we have not touched yet.  So shall we talk about counter-productivity?

<Leslie> Don’t you mean productivity … the fourth dimension of system improvement.

<Bob>They are related of course but we will approach the issue of productivity from a different angle. Rather like we did with safety. To improve safety we considered at the causes of un-safety and focussed our efforts there.

<Leslie> Ah yes, I see.  So to improve productivity we look at the causes of un-productivity … in other words counter-productive beliefs and behaviours that are manifest as system design flaws.

<Bob> Exactly. So remind me what the definition of a productivity metric is from your FISH course.

<Leslie> Productivity is the ratio of a stream metric and a stage metric.  Value-for-Money for example.

<Bob> Good.  So counter-productivity is also a ratio of a stream and a stage metric.

<Leslie> Um, I’m not sure I quite get that. Can you explain a bit more.

<Bob> OK. To explore deeper we need to be clear about how each metric relates to our intended outcome.  Remember in safety-by-design we count the number and severity of risks and harm because  as harm is going up then safety is going down.  So harm is an un-safety stream metric.

<Leslie> Ah! Yes I see.  So if we look at cycle-time, which is a stage metric; as cycle-time increases, the activity falls and productivity falls. So cycle-time is actually a counter-productivity metric.

<Bob>Excellent. You are getting the hang of the concept of counter-productivity.

<Leslie> And we need to be careful because productivity is a ratio so the numerator and denominator metrics work in opposite ways: increasing the magnitude of the numerator is equivalent to decreasing the magnitude of the denominator – the ratio increases.

<Bob> Indeed, there are many hazards with ratios as we have explored before. So let is consider a real and rather useful example.  Let us look at Little’s Law from the perspective of counter-productivity. Remind me of the definition of Little’s Law for a single step system.

<Leslie> Little’s Law is a mathematically proven law of flow physics which states that the average lead-time is the product of the average work-in-progress and the average cycle-time.

LT = WIP * CT

<Bob> Good and I am pleased to see that you have used cycle-time. We are considering a single stream, single stage, single step system.

<Leslie> Yes, I avoided using the unqualified term ‘activity’. I have learned that lesson the hard way too!

<Bob> So how do the terms in Little’s Law relate to streams, stages and systems?

<Leslie> Lead-time is a stream metric, cycle-time is a stage metric and work-in-progress is a …. h’mm. What it is? A stream metric or a stage metric?

<Bob>Or?

<Leslie>A system metric?  WIP is a system metric!

<Bob> Good. So now re-arrange Little’s Law as a productivity formula.

<Leslie> Work-in-Progress equals lead-time divided by cycle-time

WIP = LT / CT

<Bob> So is WIP a productivity or a counter-productivity metric?

<Leslie> H’mmm …. I will need to work this through logically and step-by-step. I do not trust my intuition on this flow stuff.

Increasing cycle-time is counter-productive because it implies activity is falling while costs are not.

But cycle-time is on the bottom of the ratio so it’s effect reverses.

So if lead-time stays the same and cycle-time increases then because it is on the bottom of the ratio that implies a more productive design. And at the same time work in progress must be falling. Urrgh! This is hurting my head.

<Bob> Good, keep going … you are nearly there.

<Leslie> So a falling WIP is a sign of increasing productivity.

<Bob> Good … and that implies?

<Leslie> WIP is a counter-productivity system metric!

<Bob> Well done. Your logic is flawless.

<Leslie> So that  is why we focus on WIP so much!  Whatever causes WIP to increase is counter-productive!

Ahhhh …. that makes complete sense.

Lo-WIP  designs are more productive than Hi-WIP designs.

<Bob> Bravo!  And translating this into financial metrics … it is because a big queue of waiting work incurs costs. Storage cost, maintenance cost, processing cost and so on. So WIP is a liability. It is not an asset!

<Leslie> But doesn’t that imply treating work-in-progress as an asset on the financial balance sheet is counter-productive?

<Bob> It does indeed.

<Leslie> Oh dear! That revelation is going to upset a lot of people in the accounting department!

<Bob> The painful reality is that  the Laws of Flow Physics are completely indifferent to what any of us believe or do not believe.

<Leslie> Wow!  I like this concept of counter-productivity … it really helps to expose some of our invalid assumptions that invisibly block improvement!

