Project-001

项目-001

基本信息

批准号：
10863455
负责人：
MATTHEW K NOCK
金额：
$ 89.09万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
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：
2023-08-08 至 2028-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10863455
关键词：
Accident and Emergency department Adherence Adolescent Algorithms Behavior Biometry Boston Caring Cause of Death Cessation of life Clinical Collaborations Communities Country Data Data Reporting Data Science Data Scientist Decision Making Dedications Development Economics Education Electronic Health Record Emergency Department patient Emergency department visit Evaluation General Hospitals Goals Grant Health Personnel Health system Healthcare Systems Hospital Administration Hospitalization Hospitals Inpatients Intervention Learning Massachusetts Methods Patient Self-Report Patient risk Patients Persons Prevention Probability Psychiatry Psychology Public Health Schools Randomized Research Research Personnel Research Project Grants Risk Safety Scientific Advances and Accomplishments Scientist Societies Structure Suicide Suicide attempt Suicide prevention Target Populations Technology Testing Time Training Translating Universities Vision Visit Woman Work acute care adaptive intervention alternative treatment biomedical informatics career clinical care clinical decision support clinical decision-making clinical practice community building community partnership computer science digital digital platform effective therapy effectiveness testing health care settings health data high risk implementation efforts improved innovation innovative technologies inpatient psychiatric settings machine learning algorithm machine learning prediction algorithm medical schools member minority children novel outreach population health practice setting predictive modeling programs randomized trial risk prediction risk stratification social health determinants statistics suicidal behavior suicidal risk suicide rate therapy development tool treatment optimization trial design

项目摘要

Suicide is one of the leading causes of death worldwide and the 10th leading cause of death in the US. A major barrier to suicide prevention has been that the cutting-edge scientific advances that have occurred in the past few decades have not yet been translated and implemented into clinical practice settings. We propose the development of a practice-based Center for Suicide Research and Prevention (CSRP) that will support the development, deployment, and evaluation of practice-ready and deployment-focused interventions aimed at improving the identification and effective treatment of those at risk of suicide. This Center will be a collaborative effort between researchers, clinicians, and stakeholders at Mass General Brigham (MGB) and Harvard University. Our focus is on improving the identification and prevention of suicide-related behaviors (SRBs) among patients presenting for treatment at emergency departments (EDs) and psychiatric inpatient units. Decades of research have shown that 50% of people who die by suicide are seen in a healthcare setting within one month before their death, 40% visit an ED the year before their death, and the suicide rate is highest in the weeks immediately following discharge from a psychiatric inpatient hospitalization. Our first aim is to build and maintain a cohesive and innovative transdisciplinary Center dedicated to advancing suicide prevention. This will be accomplished via the work of our proposed Administrative Core and Methods Core. Our second aim is to conduct four practice-focused research projects that target prediction and prevention of suicidal behaviors in ED and inpatient settings. Our Signature Project (SIG) will implement a previously-develop machine learning prediction algorithm based on electronic health record (EHR) and self-report data collected in the ED and randomly assign the clinicians of 4,000 patients to receive (experimental condition) or not receive (control condition) the predicted probability that their patient will make a suicide attempt after ED discharge. We will test the impact of this intervention on the suicide attempt rate and clinician decision-making. The SIG also will examine clinician acceptability and adherence, prediction model improvement, and the development of treatment optimization rules regarding patients' likelihood of benefiting from hospitalization versus alternative treatments. Our three Exploratory Projects (EXP): (EXP1) will use the SIG prediction model to identify ED patients at risk of suicidal behavior and experimentally test the effectiveness of an enhanced outreach intervention administered in collaboration with a community partner – Samaritans of Boston; (EXP2) will implement and test EHR-based risk algorithms in two inpatient units with a special focus on the use of social determinants of health to improve prediction among under-represented adolescents; and (EXP3) will test a just-in-time adaptive intervention using an innovative micro-randomized trial design to connect at-risk patients with care when needed most. The third aim of the Center is to support education, dissemination, and implementation efforts that will have a significant and sustained effect on suicide prevention.

自杀是全球主要死亡原因之一，也是美国第十大死亡原因。预防自杀的障碍是过去发生的尖端科学进步几十年来尚未转化并实施到临床实践环境中。建立一个以实践为基础的自杀研究和预防中心（CSRP），该中心将支持开发、部署和评估可供实践和以部署为重点的干预措施，旨在该中心将是一个合作中心，旨在提高对自杀风险者的识别和有效治疗。麻省布里格姆总医院 (MGB) 和哈佛大学的研究人员、新来者和利益相关者之间的努力大学。我们的重点是改善自杀相关行为 (SRB) 的识别和预防。在急诊科 (ED) 和精神科住院部接受治疗的患者中。数十年的研究表明，50% 的自杀者是在医疗机构中看到的 40% 的人在去世前一个月就去急诊室就诊，自杀率最高的国家是精神科住院治疗出院后几周，我们的首要目标是建立和实现。维持一个有凝聚力和创新性的跨学科中心，致力于推进自杀预防。将通过我们提议的管理核心和方法核心的工作来实现我们的第二个目标。是开展四个以实践为重点的研究项目，旨在预测和预防自杀行为我们的标志项目（SIG）将在急诊室和住院环境中实施先前开发的机器学习。基于电子健康记录 (EHR) 和 ED 收集的自我报告数据的预测算法将 4,000 名新患者随机分配接受（实验条件）或不接受（对照）条件）患者在急诊室出院后尝试自杀的预测概率我们将进行测试。 SIG 还将了解这种干预措施对自杀未遂率和临床医生决策的影响。检查临床医生的可接受性和依从性、预测模型的改进以及开发关于患者从住院与替代方案中受益的可能性的治疗优化规则我们的三个探索性项目 (EXP)：(EXP1) 将使用 SIG 预测模型来识别 ED。有自杀行为风险的患者并通过实验测试加强外展服务的有效性与社区合作伙伴——波士顿撒玛利亚人组织 (EXP2) 合作实施干预措施；在两个住院病房实施和测试基于 EHR 的风险算法，特别关注社交媒体的使用健康的决定因素，以改善代表性不足的青少年的预测；并且（EXP3）将测试使用创新的微随机试验设计来连接高危患者的及时适应性干预该中心的第三个目标是支持教育、传播和发展。实施将对自杀预防产生重大和持续的影响。