NHS and AI: Reducing Hospital Overload in the UK

The NHS (National Health Service) in England is facing increasing pressure, with a waiting list of 7.25 million patients. This situation is exacerbated by impending doctors' strikes and a growing staff shortage. To alleviate this burden, new policies are being implemented to shift care from hospitals to the community, despite warnings from general practitioners about rising workloads and potential risks to patients.

In this challenging context, virtual care supported by artificial intelligence (AI) emerges as a promising solution to manage the growing number of patients outside hospital settings. This technology is being implemented to assist in three key areas: waiting lists, hospital capacity, and corridor care.

Michael Macdonnell, Deputy CEO at Doccla, a European virtual care provider, has firsthand experience within the NHS. He stated, “The NHS is facing unprecedented pressure, with a waiting list of 7.2 million patients, patients waiting in ambulances and in corridors, without the increasing budgets of previous years.”

AI is at the heart of the operation of large-scale virtual care. Machine learning models are used to identify patients at risk of deterioration by combining NHS datasets with proprietary data. Continuous data from clinical-grade wearable devices, such as oxygen saturation, blood pressure, and ECG, are analyzed to detect early warning signs. This enables clinical teams to intervene earlier and safely manage much larger groups of patients than would otherwise be possible.

Doccla and the Impact of Virtual Care

Doccla is a company that provides remote patient monitoring and virtual care services to NHS trusts. Doccla's model is designed to support earlier discharge and prevent avoidable admissions, particularly for those with chronic illnesses.

There is already evidence of Doccla's effectiveness. The NHS has reported a 61% reduction in hospital days, an 89% reduction in GP appointments, and a 39% decrease in non-elective admissions. Not only has this AI-powered software improved efficiency, but it also saves approximately £450 per day compared to the cost of a hospital bed, according to the company. The figures suggest that for every £1 spent on this technology, the NHS saves about £3 compared to non-technological models.

Michael Macdonnell stated, “At Doccla, we use machine learning to identify patients at risk of deterioration before they reach a crisis point. Continuous data from clinical-grade wearable devices like oxygen saturation, blood pressure, and ECGs are analyzed alongside medical records to detect early warning signs.”

The information gathered allows clinical teams to intervene earlier and manage larger workloads compared to more traditional systems. AI could also streamline administrative tasks through the use of large language models (LLMs) to simplify clinical notes and present complex information to patients in a more accessible manner. AI is not intended to replace clinicians, but rather to make them more efficient, which should reassure healthcare professionals.

Clinical trust in this technology remains low and can only grow through transparency and additional evidence of success. Predictive models must also deliver accurate and equitable outcomes across diverse patient groups before being deployed at scale in real clinical environments.

As the NHS in the UK strives to shift more care from hospitals to the community, with its “10-Year Health Plan for England: Ready for the Future,” AI is at the forefront of this transformation. The future of AI-based healthcare is expected to enable patients to remain more independent and receive the care they need in familiar environments.