Technologic Innovation to Enhance the Scalability and Sustainability of Trauma Center Provider Training in Suicide Safety Planning

技术创新增强创伤中心提供者自杀安全规划培训的可扩展性和可持续性

• 批准号: 9973232
• 负责人: Doyanne Aspen Darnell
• 金额: $ 15.46万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-05 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9973232
• 关键词: Accident and Emergency department; Acute; Address; Adult; Alaska; American; Artificial Intelligence; Asses; Behavior Therapy; Cessation of life; Clinical Trials; Collaborations; Computers; Counseling; County; Data; Development; Discipline of Nursing; Effectiveness; Emergency department visit; Empathy; Enhancement Technology; Feedback; Feeling suicidal; Focus Groups; Goals; Health Personnel; Health Services; Health behavior change; Healthcare Systems; Hospitalization; Hospitals; Idaho; Individual; Information Technology; Injury; Inpatients; Intervention; Knowledge; Learning; Life; Machine Learning; Medical; Medical center; Mental Health Services; Methods; Montana; Motivation; National Institute of Mental Health; Natural Language Processing; Needs Assessment; Nurses; Online Systems; Outcome; Partnership Practice; Patients; Performance; Play; Prevention approach; Preventive Intervention; Provider; Randomized; Recommendation; Research; Research Activity; Research Priority; Resources; Risk; Role; Safety; Savings; Science; Statistical Methods; Strategic Planning; Suicide; Suicide prevention; Surveys; System; Technology; Time; Training; Training Activity; Trauma Nursing; Trauma patient; United States; Universities; Washington; Wyoming; acute care; base; care providers; college; computerized; coping; evidence base; feasibility trial; health care settings; implementation science; implementation strategy; improved; innovation; interest; motivational enhancement therapy; patient population; preference; prevention clinical trial; psychosocial; randomized trial; recruit; responsible research conduct; safety net; screening; severe injury; skill acquisition; skills; suicidal behavior; suicidal patient; suicidal risk; task analysis; technological innovation; theories; trauma care; trauma centers; trauma units; usability; user centered design

PROJECT SUMMARY/ABSTRACT Over 44,000 people died by suicide in the U.S. in 2016 and national rates continue to increase. The majority of people who died by suicide had contact with the health care system in the year prior to their death. Major hurdles to implementing suicide prevention in healthcare settings include the lack of scalable and sustainable methods for training routine healthcare providers in suicide prevention. Innovations in machine learning and artificial intelligence may overcome these hurdles as it is now possible for technology to assess the quality of provider skill in intervention delivery and provide opportunities for skill acquisition and practice. The candidate's long-term goal is to harness technological advances in artificial intelligence, natural language processing, and machine learning to improve the scalability and sustainability of training among general medical providers in suicide prevention. The proposed research and training activities will take place at the University of Washington at Harborview Medical Center in Seattle, WA, a county safety-net hospital and level I trauma center serving patients across Washington, Wyoming, Alaska, Montana and Idaho. The research aims to adapt and deploy existing scalable technology to train frontline trauma center providers (e.g., nurses) to collaboratively engage patients in a suicide safety planning intervention (SPI) and conduct a pilot feasibility trial of the resultant training. Aim 1 includes focus groups with trauma nurses to identify individual, setting, and organizational-level implementation barriers and facilitators based on the Theoretical Domains Framework and inform strategies for engaging nurses in training and delivery of the SPI with patients. Aim 1 also includes the user-centered design method of contextual inquiry, including task analysis, with nurses to inform workflow- integration. Aim 2 includes user-centered design methods to identify technology refinements and adaptations based on nurse preferences to increase usability. The technologies are a 1) conversational agent, with simulated patient role-play and real-time feedback, and 2) AI-based feedback of counseling performance from SPI audio recordings. Aim 3 is to conduct a pilot randomized trial of a technology-enhanced provider training as compared to a web-based didactic only condition. The longitudinal trial will include 20 nurses (10 per condition), each with 3 patients, and support submission of an NIMH R01 full-scale trial. The K23 training goals include building knowledge and skills in 1) technology-focused team science, 2) the application and integration of implementation science, user-centered design, and adult learning theory for technology adaptation and integration for nurse training, 3) acute care suicide prevention clinical trials research, including the responsible conduct of research with patients at-risk for suicide, and statistical methods for low base-rate outcomes and nested longitudinal clinical trials data. This K23 application addresses the NIMH Strategic Plan by developing strategies incorporating information technology and pragmatic feedback systems for suicide prevention efforts in real-world practice, reaching the full breadth of patients presenting to the health care system after injury.

项目摘要/摘要 2016年，美国有超过4.4万人死于自杀，而且全国的自杀率还在继续上升。大多数人 自杀身亡的人在死前一年与医疗保健系统有过接触。主修 在医疗保健环境中实施自杀预防的障碍包括缺乏可扩展和可持续的 对常规医护人员进行自杀预防培训的方法。机器学习方面的创新和 人工智能可能会克服这些障碍，因为现在技术可以评估 提供干预交付方面的技能，并提供获得和练习技能的机会。候选人的 长期目标是利用人工智能、自然语言处理和 机器学习可提高全科医疗服务提供者培训的可扩展性和可持续性 防止自杀。拟议的研究和培训活动将在华盛顿大学进行。 华盛顿，华盛顿州西雅图的哈博维尤医疗中心，一家县安全网医院和I级创伤 该中心为华盛顿、怀俄明州、阿拉斯加、蒙大拿州和爱达荷州的患者提供服务。这项研究旨在适应 并部署现有的可扩展技术，以培训一线创伤中心提供者(例如护士) 协作性地让患者参与自杀安全计划干预(SPI)，并进行试点可行性试验 由此产生的训练。目标1包括由创伤护士组成的焦点小组，以确定个人、背景和 基于理论领域框架和组织级实施障碍和促进者 告知让护士参与培训和向患者提供SPI的策略。目标1还包括 以用户为中心的上下文查询设计方法，包括任务分析，用护士告知工作流程- 整合。目标2包括以用户为中心的设计方法，以确定技术改进和改编 根据护士的喜好来增加可用性。这些技术是一种对话代理，具有 模拟患者角色扮演和实时反馈，以及2)基于人工智能的咨询绩效反馈 SPI录音。目标3是进行技术增强型提供商培训的试点随机试验 与基于网络的纯说教条件相比。纵向试验将包括20名护士(10% 条件)，每个有3名患者，并支持提交NIMH R01全面试验。K23培训目标 包括1)以技术为中心的团队科学，2)应用和集成方面的知识和技能 实施科学、以用户为中心的设计和成人学习理论的技术适应和 整合护士培训，3)急性护理预防自杀临床试验研究，包括负责 对有自杀风险的患者进行研究，并对低基本比率结果和 嵌套的纵向临床试验数据。此K23应用程序通过以下方式解决NIMH战略计划 结合信息技术和实用反馈系统的战略，以预防自杀 在现实世界的实践中，达到所有患者在受伤后向医疗保健系统提出的要求。