Archive for the ‘Flow’ Category

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

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

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

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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


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

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

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

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


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

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


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

From the same heat-map perhaps?

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

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

H’mm.  “Houston, we have a problem“.


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

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


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

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

 

It is that time of year – again.

Winter.

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

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


And the evidence seems plausible.

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

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

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


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


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


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

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

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

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

Health care is a victim of its own success.

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

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

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

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


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

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


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

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

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

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

So the first gap to fill is the Awareness gap.

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

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

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

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

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

So what are HROs?


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

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

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

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


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

And that was no accident.

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


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

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

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

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


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

Monklands A&E Improvement

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


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

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

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

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

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

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

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

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

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

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

<Bob> OK. What is the problem?

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


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

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

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

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

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

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

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

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

<Bob> OK.  So what is your diagnosis?

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


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

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

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

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

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

<Leslie>Tee-Four?

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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

It also requires skills, training, practice and experience.

The same is true in healthcare improvement.


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

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

Logically, rationally, enjoyably and surprisingly quickly.

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

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

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


How do we do that?

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

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

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


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

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

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

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

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

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

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

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

So what happened?

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

It certainly looks that way.

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


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

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

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


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

Will that happen in healthcare too?

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

Could it be that flow and productivity are also important?

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

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

Is it too politically embarrassing?

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


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

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

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

OMG!

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

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

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

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

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

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

What opportunity does systems engineering offer health care?

That question is being actively explored … here.

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

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

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

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

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

Read on »

Dr Bill Hyde was already at the bar when Bob Jekyll arrived.

Bill and  Bob had first met at university and had become firm friends, but their careers had diverged and it was only by pure chance that their paths had crossed again recently.

They had arranged to meet up for a beer and to catch up on what had happened in the 25 years since they had enjoyed the “good old times” in the university bar.

<Dr Bill> Hi Bob, what can I get you? If I remember correctly it was anything resembling real ale. Will this “Black Sheep” do?

<Bob> Hi Bill, Perfect! I’ll get the nibbles. Plain nuts OK for you?

<Dr Bill> My favourite! So what are you up to now? What doors did your engineering degree open?

<Bob> Lots!  I’ve done all sorts – mechanical, electrical, software, hardware, process, all except civil engineering. And I love it. What I do now is a sort of synthesis of all of them.  And you? Where did your medical degree lead?

<Dr Bill> To my hearts desire, the wonderful Mrs Hyde, and of course to primary care. I am a GP. I always wanted to be a GP since I was knee-high to a grasshopper.

<Bob> Yes, you always had that “I’m going to save the world one patient at a time!” passion. That must be so rewarding! Helping people who are scared witless by the health horror stories that the media pump out.  I had a fright last year when I found a lump.  My GP was great, she confidently diagnosed a “hernia” and I was all sorted in a matter of weeks with a bit of nifty day case surgery. I was convinced my time had come. It just shows how damaging the fear of the unknown can be!

<Dr Bill> Being a GP is amazingly rewarding. I love my job. But …

<Bob> But what? Are you alright Bill? You suddenly look really depressed.

<Dr Bill> Sorry Bob. I don’t want to be a damp squib. It is good to see you again, and chat about the old days when we were teased about our names.  And it is great to hear that you are enjoying your work so much. I admit I am feeling low, and frankly I welcome the opportunity to talk to someone I know and trust who is not part of the health care system. If you know what I mean?

<Bob> I know exactly what you mean.  Well, I can certainly offer an ear, “a problem shared is a problem halved” as they say. I can’t promise to do any more than that, but feel free to tell me the story, from the beginning. No blood-and-guts gory details though please!

<Dr Bill> Ha! “Tell me the story from the beginning” is what I say to my patients. OK, here goes. I feel increasingly overwhelmed and I feel like I am drowning under a deluge of patients who are banging on the practice door for appointments to see me. My intuition tells me that the problem is not the people, it is the process, but I can’t seem to see through the fog of frustration and chaos to a clear way forward.

<Bob> OK. I confess I know nothing about how your system works, so can you give me a bit more context.

<Dr Bill> Sorry. Yes, of course. I am what is called a single-handed GP and I have a list of about 1500 registered patients and I am contracted to provide primary care for them. I don’t have to do that 24 x 7, the urgent stuff that happens in the evenings and weekends is diverted to services that are designed for that. I work Monday to Friday from 9 AM to 5 PM, and I am contracted to provide what is needed for my patients, and that means face-to-face appointments.

<Bob> OK. When you say “contracted” what does that mean exactly?

<Dr Bill> Basically, the St. Elsewhere’s® Practice is like a small business. It’s annual income is a fixed amount per year for each patient on the registration list, and I have to provide the primary care service for them from that pot of cash. And that includes all the costs, including my income, our practice nurse, and the amazing Mrs H. She is the practice receptionist, manager, administrator and all-round fixer-of-anything.

<Bob> Wow! What a great design. No need to spend money on marketing, research, new product development, or advertising! Just 100% pure service delivery of tried-and-tested medical know-how to a captive audience for a guaranteed income. I have commercial customers who would cut off their right arms for an offer like that!

<Dr Bill> Really? It doesn’t feel like that to me. It feels like the more I offer, the more the patients expect. The demand is a bottomless well of wants, but the income is capped and my time is finite!

<Bob> H’mm. Tell me more about the details of how the process works.

<Dr Bill> Basically, I am a problem-solving engine. Patients phone for an appointment, Mrs H books one, the patient comes at the appointed time, I see them, and I diagnose and treat the problem, or I refer on to a specialist if it’s more complicated. That’s basically it.

<Bob> OK. Sounds a lot simpler than 99% of the processes that I’m usually involved with. So what’s the problem?

<Dr Bill> I don’t have enough capacity! After all the appointments for the day are booked Mrs H has to say “Sorry, please try again tomorrow” to every patient who phones in after that.  The patients who can’t get an appointment are not very happy and some can get quite angry. They are anxious and frustrated and I fully understand how they feel. I feel the same.

<Bob> We will come back to what you mean by “capacity”. Can you outline for me exactly how a patient is expected to get an appointment?

<Dr Bill> We tell them to phone at 8 AM for an appointment, there is a fixed number of bookable appointments, and it is first-come-first-served.  That is the only way I can protect myself from being swamped and is the fairest solution for patients.  It wasn’t my idea; it is called Advanced Access. Each morning at 8 AM we switch on the phones and brace ourselves for the daily deluge.

<Bob> You must be pulling my leg! This design is a batch-and-queue phone-in appointment booking lottery!  I guess that is one definition of “fair”.  How many patients get an appointment on the first attempt?

