Patient flow in most hospitals is about patients moving from one staging post to another. For example, from the Emergency Department to the Acute Medical Unit (AMU), or from the AMU to a specialty ward. These moves are rarely smooth. There are usually delays.
The delays we get most fixated on are the delays in the Emergency Department: the four-hour target delays. But we record and report other delays and they are problematic, too. Delays prior to discharge are particularly irksome because they can last so long. And there are delays that are less 'well-recorded': for example, the delays experienced by patients while they're in the AMU because there aren't any specialty beds available downstream for them to transfer into.
It's worth mentioning that it's not just about delays. There are other problems: patients getting moved to the wrong wards, for example, or patients experiencing too many ward-to-ward moves. And all of these things are symptoms of dysfunction in the wider whole system.
FlowStories proposes a way of using data to address these patient flow problems. It focuses on exit block at all the different places where exit block happens, and it shows how you can use data to not just show or describe the various levels of occupancy or fullness that cause exit block, but also to stipulate what the levels of fullness ought to be if we want to eliminate exit block.
But FlowStories doesn't stop there. Fullness levels won't magically change on their own. If we find ourselves saying—for example—that the AMU needs to work at 75% occupancy instead of 90% occupancy in order for exit block to be eliminated, we're only going to be able to achieve that 75% if we change something else. And that something else is usually length of stay, so FlowStories shows us how we can work out what the new—'ought-to-be'—length of stay numbers should be for each staging post.
Once we've got a full set of 'ought-to-be' numbers to work with and work towards, we need to test them to make sure they will work. So we need to be able to model the impact these new 'ought-to-be' numbers will have. We need to show they will work. And we do this by using table-top methods. Boards and cards. Analogue not digital.
Next we have to try and persuade people to adopt those numbers. And then make them a reality. So we need to prepare the data in such a way that it will convince teams of clinicians to make a realistic and credible plan to change their length of stay.
And finally—assuming our persuasion has worked!—we have to design data visualizations data that will allow us to monitor the changes that get implemented and then check that the improvement is maintained on an ongoing basis.
FlowStories does all of these things by using three underlying principles.
The first principle is that we use individual patient stories to illustrate the issues. Data analysts need to be aware that the people who need to look at our data are probably more receptive to actual real-life patient examples than they are to some high-level summarised calculation.
Secondly, we show what's happening in all of the staging posts—all the silos—simultaneously. It's no good just showing the AMU people what's happening in the AMU if their activity and workload is profoundly affected by what's happening in the silos both upstream and downstream of them. They have to see what's happening in those neighbouring silos, too. In fact, everybody has to see everything. It's a whole system, so let's start treating it as such and let's stop just looking at it one silo at a time.
And—thirdly—we emphasize that using data to give us insight about patient flow is an approach that requires a narrative line. It's about looking at a series of data exhibits in a sequence, not just randomly plonking isolated graphs and tables here and there with no attempt at writing the storyline that connects them together.
FlowStories is an approach to the data on patient flow that restores its relevance and credibility. There is an open workshop in central London scheduled for Wednesday 20th July 2022. More details on the FlowStories webpage.
[29 June 2022]
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