基于排队论的急诊预检分诊最佳窗口数测算及仿真研究

doi:10.3761/j.issn.2096-7446.2026.10.015

摘要/Abstract

摘要：

目的构建急诊预检分诊窗口数与患者到达率的量化关系模型，并提出多场景下的动态优化策略，为急诊人力配置提供数据驱动的决策工具。方法回顾性分析上海市某三级甲等医院2024年全年急诊数据，首先基于多队列多服务台M/M/S排队模型，推导预检分诊最佳窗口数的静态测算公式；其次利用Anylogic仿真软件构建涵盖常规分诊、救护车激增、发热患者分流3类典型场景的动态仿真模型，对静态测算公式进行验证与修正；最后基于仿真结果提出三级负荷分级响应策略。结果患者到达规律呈日间及季节性双高峰，平均分诊服务时间为（127.56±26.48） s。常规场景下拟合得到线性配置函数：y=0.035x+0.5（y为分诊护士数量，x为每小时患者到达数量）。仿真验证显示，模型整体均方根百分比误差（root mean square percentage error，RMSPE）为4.5%，优于单一静态模型（RMSPE=12.3%）。救护车激增场景提出双变量动态公式N_窗口=λ/30+0.3·P_救护车·λ（P为救护车占比），较静态公式使卸载延迟缩短46.9%。发热患者分流场景因筛查复杂度提升，窗口需求呈二次函数关系：N_窗口=0.000 08λ²+0.016λ+1.2。结论该研究的“静态+动态”整合模型为急诊预检分诊人力配置提供了可量化的递进式决策工具，有助于推动急诊管理从经验驱动向数据驱动转型。该研究为单中心回顾性设计，模型在非三级医院、不同区域的普适性有待验证，后续将开展多中心研究并纳入成本效益分析，以优化模型参数与推广适用性。

关键词: 急诊科, 预检分诊, 排队论, 资源配置, Anylogic仿真, 护理管理研究

Abstract:

Objective To construct a quantitative relationship model between the number of emergency triage windows and patient arrival rates，and to propose dynamic optimization strategies for multiple scenarios，thereby providing a data-driven decision-making tool for emergency workforce allocation. Methods A retrospective analysis was conducted on the full-year 2024 emergency department data from a tertiary-grade A hospital in Shanghai. Based on the multi-queue multi-server M/M/S queuing model，a static calculation formula for the optimal number of triage windows was derived. Subsequently，Anylogic simulation software was employed to construct a dynamic simulation model covering three typical scenarios—routine triage，ambulance surge，and febrile patient diversion—to validate and refine the static formula. Finally，a three-level graded load response strategy was proposed based on the simulation results. Results Patient arrivals exhibited diurnal and seasonal bimodal peaks，with an average triage service time of （127.56±26.48） seconds. For the routine triage scenario，a linear configuration function was fitted：y= 0.035x+0.5，where y represents the number of triage nurses and x denotes the number of patient arrivals per hour. Simulation validation showed an overall root mean square percentage error（RMSPE） of 4.5%，outperforming the static model alone（RMSPE=12.3%）. For the ambulance surge scenario，a bivariate dynamic formula was proposed：N_windows=λ/30+0.3·P_ambulance·λ（where P_ambulance represents the proportion of ambulance arrivals），which reduced offload delay by 46.9% compared with the static formula. For the febrile patient diversion scenario，owing to increased screening complexity，the window requirement followed a quadratic function：N_windows=0.000 08λ²+ 0.016λ+1.2. Conclusion The integrated “static plus dynamic” model developed in this study provides a quantifiable，progressive decision-making tool for emergency triage workforce allocation，facilitating the transition of emergency management from experience-driven to data-driven approaches. As a single-center retrospective study，the generalization of the model to non-tertiary hospitals and other regions requires further validation. Future multicenter studies incorporating cost-effectiveness analysis are warranted to optimize model parameters and promote broader applicability.

Key words: Emergency Department, Pre-Examination and Triage, Queuing Theory, Resource Allocation, Anylogic Simulation, Nursing Management Research

李蕊, 周丽金, 张月. 基于排队论的急诊预检分诊最佳窗口数测算及仿真研究[J]. 中华急危重症护理杂志, 2026, 7(10): 1235-1242.

LI Rui, ZHOU Lijin, ZHANG Yue. Calculation and simulation study of optimal window numbers for emergency triage based on queuing theory[J]. Chinese Journal of Emergency and Critical Care Nursing, 2026, 7(10): 1235-1242.

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