Hoot Nathan R, Epstein Stephen K, Allen Todd L, Jones Spencer S, Baumlin Kevin M, Chawla Neal, Lee Anna T, Pines Jesse M, Klair Amandeep K, Gordon Bradley D, Flottemesch Thomas J, LeBlanc Larry J, Jones Ian, Levin Scott R, Zhou Chuan, Gadd Cynthia S, Aronsky Dominik
Vanderbilt University Medical Center, Nashville, TN, USA.
Ann Emerg Med. 2009 Oct;54(4):514-522.e19. doi: 10.1016/j.annemergmed.2009.06.006. Epub 2009 Aug 29.
We apply a previously described tool to forecast emergency department (ED) crowding at multiple institutions and assess its generalizability for predicting the near-future waiting count, occupancy level, and boarding count.
The ForecastED tool was validated with historical data from 5 institutions external to the development site. A sliding-window design separated the data for parameter estimation and forecast validation. Observations were sampled at consecutive 10-minute intervals during 12 months (n=52,560) at 4 sites and 10 months (n=44,064) at the fifth. Three outcome measures-the waiting count, occupancy level, and boarding count-were forecast 2, 4, 6, and 8 hours beyond each observation, and forecasts were compared with observed data at corresponding times. The reliability and calibration were measured following previously described methods. After linear calibration, the forecasting accuracy was measured with the median absolute error.
The tool was successfully used for 5 different sites. Its forecasts were more reliable, better calibrated, and more accurate at 2 hours than at 8 hours. The reliability and calibration of the tool were similar between the original development site and external sites; the boarding count was an exception, which was less reliable at 4 of 5 sites. Some variability in accuracy existed among institutions; when forecasting 4 hours into the future, the median absolute error of the waiting count ranged between 0.6 and 3.1 patients, the median absolute error of the occupancy level ranged between 9.0% and 14.5% of beds, and the median absolute error of the boarding count ranged between 0.9 and 2.8 patients.
The ForecastED tool generated potentially useful forecasts of input and throughput measures of ED crowding at 5 external sites, without modifying the underlying assumptions. Noting the limitation that this was not a real-time validation, ongoing research will focus on integrating the tool with ED information systems.
我们应用一种先前描述的工具来预测多个机构的急诊科拥挤情况，并评估其在预测近期候诊人数、占用水平和滞留人数方面的通用性。
使用来自开发地点以外5个机构的历史数据对ForecastED工具进行验证。采用滑动窗口设计将数据分为参数估计和预测验证两部分。在4个地点的12个月（n = 52,560）和第5个地点的10个月（n = 44,064）期间，以连续10分钟的间隔对观察数据进行采样。对三个结果指标——候诊人数、占用水平和滞留人数——在每次观察后的2、4、6和8小时进行预测，并将预测结果与相应时间的观察数据进行比较。按照先前描述的方法测量可靠性和校准情况。经过线性校准后，用中位数绝对误差测量预测准确性。
该工具成功应用于5个不同的地点。其预测在2小时时比8小时时更可靠、校准更好且更准确。该工具在原始开发地点和外部地点之间的可靠性和校准情况相似；滞留人数是个例外，在5个地点中的4个地点可靠性较低。各机构之间在准确性方面存在一些差异；在预测未来4小时时，候诊人数的中位数绝对误差在0.6至3.1名患者之间，占用水平的中位数绝对误差在床位的9.0%至14.5%之间，滞留人数的中位数绝对误差在0.9至2.8名患者之间。
ForecastED工具在5个外部地点生成了关于急诊科拥挤的输入和产出指标的潜在有用预测，且无需修改基本假设。注意到这不是实时验证这一局限性，正在进行的研究将专注于将该工具与急诊科信息系统集成。
