Why was this work needed?
A&E departments across the country are facing unprecedented pressure dealing with patients requiring urgent care. The total number of attendances in June 2023 was 2,221,000, an increase of 1.3% on June 2022.
Historically, it has been difficult for decision makers to effectively plan and make sure crucial resources are available without knowing anticipated surges in levels of demand.
An A&E demand forecasting tool has been developed which provides hospitals in England with anticipated A&E admissions three weeks in advance and alerts them to potential upcoming surges. This helps local and frontline NHS staff make more informed decisions on their allocation of resources by understanding when emergency demand pressures are likely to be higher or lower, ensuring there is capacity for patients when they need it.
Accounting for historical trends in the data as well as variations such as seasonality, weather and public holidays, forecasts are broken down by age so that staff can plan for specific bed needs, such as for paediatric patients, ensuring that they are provided with the relevant care they require.
What were the benefits?
NHS leaders are now able to proactively plan for surges in demand and divert resources to other areas such as elective care when demand is lower with the aim of treating patients as fast as possible.
The provision of accurate three-week ahead forecasts places the NHS in a better position to anticipate and prepare for surges in demand rather than react once they’ve started. This benefits patients as well as frontline staff who are better placed to deal with all the different situations they are faced within A&E.
The tool – which has been co-developed in collaboration with frontline, clinical and operational staff in nine pilot NHS trusts – is now available to 123 hospital trusts and has been shown to be approximately twice as accurate at predicting admissions than a baseline comparison model.
What type of data was involved?
The AI tool uses historical data about A&E admissions, e.g. when and where they happen, who is accessing the service, to predict what future demand will look like. For example, if the tool can see that demand inevitably increases around the first week of December, it can alert staff three weeks in advance so that they can ensure staff rotas and resource allocation reflects this expected demand increase, preventing them from being overwhelmed and under resourced when that week comes around.
The upcoming NHS Federated Data Platform will continue to support functions like the A&E forecasting tool to provide trusts with the information they need to understand patterns, solve problems, and plan services for their local populations and patients.
Who funded and collaborated on this work?
NHS England data scientists worked with Faculty, a private AI organisation, to develop the tool. The developers would not have had access to any identifiable personal health information as part of their work on this tool.
Where can I go for more information?
- Page updated: 12 December 2023
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