Project 3: Suicide Risk Identification in Jails using Data Linkage and Automation

项目 3：使用数据链接和自动化识别监狱中的自杀风险

基本信息

批准号：
10441875
负责人：
Brian Kenneth Ahmedani
金额：
$ 27.96万
依托单位：
MICHIGAN STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-22 至 2027-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10441875
关键词：
Accreditation African American population Algorithms Area Automation Behavior Books Caring Cessation of life Communities Community Health Systems Continuity of Patient Care County Coupled Criminal Justice Crowding Data Data Analyses Data Linkages Detection Disease Early Intervention Electronic Health Record Ethnic Origin Evidence based intervention Familiarity Family Family member Future General Population Geography Goals Health Health Care Visit Health Insurance Health Insurance Portability and Accountability Act Health Personnel Health system Hybrids Individual Infrastructure Institution Insurance Carriers Intake Intercept Intervention Jail Justice Lead Length Link Machine Learning Medicaid Medical Records Mental Health Methods Michigan Modeling Population Process Provider ROC Curve Race Recording of previous events Records Relative Risks Reporting Research Risk Sampling Screening procedure Severities Standardization Suicide Suicide attempt Suicide prevention System Targeted Research Testing Thinking Time United States Dept. of Health and Human Services Validation Work base cost cost effectiveness data sharing design effectiveness implementation study effectiveness implementation trial evidence base experience future implementation health care availability health care service utilization health data health record high risk hybrid type 2 trial implementation determinants implementation evaluation implementation outcomes implementation process implementation strategy improved insurance claims model development predictive modeling reducing suicide risk prediction model scale up screening suicidal morbidity suicidal risk suicide model time use

项目摘要

Abstract Project #3 Although professional and accreditation standards exist to guide identification of suicide risk, few jails effectively screen for such risk at booking (Intercept 2). Given that individuals booking into jails may be less forthcoming in reporting thoughts and behaviors to correctional officers, current identification practices are insufficient. It may be possible to enhance identification methods in jails, replicating a method developed in community health systems. Using a general population sample from seven health systems, the Mental Health Research Network developed a suicide risk model to predict suicide attempts/deaths using electronic health records and insurance claims data. Claims records were used to create the model that resulted in a risk score that could be available for medical personnel, alerting them to the possibility of heightened suicide risk. Replicating this validation, using a jail population with integrated Medicaid claims data, could result in a similar identification process for justice- involved individuals available at jail intake (booking) that could assist in detecting who among those entering jail could be at risk for suicide attempts and suicide deaths. Risk identified through the model will be compared to the practice-as-usual identification within the jail. Because there is no standardized process for identification of suicide risk within jails, each jail’s screening process will be assessed separately. This proposal would leverage three geographically and demographically diverse jails in one state, increasing the generalizability of the findings. Aim 1. Validate the suicide risk model with a jail population sample (three jails; on all of those who enter during a specific length of time), using Medicaid claims and vital record data. Aim 2. Compare the risk flag to the current suicide risk identification process (e.g. practice as usual) within 3 diverse jails. Aim 3. Evaluate implementation factors to inform the design of a future hybrid trial and integration within jails, working with state Medicaid and the Department of Health and Human Services. Improved suicide risk identification in jails could decrease the adverse impacts that suicide has on those who are detained, family members, correctional staff, the institution and community (i.e. liability, costs). Our long-term goal of this research targets jail systems by implementing an automated ‘suicide risk flag’ – derived from prior health records, resulting in improved detection at intake that would lead to intervention to reduce suicide attempts and suicide deaths within the jail. The assembled team has experience with development of the model, familiarity and experience implementing screening tools within jails, and integrating and analyzing jail and Medicaid data. The project leverages an established partnership between the team and criminal justice system. This project will inform an R01 hybrid effectiveness-implementation trial to assess whether the use of a suicide risk flag derived from this algorithm results in access to evidence based intervention within the jail resulting in a reduction in suicide attempts and death within these jail settings and post-release.

抽象项目#3 尽管存在专业和认证标准来指导自杀风险的识别，但很少有监狱能够有效地在预订时筛查此类风险（拦截 2）。鉴于在监狱中预订的个人可能不太愿意。向惩教人员报告想法和行为，目前的识别做法可能还不够。有可能加强监狱中的识别方法，复制社区卫生中开发的方法心理健康研究网络使用来自七个卫生系统的一般人群样本。开发了一个自杀风险模型，利用电子健康记录和保险来预测自杀企图/死亡索赔数据。索赔记录用于创建模型，从而得出可用的风险评分。对于医务人员，提醒他们气球自杀风险的可能性，使用重复此验证。具有综合医疗补助索赔数据的监狱人口，可能会导致类似的司法识别过程 - 涉案人员可在监狱入狱（预约），以帮助查明入狱者中有哪些人可能面临自杀企图和自杀死亡的风险将与通过该模型确定的风险进行比较。监狱内的身份识别照常进行，因为没有标准化的身份识别流程。监狱内的自杀风险，每个监狱的筛查过程将单独评估。在一个州设立三座地理位置和人口结构各异的监狱，增加了研究结果的普遍性。目标 1. 使用监狱人口样本（三个监狱；针对所有在监狱中进入的人）验证自杀风险模型目标 2. 将风险标志与当前风险标志进行比较 3 个不同监狱内的自杀风险识别流程（例如照常实践）目标 3. 评估实施情况。为未来混合试验的设计和监狱内整合提供信息的因素，与州医疗补助和卫生与公共服务部改进监狱中的自杀风险识别可以减少自杀风险。自杀对被拘留者、家庭成员、惩教人员、机构的不利影响我们这项研究的长期目标是通过监狱系统。实施自动“自杀风险标记”——源自之前的健康记录，从而改善在入院时进行检测，从而采取干预措施，减少自杀未遂和自杀死亡组建的团队具有模型开发经验、熟悉度和经验。在监狱内实施筛查工具，并整合和分析监狱和医疗补助数据该项目。该项目将利用团队与刑事司法系统之间已建立的伙伴关系。 R01 混合有效性实施试验，评估自杀风险标记的使用是否源于此算法导致监狱内获得基于证据的干预，从而减少自杀率在这些监狱环境中和释放后的尝试和死亡。