Predicting Emergency Department Volume Using Forecasting Methods to Create a "Surge Response" for Noncrisis Events
Predicting Emergency Department Volume Using Forecasting Methods to Create a "Surge Response" for Noncrisis Events
复制标题
DOI：
10.1111/j.1553-2712.2012.01359.x
复制
发表时间：
2012-05-01
影响因子：
4.4
通讯作者：
Lavieri, Mariel S.
中科院分区：
文献类型：
--
作者：
Chase, Valerie J.;Cohn, Amy E. M.;Lavieri, Mariel S.
目的：本研究调查了急诊科（艾德）变量是否可以用于数学模型，以预测未来激增的艾德量的基础上，最近的水平使用的医生的能力。该模型可用于指导相关的决定，在非危机相关的浪涌patient volume.Methods：一个回顾性分析，使用的信息跨越2009年7月至2010年6月从一个大型城市教学医院与一级创伤中心。显著性比较用于评估多个患者特异性变量对ED状态的影响。根据历史医生治疗能力和生产力对医生能力进行建模。使用二元逻辑回归分析来确定可用医生能力足以治疗预计在下一时间段到达的所有患者的概率。使用的预测范围为15分钟、30分钟、1小时、2小时、4小时、8小时和12小时。从2010年7月到2010年11月连续5个月的患者数据（与用于生成模型的数据相似）用于验证模型。正预测值、I型和II型误差以及预测非危机激增事件的实时准确性被用来评估模型的预测准确性。需要治疗的新病人与医生总能力的比率（称为护理利用率[CUR]）被认为是艾德状态的可靠预测因子（CUR大于1表示医师能力不足以治疗所有预计到达的患者）。30分钟、8小时和12小时的预测间隔在所有分析的模型中表现最好，偏差分别为1.000、0.951和0.864。使用95%的显著性来验证2010年7月至2010年11月数据集的模型。阳性预测值范围为0.738至0.872，真阳性范围为74%至94%，真阴性范围为70%至90%，这取决于用于确定艾德的状态与30分钟的预测model.Conclusions：CUR是一个新的和强大的指标的艾德系统的性能。该研究能够通过调查不同的预测区间，对较长的响应时间与较短但更准确的预测之间的权衡进行建模。通过使用所提出的模型，当前的实践将得到改进，并且将在非危机日更早地识别出患者数量的激增。学术急诊医学2012; 19：569-576（C）2012由学术急诊医学学会
Objectives: This study investigated whether emergency department (ED) variables could be used in mathematical models to predict a future surge in ED volume based on recent levels of use of physician capacity. The models may be used to guide decisions related to on-call staffing in non-crisis-related surges of patient volume.Methods: A retrospective analysis was conducted using information spanning July 2009 through June 2010 from a large urban teaching hospital with a Level I trauma center. A comparison of significance was used to assess the impact of multiple patient-specific variables on the state of the ED. Physician capacity was modeled based on historical physician treatment capacity and productivity. Binary logistic regression analysis was used to determine the probability that the available physician capacity would be sufficient to treat all patients forecasted to arrive in the next time period. The prediction horizons used were 15 minutes, 30 minutes, 1 hour, 2 hours, 4 hours, 8 hours, and 12 hours. Five consecutive months of patient data from July 2010 through November 2010, similar to the data used to generate the models, was used to validate the models. Positive predictive values, Type I and Type II errors, and real-time accuracy in predicting noncrisis surge events were used to evaluate the forecast accuracy of the models.Results: The ratio of new patients requiring treatment over total physician capacity (termed the care utilization ratio [CUR]) was deemed a robust predictor of the state of the ED (with a CUR greater than 1 indicating that the physician capacity would not be sufficient to treat all patients forecasted to arrive). Prediction intervals of 30 minutes, 8 hours, and 12 hours performed best of all models analyzed, with deviances of 1.000, 0.951, and 0.864, respectively. A 95% significance was used to validate the models against the July 2010 through November 2010 data set. Positive predictive values ranged from 0.738 to 0.872, true positives ranged from 74% to 94%, and true negatives ranged from 70% to 90% depending on the threshold used to determine the state of the ED with the 30-minute prediction model.Conclusions: The CUR is a new and robust indicator of an ED system's performance. The study was able to model the tradeoff of longer time to response versus shorter but more accurate predictions, by investigating different prediction intervals. Current practice would have been improved by using the proposed models and would have identified the surge in patient volume earlier on noncrisis days.ACADEMIC EMERGENCY MEDICINE 2012; 19: 569-576 (C) 2012 by the Society for Academic Emergency Medicine