Storrow Alan B, Zhou Chuan, Gaddis Gary, Han Jin H, Miller Karen, Klubert David, Laidig Andy, Aronsky Dominik
Department of Emergency Medicine, Vanderbilt University, Nashville, TN, USA.
Acad Emerg Med. 2008 Nov;15(11):1130-5. doi: 10.1111/j.1553-2712.2008.00181.x. Epub 2008 Jul 14.
The effect of decreasing lab turnaround times on emergency department (ED) efficiency can be estimated through system-level simulation models and help identify important outcome measures to study prospectively. Furthermore, such models may suggest the advantage of bedside or point-of-care testing and how they might affect efficiency measures.
The authors used a sophisticated simulation model in place at an adult urban ED with an annual census of 55,000 patient visits. The effect of decreasing turnaround times on emergency medical services (EMS) diversion, ED patient throughput, and total ED length of stay (LOS) was determined.
Data were generated by using system dynamics analytic modeling and simulation approach on 90 separate days from December 2, 2007, through February 29, 2008. The model was a continuous simulation of ED flow, driven by real-time actual patient data, and had intrinsic error checking to assume reasonable goodness-of-fit. A return of complete laboratory results incrementally at 120, 100, 80, 60, 40, 20, and 10 minutes was compared. Diversion calculation assumed EMS closure when more than 10 patients were in the waiting room and 100% ED bed occupancy had been reached for longer than 30 minutes, as per local practice. LOS was generated from data insertion into the patient flow stream and calculation of time to specific predefined gates. The average accuracy of four separate measurement channels (waiting room volume, ED census, inpatient admit stream, and ED discharge stream), all across 24 hours, was measured by comparing the area under the simulated curve against the area under the measured curve. Each channel's accuracy was summed and averaged for an overall accuracy rating.
As lab turnaround time decreased from 120 to 10 minutes, the total number of diversion days (maximum 57 at 120 minutes, minimum 29 at 10 minutes), average diversion hours per day (10.8 hours vs. 6.0 hours), percentage of days with diversion (63% vs. 32%), and average ED LOS (2.77 hours vs. 2.17 hours) incrementally decreased, while average daily throughput (104 patients vs. 120 patients) increased. All runs were at least 85% accurate.
This simulation model suggests compelling improvement in ED efficiency with decreasing lab turnaround time. Outcomes such as time on EMS diversion, ED LOS, and ED throughput represent important but understudied areas that should be evaluated prospectively. EDs should consider processes that will improve turnaround time, such as point-of-care testing, to obtain these goals.
通过系统层面的模拟模型可以估算缩短实验室周转时间对急诊科（ED）效率的影响，这有助于确定前瞻性研究的重要结果指标。此外，此类模型可能会显示床边检测或即时检测的优势以及它们对效率指标的影响方式。
作者在一家年接诊量为55000人次的成人城市急诊科使用了一个复杂的模拟模型。确定了缩短周转时间对紧急医疗服务（EMS）分流、急诊科患者流量以及急诊科总住院时间（LOS）的影响。
通过系统动力学分析建模和模拟方法，在2007年12月2日至2008年2月29日的90个不同日期生成数据。该模型是对急诊科流程的连续模拟，由实时实际患者数据驱动，并具有内在的误差检查以确保合理的拟合优度。比较了实验室完整结果分别在120、100、80、60、40、20和10分钟时逐步返回的情况。按照当地惯例，当候诊室有超过10名患者且急诊科床位占用率达到100%超过30分钟时，分流计算假定EMS关闭。住院时间是通过将数据插入患者流程并计算到达特定预定义关卡的时间生成的。通过比较模拟曲线下的面积与测量曲线下的面积，测量了24小时内四个独立测量通道（候诊室容量、急诊科普查、住院患者收治流和急诊科出院流）的平均准确性。将每个通道的准确性相加并求平均值以获得总体准确性评级。
随着实验室周转时间从120分钟减少到10分钟，分流天数总数（120分钟时最多57天，10分钟时最少29天）、每天平均分流小时数（10.8小时对6.0小时）、出现分流的天数百分比（63%对32%）以及急诊科平均住院时间（2.77小时对2.17小时）逐渐减少，而每日平均流量（104例患者对120例患者）增加。所有运行的准确性至少为85%。
该模拟模型表明，随着实验室周转时间的缩短，急诊科效率有显著提高。诸如EMS分流时间、急诊科住院时间和急诊科流量等结果代表了重要但研究不足的领域，应进行前瞻性评估。急诊科应考虑采用如即时检测等能改善周转时间的流程来实现这些目标。
