Falls risk: over 65% of at-risk people missed by routine assessment


Wednesday, 04 May, 2022

Falls risk: over 65% of at-risk people missed by routine assessment

New research has revealed that the commonly used method of identifying people at risk of a fall is not effective.

Macquarie University researchers analysed records of nearly 6000 residents from 25 residential aged-care facilities that use the Peninsula Health Falls Risk Assessment Tool to identify people at the highest risk of falling.

The tool — included in the Australian Commission on Safety and Quality in Health Care’s guidelines for fall prevention — considers factors such as recent falls, medications, psychological status and cognitive status to ascertain the level of risk. A value is then assigned to identify how at risk the person is of falling and this may guide care plans for fall prevention.

Widely used by residential aged-care facilities across Australia, the tool accurately predicted a fall, within six months of the use of the tool, in only 33.6% of residents, which statistically demonstrates poor performance, according to the Macquarie University research.

The research, led by Dr Nasir Wabe from the Australian Institute of Health Innovation (AIHI) at Macquarie University, claims to be 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.

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,” 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resident and use it to predict their risk in real time, rather than relying on what could be a one-off and potentially out-of-date assessment.”

Image caption: Dr Nasir Wabe. Image: Supplied.

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