The Prince Charles Hospital adopts support tool for determining delirium

This world's first electronic tool is now implemented in its medical wards.
By Adam Ang
12:28 AM

Photo courtesy of Metro North Health

Medical wards at The Prince Charles Hospital, a public hospital in Queensland, have recently trialled what may be the world's first electronic tool for assessing the likely causes of delirium. 

According to Metro North Health, the electronic tool employs a series of yes/no questions that help narrow down causes to select groups such as infection, organ dysfunction, or drugs. It is built with algorithms that use "conventional principles," combined with smart design features such as adaptive testing.

WHY IT MATTERS

Delirium, one of the most underdiagnosed conditions during hospitalisation, affects around one in five patients in Australia, mostly senior folks. It apparently costs the Australian health system A$8.8 billion ($5.8 billion) every year. 

The condition is often missed in around a half of cases given its many possible causes, said Dr Eamonn Eeles, a geriatrician at TPCH and developer of the delirium assessment tool.

"Examination might be a challenge because of drowsiness where a patient may be quiet or seemingly withdrawn. Alternatively, a patient may be agitated. In these situations, a diagnosis may be missed."

Timely diagnosis cannot be overstated as delays can "quadruple" the risk of mortality, Dr Eeles stressed. 

Dr Eeles spent the last decade developing the tool together with the TPCH Internal Medicine Service and The Dementia and Neuro Mental Health Research Unit at The University of Queensland Centre for Clinical Research.

They are now working with other TCPH departments, including emergency medicine, to expand the application of the delirium assessment tool. 

THE LARGER TREND

Over the past year, at least two studies examined the application of AI models in assessing delirium. For example, a research from Johns Hopkins University revealed how AI models predicted delirium risks based on EMR data of 200,000 ICU stays. Another study leveraged a supervised deep learning model, along with an EEG device, to better assess delirium.

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