The government has stated its vision for artificial intelligence use in the NHS. With a plan to inject £1.8bn into the NHS as part of It’s pledge to upgrade 20 hospitals across the country and NHSX (the department responsible for moving forward digital transformation), the government has announced that £250m will be available to establish AI labs, to allow patients and staff to benefit from the latest technology. The government also wants AI, data and innovation to “transform the prevention, early diagnosis and treatment of chronic diseases by 2030“, with the UK being “at the forefront of the use of AI and data in early diagnosis, innovation, prevention and treatment.”
It’s a massive ambition for a set of technologies that are still developing, and which use is relatively restricted in the health service today. But can the NHS make this a reality? And is 2020 the year of digital transformation for NHS?
NHS faces three major challenges: increasing demand, workforce planning and financial planning. With its focus on technology, our healthcare experts, Prof Matthew Cooke and Richard Beet, shared their views on what 2020 could hold for the NHS
Meeting increasing demand
The activity of the NHS is increasing every year. Recent work at Capgemini has demonstrated that this increase is mostly due to the activity per person rather than more people being treated. As people live longer with chronic conditions, they are using services more each year. AI can help the NHS to redesign services around the individual. For example, many patients have to attend multiple appointments because the system is designed around individual medical specialities rather than the standard disease sets that affect patients with numerous conditions.
Exploiting the potential of AI can help the NHS meet its long-term target of making up to 30 million outpatient appointments unnecessary, resulting in saving over £1 billion a year, in new expenditure averted, which can then be reinvested in front line care.
Planning for workforce
The NHS is the biggest employer in Europe and the world’s largest employer of highly skilled professionals. But over the past decade, workforce growth has not kept up with the increasing demands on the NHS.
As workforce will continue to be a challenge for the healthcare industry and NHS in the UK, predictive analysis (moving from a single spreadsheet to complex AI-supported predictions) will help bring the change and allow for detailed planning of workforce and capacity in clinics and A & Es.
AI can help the NHS plan its workforce better by building a roster to ensure no facility ever has lesser than needed or more than needed staff. Smart rostering will enable staffing to better fit demand, to free up staff time (by automation) and can help to more efficiently and accurately identify likely pinch-points where demand exceeds the service’s capacity. Additionally, AI can help identify ways to mitigate these pressures, simulate and estimate the possible knock-on impact of unplanned resource or site downtime, or workforce changes. Currently, only a few NHS organisations have adopted this level of capacity and demand planning, hoping to provide services comparable to the best of the private sector.
Increasingly the problem for the NHS is not the funding of staff but availability. Many organisations have the budget to employ staff, but there are none available. Recruitment and retention drivers are unlikely to be able to resolve these issues. Automated systems, image recognition and other aspects of AI will mean that professionals can focus on the tasks that only humans can undertake, including the caring, compassionate and empathetic elements of care.
In 2020 we should see the use of automation technology to support decision making, resulting in more effective working and enabling staff to focus on the areas where they add the most value.
AI-led diagnostics has a strong evidence base in the area of image interpretation, allowing x-rays and scans to be interpreted by a computer. These systems work alongside radiologists and continue to learn and have the advantage that accuracy does not deteriorate during the latter part of a shift. AI systems are also being trained to recognise characteristic features of different conditions and individuals responses to treatment. This offers the potential to predict a patient’s response to a specific procedure and deliver more focussed and personalised care. There has been limited progress in the adoption of such technologies in the last couple of years, despite the opportunities presented by AI.
With the government’s announcements, we hope to see some steps taken in this direction in 2020. However, we also predict the spending on AI and digital transformation to be conservative as the NHS is still in the process of defining its digital strategies aiming to offer improved experiences and outcomes, making this improvement journey slow.
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