The UK government’s recent budget announcement delivered an unexpected and much-needed boost for the NHS, with an additional £25.7 billion allocated over the next two years. Notably, this increase spans both operational and capital expenditure, aiming to address immediate needs while also investing in the long-term sustainability of healthcare services. Recognising the essential role of technology in driving productivity, £2 billion has specifically been earmarked for technology and digital improvements, with a focus on saving staff time and enhancing efficiency.1 But how can the NHS spend this money wisely, leveraging AI to make the biggest impact?
From reducing treatment delays to supporting staff well-being, this article explores key areas where AI could help maximise the NHS’s investment.
Reducing Treatment Times
In understaffed radiology departments and overcrowded hospitals, clinical AI is already demonstrating its potential to deliver faster treatment for patients by significantly reducing treatment times. For example, in Sweden, AI shortened the time-to-treatment for patients with incidental pulmonary embolism (iPE) by 97%, flagging suspected positive CT scans for radiologists to review as a priority.2 Expedited treatment not only improves patient outcomes but also increases efficiency by reducing the likelihood of follow-up hospital visits for delayed treatment or to manage complications. With the new budget, investing in similar AI solutions could help the NHS deliver timely care to more patients.
Shortening Length of Stay
According to the Royal College of Emergency Medicine (RCEM), Emergency Department crowding is one of the biggest threats to timely care.3 AI can support efforts to reduce patient length of stay, which directly affects hospital capacity and resource allocation. For instance, in the US, AI has been used to identify suspected positive pulmonary embolism (PE) cases and activate a hospital’s PE Response Team (PERT), cutting time to intervention by nearly 50% and reducing ICU stays by around 60%.4 AI-supported protocols in hospitals like Yale New Haven and Cedars-Sinai have similarly reduced inpatient stays for patients with intracerebral haemorrhage (ICH) by 12% and 13%, respectively.5,6 Applying such AI-driven efficiencies within the NHS could ease hospital overcrowding, helping patients move through, and out of, the system more swiftly.
Saving Time and Improving Outcomes
Workforce shortages continue to create backlogs and delays, with 97% of clinical directors citing staff shortages as a primary challenge in the latest RCR census report.7 AI can help reduce clinician workloads, as seen in Swedish mammogram screenings, where AI-assisted workflows cut reading workloads by 44%, translating to 36,000 fewer reads per year for radiologists.8 Meanwhile, the same AI-supported screening protocols flagged 28% more cancers compared to traditional double reading protocols without AI, demonstrating not only efficiency gains, but significant improvement to patient outcomes.9 Allocating part of the budget to clinical AI could enable the NHS to serve more patients while maintaining quality care.
Supporting Staff Well-being
Workforce shortages are also impacting staff morale, with 100% of clinical directors expressing concern about the toll on workforce well-being, according to the RCR.7 AI can help alleviate some of this pressure. A recent study presented at the European Congress of Radiology showed that 98% of radiologists who use AI would not want to return to pre-AI workflows, with 85% reporting higher job satisfaction. In the long run, integrating AI could foster a more sustainable work environment for NHS staff, helping to reduce stress and burnout associated with heavy workloads.10
A Strategic Investment in the Future
As the NHS considers how to use its new funding, AI stands out as a valuable tool for enhancing productivity and quality of care. By investing strategically in AI, the NHS has an opportunity to make a lasting impact, creating a more resilient healthcare system that supports both patients and staff. From streamlined workflows and diagnostic precision to improved staff well-being and shorter hospital stays, AI offers the NHS multiple pathways to build a more efficient, patient-centred healthcare model.
References
- https://www.gov.uk/government/news/what-you-need-to-know-from-the-budget
- Wiklund, P., & Medson, K. (2023). Use of a Deep Learning Algorithm for Detection and Triage of Cancer-associated Incidental Pulmonary Embolism. Radiology. Artificial intelligence, 5(6), e220286. https://doi.org/10.1148/ryai.220286
- https://rcem.ac.uk/emergency-department-crowding/
- Burch et al. “Improving Patient Outcomes with an AI-Enhanced Pulmonary Embolism Response Team in a Large Healthcare Network” – PE Symposium 2024 Poster Presentation
- Davis, Melissa A et al. “Machine Learning and Improved Quality Metrics in Acute Intracranial Hemorrhage by Non-contrast Computed Tomography.” Current problems in diagnostic radiology vol. 51,4 (2022): 556-561. doi:10.1067/j.cpradiol.2020.10.007
- Petry M, Lansky C, Chodakiewitz Y, Maya M, Pressman B. “Decreased Hospital Length of Stay for ICH and PE after Adoption of an Artificial Intelligence-Augmented Radiological Worklist Triage System.” Radiol Res Pract. 2022 Aug 18;2022:2141839. doi: 10.1155/2022/2141839. PMID: 36034496; PMCID: PMC9411003.
- https://www.rcr.ac.uk/media/5befglss/rcr-census-clinical-radiology-workforce-census-2023.pdf
- Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study.” The Lancet Oncology 24.8 (2023): 936-944.
- Cancer detection in relation to type and stage in the randomised Mammography Screening with Artificial Intelligence trial (MASAI), Kristina Lang, Malmö / Sweden, European Congress of Radiology 2024
- European Congress of Radiology, 2024: Poster no. C-13783: AI in Routine use across Germany and Austria – What are the experiences of Teleradiologists? Torsten Bert Thomas Moeller; Dillingen / Germany