<Dr Bill> Not many.  The appointments are usually all gone by 9 AM and a lot are to people who have been trying to get one for several days. When they do eventually get to see me they are usually grumpy and then spring the trump card “And while I’m here doctor I have a few other things that I’ve been saving up to ask you about“. I help if I can but more often than not I have to say, “I’m sorry, you’ll have to book another appointment!“.

<Bob> I’m not surprised you patients are grumpy. I would be too. And my recollection of seeing my GP with my scary lump wasn’t like that at all. I phoned at lunch time and got an appointment the same day. Maybe I was just lucky, or maybe my GP was as worried as me. But it all felt very calm. When I arrived there was only one other patient waiting, and I was in and out in less than ten minutes – and mightily reassured I can tell you! It felt like a high quality service that I could trust if-and-when I needed it, which fortunately is very infrequently.

<Dr Bill> I dream of being able to offer a service like that! I am prepared to bet you are registered with a group practice and you see whoever is available rather than your own GP. Single-handed GPs like me who offer the old fashioned personal service are a rarity, and I can see why. We must be suckers!

<Bob> OK, so I’m starting to get a sense of this now. Has it been like this for a long time?

<Dr Bill> Yes, it has. When I was younger I was more resilient and I did not mind going the extra mile.  But the pressure is relentless and maybe I’m just getting older and grumpier.  My real fear is I end up sounding like the burned-out cynics that I’ve heard at the local GP meetings; the ones who crow about how they are counting down the days to when they can retire and gloat.

<Bob> You’re the same age as me Bill so I don’t think either of us can use retirement as an exit route, and anyway, that’s not your style. You were never a quitter at university. Your motto was always “when the going gets tough the tough get going“.

<Dr Bill> Yeah I know. That’s why it feels so frustrating. I think I lost my mojo a long time back. Maybe I should just cave in and join up with the big group practice down the road, and accept the inevitable loss of the personal service. They said they would welcome me, and my list of 1500 patients, with open arms.

<Bob> OK. That would appear to be an option, or maybe a compromise, but I’m not sure we’ve exhausted all the other options yet.  Tell me, how do you decide how long a patient needs for you to solve their problem?

<Dr Bill> That’s easy. It is ten minutes. That is the time recommended in the Royal College Guidelines.

<Bob> Eh? All patients require exactly ten minutes?

<Dr Bill> No, of course not!  That is the average time that patients need.  The Royal College did a big survey and that was what most GPs said they needed.

<Bob> Please tell me if I have got this right.  You work 9-to-5, and you carve up your day into 10-minute time-slots called “appointments” and, assuming you are allowed time to have lunch and a pee, that would be six per hour for seven hours which is 42 appointments per day that can be booked?

<Dr Bill> No. That wouldn’t work because I have other stuff to do as well as see patients. There are only 25 bookable 10-minute appointments per day.

<Bob> OK, that makes more sense. So where does 25 come from?

<Dr Bill> Ah! That comes from a big national audit. For an average GP with and average  list of 1,500 patients, the average number of patients seeking an appointment per day was found to be 25, and our practice population is typical of the national average in terms of age and deprivation.  So I set the upper limit at 25. The workload is manageable but it seems to generate a lot of unhappy patients and I dare not increase the slots because I’d be overwhelmed with the extra workload and I’m barely coping now.  I feel stuck between a rock and a hard place!

<Bob> So you have set the maximum slot-capacity to the average demand?

<Dr Bill> Yes. That’s OK isn’t it? It will average out over time. That is what average means! But it doesn’t feel like that. The chaos and pressure never seems to go away.


There was a long pause while Bob mulls over what he had heard, sips his pint of Black Sheep and nibbles on the dwindling bowl of peanuts.  Eventually he speaks.


<Bob> Bill, I have some good news and some not-so-good news and then some more good news.

<Dr Bill> Oh dear, you sound just like me when I have to share the results of tests with one of my patients at their follow up appointment. You had better give me the “bad news sandwich”!

<Bob> OK. The first bit of good news is that this is a very common, and easily treatable flow problem.  The not-so-good news is that you will need to change some things.  The second bit of good news is that the changes will not cost anything and will work very quickly.

<Dr Bill> What! You cannot be serious!! Until ten minutes ago you said that you knew nothing about how my practice works and now you are telling me that there is a quick, easy, zero cost solution.  Forgive me for doubting your engineering know-how but I’ll need a bit more convincing than that!

<Bob> And I would too if I were in your position.  The clues to the diagnosis are in the story. You said the process problem was long-standing; you said that you set the maximum slot-capacity to the average demand; and you said that you have a fixed appointment time that was decided by a subjective consensus.  From an engineering perspective, this is a perfect recipe for generating chronic chaos, which is exactly the symptoms you are describing.

<Dr Bill> Is it? OMG. You said this is well understood and resolvable? So what do I do?

<Bob> Give me a minute.  You said the average demand is 25 per day. What sort of service would you like your patients to experience? Would “90% can expect a same day appointment on the first call” be good enough as a starter?

<Dr Bill> That would be game changing!  Mrs H would be over the moon to be able to say “Yes” that often. I would feel much less anxious too, because I know the current system is a potentially dangerous lottery. And my patients would be delighted and relieved to be able to see me that easily and quickly.

<Bob> OK. Let me work this out. Based on what you’ve said, some assumptions, and a bit of flow engineering know-how; you would need to offer up to 31 appointments per day.

<Dr Bill> What! That’s impossible!!! I told you it would be impossible! That would be another hour a day of face-to-face appointments. When would I do the other stuff? And how did you work that out anyway?

<Bob> I did not say they would have to all be 10-minute appointments, and I did not say you would expect to fill them all every day. I did however say you would have to change some things.  And I did say this is a well understood flow engineering problem.  It is called “resilience design“. That’s how I was able to work it out on the back of this Black Sheep beer mat.

<Dr Bill> H’mm. That is starting to sound a bit more reasonable. What things would I have to change? Specifically?

<Bob> I’m not sure what specifically yet.  I think in your language we would say “I have taken a history, and I have a differential diagnosis, so next I’ll need to examine the patient, and then maybe do some tests to establish the actual diagnosis and to design and decide the treatment plan“.

<Dr Bill> You are learning the medical lingo fast! What do I need to do first? Brace myself for the forensic rubber-gloved digital examination?

<Bob> Alas, not yet and certainly not here. Shall we start with the vital signs? Height, weight, pulse, blood pressure, and temperature? That’s what my GP did when I went with my scary lump.  The patient here is not you, it is your St. Elsewhere’s® Practice, and we will need to translate the medical-speak into engineering-speak.  So one thing you’ll need to learn is a bit of the lingua-franca of systems engineering.  By the way, that’s what I do now. I am a systems engineer, or maybe now a health care systems engineer?