<Bob> So here is a question to ponder.  Is zero WIP desirable or even possible?

<Leslie> H’mmm.  I will have to think about that.  I know you would not have asked the question for no reason.

F4P_PillsWe all want a healthcare system that is fit for purpose.

One which can deliver diagnosis, treatment and prognosis where it is needed, when it is needed, with empathy and at an affordable cost.

One that achieves intended outcomes without unintended harm – either physical or psychological.

We want safety, delivery, quality and affordability … all at the same time.

And we know that there are always constraints we need to work within.

There are constraints set by the Laws of the Universe – physical constraints.

These are absolute,  eternal and are not negotiable.

Dr Who’s fantastical tardis is fictional. We cannot distort space, or travel in time, or go faster than light – well not with our current knowledge.

There are also constraints set by the Laws of the Land – legal constraints.

Legal constraints are rigid but they are also adjustable.  Laws evolve over time, and they are arbitrary. We design them. We choose them. And we change them when they are no longer fit for purpose.

The third limit is often seen as the financial constraint. We are required to live within our means. There is no eternal font of  limitless funds to draw from.  We all share a planet that has finite natural resources  – and ‘grow’ in one part implies ‘shrink’ in another.  The Laws of the Universe are not negotiable. Mass, momentum and energy are conserved.

The fourth constraint is perceived to be the most difficult yet, paradoxically, is the one that we have most influence over.

It is the cultural constraint.

The collective, continuously evolving, unwritten rules of socially acceptable behaviour.


Improvement requires challenging our unconscious assumptions, our beliefs and our habits – and selectively updating those that are no longer fit-4-purpose.

To learn we first need to expose the gaps in our knowledge and then to fill them.

We need to test our hot rhetoric against cold reality – and when the fog of disillusionment forms we must rip up and rewrite what we have exposed to be old rubbish.

We need to examine our habits with forensic detachment and we need to ‘unlearn’ the ones that are limiting our effectiveness, and replace them with new habits that better leverage our capabilities.

And all of that is tough to do. Life is tough. Living is tough. Learning is tough. Leading is tough. But it energising too.

Having a model-of-effective-leadership to aspire to and a peer-group for mutual respect and support is a critical piece of the jigsaw.

It is not possible to improve a system alone. No matter how smart we are, how committed we are, or how hard we work.  A system can only be improved by the system itself. It is a collective and a collaborative challenge.


So with all that in mind let us sketch a blueprint for a leader of systemic cultural improvement.

What values, beliefs, attitudes, knowledge, skills and behaviours would be on our ‘must have’ list?

What hard evidence of effectiveness would we ask for? What facts, figures and feedback?

And with our check-list in hand would we feel confident to spot an ‘effective leader of systemic cultural improvement’ if we came across one?


This is a tough design assignment because it requires the benefit of  hindsight to identify the critical-to-success factors: our ‘must have and must do’ and ‘must not have and must not do’ lists.

H’mmmm ….

So let us take a more pragmatic and empirical approach. Let us ask …

“Are there any real examples of significant and sustained healthcare system improvement that are relevant to our specific context?”

And if we can find even just one Black Swan then we can ask …

Q1. What specifically was the significant and sustained improvement?
Q2. How specifically was the improvement achieved?
Q3. When exactly did the process start?
Q4. Who specifically led the system improvement?

And if we do this exercise for the NHS we discover some interesting things.

First let us look for exemplars … and let us start using some official material – the Monitor website (http://www.monitor.gov.uk) for example … and let us pick out ‘Foundation Trusts’ because they are the ones who are entrusted to run their systems with a greater degree of capability and autonomy.

And what we discover is a league table where those FTs that are OK are called ‘green’ and those that are Not OK are coloured ‘red’.  And there are some that are ‘under review’ so we will call them ‘amber’.

The criteria for deciding this RAG rating are embedded in a large balanced scorecard of objective performance metrics linked to a robust legal contract that provides the framework for enforcement.  Safety metrics like standardised mortality ratios, flow metrics like 18-week and 4-hour target yields, quality metrics like the friends-and-family test, and productivity metrics like financial viability.

A quick tally revealed 106 FTs in the green, 10 in the amber and 27 in the red.

