Data with a human face

Individual stories help us understand the big picture

When we think about patient flow, we probably think of it as individual patients flowing from one hospital staging post to another. A person arrives in the Emergency Department, then gets admitted to the Acute Medical Unit, then gets transferred to one of the specialty wards.

A patient moves from the Acute Medical Unit to Ward 8. Flow Data Symposium, Edinburgh: 11 December 2015. Photo by Ellie Morag

But when we try to make sense of patient flow using data, we often abandon this patient-centric view and rely instead on aggregated numbers and forbidding-looking graphs. Counts. Totals. Averages. Percentages. Scatterplots. And these are a turn-off for lots of people, particularly the people who are actually working with patient flow at the coalface.

FlowStories is a workshop that restores the individual patient journey to the way we look at the data. It self-consciously tries to replace aggregated data with human-centred stories.

Patient stories not system stories

I made a few steps in this human direction a few years ago. Michael Fox and I developed a workshop called Flowopoly that was full of patient stories. Cards the size of credit cards represented individual patients. Boards on tables represented the wards and departments the patients moved through. On the face of it, this was a feast of flow stories. Except that it wasn't. Flowopoly wasn't about patient stories; it was the story of a day: a day in the life of a system. So although Flowopoly looked as if it was full of individual patient stories, most of them were either 'short' stories (the patients who arrived at the Emergency Department, got treated, then went home again a few hours later) or—if they were longer stories (they went through all the silos)—they were incomplete because the journeys took longer than a day.

(Also, the 'short' stories were split in half, which made them difficult to follow. The arrival would happen and then 150 other things would happen elsewhere and then that patient that arrived at 10:21 would finally go home at 13:11 but because of all the intervening events, it'd be virtually impossible to associate the two events. So we end up with the 'de-humanized' arrival at 10:21 and the 'completely disconnected (and equally 'de-humanized') departure at 13:11.)

The best flow stories are the long flow stories

This led me to the idea that the most important stories were probably the ones that involved journeys through all the silos. The patients who go from the Emergency Department, then the Acute Medical Unit, then to one of the specialty wards. And it is the experience of these 'long story' patients that has a disproportionate effect on everyone else's experience. (It's long been a obsession of mine—for example—to show that the 30% of ED patients who end up needing to be admitted have a massively disproportionate effect on the experience of the non-admitted patients.)

But if I was going to understand the whole journey, I had to get more specific about the third staging post. It wasn't just some nebulous generality called 'downstream'; it was a series of individual ecosystems—the subspecialties or the ward 'firms'—each with their own dynamics. And the way we looked at these individual entities differed according to whether they were a bed borrowing ecosystem or a bed lending ecosystem.

Start at the end and work backwards

This segmentation of the 'downstream' into its constituent elements led me to the idea that we have to start at the end and work backwards. We need to take all the patients who ended up in one particular downstream specialty—e.g. Respiratory Medicine—and then work back and look at their experience from the beginning. The cohort should be defined by where they ended up, not where they started, and then we could then start form the endpoint and track their journeys back to the start.

Relevant and credible

The political scientist William Davies wrote an article for the Guardian in 2017 called How statistics lost their power—and why we should fear what comes next. He sought to explain why people have come to distrust official statistics and one of the reasons is that summarized aggregate data is not only impersonal and abstract, but it also offends and betrays. By always seeking to make a claim for a generality, it ends up denying the richness of the individual experience: "Official knowledge becomes ever more abstracted from lived experience, until that knowledge simply ceases to be relevant or credible."

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.

[7 June 2022]