<Dr Bill> Point me in the direction of the HCSE dictionary! The next round is on me. And the nuts!

<Bob> Excellent. I’ll have another Black Sheep and some of those chilli-coated ones. We have work to do.  Let me start by explaining what “capacity” actually means to an engineer. Buckle up. This ride might get a bit bumpy.


This story is fictional, but the subject matter is factual.

Bob’s diagnosis and recommendations are realistic and reasonable.

Chapter 1 of the HCSE dictionary can be found here.

And if you are a GP who recognises these “symptoms” then this may be of interest.

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

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


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

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

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


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

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

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

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


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

Here is the back story …

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

The approach the NPDT chose was:

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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


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


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

Remember the essential ingredients?

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

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

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

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


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

You have demonstrated what was always possible.

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

To read the fuller story click here.

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


thinker_figure_unsolve_puzzle_150_wht_18309Many of the challenges that we face in delivering effective and affordable health care do not have well understood and generally accepted solutions.

If they did there would be no discussion or debate about what to do and the results would speak for themselves.

This lack of understanding is leading us to try to solve a complicated system design challenge in our heads.  Intuitively.

And trying to do it this way is fraught with frustration and risk because our intuition tricks us. It was this sort of challenge that led Professor Rubik to invent his famous 3D Magic Cube puzzle.

It is difficult enough to learn how to solve the Magic Cube puzzle by trial and error; it is even more difficult to attempt to do it inside our heads! Intuitively.


And we know the Rubik Cube puzzle is solvable, so all we need are some techniques, tools and training to improve our Rubik Cube solving capability.  We can all learn how to do it.


Returning to the challenge of safe and affordable health care, and to the specific problem of unscheduled care, A&E targets, delayed transfers of care (DTOC), finance, fragmentation and chronic frustration.

This is a systems engineering challenge so we need some systems engineering techniques, tools and training before attempting it.  Not after failing repeatedly.

se_vee_diagram

One technique that a systems engineer will use is called a Vee Diagram such as the one shown above.  It shows the sequence of steps in the generic problem solving process and it has the same sequence that we use in medicine for solving problems that patients present to us …

Diagnose, Design and Deliver

which is also known as …

Study, Plan, Do.


Notice that there are three words in the diagram that start with the letter V … value, verify and validate.  These are probably the three most important words in the vocabulary of a systems engineer.


One tool that a systems engineer always uses is a model of the system under consideration.

Models come in many forms from conceptual to physical and are used in two main ways:

  1. To assist the understanding of the past (diagnosis)
  2. To predict the behaviour in the future (prognosis)

And the process of creating a system model, the sequence of steps, is shown in the Vee Diagram.  The systems engineer’s objective is a validated model that can be trusted to make good-enough predictions; ones that support making wiser decisions of which design options to implement, and which not to.


So if a systems engineer presented us with a conceptual model that is intended to assist our understanding, then we will require some evidence that all stages of the Vee Diagram process have been completed.  Evidence that provides assurance that the model predictions can be trusted.  And the scope over which they can be trusted.


Last month a report was published by the Nuffield Trust that is entitled “Understanding patient flow in hospitals”  and it asserts that traffic flow on a motorway is a valid conceptual model of patient flow through a hospital.  Here is a direct quote from the second paragraph in the Executive Summary:

nuffield_report_01
Unfortunately, no evidence is provided in the report to support the validity of the statement and that omission should ring an alarm bell.

The observation that “the hospitals with the least free space struggle the most” is not a validation of the conceptual model.  Validation requires a concrete experiment.


To illustrate why observation is not validation let us consider a scenario where I have a headache and I take a paracetamol and my headache goes away.  I now have some evidence that shows a temporal association between what I did (take paracetamol) and what I got (a reduction in head pain).

But this is not a valid experiment because I have not considered the other seven possible combinations of headache before (Y/N), paracetamol (Y/N) and headache after (Y/N).

An association cannot be used to prove causation; not even a temporal association.

When I do not understand the cause, and I am without evidence from a well-designed experiment, then I might be tempted to intuitively jump to the (invalid) conclusion that “headaches are caused by lack of paracetamol!” and if untested this invalid judgement may persist and even become a belief.


Understanding causality requires an approach called counterfactual analysis; otherwise known as “What if?” And we can start that process with a thought experiment using our rhetorical model.  But we must remember that we must always validate the outcome with a real experiment. That is how good science works.

A famous thought experiment was conducted by Albert Einstein when he asked the question “If I were sitting on a light beam and moving at the speed of light what would I see?” This question led him to the Theory of Relativity which completely changed the way we now think about space and time.  Einstein’s model has been repeatedly validated by careful experiment, and has allowed engineers to design and deliver valuable tools such as the Global Positioning System which uses relativity theory to achieve high positional precision and accuracy.


So let us conduct a thought experiment to explore the ‘faster movement requires more space‘ statement in the case of patient flow in a hospital.

First, we need to define what we mean by the words we are using.

The phrase ‘faster movement’ is ambiguous.  Does it mean higher flow (more patients per day being admitted and discharged) or does it mean shorter length of stage (the interval between the admission and discharge events for individual patients)?

The phrase ‘more space’ is also ambiguous. In a hospital that implies physical space i.e. floor-space that may be occupied by corridors, chairs, cubicles, trolleys, and beds.  So are we actually referring to flow-space or storage-space?

What we have in this over-simplified statement is the conflation of two concepts: flow-capacity and space-capacity. They are different things. They have different units. And the result of conflating them is meaningless and confusing.


However, our stated goal is to improve understanding so let us consider one combination, and let us be careful to be more precise with our terminology, “higher flow always requires more beds“. Does it? Can we disprove this assertion with an example where higher flow required less beds (i.e. space-capacity)?

The relationship between flow and space-capacity is well understood.

The starting point is Little’s Law which was proven mathematically in 1961 by J.D.C. Little and it states:

Average work in progress = Average lead time  X  Average flow.

In the hospital context, work in progress is the number of occupied beds, lead time is the length of stay and flow is admissions or discharges per time interval (which must be the same on average over a long period of time).

(NB. Engineers are rather pedantic about units so let us check that this makes sense: the unit of WIP is ‘patients’, the unit of lead time is ‘days’, and the unit of flow is ‘patients per day’ so ‘patients’ = ‘days’ * ‘patients / day’. Correct. Verified. Tick.)

So, is there a situation where flow can increase and WIP can decrease? Yes. When lead time decreases. Little’s Law says that is possible. We have disproved the assertion.


Let us take the other interpretation of higher flow as shorter length of stay: i.e. shorter length of stay always requires more beds.  Is this correct? No. If flow remains the same then Little’s Law states that we will require fewer beds. This assertion is disproved as well.

