The NHS has announced that it is using AI to forecast emergency department demand so it can deploy staff more effectively. This comes as UK hospitals battle record winter flu cases that threaten to disrupt services.
The predictive AI tool, now active across 50 NHS organizations in England, is helping trusts anticipate patient surges and plan resources before pressure points emerge.
Health Innovation Minister Dr. Zubir Ahmed described the development as evidence that the “AI revolution is here.”
The system analyzes historical attendance data, seasonal health patterns, and external factors like weather forecasts to predict A&E demand. This enables hospital managers to schedule shifts more intelligently and allocate resources where they are needed most.
How the AI Forecasting System Works
The technology is designed to help patients move through the system more efficiently by ensuring the right resources are in place at the right time.
With more than 18 million flu vaccines administered in autumn, hundreds of thousands more than in the same period in 2024, it underscores the scale of demand facing the health service.
The AI forecasting tool continuously ingests seasonal health data and multiple demand signals to generate forecasts of patient attendance at emergency departments. These inputs include Met Office temperature predictions, historical records showing which days of the week typically experience higher volumes, and patterns from previous years. By processing this information, the system produces forecasts that trusts can use to manage staffing levels and resource allocation proactively rather than reactively.
A government statement from December said the tool currently has 170 active users each month across the 50 NHS organizations using it.
Trusts currently using the system include NHS Coventry and Warwickshire Integrated Care Board and NHS Bedfordshire, Luton, and Milton Keynes Integrated Care Board. However, the tool is available to all NHS trusts in England, creating a standardized approach to demand forecasting across the service.
Its design allows hospital managers to identify potential bottlenecks before they materialize, reducing the frequency of last-minute staffing decisions that can strain teams.
The practical impact centers on smarter shift scheduling. Rather than relying on generalized staffing patterns or responding to crises as they unfold, managers can adjust schedules based on predicted demand. This means deploying additional staff during anticipated surge periods and avoiding overstaffing during quieter times. The result is a more efficient allocation of clinical resources and reduced pressure on frontline workers who might otherwise face understaffed shifts during peak demand.
Technology Secretary Liz Kendall emphasized that AI is “already improving healthcare by speeding up diagnosis and unlocking new treatments” but described this forecasting capability as another significant step.
Health Innovation Minister Dr. Ahmed said this represents part of a “10-Year Health Plan to shift healthcare from analog to digital as we build an NHS fit for the future.”
The NHS’s Broader AI Strategy
This A&E forecasting tool sits within a larger effort to embed AI throughout NHS operations. The government’s AI Exemplars program, which introduced this tool, aims to deploy AI more extensively across public services and modernize legacy systems that have hampered efficiency.
The initiative reflects a strategic decision to position AI not as experimental technology but as infrastructure for everyday operations. The NHS’s embrace of AI extends far beyond demand forecasting.
A landmark trial of Microsoft 365 Copilot across 90 NHS organizations involved more than 30,000 workers and showed that staff could save an average of 43 minutes daily using the AI assistant. If scaled across the entire NHS, the results suggest up to 400,000 work hours could be reclaimed each month, time currently consumed by administrative tasks such as note-taking, email management, and documentation.
The Copilot trial focused on administrative staff rather than clinical roles, yet the efficiencies identified were substantial. The tool could potentially save 83,333 hours monthly in note-taking alone, while summarizing the 10.3 million emails handled by clinicians and staff each month could free up a further 271,000 hours. These gains matter in a health service where administrative burdens have long diverted attention from patient care.
Microsoft’s deepening presence in the NHS extends beyond Copilot. Earlier this year, the company signed an agreement making Microsoft 365 available to 1.2 million healthcare staff in England. In September, it launched Dragon Copilot, a specialized AI assistant that captures clinical conversations to draft documentation and automate follow-up tasks. Trialed across seven organizations with more than 200 clinicians, Dragon Copilot represents a move into clinical workflows rather than purely administrative functions.
This ongoing adoption shows how AI is being woven into multiple layers of NHS operations. From forecasting patient demand to automating documentation and summarizing communications, the technology addresses bottlenecks at different points in the care pathway.
AI as NHS Infrastructure
The deployment of AI forecasting tools across the NHS marks a turning point in workforce management for one of the UK’s largest employers. With over 1.2 million staff, the health service is testing predictive AI at a scale that few organizations can match.
For the NHS, the immediate benefit lies in navigating winter pressures with greater agility. The ability to predict A&E demand and deploy staff accordingly helps trusts manage surges without resorting to crisis staffing measures. But the implications extend beyond emergency care. If AI can help the NHS schedule staff more effectively amid complex shift patterns and unpredictable demand, similar approaches could transform workforce planning across sectors.
Success here provides a compelling proof point for HR and operations leaders in retail, logistics, hospitality, and professional services watching to see whether predictive staffing tools deliver on their promises.
