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

使用系统动力学建模促进实时护理联系

• 批准号: 10590186
• 负责人: Rebekah Heckmann
• 金额: $ 114.75万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2024-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10590186
• 关键词: Accident and Emergency department; Artificial Intelligence; Calibration; Caring; Cessation of life; Clinical; Communities; Connecticut; County; Data; Data Collection; Development; Drug Prescriptions; Elements; Emergency Medicine; Emergency department visit; Emergency medical service; Event; Evidence based intervention; Face; Feedback; Fire - disasters; Fostering; Future; Gender; Goals; Harm Reduction; Health; Improve Access; Individual; Intervention; Intervention Trial; Laws; Life; Linguistics; Link; Location; Maps; Measures; Modeling; Morbidity - disease rate; Naloxone; Opioid; Outcome; Output; Overdose; Overdose reduction; Participant; Persons; Pharmaceutical Preparations; Phase; Police; Policies; Prevention; Process; Provider; Public Health; Randomized Clinical Trials; Recovery Support; Research; Resuscitation; Savings; Services; System; Time; Update; age group; authority; base; chatbot; evidence base; experience; first responder; follow-up; high risk; improved; innovation; mortality; novel; opioid epidemic; opioid overdose; opioid use disorder; overdose death; predict clinical outcome; prevent; primary outcome; public health intervention; racial and ethnic; response; secondary outcome; simulation; telehealth; treatment as usual; trial comparing

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.

项目摘要 自1999年以来，美国药物过量（OD）死亡率增加了400%，拥有超过 2019年70%的死亡与阿片类药物有关。康涅狄格州的阿片类药物危机继续恶化 (CT)所有种族/民族，性别和年龄组，过量死亡人数增加了285% 从2012年到2020年。虽然在现场部署了第一反应者，包括警察，消防， 紧急医疗服务，提供救生复苏和纳洛酮，目前尚不清楚是否其他 有循证干预措施，并正在加以利用。到目前为止，我们缺乏关键的和可操作的实时 来自第一反应者和急诊科（ED）的数据，包括治疗转诊是否 提供给那些谁已经过量，并从个人谁过量，如时间从过量， 治疗参与。这些实时数据可以帮助地方当局预测利率，时间和位置 以及所需服务的类型。作为回应，我们的研究团队与 CT公共卫生部（DPH）开发一个系统动力学（SD）模型，使我们能够评估 关键干预措施的影响，包括实施《好撒玛利亚人法》和广泛的 纳洛酮的分布，对重要的临床结局，如OD死亡人数。该模型 仔细校准CT，并已用于确定数据差距限制发展的地方 基于证据的干预措施（例如，缺乏关于OD期间旁观者使用纳洛酮的信息 事件等）并预测如果特定政策改变或干预， 被追捕。我们的团队还开发了一个全面的远程医疗平台，可以部署在 发生药物过量的现场或艾德，现有工作人员只需花费最少的时间或精力。这个平台 将提供实时访问提供者谁处方药物阿片类药物使用障碍（MOUD）和其他 为高风险个体提供减少伤害的服务，我们假设它将消除许多障碍， 这些人所面临的问题。因此，这项建议的主要目标有两个：（1）实施一项 新颖的、可扩展的、基于证据的干预（即，我们的远程医疗平台）在阿片类药物过量时 将过量服用阿片类药物的人与获得阿片类药物使用障碍（MOUD）的人联系起来， 减少服务和恢复支持，以及（2）收集有关流程的高质量数据， 与此平台部署相关的成果，可以与我们现有的SD模型集成， 确定今后是否、在何处、何时以及应采取何种干预措施。非常需要 加快和促进MOUD的访问，并有效地应对目击的过量用药。我们的长期目标是 在CT中实施这些新的SD建模和远程医疗策略，并随后进行传播 在全国范围内，最终改善MOUD的使用，减少OD事件和死亡。