Wretborn Jens, Khoshnood Ardavan, Wieloch Mattias, Ekelund Ulf
Department of Emergency Medicine, Skåne University Hospital, Lund, Sweden.
Department of Emergency Medicine, Skåne University Hospital, Malmö, Sweden.
PLoS One. 2015 Jun 17;10(6):e0130020. doi: 10.1371/journal.pone.0130020. eCollection 2015.
Emergency department (ED) crowding is an increasing problem in many countries. The purpose of this study was to develop a quantitative model that estimates the degree of crowding based on workload in Swedish EDs.
At five different EDs, the head nurse and physician assessed the workload on a scale from 1 to 6 at randomized time points during a three week period in 2013. Based on these assessments, a regression model was created using data from the computerized patient log system to estimate the level of crowding based on workload. The final model was prospectively validated at the two EDs with the largest census.
Workload assessments and data on 14 variables in the patient log system were collected at 233 time points. The variables Patient hours, Occupancy, Time waiting for the physician and Fraction of high priority (acuity) patients all correlated significantly with the workload assessments. A regression model based on these four variables correlated well with the assessed workload in the initial dataset (r2 = 0.509, p < 0.001) and with the assessments in both EDs during validation (r2 = 0.641; p < 0.001 and r2 = 0.624; p < 0.001).
It is possible to estimate the level of crowding based on workload in Swedish EDs using data from the patient log system. Our model may be applicable to EDs with different sizes and characteristics, and may be used for continuous monitoring of ED workload. Before widespread use, additional validation of the model is needed.
急诊科拥挤在许多国家都是一个日益严重的问题。本研究的目的是开发一种定量模型,该模型可根据瑞典急诊科的工作量来估计拥挤程度。
在2013年为期三周的时间里,在五个不同的急诊科,护士长和医生在随机时间点按1至6的等级评估工作量。基于这些评估,利用来自计算机化患者日志系统的数据创建了一个回归模型,以根据工作量估计拥挤程度。最终模型在患者人数最多的两个急诊科进行了前瞻性验证。
在233个时间点收集了工作量评估数据和患者日志系统中14个变量的数据。变量患者小时数、占用率、等待医生的时间以及高优先级(急症)患者的比例均与工作量评估显著相关。基于这四个变量的回归模型与初始数据集中评估的工作量相关性良好(r2 = 0.509,p < 0.001),并且在验证期间与两个急诊科的评估结果相关性良好(r2 = 0.641;p < 0.001和r2 = 0.624;p < 0.001)。
利用患者日志系统的数据可以根据瑞典急诊科的工作量来估计拥挤程度。我们的模型可能适用于不同规模和特点的急诊科,可用于持续监测急诊科工作量。在广泛应用之前,需要对该模型进行进一步验证。