U of A researchers test AI to measure risk of prescription opioids

With this tool, physicians could predict the impacts of a prescription opioid on patients and save them from unnecessary emergency department visits or even death within 30 days.

Researchers in Alberta are experimenting with artificial intelligence to measure the risks of prescription opioids amid the ongoing drug overdose crisis across Canada.

While doctors have a set protocol to identify patients at risk of opioid addiction, Dr. Dean Eurich said machine learning “could do a better job” of pinning down who is most susceptible.

The AI-assisted system could provide an additional “level of comfort to clinicians, (knowing) there are also other supports they can use to help (in) making sure the patient is getting the right drug at the right time,” said Eurich, program director for the clinical epidemiology program at the University of Alberta.

With this tool, physicians could predict the impacts of a prescription opioid on patients and save them from unnecessary emergency department visits or even death within 30 days of starting the medication.

Eurich was lead investigator on research published in December with JAMA Network, which analyzed medical data of more than 850,000 Albertans anonymously and predicted the best outcomes for the patients.

The data sets were mainly provided by Alberta Health.

Dr. Fizza Gliani of the College of Physicians and Surgeons of Alberta said machine learning could be an effective way to reduce hospitalizations and morbidity for patients once integrated in the health system.

“The model (could) predict risks of hospitalization,” said Gilani, who is the program manager of prescribing, analytics and the tracked prescription program at the college.