And we need to remember that Little’s Law is proven to be valid for averages, does that shed any light on the source of our confusion? Could the assertion about flow and beds actually be about the variation in flow over time and not about the average flow?


And this is also well understood. The original work on it was done almost exactly 100 years ago by Agner Krarup Erlang and the problem he looked at was the quality of customer service of the early telephone exchanges. Specifically, how likely was the caller to get the “all lines are busy, please try later” response.

What Erlang showed was there there is a mathematical relationship between the number of calls being made (the demand), the probability of a call being connected first time (the service quality) and the number of telephone circuits and switchboard operators available (the service cost).


So it appears that we already have a validated mathematical model that links flow, quality and cost that we might use if we substitute ‘patients’ for ‘calls’, ‘beds’ for ‘telephone circuits’, and ‘being connected’ for ‘being admitted’.

And this topic of patient flow, A&E performance and Erlang queues has been explored already … here.

So a telephone exchange is a more valid model of a hospital than a motorway.

We are now making progress in deepening our understanding.


The use of an invalid, untested, conceptual model is sloppy systems engineering.

So if the engineering is sloppy we would be unwise to fully trust the conclusions.

And I share this feedback in the spirit of black box thinking because I believe that there are some valuable lessons to be learned here – by us all.


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motorway[Beep] Bob’s computer alerted him to Leslie signing on to the Webex session.

<Bob> Good afternoon Leslie, how are you? It seems a long time since we last chatted.

<Leslie> Hi Bob. I am well and it has been a long time. If you remember, I had to loop out of the Health Care Systems Engineering training because I changed job, and it has taken me a while to bring a lot of fresh skeptics around to the idea of improvement-by-design.

<Bob> Good to hear, and I assume you did that by demonstrating what was possible by doing it, delivering results, and describing the approach.

<Leslie> Yup. And as you know, even with objective evidence of improvement it can take a while because that exposes another gap, the one between intent and impact.  Many people get rather defensive at that point, so I have had to take it slowly. Some people get really fired up though.

 <Bob> Yes. Respect, challenge, patience and persistence are all needed. So, where shall we pick up?

<Leslie> The old chestnut of winter pressures and A&E targets.  Except that it is an all-year problem now and according to what I read in the news, everyone is predicting a ‘melt-down’.

<Bob> Did you see last week’s IS blog on that very topic?

<Leslie> Yes, I did!  And that is what prompted me to contact you and to re-start my CHIPs coaching.  It was a real eye opener.  I liked the black swan code-named “RC9” story, it makes it sound like a James Bond film!

<Bob> I wonder how many people dug deeper into how “RC9” achieved that rock-steady A&E performance despite a rising tide of arrivals and admissions?

<Leslie> I did, and I saw several examples of anti-carve-out design.  I have read though my notes and we have talked about carve out many times.

<Bob> Excellent. Being able to see the signs of competent design is just as important as the symptoms of inept design. So, what shall we talk about?

<Leslie> Well, by co-incidence I was sent a copy of of a report entitled “Understanding patient flow in hospitals” published by one of the leading Think Tanks and I confess it made no sense to me.  Can we talk about that?

<Bob> OK. Can you describe the essence of the report for me?

<Leslie> Well, in a nutshell it said that flow needs space so if we want hospitals to flow better we need more space, in other words more beds.

<Bob> And what evidence was presented to support that hypothesis?

<Leslie> The authors equated the flow of patients through a hospital to the flow of traffic on a motorway. They presented a table of numbers that made no sense to me, I think partly because there are no units stated for some of the numbers … I’ll email you a picture.

traffic_flow_dynamics

<Bob> I agree this is not a very informative table.  I am not sure what the definition of “capacity” is here and it may be that the authors may be equating “hospital bed” to “area of tarmac”.  Anyway, the assertion that hospital flow is equivalent to motorway flow is inaccurate.  There are some similarities and traffic engineering is an interesting subject, but they are not equivalent.  A hospital is more like a busy city with junctions, cross-roads, traffic lights, roundabouts, zebra crossings, pelican crossings and all manner of unpredictable factors such as cyclists and pedestrians. Motorways are intentionally designed without these “impediments”, for obvious reasons! A complex adaptive flow system like a hospital cannot be equated to a motorway. It is a dangerous over-simplification.

<Leslie> So, if the hospital-motorway analogy is invalid then the conclusions are also invalid?

<Bob> Sometimes, by accident, we get a valid conclusion from an invalid method. What were the conclusions?

<Leslie> That the solution to improving A&E performance is more space (i.e. hospital beds) but there is no more money to build them or people to staff them.  So the recommendations are to reduce volume, redesign rehabilitation and discharge processes, and improve IT systems.

<Bob> So just re-iterating the habitual exhortations and nothing about using well-understood systems engineering methods to accurately diagnose the actual root cause of the ‘symptoms’, which is likely to be the endemic carveoutosis multiforme, and then treat accordingly?

<Leslie> No. I could not find the term “carve out” anywhere in the document.

<Bob> Oh dear.  Based on that observation, I do not believe this latest Think Tank report is going to be any more effective than the previous ones.  Perhaps asking “RC9” to write an account of what they did and how they learned to do it would be more informative?  They did not reduce volume, and I doubt they opened more beds, and their annual report suggests they identified some space and flow carveoutosis and treated it. That is what a competent systems engineer would do.

<Leslie> Thanks Bob. Very helpful as always. What is my next step?

<Bob> Some ISP-2 brain-teasers, a juicy ISP-2 project, and some one day training workshops for your all-fired-up CHIPs.

<Leslie> Bring it on!


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reading_a_book_pa_150_wht_3136An effective way to improve is to learn from others who have demonstrated the capability to achieve what we seek.  To learn from success.

Another effective way to improve is to learn from those who are not succeeding … to learn from failures … and that means … to learn from our own failings.

But from an early age we are socially programmed with a fear of failure.

The training starts at school where failure is not tolerated, nor is challenging the given dogma.  Paradoxically, the effect of our fear of failure is that our ability to inquire, experiment, learn, adapt, and to be resilient to change is severely impaired!

So further failure in the future becomes more likely, not less likely. Oops!


Fortunately, we can develop a healthier attitude to failure and we can learn how to harness the gap between intent and impact as a source of energy, creativity, innovation, experimentation, learning, improvement and growing success.

And health care provides us with ample opportunities to explore this unfamiliar terrain. The creative domain of the designer and engineer.


The scatter plot below is a snapshot of the A&E 4 hr target yield for all NHS Trusts in England for the month of July 2016.  The required “constitutional” performance requirement is better than 95%.  The delivered whole system average is 85%.  The majority of Trusts are failing, and the Trust-to-Trust variation is rather wide. Oops!

