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      Rapid opioid overdose response system technologies

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          Abstract

          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.

          Related collections

          Most cited references39

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          Dissecting racial bias in an algorithm used to manage the health of populations

          Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.
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            The rise of illicit fentanyls, stimulants and the fourth wave of the opioid overdose crisis

            This review provides an update on recently published literature on the rise of illicit fentanyls, risks for overdose, combinations with other substances, e.g. stimulants, consequences, and treatment.
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              Opioid use disorder

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                Author and article information

                Journal
                Current Opinion in Psychiatry
                Ovid Technologies (Wolters Kluwer Health)
                0951-7367
                1473-6578
                2023
                July 2023
                April 25 2023
                : 36
                : 4
                : 308-315
                Affiliations
                [1 ]DigitAS Project, Population and Behavioural Science Division, School of Medicine, University of St Andrews, St Andrews
                [2 ]Forward Leeds and Humankind Charity, Durham, UK
                Article
                10.1097/YCO.0000000000000870
                37185583
                c1eea0d6-ad4b-4098-a4bc-b60149111bde
                © 2023
                History

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