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

项目总结 2020年，自杀是青少年和年轻人(10-34岁)的前三大死因之一， 进入成人前九名(35岁-)。最近，研究人员成功地研究出自杀风险 预测算法，有可能极大地改进对自杀高危个人的识别和 支持以初级保健为基础的自杀预防做法。然而，几乎没有证据来指导日常工作。 在医疗保健会面期间使用自杀风险预测算法。最著名的实现 到目前为止，工作的重点是推广，比如ReachVet计划，研究人员最近报告说， 与更多的治疗参与度和安全计划文档以及更少的精神病患者相关 住院、急诊科就诊和自杀未遂。以访问为基础的实施努力 不那么常见了。Kaiser Permanente华盛顿领导人试用了这种方法，使用基于访问的 在一家心理健康专科诊所的提供者中打出了“旗帜”。这项提案的领导者与 医疗系统领导进行混合方法实施评估，其中发现以走访为主 风险“旗帜”并不像心理健康所希望的那样持续地促使进行额外的自杀风险评估。 并为提高质量和今后的执行工作提供了路线图。 因此，本项目的目的是通过开展以下实践来实现RFA-MH-22-120目标- 基于试点实施评估，旨在指导在小学使用自杀风险预测分析 关心。具体地说，一个多学科的研究团队，包括自杀风险预测的开发者 分析和初级保健提供商将合作建立和支持实施临床 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 风险缓解和后续护理路径中的自杀风险。研究团队将使用发现、设计 并构建和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，以确定使用预测性分析的机会 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和迭代改进临床决策支持 分析将增强识别和参与初级保健患者(13岁以上)的工作流 自杀的风险很高。 3(测试)：在1-3个初级保健诊所实施临床决策支持原型的试点测试和 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 尝试结果。 这项工作将支持全国医疗系统使用自杀风险预测分析，并为 为今后评估预防自杀的干预措施的有效性奠定了坚实的基础。

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.