This stark picture of the gap between intent (95%) and impact (85%) prompts some uncomfortable questions:

Q1: How can one Trust achieve 98% and yet another can do no better than 64%?

Q2: What can all Trusts learn from these high and low flying outliers?

[NB. I have not asked the question “Who should we blame for the failures?” because the name-shame-blame-game is also a predictable consequence of our fear-of-failure mindset.]


Let us dig a bit deeper into the information mine, and as we do that we need to be aware of a trap:

A snapshot-in-time tells us very little about how the system and the set of interconnected parts is behaving-over-time.

We need to examine the time-series charts of the outliers, just as we would ask for the temperature, blood pressure and heart rate charts of our patients.

Here are the last six years by month A&E 4 hr charts for a sample of the high-fliers. They are all slightly different and we get the impression that the lower two are struggling more to stay aloft more than the upper two … especially in winter.


And here are the last six years by month A&E 4 hr charts for a sample of the low-fliers.  The Mark I Eyeball Test results are clear … these swans are falling out of the sky!


So we need to generate some testable hypotheses to explain these visible differences, and then we need to examine the available evidence to test them.

One hypothesis is “rising demand”.  It says that “the reason our A&E is failing is because demand on A&E is rising“.

Another hypothesis is “slow flow”.  It says that “the reason our A&E is failing is because of the slow flow through the hospital because of delayed transfers of care (DTOCs)“.

So, if these hypotheses account for the behaviour we are observing then we would predict that the “high fliers” are (a) diverting A&E arrivals elsewhere, and (b) reducing admissions to free up beds to hold the DTOCs.

Let us look at the freely available data for the highest flyer … the green dot on the scatter gram … code-named “RC9”.

The top chart is the A&E arrivals per month.

The middle chart is the A&E 4 hr target yield per month.

The bottom chart is the emergency admissions per month.

Both arrivals and admissions are increasing, while the A&E 4 hr target yield is rock steady!

And arranging the charts this way allows us to see the temporal patterns more easily (and the images are deliberately arranged to show the overall pattern-over-time).

Patterns like the change-for-the-better that appears in the middle of the winter of 2013 (i.e. when many other trusts were complaining that their sagging A&E performance was caused by “winter pressures”).

The objective evidence seems to disprove the “rising demand”, “slow flow” and “winter pressure” hypotheses!

So what can we learn from our failure to adequately explain the reality we are seeing?


The trust code-named “RC9” is Luton and Dunstable, and it is an average district general hospital, on the surface.  So to reveal some clues about what actually happened there, we need to read their Annual Report for 2013-14.  It is a public document and it can be downloaded here.

This is just a snippet …

… and there are lots more knowledge nuggets like this in there …

… it is a treasure trove of well-known examples of good system flow design.

The results speak for themselves!


Q: How many black swans does it take to disprove the hypothesis that “all swans are white”.

A: Just one.

“RC9” is a black swan. An outlier. A positive deviant. “RC9” has disproved the “impossibility” hypothesis.

And there is another flock of black swans living in the North East … in the Newcastle area … so the “Big cities are different” hypothesis does not hold water either.


The challenge here is a human one.  A human factor.  Our learned fear of failure.

Learning-how-to-fail is the way to avoid failing-how-to-learn.

And to read more about that radical idea I strongly recommend reading the recently published book called Black Box Thinking by Matthew Syed.

It starts with a powerful story about the impact of human factors in health care … and here is a short video of Martin Bromiley describing what happened.

The “black box” that both Martin and Matthew refer to is the one that is used in air accident investigations to learn from what happened, and to use that learning to design safer aviation systems.

Martin Bromiley has founded a charity to support the promotion of human factors in clinical training, the Clinical Human Factors Group.

So if we can muster the courage and humility to learn how to do this in health care for patient safety, then we can also learn to how do it for flow, quality and productivity.

Our black swan called “RC9” has demonstrated that this goal is attainable.

And the body of knowledge needed to do this already exists … it is called Health and Social Care Systems Engineering (HSCSE).


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Postscript: And I am pleased to share that Luton & Dunstable features in the House of Commons Health Committee report entitled Winter Pressures in A&E Departments that was published on 3rd Nov 2016.

Here is part of what L&D shared to explain their deviant performance:

luton_nuggets

These points describe rather well the essential elements of a pull design, which is the antidote to the rather more prevalent pressure cooker design.

This is a snapshot of an experiment in progress.  The question being asked is “Can consultant surgeons be trained to be system flow designers in one day?”

On the left are Kate Silvester and Phil Debenham … their doctor/trainers. On the right are some brave volunteer consultant surgeons.

It is a tense moment. The focused concentration is palpable. It is a tough design assignment … a chronically chaotic one-stop outpatient clinic. They know it well.


They have the raw, unprocessed, data and they are deep into diagnosis mode.  On the other side of the room is another team of consultant surgeon volunteers who are struggling with the same challenge. Competition is in the air. Reputations are on the line. The game is on.

They are racing to generate this … a process template chart … that illustrates the conversion of raw event data into something visible and meaningful. A Gantt chart.

Their tools are basic – coloured pens and squared paper – just as Henry L. Gantt used in 1916 – a hundred years ago.

Hidden in this Gantt chart is the diagnosis, the open door to the path to improving this clinic design.  It is as plain as the nose on your face … if you know what to look for. They don’t. Well, … not yet.


Skip forwards to later in the experiment. Both teams have solved the ‘impossible’ problem. They have diagnosed the system design flaw that was causing the queues, chaos and waiting … and they have designed and verified a solution. With no more than squared paper and coloured pens.  Henry G would be delighted.

And they are justifiably proud of their achievement because, when they tested their design in the real world, it showed that the queues and chaos had “evaporated”.  And it cost … nothing.


At the start of the experiment they were unaware of what was possible. At the end of the experiment they knew how to do it. In one day.

The question: ‘”Can consultant surgeons be trained to be system flow designers in one day?”

The answer: “Yes”


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About a year ago we looked back at the previous 10 years of NHS unscheduled care performance …

click here to read

… and warned that a catastrophe was on the way because we had unintentionally created a urgent care “pressure cooker”.

 

Did waving the red warning flag make any difference? It seems not.

The catastrophe unfolded as predicted … A&E performance slumped to an all-time low, and has not recovered.


A pressure cooker is an elegantly simple self-regulating system.  A strong metal box with a sealed lid and a pressure-sensitive valve.  Food cooks more quickly at a higher temperature, and we can increase the boiling point of water by increasing the ambient pressure.  So all we need to do is put some water in the cooker, close the lid, set the pressure limit we need (i.e. the temperature we want) and apply some heat.  Simple.  As the water boils the steam increases the pressure inside, until the regulator valve opens and lets a bit of steam out.  The more heat we apply – the faster the steam comes out – but the internal pressure and temperature remain constant.  An elegantly simple self-regulating system.


