Good question! I need to try and get hold of data from different types of AMUs to see if there's a difference!
(I've recently embarked on a similar-ish quest following advice from @mancunianmedic.bsky.social about how medical specialties are organized differently from hospital to hospital...)

The important thing would be to try and get the clinicians themselves (with a bit of help from the analysts!) to define the dimensions of the grey zone. "Ought-to-be" zones are probably better calculated 'bottom-up' rather than 'top-down', I think.

Here's the graph after the correction. I'm trying out different ways of showing how a specialty's inpatients get more displaced as its workload increases. As @em-dr-jacklin.bsky.social might put it: the fuller the specialty, the greater the "wrongward-ness".
#rstats #ggplot2

Has anyone done 'loathed to say' yet?

Speak for yourself. It's a 'doggy-dog world' for me from now on!

The result is that hospital beds are managed “reactively” in the permanent-crisis here-and-now: a bed manager sees an empty bed, so they immediately fill it. It would be better if clinicians could see and discuss their flow metrics, to give them at least a chance of managing beds "proactively".
6/6

Preconceptions apart, the real problem is that we hardly ever report average bed occupancy to the clinicians whose patients occupy the beds. Most clinicians – and most managers– are not given the numbers that retrospectively describe demand and capacity in a general hospital.
5/6

There are some “NHS-specific” preconceptions, too. For example, there’s an “aura” surrounding “85%: the cure for all our ills”. In fact, the “right” percentage will be different for each part of the hospital. Some wards/specialties can work optimally at 95%; others need it to be lower than 60%.
4/6

Most of us bring “generic” preconceptions to percentages. We think higher numbers are better. Like the anecdote about the optimist who sees a glass half-full, a pessimist who sees a glass half-empty and a hospital manager who sees a glass twice as big as it needs to be.
3/6

Average percentage bed occupancy is the conventional way we measure bed usage in the NHS. (Here’s a visual example: 8,784 consecutive hour-by-hour snapshots of the number of beds occupied in a 46-bed hospital ward.) But the NHS has problems with bed occupancy as a metric.
2/6

Yes, perhaps we do need to be careful. One of my objectives here is to find a way of engaging clinicians in the specialties by giving them an indicator of dysfunction "of their own".

Thanks for this - I like this WrongWardHours idea a lot. Moving away from snapshots and building in a bit of history (including ED history) to the indicator. This might also allow me to add "number of non-clinical ward moves" into the mix...

As it stands, it's a snapshot metric. I tried doing it for one specialty (Respiratory Medicine) based on hour-by-hour snapshots from two years ago. I posted it on X; can't remember if I posted it here. So here it is - in all its dual-vertical-axis glory...

...and called the package {abracadabra}...