Comfort Blanket

When Stick Child was a bit younger, he had a little comfort blanket. Here he is with it, dreaming about smashing up some arbitrary numerical targets. (That’s supposed to be the moon in the window by the way – not a banana).

Stick Child dreaming

Stick Child believed that his comfort blanket had magical powers – it kept monsters away at night and helped him sleep soundly. It felt soft and warm, so if he was ever sad he would hold it against the side of his face. It was familiar and reassuring.

Some adults adopt a similar approach.

Despite evidence that binary comparisons are incapable of telling us anything about trends or trajectories, are prone to causing unwarranted assumptions, consistently impair decision making and lead to unnecessary action as a direct result, some adults still cling to them like a comfort blanket. (The same applies to numerical targets and league tables).

In case of any confusion, here’s a simple guide:

Two types of comfort blanket

So, to conclude: binary comparisons do not have magical powers and any ‘reassurance’ they appear to provide is false.

Comfort blankets can be great for children, but adults need to let go.

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The Wrong Conversation

Stick Child facepalmStick Child has been getting increasingly irritated by the slack methods some adults use to present information about really important stuff, such as how long it takes for patients to be seen in Accident and Emergency (A&E) departments. Many a time recently he’s had to do a facepalm at the way hospitals and other institutions are judged and compared against each other, supposedly to inform the public about how well each is performing.

The problem is that the starting point of the conversation – the frame by which performance is judged – is totally wrong. Plenty has been written about the arbitrary nature of numerical targets and their propensity for triggering dysfunctional behaviour, so we’ll leave that to one side for now, and just look at why using them as the focal point for judging performance simply means people engage in the wrong conversation.

Stick Child has drawn a couple of charts, which plot the distribution curves of A&E admission times for two hospitals.

Wrong conversation Hospital A


Wrong conversation Hospital B

As you can see, the curves are different. Hospital ‘A’ manages to get 95% of patients seen within 4 hours, after which, a steep drop off in the curve indicates the remaining 5% are seen before 4 hours and 30 minutes has elapsed.

Hospital ‘B’ also manages to see 95% of patients within 4 hours, but the tail of the curve beyond this point is much longer, meaning that the remaining 5% of patients take much longer to be seen – some waits are as long as 7 hours. It’s also apparent that Hospital ‘A’ sees more patients during the early stages of their wait than Hospital ‘B’. This is evidenced by the fact that Hospital ‘B’s curve is weighted more to the right.

So, its quite clear that the patterns of waiting times are different for the two hospitals.

Not according to the target.

According to the target, the two hospitals’ performance is exactly the same. This means that opportunities to understand why some patients wait up to 7 hours in Hospital ‘B’ are missed. It means that managers don’t get the chance to understand their system, as the usefulness of A&E admission time data is undermined by using the target as a focal point, thereby degrading potentially useful information into a simplistic PASS / FAIL scenario.

Now consider what would happen if Hospital ‘A’s distribution curve actually showed that 94.9% of patients were seen within 4 hours, whilst Hospital ‘B’ achieved 95.1%.

Wrong conversation table

Yep, despite the fact that Hospital ‘A’ demonstrates better overall performance, it FAILS, whilst Hospital ‘B’ PASSES. This fixation on the target and a binary definition of ‘good’ or ‘bad’ performance means no one learns anything about either hospital.

That’s the real FAIL.

Then there’s those convincing-looking charts which look authoritative, but actually spew out what can only be referred to as pseudo performance data, such as this:

Wrong conversation chart

Looks good, doesn’t it? Well it’s not. All it does is tell you the percentage of cases where performance has crossed that invisible and imaginary dividing line between ‘good’ and ‘bad’, as defined by the target. A chart that tells you about a target to hit a target. Utter waste of time.

If you’ve got the data, simply plot them and learn from them. Why throw in a target and thereby replace the richness of potentially useful performance information with a meaningless and mind-numbing YES / NO game? Seriously, why?

It drives the wrong conversation.

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Avoidable Harm

I recently had a conversation on Twitter about a national campaign called ‘Sign up to Safety’, which aims to reduce avoidable harm in the NHS. Now, avoidable harm is clearly something worth tackling. The sticking point for me was that they have a numerical target to reduce avoidable harm by 50%.

What I can’t understand is why anyone would aim to reduce avoidable harm by 50% – if it’s avoidable, we should avoid it! Not just some of it. Why would you want to be ‘half-safe’?

It’s like deliberately planning to retain the other 50% of harm! Which, of course, sounds silly – because it is silly.

