Nothing in random in any mystical causative sense. Many things are unpredictable. And, indeed, unpredictable is what people mean when they say random. That, or unknown cause.
I was reminded of this after pointed by Wrath of Gnon to the paper “Deaths by horsekick in the Prussian army – and other ‘Never Events’ in large organisations” by J. J. Pandit in Anaesthesia.
I don’t disagree, in general, with the author’s broad conclusion, but I think his views can be improved by the switches I mentioned.
Background is Moppet and Moppet wrote a paper discussing “Never Events”, which are “serious adverse incidents like wrong site surgery”, events which M&M say “follow” a Poisson distribution.
In attempting to clarify this, Pandit reminds us of the work of Ladislaus Bortkiewicz, who cataloged the number of times each year Prussian cavalrymen were kicked to death by their horses. The numbers are in this picture:
Most years (counting by cavalry units) have no deaths, some had one, fewer had two, and so on as you see. Now this:
Bortkiewicz went on to explain how this is a formal mathematical description of random, rare events. In other words, any real distribution (e.g. Prussian horsekicks) that resembles a Poisson in turn can be readily assumed to have arisen through chance events and not as a result of intent or design (e.g. inherent systems failure in the military). Similarly, if Moppett and Moppett have concluded that NHS Never Events are also Poisson-distributed and so random, then does it follow that they have no ‘cause’ and, in turn, that they cannot be prevented? We discuss below why this does not follow.
As I said, Pandit sees the essential problem, but missed his chance (good pun!) to go a bit farther.
He opens his gambit with this: “A common Oxford entrance interview question asks candidates: ‘What is the opposite of “random”?‘”
You, dear reader, must say “predictable or with a known cause.” Let’s see why.
Now nothing follows a Poisson distribution, but that probability model can mathematically represent the uncertainty you have in some event, like number of wrong surgeries or horse kicks—or anything you assign.
This implies the event is not completely unpredictable, because if it was then the best you could say is “I do not know.” And take in the strictest sense: you know nothing. Since you can or do assign a Poisson—or perhaps even deduce it from known premises about the event, a very rare thing to do—you are saying you do know something, but only enough to quantify the chance of the event.
The event is in common parlance random. Which only means you cannot foresee whether it will happen, or when, or precisely how. You allow only that it can, and with this-and-such uncertainty.
But when the event happens, as Pandit sees, “If we are the patient (or the family), then none of the statistics matter: the event has happened to us and naturally we are always concerned.”
And then there must be a cause of the event. A doctor screwed up, or a nurse did, or somebody put the wrong information into the chart, or all three, or more, or any other form of incompetence: some form of incompetence or error.
Blame can and must be assigned. It is no good to say “Oh, well, the event was a Never Event because it followed a Poisson.”
Something always causes everything to happen, even if we can never know the cause in advance, or even after, as with quantum mechanics.
Here’s his conclusion:
Moppett and Moppett have nicely shown that the distribution of Never Events in the NHS – just like that of deaths by horsekick in the Prussian cavalry over a century ago – follows a quasi-Poisson distribution. This means that the events, when viewed in totality, are rare and random. There is no reason to suspect any fundamental systems failure across the NHS or in any one hospital based on the Never Event data alone.
I disagree. Because, as I said, every wrong surgery (or whatever) represents an error, something happened that should not have, and we should learn why.
A Poisson, or other probability model, is only useful to make predictions of the future, given the assumption nothing known has changed causally.
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