Wang Hao, Robinson Richard D, Cowden Chad D, Gorman Violet A, Cook Christopher D, Gicheru Eugene K, Schrader Chet D, Jayswal Rani D, Zenarosa Nestor R
Department of Emergency Medicine, Integrative Emergency Services Physician Group, John Peter Smith Health Network, Fort Worth, Texas, USA.
Department of Emergency Medicine and Urgent Care Center, Integrative Emergency Services Physician Group, John Peter Smith Health Network, Fort Worth, Texas, USA.
BMJ Open. 2015 Apr 14;5(4):e006860. doi: 10.1136/bmjopen-2014-006860.
To derive a tool to determine Urgent Care Center (UCC) crowding and investigate the association between different levels of UCC overcrowding and negative patient care outcomes.
Prospective pilot study.
Single centre study in the USA.
3565 patients who registered at UCC during the 21-day study period were included. Patients who had no overcrowding statuses estimated due to incomplete collection of operational variables at the time of registration were excluded in this study. 3139 patients were enrolled in the final data analysis.
A crowding estimation tool (SONET: Severely overcrowded, Overcrowded and Not overcrowded Estimation Tool) was derived using the linear regression analysis. The average length of stay (LOS) in UCC patients and the number of left without being seen (LWBS) patients were calculated and compared under the three different levels of UCC crowding.
Four independent operational variables could affect the UCC overcrowding score including the total number of patients, the number of results pending for patients, the number of patients in the waiting room and the longest time a patient was stationed in the waiting room. In addition, UCC overcrowding was associated with longer average LOS (not overcrowded: 133±76 min, overcrowded: 169±79 min, and severely overcrowded: 196±87 min, p<0.001) and an increased number of LWBS patients (not overcrowded: 0.28±0.69 patients, overcrowded: 0.64±0.98, and severely overcrowded: 1.00±0.97).
The overcrowding estimation tool (SONET) derived in this study might be used to determine different levels of crowding in a high volume UCC setting. It also showed that UCC overcrowding might be associated with negative patient care outcomes.
开发一种用于确定紧急护理中心(UCC)拥挤程度的工具,并研究不同程度的UCC过度拥挤与负面患者护理结果之间的关联。
前瞻性试点研究。
美国的单中心研究。
纳入了在21天研究期间在UCC登记的3565名患者。本研究排除了因登记时操作变量收集不完整而无法估计拥挤状态的患者。3139名患者纳入最终数据分析。
使用线性回归分析得出拥挤估计工具(SONET:严重拥挤、拥挤和不拥挤估计工具)。计算并比较了UCC患者在三种不同拥挤程度下的平均住院时间(LOS)和未就诊离开(LWBS)患者的数量。
四个独立的操作变量可影响UCC拥挤评分,包括患者总数、患者待处理结果数量、候诊室患者数量以及患者在候诊室停留的最长时间。此外,UCC过度拥挤与更长的平均住院时间相关(不拥挤:133±76分钟,拥挤:169±79分钟,严重拥挤:196±87分钟,p<0.001)以及LWBS患者数量增加(不拥挤:0.28±0.69名患者,拥挤:0.64±0.98名,严重拥挤:1.00±0.97名)。
本研究得出的拥挤估计工具(SONET)可用于确定高流量UCC环境中的不同拥挤程度。研究还表明,UCC过度拥挤可能与负面患者护理结果相关。