Wearable Cameras Use AI to Detect Medication Errors

In a test, the wearable cameras recognised and identified medication errors with high proficiency in busy clinical settings, the Innovation News Network reported.

The AI achieved 99.6% sensitivity and 98.8% specificity in detecting vial-swap errors.

The system could become a critical safeguard, especially in operating rooms, intensive-care units and emergency medicine settings.

Dr Kelly Michaelsen, assistant professor of anesthesiology and pain medicine at the University of Washington School of Medicine, explained: “The thought of being able to help patients in real-time or to prevent a medication error before it happens is very powerful.

“In a survey of more than 100 anaesthesia providers, the majority desired the system to be more than 95% accurate, which is a goal we achieved.”

Medication errors are the most frequently reported critical incidents in anaesthesia and the most common cause of serious medical errors in intensive care.

In the bigger picture, an estimated 5-10% of all drugs given are associated with errors. Adverse events associated with injectable medications are estimated to affect 1.2 million patients annually at a cost of $5.1bn.

Syringe and vial-swap errors most often occur during intravenous injections, during which a clinician must transfer the medication from the vial to the syringe and then to the patient. About 20% of mistakes are substitution errors in which the wrong vial is selected, or a syringe is mislabelled.

Another 20% of medication errors occur when the drug is labelled correctly but administered in error.

Safety measures, such as a barcode system that quickly reads and confirms a vial’s contents, are in place to guard against such accidents.

However, practitioners might sometimes forget this check during high-stress situations because it is an extra step in their workflow.

The researchers aimed to build a deep-learning model sophisticated enough to recognise the contents of cylindrical vials and syringes and appropriately warn the patient before the medication enters the patient, paired with a GoPro camera.

The investigators collected 4K videos of 418 drug draws by 13 anaesthesiology providers in operating rooms with varied setups and lighting. The video captured clinicians managing vials and syringes of select medications.

These video snippets were later logged, and the contents of the syringes and vials were denoted to train the wearable cameras to recognise the contents and containers.

The video system does not directly read the wording on each vial but scans for other visual cues, such as the size and shape of the vial and syringe, the colour of the vial cap, and the size of the label print.

“It was particularly challenging because the person in the OR is holding a syringe and a vial, and you don’t see either of those objects completely. Some letters (on the syringe and vial) are covered by the hands,” said Shyam Gollakota, professor at the UW’s Paul G. Allen School of Computer Science & Engineering.

Further, the computational model had to be trained to focus on medications in the frame’s foreground and ignore vials and syringes in the background.

“AI is doing all that: detecting the specific syringe that the healthcare provider is picking up and not detecting a syringe that is lying on the table,” Gollakota said.

This work shows that AI and deep learning have the potential to improve safety and efficiency in several healthcare practices.

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