Industry, university, and healthcare organisations are working together on a research project that shows how improved data analytics within telecare equipment could help with the prediction and prevention of falls.

It is a collaborative effort between University of Strathclyde, Glasgow City Health and Social Care Partnership (HSCP), telecare provider Tunstall Healthcare, and the Digital Health and Care Innovation Centre.

The key challenge was to investigate how to routinely use the vast amount of data collected across the health and social care system to identify or predict people who are at risk of falling, hospitalisation, or needing other specific telecare or social care services.

Telecare devices gather and electronically communicate information 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.

Data from more than 28,000 Glasgow residents who use a telecare system was analysed to understand what is collected on whom, about what, and what questions need to be answered about telecare users and their usage.

Other questions were what, if any, data is missing and how can it be better captured to be ‘analytics ready’, and how data access, management, and sharing for future research and innovation projects can be facilitated.

Marilyn Lennon, Professor of Digital Health and Care at the University of Strathclyde, said: “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 research concluded that better data analysis could help predict service users’ 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. The researchers further recommended steps to improve how data could be used in a more preventative way.

Additionally, the research findings encouraged a less risk-averse approach to data sharing across organisations, to identify and anticipate who is at risk of falling.

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

This work has the potential to make a difference for the better, resulting in timely and early interventions that can ultimately prevent falls, the University of Strathclyde emphasises.

Lucille Whitehead from Tunstall Healthcare added: “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.”

The research also identifies that AI can help to build more personalised, predictive, and proactive models for allocating health resources more efficiently and effectively, at the right time and in the right place.

Gavin Bashar, UK Managing Director at Tunstall Healthcare, has previously discussed what technology can be used to help prevent falls, such as wearable fall detectors and bed occupancy sensors.