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in a nutshell
Why we need to start looking at unscheduled care data in a different way
The problem the NHS has with unscheduled care has a lot to do with a failure to match demand and supply. The demand for unscheduled care. The supply of unscheduled care. It's a problem that ought to have been solved long ago, and data should have played a leading role in the solving of it. But the problem hasn't been solved. And it hasn't been solved because the NHS is not good at using data.
One reason for this—perhaps the main reason—is because of a general "cultural" antipathy towards data that exists in the NHS. That's a depressing reason, because there's not much we can do about it. But we don't need to get too depressed because—quite apart from this—there are also three other, "fix-able" reasons why we don't use data effectively.
The first "fix-able" reason is that the custodians of the data—NHS information analysts—can often be too remote from the clinical and managerial coalface to be able to use data to describe unscheduled care with any resonance. When I use the word "describe" here, I mean the steps you have to take to convert the complex, messy reality that is emergency care into tables and charts that people can easily understand. These steps are rarely taken. For example, we still describe emergency inpatient activity using the currency of consultant episodes instead of splitting it more usefully into ward stays so that we can separate out the Assessment element of the stay from the specialty ward element of the stay. Or we still measure length of stay in key parts of the system in days instead of hours. Or we just spit the data out as a random collection of numbers, tables and charts instead of publishing it as part of an agreed framework or map that people can readily grasp. This failure to adequately describe the unscheduled care process means that managers and clinicians still have to rely—sub-optimally—on their own experience and the anecdotes of others in order to make sense of it.
Secondly, if we are having difficulty even describing what's going on, then we are going to be ill-equipped to take the next step, which is to understand why things are as they are. We need to be working out why one thing is causing another thing to happen. We need to be looking at the relationships between the different parts of the unscheduled care system. We need to be investigating cause-and-effect. But this doesn't happen nearly enough.
And—thirdly—the reason why neither meaningful description nor cause-and-effect analysis happen enough is that we've allowed a culture to develop where—by and large—we don't expect information analysts to be involved in dialogues and conversations with managers and clinicians about unscheduled care data. It's as if we've allowed the stereotypes to affect the way we think about data. Stereotypes? Here are the stereotypes. Data analysis is something that's done remotely, in dusty, darkened rooms, by data-geek-types surrounded—presumably—by half-empty, discarded pizza boxes. And the results of that data analysis are met with shrugged shoulders by managers and clinicians who sometimes simply state: "Data? No, I don't do data."
Flow_ology tackles these three reasons head-on.
It's a way of jump-starting the way NHS organisations use data to inform decisions about supply and demand in unscheduled care.
It provides a template for how to describe emergency care with data. It starts you off with examples of useful cause-and-effect analysis that help people understand why bad things happen some of the time but not all of the time. And it expressly enforces the initiation and development of dialogue between analysts, clinicians and managers as the way forward.
[21 June 2013 | revised 22 July 2013]
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