There’s a lot that’s silly (and harmful) about such targets, such as the assumption that a target is necessary to make people want to reduce harm in the first place. If they know that reducing harm is important (i.e. a priority), then the target is irrelevant. It might even be possible to measure some types of harm reduction, so that’s good too, because then you have measures to help you understand how your harm reduction efforts are going. The target is still irrelevant though.

Angry Stick Child

Anyway, why is the target 50%? How was this determined? Why not 55%, or 70%, or 81.648%? If it was set at 50% because it was deemed attainable, then what’s the point of the target, because you’re gonna attain it anyway, right?

Why is a 49.999% reduction a failure, whereas a 50.001% reduction a success? These invisible dividing lines between ‘good’ and ‘bad’ simply don’t exist in the real world. If you could reduce more harm than 50% then you would, wouldn’t you? If so, the target is irrelevant. If you wouldn’t, then why not?

How about if you reduced all the harm you possibly could, but this only amounted to 35% less harm? Have you failed? Why? What about if you had it within your gift to reduce harm by around 80-90%, but only reduced it by 55%? You’ve exceeded the target, but is this good?

Then there’s the stuff about method. How does a numerical target set at any level help you identify and address harm reduction opportunities? It doesn’t, because targets don’t provide a method.

Also, as I’ve said before, it’s better to aim for 100% (i.e. perfection) than just a fraction of your true goal. You’d then measure, learn and improve as you go along. Yes, in many domains (such as harm or crime reduction) it may not ultimately be possible to completely eradicate the object of your reduction efforts, but this shouldn’t stop anyone from trying.

Let me give you a few examples using the Stick People, to try and demonstrate why numerical targets like the 50% target for avoidable harm are pointless (not to mention arbitrary and prone to causing dysfunctional behaviour).

Here’s Stick Doctor. Today, Stick Doctor encountered two opportunities to reduce harm in her hospital. Guess how many she addressed? (Clue: It wasn’t one).

Stick Doctor Avoidable Harm

This is Stick Cop. Stick Cop currently has four investigations in his in-tray. He’s decided to investigate all of them to the best of his ability. Not just two.

Stick Cop - Avoidable Harm

Here’s Stick Child. Stick Child saw one opportunity to help a group of under-10s get their heads around some basic performance management concepts. He didn’t stop half way through.

Stick Child - avoidable harm

Get  it now?

If you have a worthwhile priority, just focus on that. Measure your progress, using the right measures in the right way. Learn and improve. You don’t need the target.

Reduce avoidable harm by reducing numerical targets!

By 100%.

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A Tale of Two Kings

Stick Child bedtime storyAt bedtime, Stick Child’s Daddy often reads his son a story from his favourite story book, “Medieval Stories from Stick Kingdom with an Inevitable Systems Thinking Moral”. One recent story they particularly enjoyed was called “A Tale of Two Kings”, which went a bit like this…

Once upon a time, many years ago, the King of Stick Kingdom decided to commission artwork to decorate his palace, so he secured the services of the greatest artist in the land – Stick Artist. He asked Stick Artist to paint him the most wonderful painting she could imagine. Stick Artist was up for the challenge and immediately fetched out her canvas, easel, brushes and paints.

Stick Artist 1

But then…just as Stick Artist was about to begin her masterpiece, Stick King said, “Oh, can you make sure you put some rhinos in the picture? I like rhinos”. Stick Artist said, “No problem, Stick King”, and began to paint.

Then Stick King stopped her again and said, “Oh, by the way, I really like the colour purple, so would you make sure there’s lots of purple in the picture?” “Okay, Stick King”, said Stick Artist.

After a few minutes, Stick King announced, “Oh, and I’d like the picture to be circular”. Stick Artist sighed quietly, doffed her beret, then began to cut her canvass into a circle.

This went on – spaceships, explosions, dinosaurs, zig-zags, mountains, more purple, hold the brush with your left hand, a little pink, some clouds, every third brush stroke to be 93.7 degrees adjacent to the previous one, and bananas. Stick Artist did as she was told, despite her mounting frustration (as she was the greatest artist in the land), until finally the picture was ready.

“Aggghhh – it’s an abomination!!” yelled the Stick King as the incoherent travesty of a painting was unveiled. “It’s a crime against art!” he raged.

Stick Artist 2

Stick Artist took the hint and ran away very quickly before she could be imprisoned. She thought it was a bit rich that the Stick King had blamed her for the mess that ensued after being so prescriptive with his daft ideas, especially as he wasn’t an artist himself.

Anyway, Stick Artist escaped to a neighbouring land, where the Stick Emperor soon heard of her reputation and commissioned her to paint him a painting for his palace. Stick Artist accepted the task, listened to Stick Emperor’s general ideas about what he wanted, then produced the greatest piece of art ever known in Stick Land.

