Using System Dynamics Modeling to Foster Real-time Connections to Care

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Project Summary Since 1999, there has been a 400% increase in the rate of drug overdose (OD) deaths in the U.S., with over 70% of the deaths in 2019 related to opioids.The opioid crisis continues to worsen in the State of Connecticut (CT) for all racial/ethnic, gender, and age groups, with the number of overdose deaths increasing by 285% from 2012 to 2020. While the deployment of first responders in the field for overdose, including police, fire, and emergency medical services, provides life-saving resuscitation and naloxone, it is unknown whether other evidence-based interventions are available and being utilized. To date, we lack critical and actionable real-time data from first responders and emergency departments (EDs), including whether a treatment referral was offered to those who have overdosed, and from individuals who overdosed, such as time from overdose to treatment engagement. This real-time data could assist local authorities in predicting rates, timing, and location of overdoses, as well as the types of services needed. In response, our research team has partnered with the CT Department of Public Health (DPH) to develop a system dynamics (SD) model that allows us to assess the impact of key interventions, including the implementation of Good Samaritan Laws (GSLs) and the widespread distribution of naloxone, on important clinical outcomes, such the number of OD deaths. This model has been carefully calibrated for CT and has already been used to identify where data gaps are limiting the development of evidence-based interventions (e.g., absence of information about bystander use of naloxone during OD event, etc.) and to predict the clinical outcomes that can anticipated if specific policy changes or interventions are pursued. Our team has also developed a comprehensive telehealth platform that can be deployed in the field where the overdose occurred or in the ED with minimal time or effort by existing staff. This platform will provide real-time access to providers who prescribe medication for opioid use disorder (MOUD) and other harm reduction services for high-risk individuals, and we hypothesize that it will remove many of the barriers to follow up that these individuals face. Thus, the main objectives of this proposal are twofold: (1) To implement a novel, scalable, evidence-based, intervention (i.e., our telehealth platform) at the time of an opioid overdose that links people who have overdosed with access to medication for opioid use disorder (MOUD), harm reduction services, and recovery supports, and (2) to collect high-quality data about the processes and outcomes associated with deployment of this platform that can be integrated with our existing SD model to determine if, where, when, and what interventions should be implemented in the future. There is a great need to expedite and facilitate MOUD access and respond effectively to witnessed overdoses. Our long-term goal is to implement these novel SD modeling and telehealth strategies in CT, with subsequent dissemination nationally, ultimately improving access to MOUD and reducing OD events and fatalities.