Medical errors are still harming patients. AI could help change that
Despite ongoing efforts to improve patient safety, it’s estimated that at least 1 in 20 patients still experience medical mistakes in the health care system. One of the most common categories of mistakes is medication errors, where for one reason or another, a patient is given either the wrong dose of a drug or the wrong drug altogether. In the US, these errors injure approximately 1.3 million people a year and result in one death each day, according to the World Health Organization.
In response, many hospitals have introduced guardrails, ranging from colour coding schemes that make it easier to differentiate between similarly named drugs, to barcode scanners that verify that the correct medicine has been given to the correct patient.
Despite these attempts, medication mistakes still occur with alarming regularity.
Dr Kelly Michaelsen, an assistant professor of anaesthesiology and pain medicine at the University of Washington wondered 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 anaesthesiologists 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.”
Michaelsen focused on vial swap errors, which account for around 20% of all medication mistakes.
All injectable drugs come in labelled vials, which are then transferred to a labelled syringe on a medication cart in the operating room. But in some cases, someone selects the wrong vial, or the syringe is labelled incorrectly, and the patient is injected with the wrong drug.
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.
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.
Source: NBC News, 25 May 2025