NHS: Why AI investment is just one piece of the puzzle
On 8th August 2019, Health Secretary Matt Hancock allocated an additional £250 million to be invested in an artificial intelligence (AI) laboratory that will lead to a better ability to screen for cancer, identify patients most at risk of diseases such as heart disease or dementia, build systems to detect people at risk of post-operative infections and more.
On the face of it, this announcement looks like very good news. Hancock commented: “We are on the cusp of a huge health tech revolution that could transform patient experience by making the NHS a truly predictive, preventive and personalised health and care service.” He also emphasised his determination to: “bring the benefits of technology to patients and staff, so the impact of our NHS Long Term Plan and this immediate, multimillion-pound cash injection are felt by all.”
Whilst some examples of the potential uses of AI have been given both in administrative and clinical contexts (which also include predicting patients most likely not to show for appointment and inspecting existing algorithms already used by the NHS to ensure patient confidentiality is protected), there are many other examples of applications of AI to support patient care. For example, the monitoring of Type 1 diabetic patients and of those that have heart conditions via body-worn devices can bring about transformational improvements in the individual’s health and also reductions in the cost to the taxpayer.
With higher patient expectations and increases in life expectancy, a growing number of citizens require pre-emptive advice to promote better health. Leveraging the insight trapped in the UK population’s medical data can make the difference. But with more data and complexity than ever, unlocking this insight is becoming increasingly difficult. Consequently, opportunities for preventive measures and the most efficient corrective care are not always being taken.
So how could AI in collaboration with other technologies improve the NHS?
AI with end-to-end process automation to improve preventative healthcare
As life expectancy rises and pressure on NHS resources grows, investing in ways to educate citizens with pre-emptive advice to staying healthy is growing in importance. To succeed, businesses need an easy, accurate and reliable way to create and incorporate predictive analytics and decisions into every process and interaction. Coupled with other leading technologies, such as interactive business process management, robotic automation and context-sensitive transparent guidance and decisions, AI should bring about both improvements in patient care at the same time as similar enhancements in operational efficiency.
AI with analytics to make cost savings and improve efficiencies
The use of AI and analytics can inform on trends on overtime and temporary staff working patterns, plus identification of likely increases in demand to help set the right numbers of doctors and nurses along with other infrastructure provision. Integration of data from different sources within the NHS and agencies outside of it could also inform where different supply options for beds provide the best value for money.
AI with external source data to inform policy
AI could be used to bring together disparate data sources to indicate the best options for how to improve patient care. For example, with sufficient online information to notify on likely shortfalls in public sector rehabilitation beds, private-sector resources could be taken advantage of more to take the strain of the public-sector. The NHS could analyse the particular needs of a patient based on case history, clinical guidance and rules with AI to decide on the best course of action.
Furthermore, data from social welfare can be used to inform policy at both a macro and local level, as health- care and social welfare are so inextricably linked – what happens in one domain often gives rise to demands on the other. The delivery of social welfare by local government versus centralised provision of healthcare has previously caused issues, so orchestration of inter-agency sharing of information is imperative.
The government says AI is already being developed in some hospitals, successfully predicting cancer survival rates and cutting the number of missed appointments. It is motivating that this technology is already saving lives, however, it is clear AI alone will not result in the desired outcomes. It needs to be part of the greater plan by also taking into account technologies such as digital process automation, smart use of data and a focus on patients to ensure this investment delivers on its promises.