{"id":52137,"date":"2024-10-22T13:07:20","date_gmt":"2024-10-22T12:07:20","guid":{"rendered":"https:\/\/www.innovationnewsnetwork.com\/?p=52137"},"modified":"2024-10-22T13:07:20","modified_gmt":"2024-10-22T12:07:20","slug":"wearable-cameras-use-ai-to-detect-medication-errors","status":"publish","type":"post","link":"https:\/\/www.innovationnewsnetwork.com\/wearable-cameras-use-ai-to-detect-medication-errors\/52137\/","title":{"rendered":"Wearable cameras use AI to detect medication errors"},"content":{"rendered":"<h2>A team of researchers has developed the first wearable camera system that, with the help of artificial intelligence, detects potential errors in medication delivery.<\/h2>\n<p>In a test published today, the wearable cameras recognised and identified\u00a0medication errors with high proficiency in busy clinical settings.<\/p>\n<p>The AI achieved 99.6% sensitivity and 98.8% specificity in detecting vial-swap errors.<\/p>\n<p>The system could become a critical safeguard, especially in operating rooms, intensive-care units and emergency medicine settings.<\/p>\n<p>Dr Kelly Michaelsen, assistant professor of anesthesiology and pain medicine at the University of Washington School of Medicine, explained: \u201cThe thought of being able to help patients in real-time or to prevent a medication error before it happens is very powerful.<\/p>\n<p>\u201cIn 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.\u201d<\/p>\n<h3>Medication errors are on the rise<\/h3>\n<p>Medication errors are the <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/23818992\/\" target=\"_blank\" rel=\"noopener\">most frequently reported critical incidents in anaesthesia<\/a> and the most common cause of serious medical errors in intensive care.<\/p>\n<p>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.<\/p>\n<p>Syringe and vial-swap errors most often occur during intravenous injections, during which a clinician must transfer the medication from the vial to the\u00a0syringe and then to the patient. About 20% of mistakes are substitution errors in which the wrong vial is selected, or a syringe is mislabelled.<\/p>\n<p>Another 20% of medication errors occur when the drug is labelled correctly but administered in error.<\/p>\n<p>Safety measures, such as a barcode system that quickly reads and confirms a vial\u2019s contents, are in place to guard against such accidents.<\/p>\n<p>However, practitioners might sometimes forget this check during high-stress situations because it is an extra step in their workflow.<\/p>\n<h3>Wearable cameras issue warnings to prevent errors<\/h3>\n<p>The researchers aimed to build a deep-learning model sophisticated enough to <a href=\"https:\/\/www.nature.com\/articles\/s41746-024-01295-2\" target=\"_blank\" rel=\"noopener\">recognise the contents of cylindrical vials and syringes<\/a> and appropriately warn the patient before the medication enters the patient, paired with a GoPro camera.<\/p>\n<p>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.<\/p>\n<p>These video snippets were later logged, and the contents of the syringes and vials were\u00a0denoted to train the wearable cameras to recognise the contents and containers.<\/p>\n<p>The video system does not directly read the wording on each vial\u00a0but 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.<\/p>\n<p>\u201cIt was particularly challenging because the person in the OR is holding a syringe and a vial, and you don\u2019t see either of those objects completely. Some letters (on the syringe and vial) are covered by the hands,\u201d said Shyam Gollakota, professor at the UW&#8217;s Paul G. Allen School of Computer Science &amp; Engineering.<\/p>\n<h3>AI\u2019s potential to improve safety and efficiency in healthcare<\/h3>\n<p>Further, the computational model had to be trained to focus on medications in the frame&#8217;s foreground and ignore vials and syringes in the background.<\/p>\n<p>\u201cAI 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,\u201d Gollakota said.<\/p>\n<p>This work shows that <a href=\"https:\/\/www.innovationnewsnetwork.com\/columbia-nursing-explores-views-on-using-ai-for-mental-health-care\/51561\/\">AI and deep learning have the potential to improve safety and efficiency in several healthcare practices<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>New wearable cameras can recognise and identify medication errors with high proficiency in busy clinical settings.<\/p>\n","protected":false},"author":22,"featured_media":52138,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[10551],"tags":[570,849,24183],"acf":[],"yoast_head":"\n<title>Wearable cameras use AI to detect medication errors<\/title>\n<meta name=\"description\" content=\"New wearable cameras can recognise and identify medication errors with high proficiency in busy clinical settings.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.innovationnewsnetwork.com\/wearable-cameras-use-ai-to-detect-medication-errors\/52137\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" 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