Increased NHS funding is not a blank cheque for success Consistently ranked as one of the most important issues facing the country, the state of the UK's healthcare system has persistently fuelled public dissatisfaction with government actions at both national and local levels.

Even with recent increases in funding, rising wait times have yet to be curbed, adding to public unrest. Labour's aim to make the NHS more efficient through technology should be commended, but a blank cheque does not equate to improved performance – the devil will be in the detail.

Chancellor Rachel Reeves used the Autumn Budget, Labour's first in Government after 14 years in Opposition, as an opportunity to establish Labour's commitment to the NHS, announcing a £22bn increase in NHS funding, including capital allocations.

The increase in health funding included a £2bn allocation specifically for digital and technology to support the analogue-to-digital transition. This is welcome news, but recent trends indicate that government funding does not directly correlate to improved health system performance, so it is important to scrutinise the specifics of funding allocation within the system.

A digitised healthcare system that puts innovation and technology at its core must lie at the heart of the Government's plans and funding.

Artificial intelligence (AI) is driving some of the most significant transformations in the healthcare industry. While doctors won't be replaced, and AI will often remain invisible to patients, the global AI in healthcare market is projected to grow substantially, from $15.1 billion in 2022 to $187.95 billion by 2030.

One of the greatest examples of this growth and its potential transformative opportunities lies in data management. Every day, 2.5 quintillion bytes of data are generated globally in the healthcare industry. Medical records, histories, treatments, and new studies multiply across different systems. Organisations will be able to accelerate processes by up to 50% with AI-driven analytics, allowing them to be more efficient.

The integration of AI within hospitals is still very limited. It is one thing to assist with medical diagnosis related to radiology or pathology, but quite another for AI tools to become part of the daily care processes of the care team. Such integration may be several years away due to multimodal challenges, the need to regulate and validate algorithms and, not least, assessing risk and ensuring responsible use of such tools.

In the short term, these tools are already having a profound impact on operational efficiencies. These include automating the management of supplies, creating dynamic diagnosis-related group models with predictive data insights, and the proliferation of uses for already validated cases in computer vision (e.g. radiology, pathology).

It's not about incorporating AI for the sake of it but using it to develop solutions to challenges that previously felt unfixable. There are plenty of success stories but, to put it in perspective, solutions that reduce hospital stays and improve operational efficiency will save $150 billion annually by 2026 for the healthcare industry in the US.

Putting innovation and technology at the heart of the British healthcare system won't just improve performance metrics; it will optimise efficiencies and cut costs, critical for a government looking to make the most out of limited fiscal headroom.