Pilot Testing Implementation of Suicide Risk Prediction Algorithms to Support Suicide Prevention in Primary Care

试点测试自杀风险预测算法的实施，以支持初级保健中的自杀预防

基本信息

批准号：
10648772
负责人：
Julie Elissa Richards
金额：
$ 25.64万
依托单位：
KAISER FOUNDATION RESEARCH INSTITUTE
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：
2023-07-07 至 2026-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10648772
关键词：
Address Adolescent Adolescent and Young Adult Adult Age Algorithms Behavioral COVID-19 COVID-19 pandemic Caring Cause of Death Cessation of life Clinic Clinical Collaborations Communication Discover, Design, Build, and Test Framework Disease Documentation Effectiveness of Interventions Emergency department visit Foundations Future Health Health Personnel Healthcare Healthcare Systems Hospitalization Individual Mental Health Methods Modeling Outcome Pathway interactions Patient-Centered Care Patients Population Predictive Analytics Primary Care Process Provider Qualitative Methods Recovery Reporting Research Research Personnel Risk Risk Assessment Safety Statistical Data Interpretation Statistical Methods Suicide Suicide attempt Suicide prevention Surveys Technology Testing Time United States Department of Veterans Affairs Veterans Visit Washington Work adolescent patient career clinical care clinical decision support clinical decision-making clinical implementation clinical practice cost design effectiveness evaluation emotional distress ethnic disparity follow-up future implementation high risk human centered design implementation efforts implementation evaluation improved medical specialties model development mortality multidisciplinary outreach pilot test predictive modeling prevention practice primary care clinic primary care clinician primary care patient primary care provider programs prototype racial disparity reducing suicide risk mitigation risk prediction model social stigma sociodemographics suicidal suicidal behavior suicidal morbidity suicidal risk support tools tool usability virtual

项目摘要

PROJECT SUMMARY In 2020, suicide was among the top three causes of death for adolescents and young adults (age 10-34) and among the top nine for adults (age 35-64). Recently, researchers have successfully developed suicide risk prediction algorithms that have potential to vastly improve identification of individuals at high risk of suicide and support primary care-based suicide prevention practices. However, there is very little evidence to guide routine use of suicide risk prediction algorithms during healthcare encounters. The most well-known implementation work to date has focused on outreach, like the ReachVet program, which researchers recently reported is associated with greater treatment engagement and safety plan documentation and fewer psychiatric hospitalizations, emergency department visits, and suicide attempts. Visit-based implementation efforts have been less common. Kaiser Permanente Washington leaders piloted this approach using a using a visit-based “flag” among providers in one mental health specialty clinic. The leader of this proposal partnered with healthcare system leaders to conduct a mixed-method implementation evaluation, which found the visit-based risk “flag” did not consistently prompt additional suicide risk assessment as intended by mental health providers and provided a roadmap for quality improvement and future implementation efforts. Therefore, the purpose of this project is to address the RFA-MH-22-120 objective by conducting a practice- based pilot implementation evaluation designed to guide the use of suicide risk predictive analytics in primary care. Specifically, a multi-disciplinary team of researchers, including developers of suicide risk predictive analytics and primary care providers, will work in partnership to build and support implementation of clinical decision support tools designed to identify and engage primary care patients (adults and adolescents) at high risk of suicide in risk mitigation and follow-up care pathways. The research team will use the Discover, Design and Build, and Test Human-Centered Design framework to support three specific aims: 1 (DISCOVER): Conduct qualitative and statistical analyses to identify opportunities to use predictive analytics to guide clinical decision making to support suicide prevention in primary care. 2 (DESIGN & BUILD): Develop and iteratively refine clinical decision support using suicide risk predictive analytics that will augment workflows for both identifying and engagement primary care patients (age 13+) at high-risk of suicide. 3 (TEST): Pilot test implementation of clinical decision support prototypes in 1-3 primary care clinics and evaluate the implementation via 1) provider surveys and 2) statistical analysis of clinical process and suicide attempt outcomes. This work will support use of suicide risk predictive analytics by healthcare systems nationwide and lay a strong foundation for future evaluations of the effectiveness of this intervention for preventing suicide.

项目摘要 2020年，自杀成为青少年和年轻人死亡的前三名（10-34岁）和在成年人的前九名中（35-64岁）。最近，研究人员成功发展了自杀风险预测算法有可能大大提高对自杀和自杀风险高的个人的识别支持基于初级保健的自杀预防实践。但是，几乎没有证据可以指导例行在医疗保健遭遇期间使用自杀风险预测算法。最著名的实施迄今为止的工作集中在外展上，例如Reachvet计划，研究人员最近报告说与更大的治疗参与和安全计划文件相关，精神病学较少住院，急诊科和自杀企图。访问基于访问的实施工作不太常见。 Kaiser Permanente华盛顿领导人使用基于访问的一家心理健康专业诊所的提供者中的“国旗”。该提案的领导人与医疗保健系统负责人进行混合方法实施评估，该评估发现了基于访问的风险“国旗”并未始终提示心理健康的额外自杀风险评估提供者并为质量改进和未来实施工作提供了路线图。因此，该项目的目的是通过进行实践来解决RFA-MH-22-120目标 - 基于基于的试点实施评估旨在指导主要的自杀风险预测分析关心。特别是一个多学科研究人员团队，包括自杀风险预测的开发人员分析和初级保健提供者将努力建立和支持临床实施旨在识别和吸引初级保健患者（成人和青少年）的决策支持工具在降低风险和后续护理途径中自杀的风险。研究团队将使用发现，设计并构建并测试以人为本的设计框架，以支持三个特定目标： 1（发现）：进行定性和统计分析以确定使用预测分析的机会指导临床决策以支持预防初级保健的自杀。 2（设计与构建）：使用自杀风险预测性发展和迭代地完善临床决策支持分析将增加用于识别和参与初级保健患者（13岁以上）的工作流程的分析。高风险自杀。 3（测试）：1-3个初级保健诊所中的临床决策支持原型的试点测试实施通过1）提供者调查和2）临床过程和自杀的统计分析评估实施尝试结果。这项工作将支持全国医疗保健系统自杀风险预测分析的使用，并铺设强大的基础是对这种干预措施预防自杀的有效性的未来评估。