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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，自杀是青少年和年轻人（10-34岁）的三大死亡原因之一，在成人（35-64岁）的前九名中。最近，研究人员成功地将自杀风险预测算法，有可能大大提高识别个人在高风险的自杀，支持以基层医疗为基础的自杀预防措施。然而，很少有证据表明，在医疗保健过程中使用自杀风险预测算法。最著名的实现到目前为止，工作重点是外展，如ReachVet计划，研究人员最近报告说，与更多的治疗参与和安全计划文件和更少的精神疾病相关住院、急诊和自杀企图。基于访问的执行工作不太常见。凯撒永久华盛顿领导人试行了这种方法，使用基于访问的在一家心理健康专科诊所的提供者中“标记”。该提案的领导人与医疗保健系统领导人进行混合方法实施评估，发现基于访问的风险“标志”并没有一贯提示额外的自杀风险评估，因为精神卫生提供商，并为质量改进和未来的实施工作提供了路线图。因此，本项目的目的是通过开展实践来实现RFA-MH-22-120目标- 基于试点实施评估，旨在指导小学自杀风险预测分析的使用在乎具体来说，一个多学科的研究团队，包括自杀风险预测的开发人员，分析和初级保健提供者将合作建立和支持实施临床决策支持工具，旨在识别和参与初级保健患者（成人和青少年）在高风险缓解和后续护理途径中的自杀风险。研究团队将使用Discover、Design 构建和测试以人为本的设计框架，以支持三个具体目标： 1（DISCOVER）：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）：使用自杀风险预测工具开发和迭代完善临床决策支持分析将增强工作流程，以识别和参与初级保健患者（13岁以上），自杀的高风险。 3（测试）：在1-3个初级保健诊所进行临床决策支持原型的试点测试，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，为今后评估这种干预措施预防自杀的有效性奠定了坚实的基础。