It’s not widely known that falls are one of the greatest costs to the NHS and social care systems.

Those that are at high risk are aged 65 years+. The aged 65 and older is projected to rise by over 40% in the next 17 years to more than 16 million. Thirty percent of people aged 65 and over will fall at least once a year. For those aged 80 and over it is 50%.

A fall can lead to pain, distress, loss of confidence and lost independence. In around 5% of cases a fall leads to fracture and hospitalisation. In human terms falls and fragility fractures can result in loss of independence, injury and death.

In health service terms they are high volume and costly with 255,000 falls-related emergency hospital admissions per year for older people in England and the annual cost of hip fractures to the UK estimated at being around £2 billion.

There are many factors that impact falls and those that are associated of being at high risk for e.g. Patients suffering from Osteoporosis, these specifically are at high risk of fracture.

However, in today’s tech health market there is considerable amount of technology that can prevent a fall from happening.

The take-up of telecare/health products has grown over the years, but the development of technology to meet the demand of service user needs around falls hasn’t. A move to use AI to identify patients at risk for specific conditions is growing, however more focus is needed around falls.

If we look at patient pathways associated with falls the costs across the system, long term care associated with a fall outweighs any other long term condition. Unplanned admission for a patient who has suffered a fracture as a result of a fall can be as much as £10,000 (subject to type of fracture) and confidence in the patient and ability to recover with reablement service from social care may result in transfer to care home setting running into thousands per month.

If technology can be developed around falls to identify behaviours of patient posture, movement, and how this is changing over a period of time, matched with data collected daily/weekly (clinical signature –data that contributes to high risk of a fall e.g. Urine infection, dehydration, medication adherence) a risk profile could be drawn up.

By identifying patients at risk, a public health intervention could be carried out to check with the patient why circumstances have changed in their health which has changed their risk status. This may result in medication or Otago exercise programme (Otago has been evidence to reduce falls).

By combing wearable technology and data falls can be avoided saving the NHS Billions annually and more importantly giving older people back their impendence and freedom to live in their homes for longer safe. The system wouldn’t be so inundated for demand in social care, hospital would have reduced lengths of stay and admission. Both patient and system would benefit tremendously.

The approach that would see such a service take up at scale would need to involve patient groups, academia (in testing of accuracy of product and data- Manchester University are widely known for their falls prevention work across the EU), social and NHS. The scale would be driven by a shared risk reward with a share of the savings made across the system by avoiding the fall. This would drive take up but also drive a different model based on service cost per user like in telecare.

I’m excited by new technologies coming on board and I have started to see some eHealth companies developing in these areas.


By Andy Cachaldora Director of Business Development at Philips Healthcare and NCUB Digital Health and Care Task Force. Read the Task Force’s report ‘The Human Factor’ here.