About A&E Live
What is it?
A&E Live is a simulation of the Emergency Departments of hospitals in England, based on statistical data that has been published by NHS England and NHS Digital.
Why was it created?
Until you start to really dig the numbers, you don’t really appreciate the size of the NHS. It is colossal. In England 1.3m people work for the NHS, every night over 100,000 people stay in a NHS hospital bed. It’s often cited that one million people have contact with the NHS every 36 hours. By my estimation, that is underestimate.
These numbers are all big, but they are often difficult to conceptualise. What does that feel like?
One thing the NHS is good at is collecting data. The NHS Digital website is a data geek’s paradise, a seemingly never ending collection of Excel spreadsheets that have all been compiled to help analysis what is happening in our health service. I’m sure these files do have some use, but exploring it does feel like going into an attic and finding a dust-covered box with treasure inside. Does anyone else know they are there?
I was interested using this treasure-trove of information to show what the NHS looks like right now. I started with A&E data, primarily because unlike much of the rest of the data there is a lot of day-and-hour information contained within. It’s this data that the media picked up on to report that 9am on Monday is apparently the busiest time for A&E (and not Saturday night as you may have suspected).
So I combined this data with NHS England’s weekly reporting on A&E performance (which changed to monthly at the start of July 2015, far less useful for this exercise) as the basis of A&E Live. Using this data, I have created a simulation of what each trust’s Emergency Department(s) looks like, and collectively create a national picture of A&E activity.
I quite like the idea of having an NHS dashboard that shows the totality of the service at any time of the day. This was an initial attempt to start turning that into a reality. I’m not sure it serves any particular purpose, but I think there is definitely some use of analysis realtime data (even if not at realtime).
Why has it been updated?
The original simulation was created in 2015 and has been running succesfully for almost four years. Almost 10 million patients have been generated in that time.
In early 2019 I noticed that the website usage had significantly increased, suggesting more people were interested; particularly at some specific trusts.
Whilst the simulation was happily continuing to function, I was aware that it was getting a little of out date: it was still using data from 2015, because it had been designed around the weekly sitrep reports that NHS England stopped publishing in June 2015.
I began the process of updating the simulation, which in reality has been a total re-write of the service. Whilst the fundamental principle of how it works remains the same, the processes to make it happen have all been re-created.
How is it done?
The data used
NHS England publish monthly key performace information on each trust that provides an Urgent and Emergency Care service. These tend to come out a few weeks after the end of the month. This data allows us to see how busy each trust is.
This data is combined with the NHS Digital annual reporting on A&E, which contains a lot more detailed information about each trust and who it has treated, including day and hour (but not combined), time of arrival, age and gender demographic information, how each patient arrived, length of stay in A&E etc. This data is referred to as annual data, and provides the rest of the information on the trust pages.
There are two more bits of data that are part of the NHS Digital annual reporting. At a national level this contains information about the combined day and hour arrival times, giving an hour-by-hour breakdown across a week. The other bit of information that I’ve used is a breakdown of ambulance call types, which is used to generate the emergency vs non-emergency ambulance arrivals in the live data.
How the data is used
Every hour, a list is of the patients is generated for the two hours ahead on a trust-by-trust basis. The last set of monthly data is used to work out how many patients would arrive at the trust in a typical hour, and then using the annual data on day and hour variance, and comparing that to the national average, a figure for that hour is reached.
Each trust is then assigned that number of patients (with a slight varience in the number to reflect unpredictability), and the patient key characteristics (demographics, arrival mode, duration etc) are each randomly selected, weighted based on the annual data published for the trust. Where available, these are compounded, so a patient’s age will determine the likelihood of them arriving by ambulance (there is a far higher percentage of older people arriving by ambulance than younger people and children). Each patient is then randomly assigned an arrival time across the hour, and a random duration that they will remain in A&E, again weighted based on the trusts’ performance data.
If the patient is arriving by ambulance then they are assigned a type, weighted based on the published national data (Category 1 - 4).
Limitations of the data
There are some limitations to the data that mean it isn’t an entirely accurate reflection of the real environment.
The main limitation is that the the duration is randomly assigned to each patient based on the data published about that trust, irrespective of any other information. But in reality, the length of time a patient will wait is determined by how busy the hospital is; so it is not simply a random occurrence.
The other primary limitation is that the data is not compounded, so that whilst there is plenty of other data that could be used (eg type of injury, initial treatment etc), it is not connected to other data, so, for example, you can not see the type of injury by arrival by ambulance etc. Without those connections simply using weighted random assignment would give you daft results such as a patient arriving by Category 1 ambulance with a broken finger; or babies having heart attacks as a regular occurrence.
Another thing that is not reflected is that there is a degree of managing ambulance arrivals so they divert to another hospital if the closest one is busy.
Finally, this is based on an interpretation of the data provided; so there could well be a misunderstanding in what the data represents or mistake in the coding!