Researchers at Monash University, together with a multidisciplinary team of healthcare professionals and medication safety experts, have developed the first national consensus list of medicines with a high risk of harm in Australian residential aged care.
Published in the Australasian Journal on Ageing, the study identified 15 high-risk medications or medication classes that require specialised monitoring in aged care settings.
These medications carry a significant risk of serious harm or death if misused or used in error.
Health professionals and health service organisations are required to have systems in place to identify and mitigate the risks associated with high-risk medications specific to their setting, but existing high-risk medication lists are not tailored to the unique and complex needs of aged care.
Dr Amanda Cross, lead author and a senior research fellow from the Centre for Medicine Use and Safety at the Monash Institute of Pharmaceutical Sciences, said the "OZ-ABCD" mnemonic she developed fills a "critical void" in Australia's aged care safety resources.
"We need a high-risk medication list that is tailored to the needs of Australian residential aged care," Dr Cross said.
"The OZ-ABCD tool gives busy clinicians and aged care staff a simple, memorable way to identify high-risk medications and ensure systems and monitoring are in place to keep residents safe."
The OZ-ABCD mnemonic, standing for Opioids, Z-drugs and benzodiazepines, Antipsychotics and lithium, Blood thinners, Chemotherapeutic agents and methotrexate, and Diabetes agents with high risk of hypoglycaemia, offers a clear, practical tool to support education and improve practice.
The list was developed through a rigorous national consensus process involving a multidisciplinary group of healthcare professionals and medication safety experts.
Researchers said the tool will support better education and clinical practice, ultimately reducing the incidence of preventable medication-related injuries among vulnerable older Australians.
A component of this research was funded by a Medical Research Future Fund (MRFF) Primary Care Data Infrastructure Grant (PHRDI000008) led by Professor Nadine Andrew.
Read the research paper: doi.org/10.1111
