Pediatric Transport Learning Laboratory

儿科交通学习实验室

• 批准号: 10768132
• 负责人: Rachel A Umoren
• 金额: $ 49.97万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10768132
• 关键词: Pediatric; Transport; Learning; Laboratory

Project Summary/Abstract The overall goal of this project is to advance the care of pediatric patients (up to 21 years of age) during medical ground or air transport from one hospital to another within a regional network. The project will build on our previous experience with neonatal patient transport and use a five-stage innovation cycle, including: problem analysis, design, development, implementation, and evaluation to identify and address the salient issues and risks of regional neonatal transportation for which new and innovative approaches are needed. In collaboration with transport providers and other stakeholders, we will analyze current workflow processes, transport records (local and statewide databases), and facilities at referral and receiving facilities to develop a complete understanding of system issues and to define the current and ideal states. This detailed problem analysis phase will enable the integration of real-time data into a transport “digital twin” model to optimize regional consultation, triage, and transport of pediatric patients to facilities with the appropriate level of care and availability of space and staffing. With the input of clinical transport team staff and stakeholders, the project team will work with collaborators from the University of Washington Industrial and Systems Engineering Department to integrate machine learning into the Transport Monitoring and Communications (T-MAC) system to support the analysis of data feeds while on transport. The machine learning augmented T-MAC system will undergo repeated testing and revision to ensure that it can functionally and efficiently facilitate information flow between the medical control physician, referring facility and transport team. In addition, we will develop robust processes to support information flow to patients and families in the peri- transport period. To accomplish this, we will work with families to identify gaps in communication and connection to local and receiving facility resources and identify transport-specific needs. The efficacy of the T-MAC system will be evaluated in a realistic in situ simulation and clinical settings. We are confident that the lessons learned through this study will improve pediatric patient safety on medical transports and will be transferable to patient populations that undergo both short and long-range interfacility transports.

项目概要/摘要 该项目的总体目标是促进儿科患者（21 岁以下）的护理 该项目将建立在区域网络内从一家医院到另一家医院的医疗地面或空中运输。 我们之前在新生儿患者转运方面的经验并采用五阶段创新周期，包括： 问题分析、设计、开发、实施和评估，以识别和解决突出问题 需要新的创新方法来解决区域新生儿运输的问题和风险。 我们将与运输提供商和其他利益相关者合作，分析当前的工作流程， 运输记录（当地和司法数据库）以及转诊和接收设施，以制定 完全了解系统问题并定义这个详细问题的当前和理想状态。 分析阶段将能够将实时数据集成到运输“数字孪生”模型中以优化 将儿科患者进行区域咨询、分诊并将其运送至具有适当护理水平的机构 以及空间和人员配备的可用性。 根据临床运输团队工作人员和利益相关者的意见，项目团队将与合作者合作 来自华盛顿大学工业与系统工程系的集成机器 学习传输监控和通信（T-MAC）系统以支持数据分析 机器学习增强型 T-MAC 系统将在运输过程中进行反复测试和喂养。 修订以确保其能够有效地促进医疗控制之间的信息流动 医生、转诊设施和运输团队。 此外，我们将开发强大的流程来支持信息流向患者和家属 为了实现这一目标，我们将与家人一起找出沟通和交流方面的差距。 连接当地和接收设施资源并确定特定于运输的需求。 T-MAC 系统的功效将在真实的原位模拟和临床环境中进行评估。 相信通过这项研究吸取的经验教训将改善医疗运输中儿科患者的安全 并将可转移到接受短程和远程设施间转运的患者群体。