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: 10210226
• 负责人: Doyanne Aspen Darnell
• 金额: $ 15.08万
• 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/10210226
• 关键词: Accident and Emergency department; Acute; Address; Adult; Alaska; American; Artificial Intelligence; Behavior Therapy; Cessation of life; Clinical Trials; Collaborations; Computers; Counseling; County; Data; Development; Discipline of Nursing; Dissemination and Implementation; 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; Provider; Randomized; Recommendation; Research; Research Activity; Research Priority; Resources; Risk; Role; Safety; Savings; Science; Statistical Methods; Strategic Planning; Suicide; Suicide prevention; Surveys; System; Technology; Theoretical Domains framework; 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 barriers; implementation facilitators; implementation science; implementation strategy; improved; innovation; interest; motivational enhancement therapy; patient population; preference; prevention clinical trial; preventive intervention; 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44，000人死于自杀，全国自杀率继续上升。大多数 死于自杀的人在死亡前一年与卫生保健系统有过接触。主要 在医疗机构实施自杀预防的障碍包括缺乏可扩展和可持续的 培训常规医疗保健提供者预防自杀的方法。机器学习的创新和 人工智能可能会克服这些障碍，因为现在技术可以评估 提供干预措施实施方面的技能，并提供获取技能和实践的机会。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，以提高一般医疗提供者培训的可扩展性和可持续性， 自杀预防拟议的研究和培训活动将在哥伦比亚大学进行。 华盛顿，华盛顿州西雅图港景医疗中心，一家县安全网医院和一级创伤 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。该研究旨在适应 并部署现有的可扩展技术来培训一线创伤中心提供者（例如，护士）， 协同患者参与自杀安全计划干预（SPI），并进行试点可行性试验 所产生的训练。目标1包括焦点小组与创伤护士，以确定个人，设置， 基于理论领域框架的组织层面实施障碍和促进因素， 告知护士参与培训和向患者提供SPI的策略。目标1还包括 以用户为中心的情境询问设计方法，包括任务分析，与护士一起告知工作流程- 一体化目标2包括以用户为中心的设计方法，以确定技术的改进和适应 根据护士的喜好来增加可用性。这些技术是一种对话式的媒介， 模拟患者角色扮演和实时反馈，以及2）基于AI的咨询绩效反馈， SPI音频记录。目标3是进行一项技术增强型供应商培训的试点随机试验 与仅基于web的教导条件相比。纵向试验将包括20名护士（10人）， 条件），每个有3名患者，并支持提交NIMH R 01全面试验。K23培训目标 包括建立知识和技能1）以技术为中心的团队科学，2）应用和整合 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）急性护理自杀预防临床试验研究，包括负责 对有自杀风险的患者进行研究，以及低基础率结局的统计方法， 嵌套纵向临床试验数据。此K23应用程序通过开发来解决NIMH战略计划 结合信息技术和实用反馈系统的自杀预防策略 在现实世界的实践中，达到受伤后向卫生保健系统提出的患者的全方位。