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Following the clues (and the data) to prevent falls

Data gleaned from TEC has to be put into context, interpreted and given meaning, says Gerry Hodgson from Cascade3d, to prevent falls in frailty and ensure interventions take place.

We all know that falls are a huge issue. They cost the NHS more than £2bn a year and it is estimated that half of people aged 80+ fall at least once a year. Furthermore, after a hip fracture around 50% of older people can no longer live independently.*

There are many measures such as exercise and mobility programmes that reduce the risk of falling but it is easy to miss the small clues that could help families and carers to identify when a fall is more likely.

CASE STUDIES:

Not eating and drinking regularly can cause fatigue in the short term and confusion, muscle weakness and a lack of co-ordination in the longer term, all of which increase the risk:

  • One elderly lady living with dementia had fallen at home three times and been admitted to hospital each time. Data from door sensors in her home showed that she had stopped going to the local community centre to have a hot meal. Identifying this change in behaviour (which she hadn’t told her carers about) enabled the team to intervene and there have been no falls since.
  • An elderly gentleman had stopped cooking regular meals and was just having hot drinks and instant soups (shown by use of the kettle instead of the fridge and hob). Data from his medicine box also indicated he was not taking his tablets regularly. He was losing weight and had become unsteady on his feet – the family were quickly able to identify the cause and to step in.

It isn’t only elderly people who are affected. Sedentary patients are at greater risk of falling and of sustaining a more severe injury due to muscle weakness and poor balance.

  • A gentleman living with a brain injury after a motorbike accident had poor reasoning and memory and little understanding of his needs. He refused to wear a pendant or tilt alarm and, despite having some mobility, it was becoming increasingly difficult to keep him safe without live-in support.

A bespoke monitoring system was built around him. The data from discreet pressure sensors showed that he was spending many hours in his wheelchair (instead of moving to a more supportive chair) which was putting him at higher risk of falling. The care team was then able to provide a much more secure wheelchair to help keep him safe.

In all these cases the data gave clues about what was happening when the system users either weren’t able to articulate the issues or were unaware of their significance. But data by itself isn’t enough to prevent falls. The door sensors in the home of the lady living with dementia only showed what times the door was being opened. The monitors on the kettle, fridge and hob only indicated what times the appliances were being used and how long for. It wasn’t until the data was put into context, interpreted and given meaning that interventions could take place.

That’s why actionable analytics is the key. Taking accurate, immediate and appropriate data and combining it with insight (both human and technological) enables us to make intelligent decisions and to keep the people we care for safe and secure at home for as long as possible.

Cascade3d Connected Care is a powerful analytics platform connecting elderly and vulnerable people to their families, clinicians, caregivers and call centres - providing cost effective, personal and enhanced quality of care. It uses the latest IoT technology to create a smarter safer world for those who need it most.

*Figures taken from the 2017 AHSN North East and North Cumbria report.



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