Rapid opioid overdose response system technologies
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Purpose of review Opioid overdose events are a time sensitive medical emergency, which is often reversible with naloxone administration if detected in time. Many countries are facing rising opioid overdose deaths and have been implementing rapid opioid overdose response Systems (ROORS). We describe how technology is increasingly being used in ROORS design, implementation and delivery. Recent findings Technology can contribute in significant ways to ROORS design, implementation, and delivery. Artificial intelligence-based modelling and simulations alongside wastewater-based epidemiology can be used to inform policy decisions around naloxone access laws and effective naloxone distribution strategies. Data linkage and machine learning projects can support service delivery organizations to mobilize and distribute community resources in support of ROORS. Digital phenotyping is an advancement in data linkage and machine learning projects, potentially leading to precision overdose responses. At the coalface, opioid overdose detection devices through fixed location or wearable sensors, improved connectivity, smartphone applications and drone-based emergency naloxone delivery all have a role in improving outcomes from opioid overdose. Data driven technologies also have an important role in empowering community responses to opioid overdose. Summary This review highlights the importance of technology applied to every aspect of ROORS. Key areas of development include the need to protect marginalized groups from algorithmic bias, a better understanding of individual overdose trajectories and new reversal agents and improved drug delivery methods.
Tay Wee Teck , J , Oteo Perez , A & Baldacchino , A M 2023 , ' Rapid opioid overdose response system technologies ' , Current Opinion in Psychiatry , vol. 36 , no. 4 , pp. 308-315 . https://doi.org/10.1097/YCO.0000000000000870
Current Opinion in Psychiatry
Copyright © 2023 the Authors. This work has been made available online in accordance with the University of St Andrews Open Access policy. This accepted manuscript is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The final published version of this work is available at https://doi.org/10.1097/YCO.0000000000000870
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