One of the biggest stories in the news this week is how the NHS has failed to meet its four-hour A&E waiting time target. From October to December, 92.6% of patients were seen within four hours – the latest target is 95%. The Figure it Out team decided to build our own A&E ward and find out what the NHS are really up against.
Unfortunately, we didn’t have the budget to build a real A&E ward, so we had to settle for a simulation model instead. These types of models are built on computers to mimic real life activities. Even 3D models are possible; this video is a great example. The method has many useful applications in the business environment such as improving passenger flows through airports and the sizing of a call centre. It can be used to replicate the impact of change on any system.
The Figure it Out A&E ward is a busy one – we have an average of one new patient arriving every minute. These patients are made up of two categories – a type A patient and a type B patient. Type A patients are patients who are using the A&E ward for life-threatening emergencies only. Type B patients are those whose condition are not life-threatening, and could be treated outside the A&E ward, such as GPs, pharmacies, out-of-hours clinics etc. Of our patients, 70% of A&E admissions are type A patients and 30% are type B (which is roughly equivalent to the UK in 2014, (NHS (2014))*.
To treat our patients, we need to hire nurses. They work 24 hours a day, 7 days a week – this is not a recommendation to solve the issues with the A&E waiting times, it is only to make the model simpler. Let’s say the nurses in our ward can treat a type A in an average of 60 minutes and a type B patient in an average of 20 minutes. We hired 50 of them, and ran the simulation for a week and measured two key metrics:
· The average waiting time
· The percentage of patients seen within an hour.
With 50 nurses, the average waiting time for patients was 3 minutes, and 100% of patients were seen within one hour. Not bad. What happens to the waiting times if we reduce the number of nurses? The chart below reveals when our A&E ward starts to breaks down.
It’s quite a surprising revelation to see that by reducing the number of nurses from 50 to 48, the average patient waiting time grows ten-fold. Remove any further nurses and the system gets out of control. We only ran the simulation for a week; if you run it for a month or a year then the waiting time will keep increasing and the queues would increase to a size that even the most patient patient couldn’t handle.
So when faced with waiting times which are getting out of hand, hiring a small number of nurses can ease the system substantially. However, rather than hire more nurses, we could educate the public so that if a patient were to get a non-life threatening condition (i.e. a type B patient) then they would seek alternative help centres than the A&E department.
Keeping 47 nurses, we tested our A&E ward to see how reducing the number of type B patients we get affects the waiting times. The chart below shows the impact.
As it would be expected, reducing the number of type B patients reduced the waiting times. By educating the public so that 20% of ‘former’ type B would use alternative healthcare resources, the A&E ward returns to a stable state. This is still a significant result, considering that in our initial state only 30% of patients were type B patients and that they only take 20minutes to treat, as opposed to the 60min it takes to treat type A patients. The unnecessary strain on A&E is an issue that the NHS have been trying to tackle for some time, and it’s quite clear why.
Of course, there are countless things to take into consideration when modelling the waiting times in A&E. Speciality nurses, shift patterns, peaks and troughs in the number of patients, patients who get priority and bed capacity, amongst other things, all have to be taken into consideration before we can paint an accurate picture.
However, shouldn’t take anything away from the lessons we learnt operating the Figure it Out A&E ward. The smallest of changes can render the queues and waiting times out of control. I think we’ll leave it in the hands of the NHS and stick to analytics.
*In reality, the NHS splits the A&E patients into Type 1, 2 and 3, but we have grouped types 1 and 2 together for simplicity
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