Reducing Frequent A&E Visits Through Predictive AI

NHS England has deployed High Intensity Use (HIU) services, supported by AI, across 125 emergency departments in England to reduce the burden of frequent A&E attendances. These services focus on identifying the root causes behind repeat visits, which are often linked to deprivation, health inequalities, and social isolation.

Key elements of HIU services include:

• Data-Driven Insights: AI analyses historical trends and patient data to identify those most likely to need A&E care repeatedly.
• Mentoring and Support: Frequent attendees are offered personalised mentoring sessions to address the underlying causes of their visits, such as managing chronic conditions or accessing community resources.
• Proactive Outreach: Staff can use predictive analytics to identify patients with conditions like asthma, diabetes, or heart failure, contacting them to offer preventative care before emergencies arise.

One example of the initiative in action is the North East London ICB, where AI is being utilised as an A&E demand forecasting tool. Since launching the pilot, emergency attendances are down by 34% and hospital bed days have been reduced by 25%. This is all a result of being able to predict which patients are at higher risk of admission, which means clinicians can intervene early, ensuring individuals receive personalised care and support at home. This not only improves patient outcomes but also alleviates pressure on emergency departments.

Amanda Pritchard, NHS Chief Executive:

“The NHS is going into winter busier than ever before, and as ever, despite huge pressure and a potential ‘quad-demic,’ our incredible staff are doing everything within their power to provide the best possible care to patients.

“We know that a small proportion of the population are much more likely to use A&E or ambulance services, so it is important we give them the targeted support they need this winter before they get to the front door of an emergency service—this is much better for them but will also help to relieve pressure on the NHS.

“Initiatives like using AI to spot those who may need extra support in the community help provide more personalised care and must be central to our 10-Year Health Plan.”

Karin Smyth, Health Minister:

“We inherited a broken NHS that is dealing with record admissions heading into winter, which is why we’re investing £26 billion in the health service and have set out our Plan for Change to get the NHS back on its feet.

“But investment must come with reform, and these fantastic services are great examples of how innovation and partnership working can transform the NHS.

“They offer a double win for getting vulnerable patients the right support and saving the precious time of busy A&E staff.

“We want to share more of this best practice through the three shifts in our 10-Year Health Plan—moving from hospital to community, analogue to digital, and treatment to prevention.”

AI is becoming a cornerstone of prevention-focused strategies within the NHS. Beyond managing frequent A&E visitors, AI applications are being used to:

• Predict Disease Outbreaks: AI models analyse trends to forecast flu or COVID-19 surges, allowing resources to be reallocated effectively.
• Enable Real-Time Monitoring: AI enhances wearables and tech-enabled care devices by analysing real-time health data, such as vital signs and glucose levels. AI’s ability to identify patterns or early warning signs enables timely interventions, empowering patients to manage chronic conditions like hypertension or diabetes at home.
• Optimise Workforce Allocation: AI forecasting tools ensure that staffing levels in hospitals match anticipated demand, particularly during peak winter periods.
• Personalise Patient Care: Platforms using AI can tailor care plans to individuals’ unique medical histories, ensuring better outcomes and fewer emergency interventions.

AI has many uses in health and social care, but its ability to predict risks and outcomes is one of its most valuable strengths. Nevertheless, we must continue to ensure it is deployed in a transparent and ethical way. These tools must address, not deepen, existing health inequalities by ensuring vulnerable populations—such as those in deprived areas—are prioritised. Transparency in how AI identifies high-risk individuals and recommends interventions is key to building trust, while safeguarding data privacy and maintaining human oversight ensures care remains proactive, personalised and patient-centred.

The NHS is taking a significant step toward creating sustainable, high-quality care for the UK population. The use of AI in initiatives like HIU services and predictive analytics is a great step forward in not just reducing A&E attendance, but creating a health and social care system focused on both prevention and intervention.

If we can continue to accurately identify at-risk patients earlier and address the root causes of their health challenges, we can not only improve patient outcomes and reduce the strain on emergency services but also build on the NHS’s already exceptional resilience, ensuring that it continues to support long-term well-being and deliver quality care for all.

Access is committed to ethical AI practices and believes that the real power of ethical AI lies in helping health and social care providers use these tools transparently and equitably. Get in touch with us today to find out how our solutions can support your organisation in delivering smarter, fairer, and more effective care.