Pioneering Wearable Camera uses AI to Spot Medication Errors
Researchers from the University of Washington (UW; USA) have developed the first AI-powered camera system capable of accurately identifying potential medication delivery errors.
The study — published in the npj Digital Medicine journal — found that the wearable camera, built by the Toyota Research Institute, can recognize drugs provided in medical settings, achieving high-level sensitivity and specificity in detecting vial-swap errors.
Drug- Related Errors: A Preventable Source of Patient Harm
According to studies, medical errors account for between 140,000 and 440,000 deaths in the United States each year.
More specifically, drug-delivery mistakes are a significant cause of avoidable patient safety incidents, with error rates in hospitals estimated to range between 5–10%. Up to 12% of these mistakes result in significant patient harm or death.
As a result, these high rates of avoidable incidents highlight the urgent need for systemic changes and improved protocols in current healthcare systems to ensure patient safety.
These improvements are critical not just for protecting patients from injury, but also for fostering trust in modern healthcare systems.
Study Methodology
Many drug delivery errors occur during clinical syringe and vial-substitution procedures, where physicians either choose the wrong vial to administer medication to a patient, a syringe is incorrectly labelled, or the drug is correctly labelled but is still administered incorrectly.
To prevent this, researchers have developed a deep-learning AI system capable of performing automated medication checks.
Funded by the Washington Research Foundation, the Foundation for Anesthesia Education and Research and a National Institutes of Health grant (K08GM153069), investigators gathered 4K videos from 418 medication draws captured by wearable action cameras worn by 13 anaesthesiologists while preparing surgery medication.
The AI system was trained to detect real-time vial swaps from the videos obtained, which were streamed to a local Graphic Processing Unit server. Then, if any errors were detected, the device would provide real-time visual or auditory warnings before patients were administered any incorrect medications.
Initially, the device faced difficulties in identifying drugs by their labels due to natural obstruction while staff went about their duties.
“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. And the hands are moving fast. They are doing the job. They aren’t posing for the camera”
Shyam Gollakota, co-author of the paper and professor at the UW’s Paul G. Allen School of Computer Science & Engineering.
To address this issue, the system was trained to identify medication by analysing vial dimensions, form, cap colour and label print sizes. Results showed that the device accurately recognized which medicine was being drawn, achieving 99.6% sensitivity and 98.8% specificity in detecting vial-swap errors.
What These Advancements Mean
AI has demonstrated the potential to advance various fields of modern medicine, including breast cancer detection, fetal health prediction and medical imaging accuracy, among others.
According to researchers, the innovative AI-powered wearable camera could become a game-changer in preventing harm in critical care units, surgical suites and emergency departments.
“The thought of being able to help patients in real time or to prevent a medication error before it happens is very powerful. One can hope for a 100% performance but even humans cannot achieve that. In a survey of more than 100 anesthesia providers, the majority desired the system to be more than 95% accurate, which is a goal we achieved.”
Dr. Kelly Michaelsen, assistant professor of anaesthesiology and pain medicine, University of Washington School of Medicine.
However, the current landscape for implementing AI in medicine faces considerable challenges, primarily due to ethical concerns involving patient data breaches, unreliable clinical validation of devices and a lack of clear regulations governing the use of these tools in healthcare.
For this reason, moving forward, efforts should focus on resolving these ethical dilemmas to allow this transformative technology to effectively advance and enhance modern medicine without compromising patient interests.