Our unscheduled care acute hospital “pressure cooker” design is very similar – but it has an additional feature – we can squeeze raw patients in through a one-way valve labelled “admissions”.  The internal pressure will eventually squeeze them out through another one-way pressure-sensitive valve called “discharges”.

But there is not much head-space inside our hospital (i.e. empty beds) so pushing patients in will increase the pressure inside, and it will trigger an internal reaction called “fire-fighting” that generates heat (but no insight).  When the internal pressure reaches the critical level, patients are squeezed out; ready-or-not.

What emerges from the chaotic internal cauldron is a mixture of under-cooked, just-right, and over-cooked patients.  And we then conduct quality control audits and we label what we find as “quality variation”, but it looks random so it gives us no clues as to the causes or what to do next.

Equilibrium is eventually achieved – what goes in comes out – the pressure and temperature auto-regulate – the chaos becomes chronic – and the quality of the output is predictably unacceptable and unpredictable, with some of it randomly spoiled (i.e. harmed).

And our acute care pressure cooker is very resistant to external influences. It is one of its key design features, it is an auto-regulating system.


Option 1: Admissions Avoidance
Squeezing a bit less in does not make any difference to the internal pressure and temperature.  It auto-regulates.  The reduced inflow means a reduced outflow and a longer cooking time and we just get less under-cooked and more over-cooked output.  Oh, and we go bust because our revenue has reduced but our costs have not.

Option 2: Build a Bigger Hospital
Building a bigger pressure cooker (i.e. adding more beds) does not make any sustained difference either.  Again the system auto-regulates.  The extra space-capacity allows a longer cooking time – and again we get less under-cooked and more over-cooked output.  Oh, and we still go bust (same revenue but increased cost).

Option 3: Reduce the Expectation
Turning down the heat (i.e. reducing the 4 hr A&E lead time target yield from 98% to 95%) does not make any difference. Our elegant auto-regulating design adjusts itself to sustain the internal pressure and temperature.  Output is still variable, but least we do not go bust.


This metaphor may go some way to explain why the intuitively obvious “initiatives” to improve unscheduled care performance appear to have had no significant or sustained impact.

And what is more worrying is that they may even have made the situation worse.

Also, working inside an urgent care pressure cooker is dangerous.  People get emotionally damaged and permanently scarred.


The good news is that a different approach is available … a health and social care systems engineering (HSCSE) approach … one that we could use to change the fundamental design from fire-fighter to flow-facilitator.

Using HSCSE theory, techniques and tools we could specify, design, build, verify, implement and validate a low-pressure, low-resistance, low-wait, low-latency, high-efficiency unscheduled care flow design that is safe, timely, effective and affordable.

But we are not training NHS staff to do that.

Why is that?  Is is because we are not aware that this is possible, or that we do not believe that it can work, or that we lack the capability to do it? Or all three?

The first step is raising awareness … so here is an example that proves it is possible.

BloodSuckerThis is a magnified picture of a blood sucking bug called a Red Poultry Mite.

They go red after having gorged themselves on chicken blood.

Their life-cycle is only 7 days so, when conditions are just right, they can quickly cause an infestation – and one that is remarkably difficult to eradicate!  But if it is not dealt with then chicken coop productivity will plummet.


We use the term “bug” for something else … a design error … in a computer program for example.  If the conditions are just right, then software bugs can spread too and can infest a computer system.  They feed on the hardware resources – slurping up processor time and memory space until the whole system slows to a crawl.


And one especially pernicious type of system design error is called an Error of Omission.  These are the things we do not do that would prevent the bloodsucking bugs from breeding and spreading.

Prevention is better than cure.


In the world of health care improvement there are some blood suckers out there, ones who home in on a susceptible host looking for a safe place to establish a colony.  They are masters of the art of mimicry.  They look like and sound like something they are not … they claim to be symbiotic whereas in reality they are parasitic.

The clue to their true nature is that their impact does not match their intent … but by the time that gap is apparent they are entrenched and their spores have already spread.

Unlike the Red Poultry Mites, we do not want to eradicate them … we need to educate them. They only behave like parasites because they are missing a few essential bits of software.  And once those upgrades are installed they can achieve their potential and become symbiotic.

So, let me introduce them, they are called Len, Siggy and Tock and here is their story:

Six Ways Not To Improve Flow

businessman_cloud_periscope_18347The path from chaos to calm is not clearly marked.  If it were we would not have chaotic health care processes, anxious patients, frustrated staff and escalating costs.

Many believe that there is no way out of the chaos. They have given up trying.

Some still nurture the hope that there is a way and are looking for a path through the fog of confusion.

A few know that there is a way out because they have been shown a path from chaos to calm and can show others how to find it.

Someone, a long time ago, explored the fog and discovered clarity of understanding on the far side, and returned with a Map of the Mind-field.


Q: What is causing The Fog?

When hot rhetoric meets cold reality the fog of disillusionment forms.

Q: Where does the hot rhetoric come from?

Passionate, well-intended and ill-informed people in positions of influence, authority and power. The orators, debaters and commentators.

They do not appear to have an ability to diagnose and to design, so cannot generate effective decisions and coordinate efficient delivery of solutions.

They have not learned how and seem to be unaware of it.

If they had, then they would be able to show that there is a path from chaos to calm.

A safe, quick, surprisingly enjoyable and productive path.

If they had the know-how then they could pull from the front in the ‘right’ direction, rather than push from the back in the ‘wrong’ one.


And the people who are spreading this good news are those who have just emerged from the path.  Their own fog of confusion evaporating as they discovered the clarity of hindsight for themselves.

Ah ha!  Now I see! Wow!  The view from the far side of The Fog is amazing and exciting. The opportunity and potential is … unlimited.  I must share the news. I must tell everyone! I must show them how-to.

Here is a story from Chris Jones who has recently emerged from The Fog.

And here is a description of part of the Mind-field Map, narrated in 2008 by Kate Silvester, a doctor and manufacturing systems engineer.

radar_screen_anim_300_clr_11649The most useful tool that a busy operational manager can have is a reliable and responsive early warning system (EWS).

One that alerts when something is changing and that, if missed or ignored, will cause a big headache in the future.

Rather like the radar system on an aircraft that beeps if something else is approaching … like another aircraft or the ground!


Operational managers are responsible for delivering stuff on time.  So they need a radar that tells them if they are going to deliver-on-time … or not.

And their on-time-delivery EWS needs to alert them soon enough that they have time to diagnose the ‘threat’, design effective plans to avoid it, decide which plan to use, and deliver it.

So what might an effective EWS for a busy operational manager look like?

