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

芝加哥数据驱动的 OUD 筛查、参与、治疗和规划 (C-DOSETaP) 系统

基本信息

批准号：
10745471
负责人：
Niranjan Subhash Karnik
金额：
$ 109.34万
依托单位：
UNIVERSITY OF ILLINOIS AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10745471
关键词：
Accident and Emergency department Address Analgesics Area COVID-19 Calibration Caring Cessation of life Chicago Clinic Clinical Clinical Trials Clinics and Hospitals Cohort Analysis Communities Consensus Continuity of Patient Care Data Drug Monitoring Drug Prescriptions Drug user Electronic Health Record Elements Epidemic Ethnic Origin Evidence based intervention Exposure to Fentanyl Gender Gender Identity Goals Harm Reduction Health Health Personnel Health system Healthcare Systems Heroin Hospital Departments Hospitals Hot Spot Illinois Individual Injury Inpatients Intervention Machine Learning Manuals Measures Monitor Morbidity - disease rate Natural Language Processing Neighborhoods Opioid Pain Patient Care Patient Outcomes Assessments Patient Self-Report Patients Persons Prevention Prevention strategy Primary Prevention Process Race Random Allocation Recovery Recovery Support Reporting Resource Allocation Retrospective cohort Sampling Screening procedure Sensitivity and Specificity Sex Orientation Side Site Structural Racism System Time Universities aged austin black men care systems clinical care community organizations computerized data streams digital disorder risk feasibility trial illicit opioid improved innovation medication for opioid use disorder mortality opioid epidemic opioid mortality opioid use disorder overdose prevention pandemic disease patient population response screening treatment services trend

项目摘要

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).

项目概要/摘要根据 HHS 过量预防战略，该 HEAL Data2Action 创新项目提案涉及初级预防、减少伤害、OUD 治疗和康复支持，使用最先进的数据驱动方法将汇集伊利诺伊大学的元素医院和诊所（UI Health）和伊利诺伊大学芝加哥分校采用单一、统一的方法，能够降低芝加哥西区 OUD 的死亡率和发病率。我们将部署一个机器学习（电子健康记录数据的自然语言处理）、状态的组合报告的处方监测数据以及患者报告的措施以确定个人和群体各个 UI 医疗保健站点的 OUD 风险级别（优先级 2，创新项目）。然后我们将使用这些数据在个人层面（以促进临床护理）和社区/系统层面（以促进临床护理）支持资源分配和规划）。