by Natasha Egan and Christopher Kelly – July 14, 2022
by Natasha Egan and Christopher Kelly – July 13, 2022
Optus media centre story on HalleyAssist® collaboration with La Trobe University and Northern Health on virtual care technologies, during and after the COVID-19 pandemic”
Three new COVID-19 projects begin – 26 August 2020
HalleyAssist® collaborates with Medibank in research project “Evaluating the sustainable, effective, and safe use of virtual care technologies, during and after the COVID-19 pandemic”
Health Research at Medibank 2021 Report – 1 July 2020 – 30 June 2021
Health Research at Medibank 2021 – See P 31
Lessons learned from remote health monitoring during COVID19 lockdown
Melbourne – 18th May 2021
HalleyAssist® technology in La Trobe Uni, Medibank, Optus funded research on mental, physical health impacts of Covid-19
Staff writers – 31 August 2020
“We’ll look at how virtual remote care technologies can be used in an effective and safe manner during and after the COVID-19 pandemic. Ideally these technologies will enable hospital patients to be discharged earlier, to safely recover at home. We’ll develop a framework for homebased virtual care, with input from clinicians and feedback from patients and families.”
The project will be run in collaboration with Northern Health, Proactive Aging and HalleyAssist.
IT Wire – News story on Grant collaboration
Matt Johnston – Aug 27 2020
Led by professors James Boyd and Ani Desai, the project will develop a framework to evaluate virtual care models, such as remotely monitoring the health of patients discharged from hospitals who might be at high risk of readmission, or long-term monitoring of patient health in residential aged care settings.
It will be run using existing technologies in partnership with Northern Health, HalleyAssist® and Proactive Aging.
IT News story
Research project, Swinburne University of Technology
The purpose of this project is to improve the ability of HalleyAssist® to detect unusual patterns of behaviour. By studying data generated by HalleyAssist® sensors during normal operation, we aim to obtain insights so we are better able to detect unusual patterns of sensor behaviour. Such patterns may indicate that the person using the solution is unwell or otherwise incapacitated. The primary activity of this phase is to evaluate the data collected from the sensors and evaluate against a number of models:
- Anomaly detection based on simple models involving single or limited sensors
- Anomaly detection based more complex models and aggregates of sensor
Data will be analysed and used to improve the design of future releases of the solution. Summaries of the data may be published in journals or presented at conferences. In such cases, only anonymous summarised data will be included.