Project Motivate: A digital motivation and prediction platform to improve treatment retention and reduce relapse in opioid use disorder

Project Motivate：一个数字动机和预测平台，可提高治疗保留率并减少阿片类药物使用障碍的复发

• 批准号: 10339079
• 负责人: Jeremy Guttman
• 金额: $ 5.5万
• 依托单位: BIOMOTIVATE, LLC
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-24 至 2022-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10339079
• 关键词: Accidents; American; Behavioral; Caring; Data; Early treatment; Emergency response; Epidemic; Family; Individual; Intervention; Intuition; Machine Learning; Medical Care Costs; Methods; Motivation; Municipalities; Overdose; Patients; Pharmaceutical Preparations; Physiologic Monitoring; Physiological; Predictive Analytics; Productivity; Provider; Recording of previous events; Relapse; Risk; Source; Symptoms; Time; Workplace; cost; craving; digital; improved; mHealth; novel strategies; opioid epidemic; opioid use disorder; overdose death; recidivism; relapse risk; response; tool; treatment program; user-friendly

PROJECT SUMMARY/ABSTRACT The opioid crisis is the deadliest drug epidemic in American history and new approaches are needed. One novel approach includes predicting likelihood of opioid use disorder (OUD) treatment retention by assessing someone’s risk of early departure from treatment. Current methods to improve treatment retention rely on providers using their intuition to identify when an individual is at risk of leaving treatment early in order to intervene, which often happens too late. Mobile health and machine learning predictive analytics offer a new opportunity to personalize OUD treatment, improve retention in OUD care, and mitigate the risk of relapse and overdose episodes. Project Motivate will combine physiological and behavioral data from disparate sources in order to predict when an individual is at risk of early departure from OUD treatment. This data will be displayed in a user-friendly manner so that providers can more effectively support patients to remain in treatment with timely intervention and responses.

项目总结/摘要 阿片类药物危机是美国历史上最致命的药物流行病，新的方法是 needed.一种新的方法包括预测阿片类药物使用障碍（OUD）的可能性 通过评估某人提前离开治疗的风险来确定治疗保留率。电流 提高治疗保留率的方法依赖于提供者利用他们的直觉来确定何时 个人有可能为了干预而过早离开治疗，而这往往为时已晚。 移动的健康和机器学习预测分析提供了个性化的新机会 OUD治疗，改善OUD护理的保留，并减轻复发和过量的风险 情节。Project Motivate将联合收割机结合来自不同领域的生理和行为数据， 来源，以预测何时个体处于早期脱离OUD治疗的风险中。 这些数据将以用户友好的方式显示，以便提供者能够更有效地 支持患者继续接受治疗，并及时采取干预和应对措施。