Stick Artist 3

This merrie tayle goes to show that you get better results out of people who know their craft if you give them broad direction, rather than interfere and micromanage what they do. If only Stick King had been able to visit the future when this was more widely known.

Concepts like ‘Commander’s Intent’ (where a clear aim is communicated by a military leader, whilst affording flexibility and autonomy to subordinates to develop their own tactics) were unknown in medieval Stick Kingdoms. Notions such as workers being intrinsically motivated by a sense of autonomy, mastery and purpose were also unheard of by Stick Kings hundreds of years ago.

The good news for us today is that this stuff is known.


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Stick Child and The Fraggles

Fraggle rock and Stick Child

Recently, Stick Child was watching the television channel ‘Stick Gold’, when he came across an episode of the 1980s children’s show, Fraggle Rock. If you’re about the same age as Stick Child’s Daddy, you may well remember it too.

Fraggle Rock was inhabited by Fraggles; colourful, furry little creatures, who spent most of their time playing games and exploring their environment. Their favourite food was radishes, and if they touched their heads together before they went to sleep they could share their dreams with each other.


Fraggle Rock was also inhabited by even smaller creatures called Doozers, who spent all of their time building constructions out of a radish-based substance, which the Fraggles liked to eat.

doozersThe Doozers actually wanted the Fraggles to eat their constructions so they could go on to build more. This was essentially the only interaction between Doozers and Fraggles; Doozers spent most of their time building, and Fraggles spent much of their time eating Doozer buildings. They thus form an odd sort of symbiosis.

This symbiosis was integral to the episode that Stick Child watched. Mokey (one of the Fraggles) called upon the Fraggles to stop eating the Doozers’ constructions – because they spent so much time making them. As a result, Fraggle Rock quickly filled with constructions, meaning the Doozers had no space left in which to build. After running out of space, the Doozers finally decided to try and find a new place to live as the Fraggles wouldn’t eat their constructions; there was even a tragic scene with a mother explaining to her daughter that Doozers must build or they will die.

Overhearing this, Mokey realised that she had inadvertently disrupted a vital symbiotic relationship through her well-intentioned, but ultimately misguided actions. As a result, she frantically rescinded her prohibition and encouraged the Fraggles to gorge on the structures – just in time to persuade the Doozers to stay.

Stick Child enjoyed the show, and even at the age of 9, recognised that there were parallels with real life. Sometimes managers make decisions, introduce new policies, initiate activity, or change systems conditions without first understanding the system, or the fact that their actions may cause unintended and unwanted side effects. This can happen even when they act with good intentions.

As Deming said, “We are being ruined by best efforts”.

Therefore, it’s important to recognise that best efforts and good intentions can destabilise symbiotic relationships necessary for the survival of the system. So… take a leaf out of the Fraggles’ book and heed the wise words of Gobo, Fraggle Rock’s resident systems thinker:*

Gobo fraggle quote

* Okay, so I made that up.

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Top of the Table

When the Chair of the House of Commons Education Committee asked Michael Gove (Secretary of State of Education at the time) about comparative performance measurement between schools, this happened:

Chair: If “good” requires pupil performance to exceed the national average, and if all schools must be good, how is this mathematically possible?

Michael Gove: By getting better all the time.

(Full transcript here)

Now, sniggers to one side, there’s a few important points here. The first is that I don’t disagree with striving to get better all the time; neither do I think performance shouldn’t be measured. I also believe it can be useful to understand apparent differences in comparative peer performance.

So, what’s the problem?

Well, it’s the way it’s so often done – league tables.

Here’s an example using police forces, although you could replace them with schools, hospitals or other institutions, if you like.

Stick Child top of the table 1

League tables are over-simplified, misleading, fundamentally illegitimate, charlatans of the performance world; they purport to convey information about comparative peer performance, when in fact they are little more than mirages. They lie to you. They tell you stuff that isn’t there. They set you off on thought processes and assumptions that are utterly unwarranted. (A bit like slightly more elaborate binary comparisons. Ugh!) But the most dangerous thing about them is that they appear so plausible.

A notable problem with league tables is that they are routinely methodologically unsound and notoriously unstable. (This is particularly true of league tables constructed from complex public sector data). Due to statistical considerations I won’t inflict on you here, it is often mathematically impossible to neatly rank institutions in the tidy fashion we are so used to (i.e. one at the top, one at the bottom, and the remainder nicely stacked in between, from best to worst). You see, in league table world, about half of those ranked end up as ‘below average’, and someone is always bottom. So not everyone can be above the national average! Why not? Because it’s an average.

