Forecasting emergency department crowding: A discrete event simulation
Forecasting emergency department crowding: A discrete event simulation
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DOI：
10.1016/j.annemergmed.2007.12.011
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发表时间：
2008-08-01
影响因子：
6.2
通讯作者：
Aronsky, Dominik
中科院分区：
文献类型：
--
作者：
Hoot, Nathan R.;LeBlanc, Larry J.;Aronsky, Dominik
研究目的：建立急诊科客流的离散事件模拟模型，以预测急诊科近期的运行状况，并用急诊科拥挤的几种度量对预测结果进行验证。研究纯粹是理论上的，而验证涉及来自学术ED的患者数据。模型的输入和输出分别是对急诊室中每个现在和将来的患者的6个变量的描述。我们使用滑动窗口设计对模型进行了验证，确保了时间序列中拟合和验证数据的分离。我们在2006年连续进行了10分钟的观察(n=52,560)。结果衡量标准--每次观察都预测未来2、4、6和8小时--是等待次数、等待时间、入住率、停留时间、登机次数、登车时间和救护车改道。结果：拥挤预测与实际结果的相关性开始较高，直到未来8小时逐渐降低(等待次数的皮尔逊相关系数最低=0.56；等待时间=0.49；入住率=0.78；停留时间=0.86；登机次数=0.79；登机时间=0.80)。除登机时间外，其余均值对所有结果都是无偏的。救护车分流的区分力在未来8小时内一直保持较高水平(接收器操作特征曲线下的最低区域=0.86)。结论：通过对病人流而不是操作摘要变量进行建模，我们的模拟预测了近期急诊拥挤的几个量度，具有不同程度的良好性能。
Study objective: To develop a discrete event simulation of emergency department (ED) patient flow for the purpose of forecasting near-future operating conditions and to validate the forecasts with several measures of ED crowding.Methods: We developed a discrete event simulation of patient flow with evidence from the literature. Development was purely theoretical, whereas validation involved patient data from an academic ED. The model inputs and outputs, respectively, are 6-variable descriptions of every present and future patient in the ED. We validated the model by using a sliding-window design, ensuring separation of fitting and validation data in time series. We sampled consecutive 10-minute observations during 2006 (n = 52,560). The outcome measures-all forecast 2, 4, 6, and 8 hours into the future from each observation-were the waiting count, waiting time, occupancy level, length of stay, boarding count, boarding time, and ambulance diversion. Forecasting performance was assessed with Pearson's correlation, residual summary statistics, and area under the receiver operating characteristic curve.Results: The correlations between crowding forecasts and actual outcomes started high and decreased gradually up to 8 hours into the future (lowest Pearson's r for waiting count = 0.56; waiting time = 0.49; occupancy level = 0.78; length of stay = 0.86; boarding count = 0.79; boarding time = 0.80). The residual means were unbiased for all outcomes except the boarding time. The discriminatory power for ambulance diversion remained consistently high up to 8 hours into the future (lowest area under the receiver operating characteristic curve = 0.86).Conclusion: By modeling patient flow, rather than operational summary variables, our simulation forecasts several measures of near-future ED crowding, with various degrees of good performance.