But this is not much help with our quest for exemplars because it is not designed to point us to who has improved the most, it only points to who is failing the most!  The league table is a name-and-shame motivation-destroying cultural-missile fuelled by DRATs (delusional ratios and arbitrary targets) and armed with legal teeth.  A projection of the current top-down, Theory-X, burn-the-toast-then-scrape-it management-of-mediocrity paradigm. Oh dear!

However,  despite these drawbacks we could make better use of this data.  We could look at the ‘reds’ and specifically at their styles of cultural leadership and compare with a random sample of all the ‘greens’ and their models for success. We could draw out the differences and correlate with outcomes: red, amber or green.

That could offer us some insight and could give us the head start with our blueprint and check-list.


It would be a time-consuming and expensive piece of work and we do not want to wait that long. So what other avenues are there we can explore now and at no cost?

Well there are unofficial sources of information … the ‘grapevine’ … the stuff that people actually talk about.

What examples of effective improvement leadership in the NHS are people talking about?

Well a little blue bird tweeted one in my ear this week …

And specifically they are talking about a leader who has learned to walk-the-improvement-walk and is now talking-the-improvement-walk: and that is Sir David Dalton, the CEO of Salford Royal.

Here is a copy of the slides from Sir David’s recent lecture at the Kings Fund … and it is interesting to compare and contrast it with the style of NHS Leadership that led up to the Mid Staffordshire Failure, and to the Francis Report, and to the Keogh Report and to the Berwick Report.

Chalk and cheese!


So if you are an NHS employee would you rather work as part of an NHS Trust where the leaders walk-DD’s-walk and talk-DD’s-talk?

And if you are an NHS customer would you prefer that the leaders of your local NHS Trust walked Sir David’s walk too?


We are the system … we get the leaders that we deserve … we make the  choice … so we need to choose wisely … and we need to make our collective voice heard.

Actions speak louder than words.  Walk works better than talk.  We must be the change we want to see.

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

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

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

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

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

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

<Lesley> That sounds like an excellent plan!

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

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

<Bob> Good. And specifically?

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

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

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

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

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

<Bob> And what is the takt time?

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

<Bob> And the cycle time?

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

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

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

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

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

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

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

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

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

<Bob> Can you give me an example?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

<Leslie> So how are they avoided?

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

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

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

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

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

<Leslie> Safety, Flow, Quality and Productivity.

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

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

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

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

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

We_Need_Small_HospitalsThis was an interesting headline to see on the front page of a newspaper yesterday!

The Top Man of the NHS is openly challenging the current Centralisation-is-The-Only-Way-Forward Mantra;  and for good reason.

Mass centralisation is poor system design – very poor.

Q: So what is driving the centralisation agenda?

A: Money.

Or to be more precise – rather simplistic thinking about money.

The misguided money logic goes like this:

1. Resources (such as highly trained doctors, nurses and AHPs) cost a lot of money to provide.
[Yes].

2. So we want all these resources to be fully-utilised to get value-for-money.
[No, not all – just the most expensive].

3. So we will gather all the most expensive resources into one place to get the Economy-of-Scale.
[No, not all the most expensive – just the most specialised]

4. And we will suck /push all the work through these super-hubs to keep our expensive specialist resources busy all the time.
[No, what about the growing population of older folks who just need a bit of expert healthcare support, quickly, and close to home?]

This flawed logic confuses two complementary ways to achieve higher system productivity/economy/value-for-money without  sacrificing safety:

Economies of Scale (EoS) and Economies of Flow (EoF).

Of the two the EoF is the more important because by using EoF principles we can increase productivity in huge leaps at almost no cost; and without causing harm and disappointment. EoS are always destructive.

But that is impossible. You are talking rubbish … because if it were possible we would be doing it!

It is not impossible and we are doing it … but not at scale and pace in healthcare … and the reason for that is we are not trained in Economy-of-Flow methods.

And those who are trained and who have have experienced the effects of EoF would not do it any other way.

Example:

In a recent EoF exercise an ISP (Improvement Science Practitioner) helped a surgical team to increase their operating theatre productivity by 30% overnight at no cost.  The productivity improvement was measured and sustained for most of the last year. [it did dip a bit when the waiting list evaporated because of the higher throughput, and again after some meddlesome middle management madness was triggered by end-of-financial-year target chasing].  The team achieved the improvement using Economy of Flow principles and by re-designing some historical scheduling policies. The new policies  were less antagonistic. They were designed to line the ducks up and as a result the flow improved.


