Predicting hospital emergency department visits accurately: A systematic review

Abstract

Objectives 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. Methods A systematic search was conducted in PubMed, Web of Science and Scopus. The review methodology followed the PRISMA statement guidelines. Results 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%. Conclusions 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.

Enhancing emergency department patient arrival forecasting: a study using feature engineering and advanced machine learning algorithms

“…The FE approach employed in this study created a set of new predictor variables based on arrival timestamps. A recent systematic review on ED arrival forecasting [76] suggests, for future studies, that exploring new variables with potential to become significant and reliable predictors is an underexplored area, requiring further research. Our study addresses this demand.…”

“…The performance of the models presented in Table A3 In Section 2, we demonstrated that the most commonly used metrics for evaluating patient arrival predictions are MAPE and RMSE. Both metrics enable direct comparison since they are scale-independent, allowing for the assessment of predictions across different scenarios [71], [76]. MAPE presents results in percentage form, which is more easily interpretable [51].…”

Predicting Hospital Ward Admission from the Emergency Department: A Systematic Review

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