Research shows majority of people at risk of a fall in aged care are missed by assessment

Dr Nasir Wabe from the Australian Institute of Health Innovation at Macquarie University.

Macquarie University research shows that the Peninsula Health Falls Risk Assessment Tool (PH-FRAT), used in residential aged care facilities across Australia, accurately predicted a fall within 6-months in only 33.6% of residents.

The tool is included in the Australian Commission on Safety and Quality in Health Care’s guidelines for fall prevention and is a key component of the recently expanded National Aged Care Mandatory Quality Indicator Program (QI Program).

The Macquarie University study is one of the very few studies into the predictive performance of PH-FRAT, analysing the records of nearly 6000 residents from 25 residential aged care facilities that use PH-FRAT to identify people at the highest risk of falling. In order to calculate the level of risk, the tool is used to identify whether major risk factors are present, being: recent falls, medications, psychological status and cognitive status. A value is then assigned to identify how at risk the person is of falling and this may guide care plans for fall prevention. 

The research was led by Dr Nasir Wabe from the Australian Institute of Health Innovation at Macquarie University. 

This study is the first to assess the effectiveness of this widely used tool and points to ways it can be improved to provide better access to fall prevention strategies for vulnerable people, Dr Wabe said. 

When the cut-off score at which people were assessed to be at higher risk of a fall was lowered during the research, the reliability of predicting a fall increased from 33.6% to 74%. 

“For residential aged care facilities already using the tool, lowering the cut-off score at which a person is deemed to be at higher risk, is a change that can be implemented easily and will immediately improve the safety of residents,” Dr Wabe said. 

“Further improvements will be made when electronic systems, such as those being developed at Macquarie University, can analyse routinely collected data about a resident and use it to predict their risk in real-time, rather than relying on what could be a one-off and potentially out of date assessment,” Dr Wabe said. 

The research report can be found here


Please enter your comment!
Please enter your name here