NHS Trusts keen on AI but are limited by poor access to data
Speech recognition is the most popular form of AI adoption, and clinical care is getting the most benefit
Despite widespread enthusiasm to adopt artificial intelligence (AI) systems across the health service, NHS organisations are being held back by poor access to the data needed to make the most of these new technologies.
Little over half of NHS Trusts, 52%, are well underway in implementing AI projects, while 16% of Trusts plan on deploying AI within the next two years.
Moreover, 75% of organisations questioned have appointed a leader for AI within their Trust, according to Freedom of Information (FOI) data obtained by NetApp.
However, just 33% of surveyed Trusts had full and complete access to the data required for successful deployments, suggesting there’s a huge barrier to the UK’s healthcare system in reaping the maximal benefits of AI technology.
Clinical data within the NHS has always been a thorn in the side of digital transformation, with patient information stored in siloed systems that barely talk with one another.
NHS Digital has embarked on changing the status quo, however, with efforts launched earlier this year to introduce a standardisation of data collected from patients.
Trusts, according to the research, are also taking a considered approach to the ethical use of patient data, with 59% of organisations having reviewed or planning to review data governance policies.
Meanwhile, a handful of Trusts, 39%, have not invested in AI projects financially at all, also suggesting the approach to AI adoption across the NHS is somewhat disjointed.
In all, the technology is being used to alleviate the pressures placed on healthcare workers, to improve the quality of care and fast-track medicine delivery to patients.
Clinical care is the area in which AI is predominately deployed by Trusts, 20%, followed by 16% of Trusts mainly using the technology for patient diagnosis. Just 5% of Trusts are mainly using AI in pharmacy, and 3% for back-office functions.
“At St Thomas’ MedTech Hub, we are at the forefront of utilising data and artificial intelligence to inform clinical decisions,” said the head of the School of Biomedical Engineering & Imaging Sciences at King’s College London (KCL), Sebastien Ourselin. “We are working on end-to-end solutions that embed AI into the clinical pathway, from early diagnostics to therapeutic interventions.”
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Although 39% of NHS Trusts have not invested in AI to date, the vast majority have, with 41% investing up to £100,000 on AI platforms, and 10% of Trusts investing up to £500,000. One Trust has even invested in the region between £500 and £1 million on the technology.
KCL poses a great example of an area in which AI has been deployed to support front-line clinical and diagnostic functions. The university has been working with Nvidia, for instance, to build an AI platform that can allow specialists to train computers to automate radiology workflows.
The implication is that machines would have the potential to classify stroke and neurological impairments, as well as recommend the best treatments for individual patients.
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