Tameside & Glossop Integrated Care NHS Foundation Trust has deployed an artificial intelligence tool that analyzes routine A&E data to identify patients at high risk of returning within 30 days, enabling early community interventions that have cut reattendance rates by 33 to 50 percent.
How the AI Tool Works
The system examines demographic details, arrival method, triage notes, long-term conditions, and prior emergency visit history. High-risk patients are flagged for multidisciplinary teams comprising NHS staff and social care partners, who design personalised follow-up support before health issues escalate.
Operational Intelligence Lead Liam Brierley stated: "The tool allows us to predict emergency department reattendance, rather than simply providing a retrospective analysis. Our ambition is ultimately to change how we anticipate patient need, moving from reactive care to intelligent, preventative intervention."
Early Results and Safety
According to the trust, early results indicate a reduction in emergency reattendance among high-risk patients of between 33 and 50 percent, though weekly impact has varied. The AI model underwent extensive testing before deployment, with patient safety specialists designing clinical processes and data security experts ensuring patient information is accessible only to authorized staff.
Future Plans
The trust expects the AI to improve over time as it learns from more data. Plans include introducing further automation across the hospital to reduce pressure on clinical staff while enhancing patient safety and care quality. The project has been shortlisted in the 'Urgent and Emergency Care Safety Initiative of the Year' category at the 2026 HSJ Patient Safety Awards, with winners announced on September 28.
Brierley added: "This project is a strong example of how we can take advantage of new, advanced technologies like AI for the benefit of both our patients and staff. The AI tool doesn't replace clinical judgement but rather empowers clinicians with the insight they need to deliver high-quality care before a crisis occurs."
