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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：
10688258
负责人：
Sheryl M PIMLOTT- KUBIAK
金额：
$ 25.14万
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/10688258
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 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 Human Resources Hybrids Individual Infrastructure Institution Insurance Carriers Intake Intercept Intervention Jail Justice Length Link Machine Learning Medicaid Medical Records Mental Health Methods Michigan Modeling Population Process Provider ROC Curve 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 cost cost effectiveness data sharing design 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 racial diversity reducing suicide risk prediction model scale up screening statistics suicidal morbidity suicidal risk suicide model

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 虽然存在专业和认证标准来指导自杀风险的识别，但很少有监狱有效地在预订时筛查此类风险（截取2）。考虑到被关进监狱的人可能不太愿意在报告的想法和行为的惩教人员，目前的识别做法是不够的。可能有可能加强监狱的识别方法，复制社区卫生部门开发的方法系统.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，使用电子健康记录和保险来预测自杀企图/死亡索赔数据。索赔记录被用于创建模型，该模型可产生可用的风险评分提醒医务人员注意自杀风险的可能性。复制此验证，使用一个监狱人口与综合医疗补助索赔数据，可能会导致一个类似的识别过程的正义- 涉及在监狱接收（预订），可以帮助检测谁在那些进入监狱的个人可能会有自杀企图和自杀死亡的风险。通过模型识别的风险将与监狱里的身份识别惯例由于没有标准化的程序来识别监狱内的自杀风险，每个监狱的筛选过程将单独评估。该提案将利用在一个州有三个地理和人口结构各异的监狱，增加了调查结果的普遍性。目标1。使用监狱人口样本（三个监狱;所有在监狱期间进入的人）来验证自杀风险模型特定的时间长度），使用Medicaid索赔和重要记录数据。目标2.将风险标志与当前在3个不同的监狱内开展自杀风险识别程序（例如，照常进行）。目标3.评估执行情况为未来混合试验的设计和监狱内的整合提供信息的因素，与州医疗补助计划合作，卫生与公众服务部改善监狱中的自杀风险识别可以减少自杀对被拘留者、家庭成员、惩教工作人员、机构社会责任（即责任、成本）。我们这项研究的长期目标是监狱系统，实施自动化的“自杀风险标志”-来自以前的健康记录，从而改善在摄入时检测，这将导致干预，以减少自杀企图和自杀死亡，监狱组建的团队拥有模型开发经验、熟悉度和经验在监狱中实施筛查工具，整合和分析监狱和医疗补助数据。项目利用该小组与刑事司法系统之间建立的伙伴关系。该项目将提供一个 R 01混合有效性-实施试验，以评估使用自杀风险标志是否源于此算法导致在监狱内获得基于证据的干预，从而减少自杀在这些监狱环境和释放后的企图和死亡。