What we should be doing is trying to establish if there are significant differences between peers, and this can be done very simply in a couple of ways, as demonstrated by Stick Child…

Stick Child top of the table 2

In this first example, the six police forces we saw earlier are assessed against each other, taking into account confidence intervals in the data. (Don’t worry if you’re unfamiliar with the term, just trust me that it’s important). As you can see, this tells us that two forces are performing significantly differently to the other four (i.e. there are no overlaps between the two groups). We can’t, however, neatly rank them from ‘best’ to ‘worst’, because we can’t separate the ‘top’ two from each other, and we can’t separate the other four from each other.

Here’s another way of understanding comparative peer performance in a more contextualised manner:

Stick Child top of the table 3

This time we can observe that the six police forces are all within the boundaries of ‘normality’ (by applying Statistical Process Control methodology). If any of them were outside of the dashed lines we might be concerned that particular force was significantly different from its peers; however, in this case, all six forces are clustered around the mean average (solid horizontal line) and within the range of anticipated performance for the group.

Therefore, there is absolutely no way the forces should be placed in ranked order – they are likely to move positions each time a snapshot is taken because of normal variation, but as long as they stay within the lines (and ideally, improve as a group), it is wrong to judge performance based on apparent position.

You see, when this happens, we encounter the other big problem associated with the league table mindset – concern about someone’s position in a league table leads to unfair assumptions about performance, unnecessary ‘remedial’ activity to address the perceived deficiencies, pressure from management, sanctions, and so on. And all based on something that essentially isn’t there. Cue gaming and dysfunctional behaviour! Like clockwork.

And a final thought – if league tables are constructed using crime data, are we even measuring the right thing? See this.

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It’s Criminal

Stick farmer

I’ve been meaning to get around to writing about the issue of using crime figures as an indicator of police performance for a while now. Aside from the risk of mis-recording crime due to target-driven performance management, I believe there is a fundamental argument against judging police performance by using crime figures. It boils down to this:

Crime rates are not the definer of effective police performance; they merely provide information about criminal activity.

What do I mean by this?

Well, we’ve been so used to judging police performance based on whether crime is higher or lower than some previous point in the past (binary comparisons), positions in league tables, or variance from arbitrary numerical targets, that it’s easy to miss the obvious question about whether we’re measuring the right thing in the first place. Even when crime trends are shown in time series format (e.g. control charts), I’d argue that it’s still a case of measuring the wrong things (albeit in the ‘right’ way) if the intention is to assess police performance.

Try these analogies:

  • Judging a vehicle repair agency (such as the AA) by the number of breakdowns reported.
  • Judging a heart surgeon’s performance by the rate of heart disease cases in a locality.
  • Judging the fire service’s performance by the number of car fires.
  • Judging Stick Farmer’s performance by the number of lambs born each spring.

(One of these measures is actually real, silly though this may sound).

I’m not saying these datasets are useless for helping these people understand their business (they’d certainly be useful for understanding demand and maybe assisting future planning), but they are not measures of performance. In the policing context, yes of course we want to reduce crime (Peel talked about it, didn’t he?) and we should take reasonable steps to prevent it, but we need to move beyond the simplistic narrative of:

“Crime up = Police bad / Crime down = Police good”.

It’s also necessary to acknowledge that multiple variables affect crime rates; factors such as economic cycles, substance abuse, the weather, societal influences, changes in legislation, and so on. None of these are directly within the gift of the police to influence. Also, what about where the police cause an increase in reported crime by having the temerity to find someone carrying a weapon? Surely proactive problem-solving should not be discouraged on the basis that finding hitherto unreported criminal offences is incongruous with an over-simplified crime reduction narrative.

Stick Burglar

At the local level, if Stick Burglar is arrested and jailed, and burglaries suddenly stop, it’s probably fair to assume that the police directly affected that particular crime series. Conversely, at force or national levels, if crime happens to go up or down a bit, it’s likely to be as a result of the plethora of external factors that influence the crime rate (as well as normal variation).

It’s time for a shift in thinking about crime rates and police performance. By removing perverse incentives for mis-recording crime, we are hopefully left with a clearer picture of criminal activity. Such data can then act as extremely useful sources of information that assist decision making about how best to tackle reported crime.

But it’s not ‘performance’ data.

In the same way, the AA should not be held accountable for a vehicle breaking down; neither should the fire service be blamed if an electrical fault causes it to catch fire. There may be opportunities to learn about causes and respond to future patterns of demand (where it’s predictable), but that’s all.

Therefore, it doesn’t seem logical to directly equate police effectiveness with crime levels, especially as the true extent of crime is unknown, and what is known is affected by a multitude of factors.

Reported crime, from whatever source, is potentially useful information about criminal activity.

Not performance data.

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