A Scottish research project has identified that improved data analysis could help with the prediction and prevention of falls—a leading cause of injury and death in the elderly.

Injuries from falls are a major cause of hospital admission and death for those aged 75 and over, and contribute to more than four thousand hospital admissions in Glasgow alone each year. Meanwhile, the fear of falling itself can result in both inactivity and increased risk of falls in older people, as well as reduced social interactions leading to isolation or loneliness.

The project from the University of Strathclyde and Glasgow City Health and Social Care Partnership (HSCP) has set out to look at how the large amount of data collected across the health and social care system can be used to identify or predict people at risk of falling and hospitalisation, and how the impact of falls can be reduced and prevented.

Data from more than 28,000 Glasgow residents who use telecare devices has been analysed by the researchers. Telecare devices gather and communicate data to health and social care providers using both “passive” technology, such as sensors and wearable devices, and “active” technology, where data is purposefully entered into the device by the user.

Marilyn Lennon, who’s professor of digital health and care at the University of Strathclyde, noted: “Telecare devices, systems and users produce vast amounts of data, and we needed to carry out detailed analysis to work out how it can be categorised and used in very pragmatic ways to predict people who are at risk of falling, so that ultimately, preventative steps can be put in place.”

The team has so far recommended that better data analysis could ultimately help predict peoples’ needs and deliver a more proactive service. Integrating systems could also improve the reliability of data and make it easier to update and access, while standardising data organisation and automating tasks could reduce the manual workload.

Artificial intelligence (AI) can also be utilised to help build more personalised, predictive, and proactive models for allocating health resources more efficiently and effectively, and at the right time and right place.

Further, they’ve encouraged a less risk averse approach to data sharing across organisations, to identify and anticipate who is at risk of falling.

“It is not straightforward to share data but when we do, we get great results,” added professor Lennon. “We have the opportunity to share innovative machine learning for the greater good.

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The initiative is part of the strategic partnership between the University of Strathclyde and the Glasgow City HSCP, working together with Tunstall as telecare providers and the Digital Health and Care Innovation Centre.

Lucille Whitehead, strategic development director at Tunstall, also commented: “These early insights on the data collected from Glasgow City HSCP, and the early analysis by the University of Strathclyde, may help to target care where and when it’s needed most.”