"For me, it was an obvious example of the type of approach we can do through machine learning," said Zaane.
Every treatment recommended by the support tool was arrived at through a set of rules created using this historical evidence. "You can trust the tool's recommendations because you know how it made that decision and why."
The tool proved about 85 per cent accurate in recommending the right treatmenta success rate that was more reliable than assessments done by physical therapists, occupational therapists and exercise therapists. When patients are referred for treatments that don't result in a return to work, the machine considers it a mistake, Zaane explained.
Currently, the tool is only being used to train students. Far more testing is required before it makes it into the hands of health professionals, with potential applications to train new staff and use in remote areas. But even then the goal isn't to replace clinicians, Gross said.
"This is about the clinicians making decisions and how we can help augment those decisions," he said. "We all make mistakes and do the best we can. We have different influences on our decisions and biases, and if there are tools out there that can help these health-care providers make better decisions, let's do it."
A study detailing their findings was published in the peer-reviewed Journal of Occupational Rehabilitation.
|Contact: Bryan Alary|
University of Alberta