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Five observations about average percentage bed occupancy The most widely-used measure of hospital bed utilisation can be problematic One of the most important things we have to do, particularly if we work in a hospital setting, is measure and describe how full hospital beds are. The trouble is, the indicator we tend to reach for in order to do this is average percentage bed occupancy. And average percentage bed occupancy brings problems with it. Here are five observations about those problems.
Average percentage bed occupancy is a number based on quite a long-winded calculation, which means that, by using this calculation, we are effectively excluding a lot of people from an important debate. This is because average percentage bed occupancy is an abstraction and many NHS managers have difficulty with abstractions. They prefer data when it describes reality in more “direct”, uncomplicated ways. Average percentage bed occupancy doesn’t do that. In case you doubt that statement, let’s have a closer look at how we calculate it. In order to measure it for
—say—General Medicine in April, we take a series of “snapshots” of the
number of General Medical patients who were occupying a bed at midnight
(midnight!!!) for each of the 30 days in the month. We then add up all
those 30 midnight snapshots and that gives us our numerator: occupied bed
days. Then we have to calculate our denominator, which is called That is a lot of steps for busy people to understand and a lot of places where the calculation can go wrong (analysts are sometimes surprisingly “disconnected” from the bed complement and every hospital has got at least three wards on whose bed complement nobody can ever agree, so it is quite easy for things to go wrong here).
The second observation is that
if you quote
Thirdly, a lot of people don’t
realise that average percentage bed occupancy is closely related to other
indicators. In fact, one of the beauties of average percentage bed
occupancy (for those people who understand it) is that it helps
The fourth observation to make
is that average percentage bed occupancy—like anything measured as a
percentage—carries baggage with it. Worryingly, some managers tend to
think in over-simplistic terms here: high average percentage bed occupancy
is good and low average percentage bed occupancy is somehow wasteful. This
is a variation on the joke about the optimist seeing a glass half-full,
the pessimist seeing a glass half-empty, and the accountant who sees a
glass that’s twice as big as it needs to be. For as long as we use only
Finally, there is a danger in
looking at an average percentage bed occupancy figure if you don’t have
any context, if you don’t have a reference–point for whether it’s the
“right” figure or the “wrong” figure. If you want to know what the “right”
level of bed occupancy is, then you need to know what “wrong” looks like.
This may seem like a glaringly obvious statement, but you do have to be
clear about what the So: using average percentage bed occupancy in isolation is fraught with problems. It follows, therefore, that we need to find new, better, more user-friendly ways of describing and visualizing bed occupancy to managers and clinicians. We will, for example, have to probably pick a time other than midnight to measure it. We will need to find a way that makes clear the day-to-day, hour-to-hour variation. We will need to show explicitly how occupancy relates to the other activity indicators and how and when it is associated with dysfunction. Will there be beds for me and all who seek? A one-day course for analysts. [28 May 2013] |
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