From the Task Force 21 MED (J.S., D.M., D.F., K.R., M.J.M.), 21st Combat Support Hospital, Baghdad Diplomatic Support Cell, Baghdad, Iraq.
J Trauma Acute Care Surg. 2019 Jul;87(1S Suppl 1):S152-S158. doi: 10.1097/TA.0000000000002283.
Capabilities for daily operations at medical facilities are determined by routine staffing levels and bed availability. Although all health care facilities must be prepared for mass casualty events, there are few tools or metrics to estimate nursing requirements, medical hold surge capacity, and critical failure points for high-volume events. We sought to create a modifiable and customizable toolkit for producing reliable capability estimates across a range of scenarios.
The inputs for key variables (patient volume, acuity, staffing, beds available, and medical evacuation) were extrapolated from the literature and interviews with subject-matter experts. Models were developed for a small austere facility, one large facility, and one expanded large facility. Inputs were serially increased to identify the "failure point" for each and the variables most contributing to failure.
Two scenarios were created, one moderate volume and one for mass casualty events. Variables most affecting capacity were identified as: average daily volume, mass casualty volume and frequency, acuity, and medical evacuation frequency. The large facility reached failure in 13 (43%) of 30 days and was attributed to bed capacity. The small facility did not reach failure point for bed capability or staffing under low volumes; however, it reached failure immediately under moderate volumes. The most significant factor was medical evacuation frequency. An automated dashboard was created to provide immediate estimates based on varying inputs.
We developed an automated and customizable toolkit to analyze mass casualty/disaster capabilities in relation to nurse staffing and hold capacity, assess the impact of key variables, and predict resource needs. Total bed capacity and hospital throughput via discharge/medical evacuation are the most critical factors in surge capacity and sustained mass casualty operations. Decreasing medical evacuation frequency is the greatest contributor to reaching "failure point."
Not Applicable.
医疗机构的日常运营能力取决于常规人员配置水平和床位可用性。尽管所有医疗保健设施都必须为大规模伤亡事件做好准备，但几乎没有工具或指标来估计护理需求、医疗储备激增能力以及高容量事件的关键故障点。我们试图创建一个可修改和可定制的工具包，以在一系列场景中生成可靠的能力估计。
关键变量（患者数量、病情严重程度、人员配置、可用床位和医疗后送）的输入数据是从文献和主题专家访谈中推断出来的。为一个小型简陋设施、一个大型设施和一个扩展的大型设施开发了模型。输入数据依次增加，以确定每个设施的“故障点”以及导致故障的主要变量。
创建了两个场景，一个是中等容量，另一个是大规模伤亡事件。对容量影响最大的变量是：平均每日量、大规模伤亡量和频率、病情严重程度和医疗后送频率。大型设施在 13 天（43%）内达到床位容量的失效点，原因是床位容量。在低容量下，小型设施的床位能力或人员配置未达到失效点；但是，在中等容量下，它立即达到失效点。最重要的因素是医疗后送频率。创建了一个自动化仪表板，可根据不同的输入提供即时估计。
我们开发了一个自动化和可定制的工具包，用于分析与护士人员配置和储备能力相关的大规模伤亡/灾害能力，评估关键变量的影响，并预测资源需求。总床位容量和通过出院/医疗后送的医院吞吐量是激增能力和持续大规模伤亡作业的最关键因素。降低医疗后送频率是达到“失效点”的最大贡献因素。
不适用。
