When ambulances’ turnaround time (TT) in emergency departments is prolonged, it not only affects the victim severely but also causes unavailability of resources in emergency medical services (EMSs) and, consequently, leaves a locality unprotected. This problem may worsen with abnormal situations, e.g., the current coronavirus disease 2019 (COVID-19) pandemic. Taking this into consideration, this paper presents a first study on the COVID-19 impact on ambulances’ TT by analyzing historical data from the Departmental Fire and Rescue Service of the Doubs (SDIS 25), in France, for three hospitals. Because the TTs of SDIS 25 ambulances increased, this paper also calculated and analyzed the number of breakdowns in services, which augmented due to shortage of ambulances that return on service in time. It is, therefore, vital to have a decision-support tool to better reallocate resources by knowing the time EMSs ambulances and personnel will be in use. Thus, this paper proposes a novel two-stage methodology based on machine learning (ML) models to forecast the TT of each ambulance in a given time and hospital. The first stage uses a multivariate model of regularly spaced time series to predict the average TT (AvTT) per hour, which considers temporal variables and external ones (e.g., COVID-19 statistics, weather data). The second stage utilizes a multivariate irregularly spaced time series model, which considers temporal variables of each ambulance departure, type of intervention, external variables, and the previously predicted AvTT as inputs. Four state-of-the-art ML models were considered in this paper, namely, Light Gradient Boosted Machine, Multilayer Perceptron, Long Short-Term Memory, and Prophet. As shown in the results, the proposed methodology provided remarkable results for practical purposes. The AvTT accuracies obtained for the three hospitals were 90.16%, 97.02%, and 93.09%. And the TT accuracies were 74.42%, 86.63%, and 76.67%, all with an error margin of $\pm$10 min.

当救护车在急诊室的周转时间 (TT) 延长时，不仅会严重影响受害者，还会导致紧急医疗服务 (EMS) 资源不可用，从而使当地得不到保护。这个问题可能会随着异常情况而恶化，例如当前的冠状病毒病 2019 (COVID-19) 大流行。考虑到这一点，本文通过分析法国杜省消防和救援局 (SDIS 25) 三家医院的历史数据，首次研究了 COVID-19 对救护车 TT 的影响。由于 SDIS 25 救护车的 TTs 增加，本文还计算和分析了服务中断的数量，由于救护车不能及时返回服务而增加。因此，它是 通过了解 EMS 救护车和人员的使用时间，拥有决策支持工具以更好地重新分配资源至关重要。因此，本文提出了一种基于机器学习 (ML) 模型的新型两阶段方法来预测给定时间和医院内每辆救护车的 TT。第一阶段使用规则间隔时间序列的多元模型来预测每小时的平均 TT (AvTT)，其中考虑了时间变量和外部变量（例如，COVID-19 统计数据、天气数据）。第二阶段使用多变量不规则间隔时间序列模型，该模型将每辆救护车出发的时间变量、干预类型、外部变量和先前预测的 AvTT 作为输入。本文考虑了四种最先进的 ML 模型，即 Light Gradient Boosted Machine，多层感知器、长短期记忆和 Prophet。如结果所示，所提出的方法为实际目的提供了显着的结果。三家医院获得的 AvTT 准确度分别为 90.16%、97.02% 和 93.09%。TT 准确率分别为 74.42%、86.63% 和 76.67%，误差范围均为$\pm$10 分钟