Why AI needs to be at the heart of the NHS

The strain on the NHS is a familiar and perpetual struggle, with everything from funding issues to staffing problems and growing waiting lists mounting. In fact, the waiting list for hospital treatments rose to a record of nearly 7.5 million in May this year and cancer treatment times are currently at an all-time high. At the beginning of this year, only 54.7% of patients waited under 62 days for treatment after an urgent GP referral, compared with the target of 85%. As the severity of the situation shows no sign of easing, new strategies need to be put into place to empower practitioners to save time and provide a consistently strong standard of patient care.

Although there are significant changes that need to be made, it’s gradually being recognised that technologies are key to addressing growing pressures. For example, draft guidance recently released from NICE on the use of AI in radiotherapy marks a positive move towards more progressive healthcare. The guidance is set to help streamline the treatment planning process for certain cancers, saving both time and money while freeing clinicians to deal with other patient-facing tasks.

When early treatment can mean the difference between life and death, these innovations will prove to be critical to building the much-needed resilience for the future. With applications extending across workflows, AI can nurture positive patient outcomes, support professionals and prevent the system from reaching a breaking point.

The path to proactive care

With a shortage of staff and growing workloads, a third of NHS staff have reported that they feel burnt out and only a quarter say they have enough staff to perform their jobs to the expected standard. To prevent them from getting weighed down by manual, repetitive tasks, AI-powered tools can automate these processes to free up their time – especially for diagnostic tasks. Not only does AI accelerate diagnoses, and therefore reduce patient waiting times, but it can also support greater accuracy to help patients get the right care quickly. Staff can also be better equipped for growing workloads with access to real-time health data, enabling them to quickly spot trends, identify treatment options before health conditions potentially worsen and prevent patient re-admission. All of this leads to a more proactive and productive approach.

However, it’s important to note that AI is most powerful when its applications are diversified across healthcare operations. It’s not enough to ‘pick and mix’ solutions from the technologies on offer or focus on siloed use cases if the NHS wants to unlock lasting efficiencies. While Large Language Models (LLMs) are currently garnering the greatest attention in the healthcare space, understanding how other AI components can integrate into systems will support long-term transformation. As AI matures, a much broader range of tasks will be handled by components spanning communication, interpretation, safety, predictive and simulation capabilities.

Beyond focusing only on LLMs (where tools are trained on extensive text datasets), this means that the NHS will be able to unlock the ability to anticipate and replicate real-world outcomes, facilitate smooth interactions between AI components and users, ensure that AI is operated within ethical standards, and more. This makes groundbreaking services like personalised medicines and treatment plans possible, as well as intelligent patient profiles that combine genomics, patient histories and real-time monitoring that can completely redefine diagnostic precision.

Investing in people

The value of AI in the NHS’s future is clear, but to get on track for success these technologies should be viewed first and foremost as an investment in the patient. Developments have powerful potential for connecting patients to their care teams and supporting positive health outcomes, but there’s always a risk that innovations can alienate individuals if careful cultural considerations aren’t made. The complex ecosystem surrounding the patient, unique medical environments and ways of working are all factors that need to be understood to support engagement. As such, patients and staff should always be the driving force for solution design and implementation, and clinical oversight in operating technologies is critical to ensuring they continue to make an impact.

Likewise, a comprehensive understanding of how AI tools complement human capabilities is essential for successful adoption. When the efficiencies of AI are harmonised with the unique skills of trained healthcare professionals, staff are empowered to bring greater value to their role and patients can gain the best of both worlds. For example, beyond their medical expertise and understanding of specific medical and personal circumstances, staff can deliver the compassion and human intuition that’s integral to patient wellbeing. Amid an abundance of challenges, a human in the loop approach to AI across its diverse forms will enable the NHS to invigorate meaningful and lasting value.