Edinburgh Napier researchers are seeking to apply the latest Big Data techniques to a specific health problem for the elderly – frailty.
The UK has a problem. It’s getting older as Baby Boomers age.
This demographic bubble will put a significant strain on the NHS and other support services. Health care providers are preparing for the crunch.
Can data help solve the problem?
The School of Computing’s Adrian Smales thinks it can and is demonstrating this through his eFrail project, funded by the Digital Health Institute.
Care in the home
Frailty is a common multidimensional health and social care challenge associated with an increased risk of physical, cognitive and functional decline. This often results in health problems among the ageing population.
Mitigating the problems associated with frailty is an important objective. Delayed hospital discharges are a major health and social care issue costing the NHS up to £900m a year.
Part of the solution is to keep people out of hospital altogether and that means preventative care. A recent Audit Scotland report on ‘Social work in Scotland’ highlighted that prevention must be an integral part of Councils’ long-term strategies.
That’s not controversial because most people prefer to stay in their own home in retirement. The problem is how to ensure that people stay healthy at home and avoid typical accidents such as falling, all while reducing overall healthcare costs.
Tracking health data
As part of the eFrail project, Adrian is working with CM2000, a care management company, to help keep people in their own homes and at the same time reduce some of healthcare needs traditionally associated with ageing.
There are many factors associated with frailty and risk of falling, such as low grip strength, muscle mass, hydration levels, low heart rate, and heart rate variability. These can be monitored and measured at home using the latest wearable technologies, even without the supervision of medical professionals.
Through a knowledge transfer partnership, the eFrail project is attempting to identify how to use the measurement of these risk factors via wearable tracking technology to intervene early and prevent falls from occurring in the first place. The purpose of the partnership is to develop and hone predictive techniques to help identify those at risk.
Big Data and preventative health care
Analytics modelling, innovative wearable technology, and social care data has led to faster preventative support and better self-management in the home to reduce demand on care services.
This data helps to highlight potential risks to enable faster preventative support. It also empowers people to take better care of themselves which can relieve pressure on care services.
Better care at a lower cost while allowing people to stay in their own home could address the key issues posed by an aging population. Dr Smales said: “We have a long-term vision of using data to detect the early signs of illness, and apply new methods that should lead to improved care and better outcomes for all.”
eFrail is applying advanced data modelling techniques to lower the risks of remaining at home in retirement. It’s a win-win situation: happier, healthier people alongside a significant reduction in healthcare costs.