Chicago Data-driven OUD Screening, Engagement, Treatment and Planning (C-DOSETaP) System

项目总结/摘要 与HHS过量预防战略保持一致，该HEAL Data 2Action创新项目 建议使用 最先进的数据驱动的方法，将汇集伊利诺伊大学的元素 医院和诊所（UI健康）和伊利诺伊大学芝加哥到一个单一的，统一的方法， 能够降低芝加哥西区OUD的死亡率和发病率。我们将部署一个 结合机器学习（电子健康记录数据的自然语言处理），状态 报告的处方监测数据，以及患者报告的用于确定个体和群体的措施- 不同UI医疗保健中心的OUD风险等级（优先级2，创新项目）。然后我们将使用 这些数据在个人水平（以促进临床护理）和社区/系统水平（以 支持资源分配和规划）。

Project Summary/Abstract In alignment with the HHS Overdose Prevention Strategy, this HEAL Data2Action Innovation Project proposal addresses primary prevention, harm reduction, treatment of OUD, and recovery support using a state-of-the-art data driven approach that will bring together elements of the University of Illinois Hospital and Clinics (UI Health) and University of Illinois Chicago into a single, unified approach that is capable of reducing mortality and morbidity from OUD on the westside of Chicago. We will deploy a combination of machine learning (natural language processing of electronic health records data), state reported prescription monitoring data, and patient reported measures to determine individual and group- level OUD risk across the various UI Health care sites (Priority 2, Innovation Project). We will then use this data at both the individual level (to facilitate clinical care) and at the community/system level (to support resource allocation and planning).