November 2016 – HCSE Blog

26/11/2016

Pride and Joy

Have you heard the phrase “Pride comes before a fall“?

What does this mean? That the feeling of pride is the reason for the subsequent fall?

So by following that causal logic, if we do not allow ourselves to feel proud then we can avoid the fall?

And none of us like the feeling of falling and failing. We are fearful of that negative feeling, so with this simple trick we can avoid feeling bad. Yes?

But we all know the positive feeling of achievement – we feel pride when we have done good work, when our impact matches our intent. Pride in our work.

Is that bad too?

Should we accept under-achievement and unexceptional mediocrity as the inevitable cost of avoiding the pain of possible failure? Is that what we are being told to do here?

The phrase comes from the Bible, from the Book of Proverbs 16:18 to be precise.

And the problem here is that the phrase “pride comes before a fall” is not the whole proverb.

It has been simplified. Some bits have been omitted. And those omissions lead to ambiguity and the opportunity for obfuscation and re-interpretation.

In the fuller New International Version we see a missing bit … the “haughty spirit” bit. That is another way of saying “over-confident” or “arrogant”.

But even this “authorised” version is still ambiguous and more questions spring to mind:

Q1. What sort of pride are we referring to? Just the confidence version? What about the pride that follows achievement?

Q2. How would we know if our feeling of confidence is actually justified?

Q3. Does a feeling of confidence always precede a fall? Is that how we diagnose over-confidence? Retrospectively? Are there instances when we feel confident but we do not fail? Are there instances when we do not feel confident and then fail?

Q4. Does confidence cause the fall or it is just a temporal association? Is there something more fundamental that causes both high-confidence and low-competence?

There is a well known model called the Conscious-Competence model of learning which generates a sequence of four stages to achieving a new skill. Such as one we need to achieve our intended outcomes.

We all start in the “blissful ignorance” zone of unconscious incompetence. Our unknowns are unknown to us. They are blind spots. So we feel unjustifiably confident.

In this model the first barrier to progress is “wrong intuition” which means that we actually have unconscious assumptions that are distorting our perception of reality.

What we perceive makes sense to us. It is clear and obvious. We feel confident. We believe our own rhetoric.

But our unconscious assumptions can trick us into interpreting information incorrectly. And if we derive decisions from unverified assumptions and invalid analysis then we may do the wrong thing and not achieve our intended outcome. We may unintentionally cause ourselves to fail and not be aware of it. But we are proud and confident.

Then the gap between our intent and our impact becomes visible to all and painful to us. So we are tempted to avoid the social pain of public failure by retreating behind the “Yes, But” smokescreen of defensive reasoning. The “doom loop” as it is sometimes called. The Victim Vortex. “Don’t name, shame and blame me, I was doing my best. I did not intent that to happen. To err is human”.

The good news is that this learning model also signposts a possible way out; a door in the black curtain of ignorance. It suggests that we can learn how to correct our analysis by using feedback from reality to verify our rhetorical assumptions. Those assumptions which pass the “reality check” we keep, those which fail the “reality check” we redesign and retest until they pass. Bit by bit our inner rhetoric comes to more closely match reality and the wisdom of our decisions will improve.

And what we then see is improvement. Our impact moves closer towards our intent. And we can justifiably feel proud of that achievement. We do not need to be best-compared-with-the-rest; just being better-than-we-were-before is OK. That is learning.

And this is how it feels … this is the Learning Curve … or the Nerve Curve as we call it.

What it says is that to be able to assess confidence we must also measure competence. Outcomes. Impact.

And to achieve excellence we have to be prepared to actively look for any gap between intent and impact. And we have to be prepared to see it as an opportunity rather than as a threat. And we will need to be able to seek feedback and other people’s perspectives. And we need to be to open to asking for examples and explanations from those who have demonstrated competence.

It says that confidence is not a trustworthy surrogate for competence.

It says that we want the confidence that flows from competence because that is the foundation of trust.

Improvement flows at the speed of trust and seeing competence, confidence and trust growing is a joyous thing.

Pride and Joy are OK.

Arrogance and incompetence comes before a fall would be a better proverb.

19/11/2016

Focus

The theme of the week has been “focus” and by that I mean the amazing ability of the human mind to concentrate on one thing to the exclusion of almost all else.

To illustrate what I mean, just reflect on what happens when we watch a television program. We do not see the TV screen, controls, or the “stuff” around it. Or to be more precise … we do see it but we do not perceive it.

Even our Mark I Eyeballs have evolved to “focus” and I do not mean just the clear bits that create a sharp image on the light-sensitive layer at the back (the retina).

Our retinas are not like a video camera … not at all … they have a very high resolution bit at the center which is quite small, and a rather low resolution bit that surrounds it and that is much bigger.

But we do not perceive that … because we have some very advanced data processing wetware … and the process actually starts in the retina.

