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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：
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.

摘要项目#3