Artificial Intelligence

AI helping ease the UK’s NHS burden

Published byAIDaily Editorial Team
3 min read
Original source author: David Thomas

The words “pressure” and “NHS” go hand in hand in the UK and unfortunately there is no sign of a reduction in the strain the institution suffers any time soon. As NHS England continues the struggle to reduce its 7.25 million waiting list, new policies are being introduced to move care away from hospitals and […] The post AI helping ease the UK’s NHS burden appeared first on AI News .

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The words “pressure” and “NHS” go hand in hand in the UK and unfortunately there is no sign of a reduction in the strain the institution suffers any time soon. As NHS England continues the struggle to reduce its 7.25 million waiting list, new policies are being introduced to move care away from hospitals and into the community, despite GPs’ warning of increased workloads and risk to patients. Add in looming doctor strikes and deepening staff shortages and the backdrop of the health service does not look rosy. In a bid to relieve some of the burden, AI-enabled virtual care is emerging as a tool to manage the growing number of patients outside hospital settings. The technology is being implemented to help around three important areas – waiting lists, hospital capacity, and corridor care. Michael Macdonnell, Deputy CEO at European virtual care provider Doccla , who has first-hand experience working in the NHS, commented, “The NHS is facing unprecedented pressure, with a 7.2 million patient waiting list, patients waiting in ambulances and in corridors, without the growing budgets of previous years.” “AI underpins how virtual care works at scale. Machine learning models are used to identify patients at risk of deterioration by combining NHS and proprietary datasets, while continuous data from clinical-grade wearables (e.g.oxygen saturation, blood pressure, ECG) is analysed to detect early warning signs. The lets clinical teams intervene sooner and safely manage far larger patient groups than would otherwise be possible.” Doccla and virtual care Doccla is a company providing remote patient monitoring and virtual wards to NHS trusts. The Doccla model is “designed both to support earlier discharge and to prevent avoidable admissions, particularly for those with long-term conditions.” There is already evidence for Doccla’s effectiveness, with the NHS seeing a 61% reduction in bed days, an 89% reduction in GP appointments, and a 39% drop in non-elective admissions. Not only has this AI-driven software improved efficiency, it is also reportedly saving the NHS approximately £450 a day compared with the cost of a hospital bed, the company says. Figures suggest that for every £1 spent on such technology, the NHS saves an estimated £3 compared with non-tech models. Mr Macdonnell said, “At Doccla, we use machine learning to identify patients at risk of deterioration before they reach crisis point. Continuous data from clinical-grade wearables like oxygen saturation, blood pressure and ECGs, are analysed with medical records to detect early warning signs.” The insights are allowing clinical teams to intervene sooner and manage larger caseloads compared with more traditional systems. AI may also be having a positive effect on clinician’s mental states, helping reduce administrative burden. For instance, large language models (LLMs) are being used to streamline clinical notes and present complex information to patients in a more accessible way. AI is not expected to replace clinicians, only make them more effective, so clinicians reading this can breathe a sigh of relief. Clinical trust in this technology remains low and this will only grow through transparency and further evidence of success. Predictive models must also deliver accurate and fair outcomes in diverse patient groups before being deployed at scale in real-world clinical settings. As the UK’s NHS works to move more care away from hospitals and into the community, with its “ Fit for the Future: 10 Year Health Plan for England ,” AI stands at the forefront of this transformation. The future of AI healthcare is set to allow patients to remain more independent and receive the care they need in familiar surroundings. (Image source: Pixabay under licence .) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media . Explore other upcoming enterprise technology events and webinars here . The post AI helping ease the UK’s NHS burden appeared first on AI News .

Key takeaways

  • AI can optimize patient and resource management in SUS, similar to what is being done in the NHS.
  • Digital health solutions with AI demonstrate effectiveness and savings, a crucial factor for health funding in Brazil.
  • The NHS experience can serve as a warning about the need for a proactive approach to adopting health technologies.

Editorial analysis

The application of AI in the UK's NHS highlights a growing shift towards health digitalization, which could serve as a model for Brazil. The Unified Health System (SUS) faces similar challenges, such as waiting lists and resource scarcity. The NHS's experience with remote monitoring technology and virtual care could inspire initiatives in Brazil, where the adoption of AI-based solutions may help optimize patient management and resource allocation. Additionally, the integration of clinical data and the use of wearables are trends that Brazilian startups, particularly those focused on digital health, could explore.

Another important point is the demonstrated effectiveness of AI solutions, which not only improve efficiency but also generate significant savings. This is crucial in a scenario where health funding is often limited. For Brazil, where public health faces a constrained budget, adopting technologies that demonstrate a return on investment could be a viable path to improving care quality without increasing costs.

Finally, the NHS situation serves as a warning about the need for a proactive approach to digital health. Brazil should observe how AI implementation can be scalable and what challenges are faced, such as resistance from healthcare professionals and the need for adequate training. The future of health in Brazil may very well depend on the ability to effectively and ethically integrate these technologies, ensuring that innovation does not compromise patient care quality.

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