And our eyes are always moving … just observe someone else’s eyes when they are looking at a picture or reading a book. If the cameras in a TV studio did that we would complain!

So what is happening here?

The answer is that our advanced data processing wetware is scanning, but not in the way that a radar scans … in a mindless cycle. Our eye scanning has purpose … it is driven by the mental model inside our heads that is looking for information, and the search is based on what we already believe and perceive.

Psychologists have studied this using cool technology that tracks the eye position and works out what the person is looking at. And what they found was surprising.

If we are presented with a picture of a face we will scan it in a very consistent way. We look at the nose first and then we look at eyes, mouth and we pattern-match to answer the question “Do I recognize this person?“

If we do then we can draw on past memories of them to help inform our interpretation of what we see. If we do not then we need to keep watching and learning. We need an answer to the question “Is this person an opportunity or a threat?“

And it is a very fast process, and it happens out of awareness, and it is hard-wired and it is automatic.

After initial recognition we will focus on the eyes and mouth because, as the Greeks said, “the eyes are the window to the soul“. We need to infer what the other person is thinking … unconsciously.

And the good news is that this amazing ability to focus is not completely automatic … it can be directed … rather like a radio can be tuned to specific frequency.

And when we learn how to do that as individuals the effect is surprising.

And when we learn how to do that as a group, in synergy, the effect is amazing!

12/11/2016

Defensive Reasoning

About 25 years ago a paper was published in the Harvard Business Review with the interesting title of “Teaching Smart People How To Learn“

The uncomfortable message was that many people who are top of the intellectual rankings are actually very poor learners.

This sounds like a paradox. How can people be high-achievers and yet be unable to learn?

Health care systems are stuffed full of super-smart, high-achieving professionals. The cream of educational crop. The top 2%. They are called “doctors”.

And we have a problem with improvement in health care … a big problem … the safety, delivery, quality and affordability of the NHS is getting worse. Not better.

Improvement implies change and change implies learning, so if smart people struggle to learn then could that explain why health care systems find self-improvement so difficult?

This paragraph from the 1991 HBR paper feels uncomfortably familiar:

The author, Chris Argyris, refers to something called “single-loop learning” and if we translate this management-speak into the language of medicine it would come out as “treating the symptom and ignoring the disease“. That is poor medicine.

Chris also suggests an antidote to this problem and gave it the label “double-loop learning” which if translated into medical speak becomes “diagnosis“. And that is something that doctors can relate to because without a diagnosis, a justifiable treatment is difficult to formulate.

We need to diagnose the root cause(s) of the NHS disease.

The 1991 HBR paper refers back to an earlier 1977 HBR paper called Double Loop Learning in Organisations where we find the theory that underpins it.

The proposed hypothesis is that we all have cognitive models that we use to decide our actions (and in-actions), what I have referred to before as ChimpWare. In it is a reference to a table published in a 1974 book and the message is that Single-Loop learning is a manifestation of a Model 1 theory-in-action.

And if we consider the task that doctors are expected to do then we can empathize with their dominant Model 1 approach. Health care is a dangerous business. Doctors can cause a lot of unintentional harm – both physical and psychological. Doctors are dealing with a very, very complex system – a human body – that they only partially understand. No two patients are exactly the same and illness is a dynamic process. Everyone’s expectations are high. We have come a long way since the days of blood-letting and leeches! Failure is not tolerated.

Doctors are intelligent and competitive … they had to be to win the education race.

Doctors must make tough decisions and have to have tough conversations … many, many times … and yet not be consumed in the process. They often have to suppress emotions to be effective.

Doctors feel the need to protect patients from harm – both physical and emotional.

And collectively they do a very good job. Doctors are respected and trusted professionals.

But … to quote Chris Argyris …

“Model I blinds people to their weaknesses. For instance, the six corporate presidents were unable to realize how incapable they were of questioning their assumptions and breaking through to fresh understanding. They were under the illusion that they could learn, when in reality they just kept running around the same track.”

This blindness is self-reinforcing because …

“All parties withheld information that was potentially threatening to themselves or to others, and the act of cover-up itself was closed to discussion.”

How many times have we seen this in the NHS?

The Mid-Staffordshire Hospital debacle that led to the Francis Report is all the evidence we need.

So what is the way out of this double-bind?

Chris gives us some hints with his Model II theory-in-use.

Valid information – Study.
Free and informed choice – Plan.
Constant monitoring of the implementation – Do.

The skill required is to question assumptions and break through to fresh understanding and we can do that with design-led approach because that is what designers do.

They bring their unconscious assumptions up to awareness and ask “Is that valid?” and “What if” questions.

It is called Improvement-by-Design.

And the good news is that this Model II approach works in health care, and we know that because the evidence is accumulating.

05/11/2016

Value, Verify and Validate

Many 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.

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:

To assist the understanding of the past (diagnosis)
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:

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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