  1. It needs to be reliable. No missed threats or false alarms.
  2. It needs to be visible. No tomes of text and tables of numbers.
  3. It needs to be simple. Easy to learn and quick to use.

And what is on offer at the moment?

The RAG Chart
This is a table that is coloured red, amber and green. Red means ‘failing’, green means ‘not failing’ and amber means ‘not sure’.  So this meets the specification of visible and simple, but it is reliable?

It appears not.  RAG charts do not appear to have helped to solve the problem.

A RAG chart is generated using historic data … so it tells us where we are now, not how we got here, where we are going or what else is heading our way.  It is a snapshot. One frame from the movie.  Better than complete blindness perhaps, but not much.

The SPC Chart
This is a statistical process control chart and is a more complicated beast.  It is a chart of how some measure of performance has changed over time in the past.  So like the RAG chart it is generated using historic data.  The advantage is that it is not just a snapshot of where were are now, it is a picture of story of how we got to where we are, so it offers the promise of pointing to where we may be heading.  It meets the specification of visible, and while more complicated than a RAG chart, it is relatively easy to learn and quick to use.

Luton_A&E_4Hr_YieldHere is an example. It is the SPC  chart of the monthly A&E 4-hour target yield performance of an acute NHS Trust.  The blue lines are the ‘required’ range (95% to 100%), the green line is the average and the red lines are a measure of variation over time.  What this charts says is: “This hospital’s A&E 4-hour target yield performance is currently acceptable, has been so since April 2012, and is improving over time.”

So that is much more helpful than a RAG chart (which in this case would have been green every month because the average was above the minimum acceptable level).


So why haven’t SPC charts replaced RAG charts in every NHS Trust Board Report?

Could there be a fly-in-the-ointment?

The answer is “Yes” … there is.

SPC charts are a quality audit tool.  They were designed nearly 100 years ago for monitoring the output quality of a process that is already delivering to specification (like the one above).  They are designed to alert the operator to early signals of deterioration, called ‘assignable cause signals’, and they prompt the operator to pay closer attention and to investigate plausible causes.

SPC charts are not designed for predicting if there is a flow problem looming over the horizon.  They are not designed for flow metrics that exhibit expected cyclical patterns.  They are not designed for monitoring metrics that have very skewed distributions (such as length of stay).  They are not designed for metrics where small shifts generate big cumulative effects.  They are not designed for metrics that change more slowly than the frequency of measurement.

And these are exactly the sorts of metrics that a busy operational manager needs to monitor, in reality, and in real-time.

Demand and activity both show strong cyclical patterns.

Lead-times (e.g. length of stay) are often very skewed by variation in case-mix and task-priority.

Waiting lists are like bank accounts … they show the cumulative sum of the difference between inflow and outflow.  That simple fact invalidates the use of the SPC chart.

Small shifts in demand, activity, income and expenditure can lead to big cumulative effects.

So if we abandon our RAG charts and we replace them with SPC charts … then we climb out of the RAG frying pan and fall into the SPC fire.

Oops!  No wonder the operational managers and financial controllers have not embraced SPC.


So is there an alternative that works better?  A more reliable EWS that busy operational managers and financial controllers can use?

Yes, there is, and here is a clue …

… but tread carefully …

… building one of these Flow-Productivity Early Warning Systems is not as obvious as it might first appear.  There are counter-intuitive traps for the unwary and the untrained.

You may need the assistance of a health care systems engineer (HCSE).

Portsmouth_News_20160609We form emotional attachments to places where we have lived and worked.  And it catches our attention when we see them in the news.

So this headline caught my eye, because I was a surgical SHO in Portsmouth in the closing years of the Second Millennium.  The good old days when we still did 1:2 on call rotas (i.e. up to 104 hours per week) and we were paid 70% LESS for the on call hours than the Mon-Fri 9-5 work.  We also had stable ‘firms’, superhuman senior registrars, a canteen that served hot food and strong coffee around the clock, and doctors mess parties that were … well … messy!  A lot has changed.  And not all for the better.

Here is the link to the fuller story about the emergency failures.

And from it we get the impression that this is a recent problem.  And with a bit of a smack and some name-shame-blame-game feedback from the CQC, then all will be restored to robust health. H’mm. I am not so sure that is the full story.


Portsmouth_A&E_4Hr_YieldHere is the monthly aggregate A&E 4-hour target performance chart for Portsmouth from 2010 to date.

It says “this is not a new problem“.

It also says that the ‘patient’ has been deteriorating spasmodically over six years and is now critically-ill.

And giving a critically-ill hospital a “good telling off” is about as effective as telling a critically-ill patient to “pull themselves together“.  Inept management.

In A&E a critically-ill patient requires competent resuscitation using a tried-and-tested process of ABC.  Airway, Breathing, Circulation.


Also, the A&E 4-hour performance is only a symptom of the sickness in the whole urgent care system.  It is the reading on an emotometer inserted into the A&E orifice of the acute hospital!  Just one piece in a much bigger flow jigsaw.

It only tells us the degree of distress … not the diagnosis … nor the required treatment.


So what level of A&E health can we realistically expect to be able to achieve? What is possible in the current climate of austerity? Just how chilled-out can the A&E cucumber run?

Luton_A&E_4Hr_Yield

This is the corresponding A&E emotometer chart for a different district general hospital somewhere else in NHS England.

Luton & Dunstable Hospital to be specific.

This A&E happiness chart looks a lot healthier and it seems to be getting even healthier over time too.  So this is possible.


Yes, but … if our hospital deteriorates enough to be put on the ‘critical list’ then we need to call in an Emergency Care Intensive Support Team (ECIST) to resuscitate us.

Kettering_A&E_4Hr_YieldA very good idea.

And how do their critically-ill patients fare?

Here is the chart of one of them. The significant improvement following the ‘resuscitation’ is impressive to be sure!

But, disappointingly, it was not sustained and the patient ‘crashed’ again. Perhaps they were just too poorly? Perhaps the first resuscitation call was sent out too late? But at least they tried their best.

An experienced clinician might comment: Those are indeed a plausible explanations, but before we conclude that is the actual cause, can I check that we did not just treat the symptoms and miss the disease?


Q: So is it actually possible to resuscitate and repair a sick hospital?  Is it possible to restore it to sustained health, by diagnosing and treating the cause, and not just the symptoms?


Monklands_A&E_4Hr_YieldHere is the corresponding A&E emotometer chart of yet another hospital.

It shows the same pattern of deteriorating health. And it shows a dramatic improvement.  It appears to have responded to some form of intervention.

And this time the significant improvement has sustained. The patient did not crash-and-burn again.

So what has happened here that explains this different picture?

