Division of Pharmaceutical Outcomes and Policy, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Am J Emerg Med. 2020 Feb;38(2):258-265. doi: 10.1016/j.ajem.2019.04.048. Epub 2019 Apr 29.
To estimate the association between adopting emergency department (ED) crowding interventions and emergency departments' core performance measures.
We analyzed the National Hospital Ambulatory Medical Care Survey (NHAMCS) data from 2007 to 2015. The outcome variables are ED length of stay for discharged and admitted patients, boarding time, wait time and percentage of patients who left ED before being seen (LWBS). The independent variables are whether or not a hospital adopted each of the 20 crowding interventions. Controlling for patient-level, hospital level and temporal confounders we analyze and report results using multivariable logit model.
Between 2007 and 2015, NHAMCS collected data for 269,721 ED visit encounters, representing a nationwide of about 1.18 billion separate ED visits. Of 20 crowding interventions we tested, using adopting bedside registration (OR = 0.89, 95% CI = 0.75-0.98, P < .05), electronic dashboard (OR = 0.86, 95% CI = 0.76-0.98, P < .05), kiosk check-in technology (OR = 0.56, 95% CI = 0.41-0.83, P < .001), physician based triage (OR = 0.86, 95% CI = 0.73-0.99, P < .05) full capacity protocol (OR = 0.91, 95% CI = 0.79-0.99, P < .05) are associated with decrease in the odds of prolonged wait time. Adopting kiosk check-in (OR = 0.55, 95% CI = 0.35-0.85, P < .05) is associated with a decrease in the odds of prolonged boarding time. Using wireless communication devices (OR = 0.77, 95% CI = 0.57-0.97, P < .05), bedside registration (OR = 0.77, 95% CI = 0.64-0.094, P < .05) and pooled nursing (OR = 0.84, 95% CI = 0.72-0.98, P < .05) are associated with decrease in the odds of a patient LWBS.
Majority of interventions did not significantly associated with ED' core performance measures.
评估采用急诊科(ED)拥挤干预措施与急诊科核心绩效指标之间的关联。
我们分析了 2007 年至 2015 年的国家医院门诊医疗调查(NHAMCS)数据。结果变量为出院和入院患者的 ED 停留时间、住院时间、等待时间以及在接受治疗前离开 ED(LWBS)的患者比例。自变量为医院是否采用了 20 项拥挤干预措施中的每一项。在控制患者水平、医院水平和时间混杂因素后,我们使用多变量逻辑回归模型进行分析并报告结果。
在 2007 年至 2015 年期间,NHAMCS 共收集了 269721 例 ED 就诊的病例,代表了全国约 11.8 亿次单独的 ED 就诊。在我们测试的 20 种拥挤干预措施中,使用床边登记(OR=0.89,95%CI=0.75-0.98,P<.05)、电子仪表板(OR=0.86,95%CI=0.76-0.98,P<.05)、自助登记技术(OR=0.56,95%CI=0.41-0.83,P<.001)、基于医生的分诊(OR=0.86,95%CI=0.73-0.99,P<.05)、全容量协议(OR=0.91,95%CI=0.79-0.99,P<.05)与延长等待时间的几率降低相关。采用自助登记(OR=0.55,95%CI=0.35-0.85,P<.05)与延长住院时间的几率降低相关。使用无线通信设备(OR=0.77,95%CI=0.57-0.97,P<.05)、床边登记(OR=0.77,95%CI=0.64-0.094,P<.05)和综合护理(OR=0.84,95%CI=0.72-0.98,P<.05)与患者 LWBS 的几率降低相关。
大多数干预措施与 ED 的核心绩效指标没有显著关联。