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，美国的药物过量（OD）死亡率增加了400％， 2019年的70％的死亡与阿片类药物有关。阿片类药物危机在康涅狄格州继续恶化 （CT）对于所有种族/种族，性别和年龄段，过量死亡人数增加了285％ 从2012年到2020 紧急医疗服务，提供挽救生命的复苏和纳洛酮，尚不清楚其他是否其他 可以提供基于证据的干预措施并被利用。迄今为止，我们缺乏关键和可行的实时 急救人员和急诊部（ED）的数据，包括治疗转诊是否为 提供给那些服药过量的人，以及从服药过量的人那里，例如从过量到的时间 治疗参与。该实时数据可以帮助地方当局预测费率，时机和位置 服用过量以及所需的服务类型。作为回应，我们的研究团队与 CT公共卫生部（DPH）开发系统动态（SD）模型，该模型使我们能够评估 关键干预措施的影响，包括实施良好的撒玛利亚法律（GSL）和广泛的影响 纳洛酮的分布，在重要的临床结果上，例如OD死亡的数量。这个模型已经 精心校准CT，已经用于确定数据差距限制开发的位置 基于证据的干预措施（例如，在OD期间缺乏旁观者使用纳洛酮的信息 事件等）并预测可以预期特定政策变更或干预措施的临床结果 被追捕。我们的团队还开发了一个综合的远程医疗平台，可以部署 现有员工的时间或努力最少的时间或努力。这个平台 将为针对阿片类药物使用障碍（MOUD）和其他药物的提供者提供实时访问 对高风险个人的减少服务，我们假设它将消除许多障碍 跟进这些人面对的。因此，该提案的主要目标是双重的：（1）实施 阿片类药物过量时，新颖，可扩展，基于证据的干预（即我们的远程医疗平台） 这与服用阿片类药物使用障碍药物（MOUD）的药物过量的人联系在一起 减少服务和恢复支持，以及（2）收集有关流程和的高质量数据 与该平台部署相关的结果，可以将我们现有的SD模型集成到 确定将来是否应实施，何时，何时和哪些干预措施。有很大的需求 加快和促进穆德访问并有效反应见证过量。我们的长期目标是 在CT中实施这些新颖的SD建模和远程医疗策略，随后传播 在全国范围内，最终改善了获得MOUD的机会，并减少了OD事件和死亡。