This hospital had enough insight and humility to seek the assistance of someone who knew what to do and who had a proven track record of doing it.  Dr Kate Silvester to be specific.  A dual-trained doctor and manufacturing systems engineer.

Dr Kate is now a health care systems engineer (HCSE), and an experienced ‘hospital doctor’.

Dr Kate helped them to learn how to diagnose the root causes of their A&E 4-hr fever, and then she showed them how to design an effective treatment plan.

They did the re-design; they tested it; and they delivered their new design. Because they owned it, they understood it, and they trusted their own diagnosis-and-design competence.

And the evidence of their impact matching their intent speaks for itself.

Transformation

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


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

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

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

Roy_Lilley_Transformation

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

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


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

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


So what sort of transformation does the NHS need?

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

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

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


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

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

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

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

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?

 

Don_Berwick_2016

This week I had the great pleasure of watching Dr Don Berwick sharing the story of his own ‘near religious experience‘ and his conversion to a belief that a Science of Improvement exists.  In 1986, Don attended one of W.Edwards Deming’s famous 4-day workshops.  It was an emotional roller coaster ride for Don! See here for a link to the whole video … it is worth watching all of it … the best bit is at the end.


Don outlines Deming’s System of Profound Knowledge (SoPK) and explores each part in turn. Here is a summary of SoPK from the Deming website.

Deming_SOPK

W.Edwards Deming was a physicist and statistician by training and his deep understanding of variation and appreciation for a system flows from that.  He was not trained as a biologist, psychologist or educationalist and those parts of the SoPK appear to have emerged later.

Here are the summaries of these parts – psychology first …

Deming_SOPK_Psychology

Neurobiologists and psychologists now know that we are the product of our experiences and our learning. What we think consciously is just the emergent tip of a much bigger cognitive iceberg. Most of what is happening is operating out of awareness. It is unconscious.  Our outward behaviour is just a visible manifestation of deeply ingrained values and beliefs that we have learned – and reinforced over and over again.  Our conscious thoughts are emergent effects.


So how do we learn?  How do we accumulate these values and beliefs?

This is the summary of Deming’s Theory of Knowledge …

Deming_SOPK_PDSA

But to a biologist, neuroanatomist, neurophysiologist, doctor, system designer and improvement coach … this does not feel correct.

At the most fundamental biological level we do not learn by starting with a theory; we start with a sensory.  The simplest element of the animal learning system – the nervous system – is called a reflex arc.

Sensor_Processor_EffectorFirst, we have some form of sensor to gather data from the outside world. Eyes, ears, smell, taste, touch, temperature, pain and so on.  Let us consider pain.

That signal is transmitted via a sensory nerve to the processor, the grey matter in this diagram, where it is filtered, modified, combined with other data, filtered again and a binary output generated. Act or Not.

If the decision is ‘Act’ then this signal is transmitted by a motor nerve to an effector, in this case a muscle, which results in an action.  The muscle twitches or contracts and that modifies the outside world – we pull away from the source of pain.  It is a harm avoidance design. Damage-limitation. Self-preservation.

Another example of this sensor-processor-effector design template is a knee-jerk reflex, so-named because if we tap the tendon just below the knee we can elicit a reflex contraction of the thigh muscle.  It is actually part of a very complicated, dynamic, musculoskeletal stability cybernetic control system that allows us to stand, walk and run … with almost no conscious effort … and no conscious awareness of how we are doing it.

But we are not born able to walk. As youngsters we do not start with a theory of how to walk from which we formulate a plan. We see others do it and we attempt to emulate them. And we fail repeatedly. Waaaaaaah! But we learn.


Human learning starts with study. We then process the sensory data using our internal mental model – our rhetoric; we then decide on an action based on our ‘current theory’; and then we act – on the external world; and then we observe the effect.  And if we sense a difference between our expectation and our experience then that triggers an ‘adjustment’ of our internal model – so next time we may do better because our rhetoric and the reality are more in sync.

The biological sequence is Study-Adjust-Plan-Do-Study-Adjust-Plan-Do and so on, until we have achieved our goal; or until we give up trying to learn.


So where does psychology come in?

Well, sometimes there is a bigger mismatch between our rhetoric and our reality. The world does not behave as we expect and predict. And if the mismatch is too great then we are left with feelings of confusion, disappointment, frustration and fear.  (PS. That is our unconscious mind telling us that there is a big rhetoric-reality mismatch).

We can see the projection of this inner conflict on the face of a child trying to learn to walk.  They screw up their faces in conscious effort, and they fall over, and they hurt themselves and they cry.  But they do not want us to do it for them … they want to learn to do it for themselves. Clumsily at first but better with practice. They get up and try again … and again … learning on each iteration.

Study-Adjust-Plan-Do over and over again.


There is another way to avoid the continual disappointment, frustration and anxiety of learning.  We can distort our sensation of external reality to better fit with our internal rhetoric.  When we do that the inner conflict goes away.

We learn how to tamper with our sensory filters until what we perceive is what we believe. Inner calm is restored (while outer chaos remains or increases). We learn the psychological defense tactics of denial and blame.  And we practice them until they are second-nature. Unconscious habitual reflexes. We build a reality-distortion-system (RDS) and it has a name – the Ladder of Inference.


And then one day, just by chance, somebody or something bypasses our RDS … and that is the experience that Don Berwick describes.

Don went to a 4-day workshop to hear the wisdom of W.Edwards Deming first hand … and he was forced by the reality he saw to adjust his inner model of the how the world works. His rhetoric.  It was a stormy transition!

The last part of his story is the most revealing.  It exposes that his unconscious mind got there first … and it was his conscious mind that needed to catch up.

Study-(Adjust)-Plan-Do … over-and-over again.


In Don’s presentation he suggests that Frederick W. Taylor is the architect of the failure of modern management. This is a commonly held belief, and everyone is equally entitled to an opinion, that is a definition of mutual respect.

But before forming an individual opinion on such a fundamental belief we should study the raw evidence. The words written by the person who wrote them not just the words written by those who filtered the reality through their own perceptual lenses.  Which we all do.

engineers_turbine_engine_16758The NHS is falling.

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

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

And many sense the impending crash.

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

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


So what is needed to avoid the crash?

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

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


We need competent and experienced health care system engineers.


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

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

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

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

How can this be?

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

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

So what can we do?

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


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


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


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

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

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

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

This vanguard of innovators have shared their feedback.

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


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


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

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

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


 

HBR_April_2016

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

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

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


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

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

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

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


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

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

It was part of the effect, not the cause.


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

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

So what is the next excuse?  Too busy?

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

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

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

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


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

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

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


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

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

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

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

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

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

That was the flow diagnosis and design done.

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

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

And that was basically it.

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

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

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


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

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

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

Injury_2011_Message

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


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

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!