Demand for urgent and same-day care is rising as the population ages and more people live with multiple long-term conditions. Yet when patients contact their GP practice needing to be seen that day, the systems used to assess them rely almost entirely on the symptoms they describe in that moment. The fuller picture – existing conditions, medications, previous admissions – sits in their medical records, invisible to the triage process.
This gap has consequences for patients. Two patients reporting the same symptoms may need very different responses, and the information that distinguishes them is held by the NHS but not used at the point it could make the most difference.
Now, a team led by Catherine Pope, Professor of Medical Sociology at the Nuffield Department of Primary Care Health Sciences, working with industry partner Visiba Group, has received £709,630 from the NIHR Invention for Innovation programme to test a system designed to fill in those details.
The project – Intelligent Navigation using AI to bust waiting times for urgent healthcare (INA) – will evaluate whether combining artificial intelligence with patients' medical histories can help clinical teams identify and prioritise the most urgent cases faster than current systems allow.
"When a patient phones their GP practice feeling unwell, it is really difficult to know, quickly, if they have a problem that needs urgent attention," said Professor Catherine Pope, who leads the project. "A patient's medical history contains useful information – about medications, other illnesses, and previous care – that can help decide if that patient needs to be seen quickly, but at the moment that information isn't part of the triage process."
Intelligent Navigation works through a text-based app, accessed via the NHS App. Patients describe their symptoms in a short text-chat, and in the background the system assembles a clinical summary that draws on relevant information from their electronic health records, incorporating a validated complexity score developed by Johns Hopkins University.
The summary goes to the clinical team, giving doctors, paramedics, pharmacists, or practice nurses all the information they need to make faster, better-informed decisions. Patients who cannot use the app will be supported by a trained receptionist who can navigate the system for them.
The system combines Visiba Triage, an established clinical decision-support tool, with the Johns Hopkins Adjusted Clinical Groups (ACG) system, which has been used across the NHS for several years to understand patient complexity.
"What makes this different is that we are testing where and how AI provides the greatest value in the triage process," said Hannah Gibson, industry partner lead at Visiba Group. "We're giving clinicians a much fuller picture of the patient before they've even spoken to them and then assessing the impact of this. In the longer term, this may open up the possibility of safely automating some clinical pathways for certain patient groups."
The project team will work with the NHS Wealden Ridge Medical Partnership, a GP partnership in East Sussex, to roll out and evaluate the system over 18 months. The evaluation will examine whether Intelligent Navigation reduces waiting times for same-day care, improves continuity of care, and offers value for money. Alongside quantitative measures, the team will study how patients, staff, and stakeholders experience the new system through surveys, interviews, and workshops.
Patient and public involvement is central to the project's design. A patients and communities panel, led by lay co-investigator Rachel Gerrard and including seven patient and public contributors, will shape the study from design through to dissemination – contributing to survey development, interview guides, implementation decisions, and the creation of public-facing materials to support wider understanding of AI in urgent care.
"Most of us have had the experience of contacting our GP surgery and trying to explain what's wrong in a few sentences," said Rachel Gerrard, lay co-investigator on the project. "We want to see if this technology works for real patients – including people who aren't comfortable with apps or who find it hard to describe their symptoms."
The project is one of six funded through the NIHR's Invention for Innovation programme as part of a wider £8.1 million investment in AI and digital technologies to reduce NHS waiting lists and waiting times. The announcement sits alongside a £20 million government commitment to roll out AI-powered chest X-ray analysis to every NHS Trust in England by 2029.
Professor Lucy Chappell, Chief Scientific Adviser to the Department of Health and Social Care and CEO of the NIHR, said: "By backing these six digital research projects, the NIHR is helping to drive the fundamental shift from an analogue to a digital health service and deliver the government's 10-year health plan. This important investment in AI and innovation will cut NHS waiting times, fast-tracking diagnoses and ensuring patients receive more accessible, efficient, and high-quality care."
The project runs from March 2026 to August 2027.