Jiang Shancheng, Liu Qize, Ding Beichen
School of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou, China.
Guangdong Provincial Key Laboratory of Fire Science and Technology, Guangzhou, China.
Quant Imaging Med Surg. 2023 Mar 1;13(3):1957-1971. doi: 10.21037/qims-22-268. Epub 2022 Oct 9.
Accident and Emergency Department (AED) is the frontline of providing emergency care in a hospital and research focusing on improving decision-makings and service level around AED has been driving a rising number of attentions in recent years. A retrospective review among the published papers shows that related research can be classified according to six planning modules: demand forecasting, days-off scheduling, shift scheduling, line-of-work construction, task assignment and staff assignment. As patient arrivals demand forecasts enable smooth AED operational planning and help decision-making, this article conducted a systematic review on the statistical modelling approaches aimed at predicting the volume of AED patients' arrival.
We carried out a systematic review of AED patient arrivals prediction studies from 2004 to 2021. The Medline, ScienceDirect, and Scopus databases were searched. A two-step screening process was carried out based on the title and abstract or full text, and 35 of 1,677 articles were selected. Our methods and results follow the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. We categorise AED methods for modelling patient arrivals into four main classes: regression, time series, artificial intelligence and time series regression. Choice of prediction model, selection of factors and model performance are compared. Finally, we discuss the advantages and limitations of the models and suggest future research directions.
A total of 1,677 papers that fulfilled the initial searching criteria was obtained from the three databases. Based on the first exclusion criteria, 1,603 articles were eliminated. The remaining 74 full text articles were evaluated based on the second exclusion criteria. Finally, 35 articles were selected for full review. We find that the use of artificial intelligence-based model has risen in recent years, from the view of predictive model selection. The calendar-based factors are most commonly used compared with other types of dependent variables, from the view of dependent variable selection.
All AEDs are inherently different and different covariables may have different effects on patient arrivals. Certain factors may play a key role in one AED but not others. Based on results of meta-analysis, when modelling patient arrivals, it is essential to understand the actual AED situation and carefully select relevant dominating factors and the most suitable modelling method. Local calibration is also important to ensure good estimates.
急诊科（AED）是医院提供紧急护理的前沿阵地，近年来，围绕急诊科改善决策和服务水平的研究受到越来越多的关注。对已发表论文的回顾表明，相关研究可根据六个规划模块进行分类：需求预测、休息日安排、轮班安排、工作流程构建、任务分配和人员分配。由于患者到达需求预测有助于急诊科进行顺畅的运营规划并辅助决策，本文对旨在预测急诊科患者到达量的统计建模方法进行了系统综述。
我们对2004年至2021年期间急诊科患者到达量预测研究进行了系统综述。检索了Medline、ScienceDirect和Scopus数据库。基于标题和摘要或全文进行了两步筛选过程，从1677篇文章中选出了35篇。我们的方法和结果遵循系统综述和荟萃分析的首选报告项目（PRISMA）指南。我们将急诊科患者到达量建模方法分为四大类：回归、时间序列、人工智能和时间序列回归。比较了预测模型的选择、因素的选择和模型性能。最后，我们讨论了模型的优点和局限性，并提出了未来的研究方向。
从三个数据库中总共获得了1677篇符合初始搜索标准的论文。根据第一个排除标准，排除了1603篇文章。根据第二个排除标准对其余74篇全文文章进行了评估。最后，选择了35篇文章进行全面审查。从预测模型选择的角度来看，我们发现近年来基于人工智能的模型的使用有所增加。从自变量选择的角度来看，与其他类型的自变量相比，基于日历的因素使用最为普遍。
所有急诊科本质上都存在差异，不同的协变量可能对患者到达量产生不同的影响。某些因素可能在一个急诊科中起关键作用，但在其他急诊科中则不然。基于荟萃分析的结果，在对患者到达量进行建模时，必须了解急诊科的实际情况，并仔细选择相关的主导因素和最合适的建模方法。进行局部校准对于确保良好的估计也很重要。
