The beds/data mismatch

We need to report data that's space-relevant

Hospitals have specialties. And specialties have beds, which we tend to call 'bed allocations' or 'bed complements'.

We gather data on the admissions, stays and discharges that take place in these specialty beds. But when we want to look at the inpatient activity in individual specialties, the bed allocations and the data often don't match up.

It's important to address this mismatch. I believe that when we report inpatient data to inpatient specialties, we need to make clear the relationship between - on the one hand - the activity and - on the other - the space that activity is supposed to fit into to.

But before I go into a bit more detail about why I think addressing the mismatch matters, let's briefly look at some of the reasons why this 'bed/data mismatch' occurs.

Firstly, in those specialties with a significant emergency care component to their workload, it's common for time spent in an admissions ward (an Acute Medical Unit (AMU)or a Surgical Assessment Unit) to be included under the specialty workload. This is particularly problematic if you want to separate out 'downstream' General Medicine from 'AMU' General Medicine. In order to generate relevant data for the downstream clinicians, you need to remove the AMU part of the stay. That part of the stay wasn’t under their control, so they won't thank us for including it.

Secondly, nearly all inpatient specialties have a relationship with Critical Care. Some specialties are affected by this more than others. (Cardiology, for example, is more affected by General Medicine), but the thing here is that time spent by patients in critical care beds should be excluded from the 'main' inpatient stay. Again, we’re trying to isolate the activity that 'belongs' in the bed allocation.

Thirdly, if the discharge lounge is treated as a separate inpatient ward, then time spent there is probably best excluded. Once the patient is in the discharge lounge, they’re as good as discharged so it doesn't help that we include time spent there in the overall length of stay.

OK, so we can see why this bed/data mismatch has come about, but why is it so important for us to address it?

The anecdotal evidence – probably the scientific evidence, too – suggests that outcomes for patients are better if we they are looked after in the 'right' beds. It’s not just better for the patients who are placed in the wrong wards; it’s also better for the patients in the wards that they’re placed into. It strikes me that one of the key things we should be aspiring to do in inpatient hospital care is have patients being looked after in the right place. Let’s reach for the stars here and see if we can get Cardiology patients in Cardiology beds, Thoracic Surgery patients in Thoracic Surgery beds and so on.

A vital stepping stone on the road to this is surely to be able to present managers and clinicians with data for a specific space that is relevant to that specific space. If there is an expectation that managers and clinicians can somehow manage the eco-system of - for example - a pair of wards that make up a bed allocation, then we need those staff to become as familiar as possible with the numbers that describe what’s happening – and what is supposed to happen – within that space.

Our repeated failure to match the beds and the activity that’s supposed to fit into those beds – this is I believe one of the key reasons why we’re struggling with patient flow.

[28 April 2024]

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