So the specific issue of  Super Hospitals vs Small Hospitals is actually an Economy of Flow design challenge.

But there is another critical factor to take into account.

Specialisation.

Medicine has become super-specialised for a simple reason: it is believed that to get ‘good enough’ at something you have to have a lot of practice. And to get the practice you have to have high volumes of the same stuff – so you need to specialise and then to sort undifferentiated work into separate ‘speciologist’ streams or sequence the work through separate speciologist stages.

Generalists are relegated to second-class-citizen status; mere tripe-skimmers and sign-posters.

Specialisation is certainly one way to get ‘good enough’ at doing something … but it is not the only way.

Another way to learn the key-essentials from someone who already knows (and can teach) and then to continuously improve using feedback on what works and what does not – feedback from everywhere.

This second approach is actually a much more effective and efficient way to develop expertise – but we have not been taught this way.  We have only learned the scrape-the-burned-toast-by-suck-and-see method.

We need to experience another way.

We need to experience rapid acquisition of expertise!

And being able to gain expertise quickly means that we can become expert generalists.

There is good evidence that the broader our skill-set the more resilient we are to change, and the more innovative we are when faced with novel challenges.

In the Navy of the 1800’s sailors were “Jacks of All Trades and Master of One” because if only one person knew how to navigate and they got shot or died of scurvy the whole ship was doomed.  Survival required resilience and that meant multi-skilled teams who were good enough at everything to keep the ship afloat – literally.


Specialisation has another big drawback – it is very expensive and on many dimensions. Not just Finance.

Example:

Suppose we have six-step process and we have specialised to the point where an individual can only do one step to the required level of performance (safety/flow/quality/productivity).  The minimum number of people we need is six and the process only flows when we have all six people. Our minimum costs are high and they do not scale with flow.

If any one of the six are not there then the whole process stops. There is no flow.  So queues build up and smooth flow is sacrificed.

Out system behaves in an unstable and chaotic feast-or-famine manner and rapidly shifting priorities create what is technically called ‘thrashing’.

And the special-six do not like the constant battering.

And the special-six have the power to individually hold the whole system to ransom – they do not even need to agree.

And then we aggravate the problem by paying them the high salary that it is independent of how much they collectively achieve.

We now have the perfect recipe for a bigger problem!  A bunch of grumpy, highly-paid specialists who blame each other for the chaos and who incessantly clamour for ‘more resources’ at every step.

This is not financially viable and so creates the drive for economy-of-scale thinking in which to get us ‘flow resilience’ we need more than one specialist at each of the six steps so that if one is on holiday or off sick then the process can still flow.  Let us call these tribes of ‘speciologists’ there own names and budgets, and now we need to put all these departments somewhere – so we will need a big hospital to fit them in – along with the queues of waiting work that they need.

Now we make an even bigger design blunder.  We assume the ‘efficiency’ of our system is the same as the average utilisation of all the departments – so we trim budgets until everyone’s utilisation is high; and we suck any-old work in to ensure there is always something to do to keep everyone busy.

And in so doing we sacrifice all our Economy of Flow opportunities and we then scratch our heads and wonder why our total costs and queues are escalating,  safety and quality are falling, the chaos continues, and our tribes of highly-paid specialists are as grumpy as ever they were!   It must be an impossible-to-solve problem!


Now contrast that with having a pool of generalists – all of whom are multi-skilled and can do any of the six steps to the required level of expertise.  A pool of generalists is a much more resilient-flow design.

And the key phrase here is ‘to the required level of expertise‘.

That is how to achieve Economy-of-Flow on a small scale without compromising either safety or quality.

Yes, there is still a need for a super-level of expertise to tackle the small number of complex problems – but that expertise is better delivered as a collective-expertise to an individual problem-focused process.  That is a completely different design.

Designing and delivering a system that that can achieve the synergy of the pool-of-generalists and team-of-specialists model requires addressing a key error of omission first: we are not trained how to do this.

We are not trained in Complex-Adaptive-System Improvement-by-Design.

So that is where we must start.