By Laura Amin, Partner and Dr Ben Bray, Partner and Evidence Generation Lead at LCP
Laura is an actuary and partner at LCP, and Ben is a medical doctor and partner in the Health Analytics department at LCP.
Harnessing the power of AI could be the key to addressing some of these pressures, especially as we build a healthcare system designed to cope with the future pressures of a growing and older society. But first, we must consider if and how AI is already being used within the NHS, as well as the current challenges and where it could make the biggest differences.
How is AI already being used within the NHS?
When someone has a suspected stroke, it is important that they receive a brain scan as quickly as possible to confirm the diagnosis and guide the correct treatment. AI is already being used in the NHS in stroke diagnosis, with various AI-powered software solutions available that help healthcare professionals interpret the images from the brain scans quickly and accurately – helping patients to be treated more quickly.
Does AI have the potential to address NHS pressures?
The government clearly thinks that AI has the potential to alleviate some of these pressures, with NHS Trusts able to bid for cash from a £21 million pot to fund promising AI projects.
Currently, any area where diagnoses rely on images (e.g. radiology and pathology) could potentially harness the power of AI. Image interpretation and generation are two of the areas where AI has advanced most quickly in recent years, and the vast majority of the commercially available applications for AI in healthcare currently are for medical imaging software.
For the more than 600,000 chest X-rays that are done every month in England, it is hoped AI tools can support doctors to diagnose lung cancer earlier, improving patients’ chances of successful treatment.
What are the challenges?
Despite the recent advances in AI, some challenges still remain in making this part of the everyday patient diagnosis and treatment. Developing AI diagnostic tools which are consistently accurate across different settings and populations is difficult, and testing and evaluation must be carried out to demonstrate that these tools are effective and safe.
The quality and volume of data required to train new models are also likely to be a big barrier to using AI in diagnostics. Although the NHS collects a huge amount of data, in practice, this is often very challenging to use for research or developing new AI systems. For medical imaging AI, high-quality labels (i.e. accurate and detailed descriptions of the important findings shown in each image) are needed to develop new AI systems, which can be very challenging and labour-intensive to generate.
One area where AI utilisation in health systems has perhaps not been explored as much to date is in prevention. As our LCP Health Analytics study with the Institute for Public Policy Research showed, there are multi-billion rewards on offer for shifting our health system to a more preventative system versus the sickness system we have today.
Where can AI make the biggest difference to healthcare?
In our view, the most significant opportunity for healthcare-related AI lies in automating logistics - appointment booking and management, hospital bed allocation and resource management, and other aspects of the unseen work of healthcare.
Tools which can automate administrative and operational tasks can free up time for more patient care and tackling current backlogs, with NHS waiting lists now at a record high of 7.6 million people and showing no sign yet of going down. Letting the AI machines do what they do best, and the human healthcare professionals do what they do best, is likely to be the best bet on using AI to save the NHS.
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