How intelligent decision making can help the NHS through winter
As we enter winter, the NHS is in survival mode. The elective backlog has hit seven million. Waiting times in A+E are routinely more than 12 hours.
As we enter winter, the NHS is in survival mode.
The elective backlog has hit seven million. Waiting times in A+E are routinely more than 12 hours. This July, over 40,000 patients suffered handover delays of over an hour from ambulance to hospital.
This is before we get to the “twindemic” of higher flu and covid cases, record staff vacancies, and many either planning to strike or quit entirely.
When lives are at risk and pressures are intense, it can seem hard – almost impossible – to find ways to improve the situation.
Frustration is rising, patience is waning, and the status quo isn’t working.
So, what can be done? Of course, more staff, hospitals and community-based care would be welcome. But these take time – and neither patients nor staff are rich in time.
The question then becomes: how do we do more with finite resources, and without further overwhelming NHS workers?
The answer has to lie in technology – including a new way of thinking about analytics and AI called decision intelligence.
Amanda Pritchard acknowledges the role tech must play, speaking of the need for “care traffic control centres” to help hospitals track demand and manage capacity.
But more data and dashboards alone are not the answer. If you’re looking for a needle in a haystack, adding more hay won’t help.
Globally, we produce 50 times more data today than we did in 2010. But has that extra data led to 50 times better performance? Are outcomes for patients 50 times better? Of course not.
In fact, have you ever heard a decisionmaker – in the NHS or elsewhere – say they have the right amount of data? If you have, we’d love to meet them…
With stretched resources, the way the NHS will deliver more is by extracting deeper insight from existing data, and therefore making better informed decisions.
This winter, every decision staff make must be spot on – because there are no extra resources to help. The NHS has already opened surgical hubs, commissioned independent sector capacity, re-designed ambulance care pathways and lots more to ease pressures.
By using machine learning, decision intelligence can dramatically improve the NHS’s ability to direct staff and vital equipment to where the need is greatest.
It can optimise resource allocation, help clinical prioritisation, and schedule appointments. It makes treating more patients with the same resources a realistic goal, not a distant pipe dream.
At the NHS in Wales, this technology is already being used to accurately predict length of stay and support complex discharge planning for patients from the point of admission into hospital. In England, they are being used to predict patients who are in the community and are at risk of admission.
Having these forecasts puts power in the hands of clinicians and hospital managers to be able to allocate staff, beds and resources efficiently, easing pressure and increasing capacity. They give reassurance to patients, and open up the possibility of earlier community interventions – such as a video call with a GP or changing a patient’s prescription – to keep people out of hospital all together.
It also offers staff a route out of “spreadsheet hell” and endless towers of paper, with decisions made in real time and based on what is going to happen – not what may happen based on guesstimates from old data.
The NHS is sick to the back teeth of being told to do more for less – and for good reason. Nurses and doctors, porters and healthcare assistants, have a finite capacity.
Technology does not. Using it to support our NHS has to be the future – this winter, and beyond.
