A crisis of overcrowding and growing demand is facing the UK healthcare industry, in particular the NHS.
Overburdened by a disproportionately-growing number of patients aged 85 years or older; the cost per capita of treating patients can be expected to rise even further beyond what current facilities, staff and funding are capable of managing.
In order to overcome these challenges, we need to look beyond our outdated care practices and capitalise on the opportunities offered to us by AI and robotics.
This field offers incredible possibilities to not only improve how patients are treated, but also to reduce the burden put on an already-over-stretched workforce who are often unable to provide the care that patients need.
Through better data gathering and analysis, AI-assisted diagnosis, sensor and AI monitoring technology, the deployment of robotic assistance devices and machine-learning mental health tracking and management software, the NHS stands to revolutionise the way we treat people in the UK and provide desperately-needed relief to a system buckling under the weight of growing demand.
According to data from the Institute of Fiscal Studies (IFS), the cost of treating an 80-year-old is as much as four times as expensive as treating a 30-year-old.
By using robotics to increase patient autonomy, AI and machine learning can offer support through adaptive conversation and even diagnostics
As the need for treatment of long-term, chronic diseases and intensive care rise, the country’s healthcare infrastructure will become even more strained than it already is, compounding year on year as the problem goes unaddressed.
Recently, Health Secretary, Matt Hancock, expressed the view that investing in emerging technologies such as AI and robotics will be crucial to creating financial savings and lessening the strain on the NHS.
Precise treatment
In an overburdened, understaffed system, the risk of misdiagnosis and oversight invariably increases and current diagnostic methods can lead to mistakes.
By introducing AI and data analysis into the process, diagnoses for things such as cancer are not only up to 10 times faster, but also given with increased accuracy, reducing the need for painful biopsies and the unnecessary emotional trauma.
This field offers incredible possibilities to not only improve how patients are treated, but also to reduce the burden put on an already-over-stretched workforce who are often unable to provide the care that patients need
Furthermore, the deployment of AI and improved sensors in treatment facilities better enables caregivers to monitor patient health and safety over time, with a reduced risk of error or overlooked problems.
These technologies could be used to diagnose symptoms as they are occurring, to predict an event, or to alert staff if there is a fall or other injury.
These systems working in unison add up to a treatment process which is altogether safer and more precise.
Improving independence
Increased patient independence and mental health support are vital to their recovery and often harmoniously impact each other.
By using robotics to increase patient autonomy, AI and machine learning can offer support through adaptive conversation and even diagnostics.
Our pro-active investment and implementation of these developing technologies will be a signpost of how serious we as a nation are about tackling our flagging healthcare infrastructure and finally bringing it into the future, for the betterment of all involved
These tools further enhance the patient treatment experience while reducing the need for constant monitoring by hospital staff.
While the field of AI and robotic technologies is nothing new, it is only recently that they have become viable solutions for unlocking better-quality, affordable healthcare solutions for both patients and staff.
Our pro-active investment and implementation of these developing technologies will be a signpost of how serious we as a nation are about tackling our flagging healthcare infrastructure and finally bringing it into the future, for the betterment of all involved.
