The Fullness Hypothesis
Flow_ology's narrative arc
In all of the Flowopoly workshops I have delivered in various hospitals in the NHS over the last two-and-half years, I have yet to encounter a healthcare system that disproves the theory I sometimes call "The Fullness Hypothesis".
Simply put, this three-stage hypothesis proposes—firstly—that hospitals usually have a problem with the four-hour target in A&E because of majors rather than minors. This is a problem that's caused by delays in transferring patients out of A&E and into the Assessment wards. These delays are caused mainly by a lack of available beds. And these delays often cause A&E to become over-full.
The second stage of the fullness hypothesis states that when we look at how these Assessment wards are working, they, too, face "exit block" difficulties. Their ability to transfer sick patients to downstream inpatient wards in the hospital is often compromised by poor bed availability in the hospital's main inpatient wards. So we often find that the Assessment wards are also operating at fullness levels that are too high.
And—thirdly—downstream of these inpatient wards, we also know that we frequently have problems with delayed discharges. These are delays often caused by problems accessing homecare or other community-based care.
It's interesting that The Fullness Hypothesis can be condensed into a series of four scatterplots that show: 1. Performance against the four-hour target is worse at times when A&E is fuller; 2. A&E is fuller at those times when the Assessment wards are fuller; 3. The Assessment wards are fuller on those occasions when the downstream wards are also full; and 4. The downstream wards tend to be fullest when they contain more patients whose discharge is being delayed.
It's also interesting that if the scatterplots aren't joined together by the right, properly-crafted narrative then the data on its own very often won't be enough to prompt a change in system behaviour.
And of course you very often can't just go wading straight into this—you have to set context first.
It's also probably the case that even data with a narrative isn't enough either. But I remain convinced that a properly narrated series of data exhibits is a necessary prerequisite—and ideally one that is tailored for different audiences.
This narrative is what Flow_ology equips you with.
Session One is the description of the status quo: a replay of what bad flow and good flow look like using a tactile, rapidly-moving, room-sized data exhibit that shows you individual patients moving from ward to ward over the course of 24 hours.
Session Two is the transition to how we can describe this status quo using more conventional data presentation techniques (tables and charts instead of boards, cards and red hotels stolen from Monopoly sets).
Session Three takes this data and develops it so that we can use it to work out and understand the cause-and-effect relationships in the system. Session Three is The Fullness Hypothesis.
And Session Four shows how we can use our new-found knowledge of cause-and-effect to arrive at the numbers we need to aim for (we call them the "ought-to-be" numbers) in order to achieve optimal patient flow. Moreover, we also need to show how we would present these numbers "on the ground" in order to monitor any progress we make towards better patient flow.
So that's Flow_ology's narrative arc. It's driven by what I've witnessed for myself in lots of different general hospitals up and down the motorways of England, Scotland and Wales. And it's driven by a belief that unless we understand that we need to present our data as a narrative then we will find it difficult to get people to pay attention to it.
There are two open course dates for Flow_ology in the diary: Friday 24th February is London; Wednesday 29th March in Manchester. Email or phone me if you want to book a place, or if you want to talk about how to organise an on-site workshop.There are more details about the session-by-session content on the Flow_ology page. And you can download a two-sides-of-A4 pdf flyer here.
[3 February 2017]
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