Predictive Fall Analytics

Ubisense in partnership with InTec Health can help care practitioners predict the likelihood of a fall in vulnerable people, triggering preventative care and avoiding unnecessary, costly and distressing outcomes associated after a fall.

The Dimension4 Ultra-Wideband (UWB) sensor system offers high fidelity, real-time location tracking of patient movement, hundreds of times per second. Healthcare providers can use this stream of precise movement data with InTec Health advanced algorithms to detect increased ‘randomness’ or small changes in movement of patients to indicate an increased propensity to fall before it happens.

  • Identify the likelihood of falls in vulnerable or recovering patients before they happen
  • Tests show fall prediction can be made up to 7 days in advance and with 85% accuracy
  • Implement preventative care and more accurately track progression or recovery from illness
  • Keep patients safe and better utilise the clinical and care staff time and resources

Contact Tracing

Ubisense Contact Tracing keeps people safe and operations open. Adapted to specifically support contact-tracing and social distancing in communities of vulnerable populations, InTec Health iDistance uses Ubisense Dimension4 sensors and the SmartSpace Contact Tracing platform to help limit the spread of infection, protect facilities from unnecessary closures and direct care to patients most in need.

Virtual Intelligent Routines

Healthcare providers are under extreme pressure to see more patients, perform more procedures and improve experiences while reducing the total cost of healthcare provision.

Virtual Intelligent Routines (VIR) from InTec Health are learnable routines that capture knowledge, experiences, and data that reside in isolation, diminishing workforce populations, or areas that require a higher degree of accountability.

VIR collects real-time location data to help streamline repetitive routines, aid compliance and provide powerful reporting and alerts to those who need it the most.

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