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，美国自杀的44,000人因自杀而死亡，全国利率继续增加。大多数 死于自杀的人在去世前一年与医疗保健系统接触。主要的 在医疗机构中实施自杀预防的障碍包括缺乏可扩展性和可持续性 培训常规医疗保健提供者预防自杀的方法。机器学习和 人工智能可能会克服这些障碍，因为技术现在有可能评估 提供者的干预交付技巧，并为技能获取和实践提供机会。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中培训的可伸缩性和可持续性 预防自杀。拟议的研究和培训活动将在大学举行 华盛顿州西雅图的Harbourview Medical Center，华盛顿州西雅图的华盛顿，县安全网医院和I级创伤 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商（例如，护士） 协作使患者进行自杀安全计划干预（SPI）并进行试验可行性试验 由此产生的培训。目标1包括与创伤护士的焦点小组，以识别个人，环境和 基于理论领域框架的组织级实施障碍和促进者 告知策略，使护士与患者一起培训和交付SPI。 AIM 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，与护士通知工作流程 - 一体化。 AIM 2包括以用户为中心的设计方法来识别技术的改进和适应 基于护士偏好以提高可用性。技术是1）对话剂， 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录音。 AIM 3是对技术增强提供商培训的飞行员随机试验 与仅基于Web的教学条件相比。纵向试验将包括20名护士（每10名 条件），每个患者有3例患者，并支持提交NIMH R01全尺度试验。 K23培训目标 在1）以技术为重点的团队科学中包括建立知识和技能，2）应用和集成 实施科学，以用户为中心的设计以及针对技术适应的成人学习理论 护士培训的整合，3）急性护理自杀预防临床试验研究，包括负责任 对患者进行自杀的患者进行研究，以及低基率结果的统计方法 嵌套的纵向临床试验数据。该K23应用程序通过开发来解决NIMH战略计划 结合信息技术和务实反馈系统的策略，以进行自杀预防工作 在实际实践中，受伤后，到达医疗保健系统的患者的全部广度。