RAPID:Triaging decisions during catastrophic events: a study of frontline triage nurses

RAPID：灾难性事件期间的分诊决策：对一线分诊护士的研究

• 批准号: 2031371
• 负责人: Priyadarshini Pennathur
• 金额: $ 9.86万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-15 至 2022-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031371&HistoricalAwards=false
• 关键词: RAPID; Triaging; decisions; during; catastrophic

The goal of this RAPID project is to model decision-making among nurses when they triage patients during the COVID-19 pandemic. Triage nurses act as frontline gatekeepers and perform a difficult balancing act during a pandemic. They must not only ensure that patients who need immediate care get it in a timely manner but must also filter incoming patients to prevent infections and to reduce undue burden on hospital resources. The complexity and risk in their decisions are influenced by how the nurses themselves perceive the risk of a pandemic, and how they associate and project their risk perception with the information a patient provides. Conventional triage decision making criteria, protocols and processes based only on a linear, discrete, “single-symptom at a time” risk screening approach are woefully inadequate to tackle triage decisions in a pandemic of this scale and complexity. Nurses play a pivotal role in ensuring safe and timely patient care and in limiting the spread of COVID-19-like pandemics. The knowledge gained from this project about decision making and evidence-based practices during crises will benefit organizations worldwide, by proving data on factors that make decision-making. These data can drive training and guidelines development. Triage nurses routinely make triage decisions about patients. But, during a pandemic, they make particularly complex and risky decisions. Triage decision making criteria and protocols must reflect a deep understanding of how nurses weigh patient symptoms, and match them to disease conditions, while also managing a multitude of complex, interrelated decision constraints, including their own risk perceptions, and limited, uncertain, confounding information, in the midst of a pandemic with major safety consequences. To identify the constraints nurses face when making triaging decisions, and to model the strategies they use when triaging, the project studies triage nurses from two large academic medical centers. The project retrospectively analyzes triage phone calls for patient risk screening, prospectively records screens nurses use as information sources, and interviews nurses about their constraints, strategies, risk perception and cognitive workload. The project maps a nurse’s patient-specific decision-making trajectories to reveal their constraints and how they managed them. Cumulatively, the data is expected to reveal generalizable strategies for triage decisions during catastrophic events.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该RAPID项目的目标是在COVID-19大流行期间为护士分流患者时建立决策模型。分诊护士作为前线的守门人，在大流行期间执行一项艰难的平衡行动。他们不仅必须确保需要立即护理的病人及时得到护理，而且还必须过滤入院病人，以防止感染，并减少对医院资源的不必要负担。他们决策的复杂性和风险受到护士自己如何看待大流行病风险的影响，以及他们如何将自己的风险感知与患者提供的信息联系起来。传统的分类决策标准、协议和流程仅仅基于线性的、离散的、“一次单一症状”的风险筛查方法，对于处理这种规模和复杂性的大流行病中的分类决策来说，是远远不够的。护士在确保安全和及时的患者护理以及限制COVID-19类大流行病的传播方面发挥着关键作用。 从该项目中获得的关于危机期间决策和循证实践的知识将通过证明决策因素的数据而使世界各地的组织受益。这些数据可以推动培训和指导方针的制定。分诊护士通常对病人进行分诊决定。但是，在大流行期间，他们会做出特别复杂和危险的决定。分诊决策标准和协议必须反映护士如何权衡患者症状的深刻理解，并将其与疾病状况相匹配，同时还要管理大量复杂的、相互关联的决策约束，包括他们自己的风险认知，以及在具有重大安全后果的流行病中有限的、不确定的、令人困惑的信息。为了确定护士在做出分诊决定时所面临的限制，并对他们在分诊时使用的策略进行建模，该项目研究了来自两个大型学术医疗中心的分诊护士。该项目回顾性地分析了病人风险筛查分诊电话，前瞻性地记录屏幕护士使用的信息源，并采访护士的限制，策略，风险感知和认知工作量。该项目绘制了护士的患者特定的决策轨迹，以揭示他们的限制，以及他们如何管理它们。累积起来，这些数据预计将揭示在灾难性事件中分类决策的可推广策略。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。