University of Minho, Braga, Portugal.
University Hospital Center of São João, Porto, Portugal.
Int J Health Plann Manage. 2023 Jul;38(4):904-917. doi: 10.1002/hpm.3629. Epub 2023 Mar 10.
The emergency department (ED) is a very important healthcare entrance point, known for its challenging organisation and management due to demand unpredictability. An accurate forecast system of ED visits is crucial to the implementation of better management strategies that optimise resources utilization, reduce costs and improve public confidence. The aim of this review is to investigate the different factors that affect the ED visits forecasting outcomes, in particular the predictive variables and type of models applied.
A systematic search was conducted in PubMed, Web of Science and Scopus. The review methodology followed the PRISMA statement guidelines.
Seven studies were selected, all exploring predictive models to forecast ED daily visits for general care. MAPE and RMAE were used to measure models' accuracy. All models displayed good accuracy, with errors below 10%.
Model selection and accuracy was found to be particularly sensitive to the ED dimension. While ARIMA-based and other linear models have good performance for short-time forecast, some machine learning methods proved to be more stable when forecasting multiple horizons. The inclusion of exogenous variables was found to be advantageous only in bigger EDs.
急诊部（ED）是一个非常重要的医疗保健入口点，由于需求的不可预测性，其组织和管理具有挑战性。准确预测 ED 就诊量对于实施更好的管理策略至关重要，这些策略可以优化资源利用、降低成本并提高公众信心。本综述旨在调查影响 ED 就诊量预测结果的不同因素，特别是预测变量和应用的模型类型。
在 PubMed、Web of Science 和 Scopus 中进行了系统搜索。综述方法遵循 PRISMA 声明指南。
共选择了 7 项研究，均探索了用于预测一般护理 ED 每日就诊量的预测模型。MAPE 和 RMAE 用于衡量模型的准确性。所有模型的准确性都很高，误差低于 10%。
模型选择和准确性特别受到 ED 维度的影响。虽然基于 ARIMA 的和其他线性模型在短期预测中表现良好，但一些机器学习方法在预测多个时间段时被证明更稳定。仅在较大的 ED 中，纳入外生变量被发现是有利的。
