Reboredo Juan C, Barba-Queiruga Jose Ramon, Ojea-Ferreiro Javier, Reyes-Santias Francisco
Department of Economics, University of Santiago (USC), Santiago de Compostela, Spain.
ECOBAS Research Centre, Santiago de Compostela, Spain.
Health Econ Rev. 2023 Oct 28;13(1):51. doi: 10.1186/s13561-023-00456-5.
Forecasting patient arrivals to hospital emergency departments is critical to dealing with surges and to efficient planning, management and functioning of hospital emerency departments.
We explore whether past mean values and past observations are useful to forecast daily patient arrivals in an Emergency Department.
We examine whether an integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) model can yield a better conditional distribution fit and forecast of patient arrivals by using past arrival information and taking into account the dynamics of the volatility of arrivals.
We document that INGARCH models improve both in-sample and out-of-sample forecasts, particularly in the lower and upper quantiles of the distribution of arrivals.
Our results suggest that INGARCH modelling is a useful model for short-term and tactical emergency department planning, e.g., to assign rotas or locate staff for unexpected surges in patient arrivals.
预测患者前往医院急诊科的人数对于应对激增情况以及医院急诊科的高效规划、管理和运作至关重要。
我们探讨过去的均值和过去的观测值是否有助于预测急诊科每日的患者就诊人数。
我们通过使用过去的就诊信息并考虑就诊人数波动的动态情况，研究整数取值的广义自回归条件异方差（INGARCH）模型是否能产生更好的条件分布拟合和患者就诊人数预测。
我们证明INGARCH模型在样本内和样本外预测方面均有改进，尤其是在就诊人数分布的较低和较高分位数处。
我们的结果表明，INGARCH建模是用于短期和战术性急诊科规划的有用模型，例如为应对患者就诊人数意外激增安排轮班或调配人员。
