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.

项目概要 2020年，自杀是青少年和年轻人（10-34岁）的三大死因之一，跻身成年人（35-64 岁）的前九名。最近，研究人员成功开发了自杀风险。预测算法有可能大大提高对自杀高风险个体的识别，支持以初级保健为基础的自杀预防实践，但是，很少有证据可以指导常规治疗。在医疗保健过程中使用自杀风险预测算法是最著名的实施方式。迄今为止的工作重点是外展，例如研究人员最近报道的 ReachVet 计划与更多的治疗参与和安全计划文件以及更少的精神病相关住院治疗、急诊就诊和自杀未遂的实施工作。华盛顿凯撒医疗机构的领导人使用基于访问的方式试行了这种方法。该提案的领导者与一家心理健康专科诊所的提供者合作。医疗保健系统领导者进行了混合方法实施评估，发现基于访问的风险“标志”并没有始终如一地提示心理健康所预期的额外自杀风险评估提供者并提供了质量改进和未来实施工作的路线图。因此，该项目的目的是通过进行实践来实现 RFA-MH-22-120 目标：基于试点实施评估，旨在指导初级阶段自杀风险预测分析的使用具体来说，是一个由多学科研究人员组成的团队，其中包括自杀风险预测的开发人员。分析和初级保健提供者将合作建立和支持临床旨在识别和吸引初级保健患者（成人和青少年）的决策支持工具研究小组将使用发现、设计来降低自杀风险。构建和测试以人为本的设计框架，以支持三个具体目标： 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初级保健中的自杀预防。 2（设计和构建）：使用自杀风险预测开发并迭代完善临床决策支持分析将增强识别和吸引初级保健患者（13 岁以上）的工作流程自杀风险高。 3（测试）：在 1-3 个初级保健诊所和通过 1) 提供者调查和 2) 临床过程和自杀的统计分析来评估实施情况尝试的结果。这项工作将支持全国医疗保健系统使用自杀风险预测分析，并奠定基础为未来评估这种预防自杀干预措施的有效性奠定了坚实的基础。