Despite these attempts, medication mistakes still occur with alarming regularity.
“I had read some studies that said basically 90% of anesthesiologists admit to having a medication error at some point in their career,” said Dr. Kelly Michaelsen, Wiederspan’s colleague at UW Medicine and an assistant professor of anesthesiology and pain medicine at the University of Washington. She started to wonder whether emerging technologies could help.
As both a medical professional and a trained engineer, it struck her that spotting an error about to be made, and alerting the anesthesiologists in real time, should be within the capabilities of AI. “I was like, ‘This seems like something that shouldn’t be too hard for AI to do,’” she said. “Ninety-nine percent of the medications we use are these same 10-20 drugs, and so my idea was that we could train an AI to recognize them and act as a second set of eyes.”
The study
Michaelsen focused on vial swap errors, which account for around 20% of all medication mistakes.
All injectable drugs come in labeled vials, which are then transferred to a labeled syringe on a medication cart in the operating room. But in some cases, someone selects the wrong vial, or the syringe is labeled incorrectly, and the patient is injected with the wrong drug.
In one particularly notorious vial swap error, a 75-year-old woman being treated at Vanderbilt University Medical Center in Tennessee was injected with a fatal dose of the paralyzing drug vecuronium instead of the sedative Versed, resulting in her death and a subsequent high-profile criminal trial.
Michaelsen thought such tragedies could be prevented through “smart eyewear” — adding an AI-powered wearable camera to the protective eyeglasses worn by all staff during operations. Working with her colleagues in the University of Washington computer science department, she designed a system that can scan the immediate environment for syringe and vial labels, read them and detect whether they match up.
“It zooms in on the label and detects, say, propofol inside the syringe, but ondansetron inside the vial, and so it produces a warning,” she said. “Or the two labels are the same, so that’s all good, move on with your day.”
Building the device took Michaelsen and her team more than three years, half of which was spent getting approval to use prerecorded video streams of anesthesiologists correctly preparing medications inside the operating room. Once given the green light, she was able to train the AI on this data, along with additional footage — this time in a lab setting — of mistakes being made.
“There’s lots of issues with alarm fatigue in the operating room, so we had to make sure it works very well, it can do a near perfect job of detecting errors, and so [if used for real] it wouldn’t be giving false alarms,” she said. “For obvious ethical reasons, we couldn’t be making mistakes on purpose with patients involved, so we did that in a simulated operating room.”
In a study published late last year, Michaelsen reported that the device detected vial swap errors with 99.6% accuracy. All that’s left is to decide the best way for warning messages to be relayed and it could be ready for real-world use, pending Food and Drug Administration clearance. The study was not funded by AI tech companies.
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