Making diamonds out of sausages
“Most hospital discharge data is useless,” said the HSJ headline recently, reporting comments by former NHS CEO and current chair of two NHS trusts Sir David Nicholson. This twitched my antenna because data is obviously neutral; it’s what you do with it that counts. On this, Sir David is spot on, because data only develops value when you analyse it to learn from it. A diamond in the ground is of no value until it has been unearthed, polished, refined, categorised and turned into a product. Data is no different; it is just there, occupying gigabytes, consuming energy and inert until analysed.
Data collection and storage is by no means an effortless automatic process; it can be expensive, complex and bureaucratic. Beyond naturally created datasets accumulated by activity, there are the manufactured and curated datasets. These are created to demonstrate or prove a point or provide accountability, and can take on a life of their own, spawning data collection industries designed to feed policy-making and political fashions of the moment. Just look at how data has been commissioned and used to support different political agendas, for example by daily newspaper The Times and think tank Reform. The use and analysis of data should mark the credibility (or lack of credibility!) of a story, proposal or policy.
The NHS produces and amasses vast quantities of data from every aspect of its functioning and misfunctioning. As The Economist said on 15 January 2023, talking about the NHS, “Britain produces excellent data … Other countries have less-comprehensive statistics”. The NHS produces much more data than is analysed and is host to even more data generation through partnership working, clinical research and medical education. Virtually everything that happens in the service is recorded in some way. Where else can you find data on everything from the number of occupants of three-wheeled motor vehicle injured in collision with two- or three-wheeled motor vehicle that were hospitalised (9 people)? Or the average age of those hospitalised due to exposure to vibration (47.143 years old) or the mean length of stay of those bitten or struck by crocodile or alligator (3 days)?
So, I wouldn’t blame the data, and I actually don’t think Sir David is. I feel the frustration that we are not using this treasure trove of potential knowledge to learn from, to discover new insights and create new outcomes. Discharge data is a snapshot of course – just one element of a patient’s pathway with many other factors contributing to that moment, including, in many cases, avoidable admissions and missed early interventions. Discharge data shows a sausage coming out of the factory; it tells you nothing about the sausage, its quality, the provenance of the ingredients, or even what the ingredients are!
This is maths, not analysis, and it is discernible to anyone, even if they stopped maths at 16 years old. And if you can’t do the maths, then “Alexa, I have 200 patients fit for discharge out of 1,000”, “yes, you have 20% of your beds blocked”. Sir David is right. It tells us nothing – and context is king. So, what is the context? Who are these patients? Why are they in hospital? How long have they been there? Where did they come from? Where will they go to? How many have we seen before? How old are they? Which teams are they under? Which GP practice covers them? Are they working or retired? Are they mobile, independent, cared for or carers? Are their conditions chronic or acute? What is their level of deprivation? Are they amenable to/impactable by new or additional interventions? What’s their ethnic and cultural background? Are they vaccinated and against what? The context and many other possibilities we can all come up with offer the opportunity for analysis to sparkle like a diamond.
And this is where analysts can and want to help. They are happy to mine for diamonds, polish them and even set them in a ring, taking all of this additional data and context, forming it into something valuable and meaningful, and providing enough understanding so that you, organisational system leaders, can make the best-informed decisions. There are an estimated 13,500 analysts doing this kind of work in the NHS and elsewhere, helping to improve the population’s health. This is work that makes patients’ lives better, work that ultimately ‘can save more lives’ and which will not only support your systems but will also get most analysts singing ‘Heigh ho’ as they go to do the work!
You don’t need a whole mining workforce of your own; ICSs offer us a space and have the right people around the table to draw the skills needed from the system, not only helping to find the resource to mine, but also helping people better see the broader context and organise themselves to action the answers across traditional boundaries of both provision and sector.
Today, the NHS feels overwhelmed by demand – the very thing the government promised its plans were designed to avoid. But if we are simply counting numbers, counting patients in, counting their stay and counting them out, it is just counting. It is not maths, and it is not analysis! We need to make our data work for us, by asking intelligent questions of it and testing the results. This takes a vision for how data can enrich our work, the resources of many more skilled people, and the imagination to ask questions that create options for improvement and progress. Otherwise, we are simply making sausages out of diamonds.
Andi Orlowski is president at AphA Analysts and director of the Health Economics Unit.
Listen to Sir David Nicholson talking to HSJ Editor Alastair McLellan during November 2022: https://www.midlandsdecisionsupport.nhs.uk/training-events/insight-2022-day-3-decision-making-in-the-21st-century-nhs-how-does-it-really-work/