Assessment of forecasting models for patients arrival at Emergency Department

Carvalho-Silva, Miguel; Monteiro, M. Teresa T.; Sá-Soares, Filipe de; Dória-Nóbrega, Sónia

doi:10.1016/j.orhc.2017.05.001

Cited by 54 publications

(40 citation statements)

References 15 publications

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“…We needed the future selection process to reduce overfitting and training time and also to improve accuracy. We determined the features on the basis of the previously conducted studies [1, 4, 5, 8, 10–12, 14, 16, 18, 19] and the data availability. After the determination of the features, we performed the exhaustive feature-selection method to eliminate any doubt about the features subset because the method selects the optimum subset by minimizing the loss function with the help of any ML techniques and tests all the combination of the features.…”

Section: Methodsmentioning

confidence: 99%

“…In addition, the most significant cause of ED crowding is the inability to manage inpatient bed availability, thereby making it critical for hospitals to manage patient flow [3]. Human resource allocation is also adjusted according to the bed demand to treat patients in the ED with the aim of providing universal access to healthcare to all the patients therein [4]. Predicting patients' arrivals rates and, consequently, the bed demand at EDs may lead to effective use of hospital resources and reduce the overcrowding and waiting time of the patients at ED [5].…”

Section: Introductionmentioning

confidence: 99%

“…All these studies used any subset of these variables. The patient arrival problems are generally related daily [7, 9–14], weekly [4], or monthly [4, 15]; however, a few studies focus on hourly arrival rates which include high variation [16–18]. The forecasting accuracy worsened as the forecast time intervals became smaller: the best MAPE rate is 2% for a month, 11% for a day, 38% for four-hour period, and 50% for an hour [18].…”

Section: Introductionmentioning

confidence: 99%

“…These results of forecasting accuracy are very close to those in the other studies in the literature, with the variation mainly due to the used dataset and prediction algorithms. The studies were performed using different types of patient data, such as one-year data [4, 5, 9–11, 16, 17], 2- to 6-year data [7, 12–15, 17], 10-year data [19], and the daily number of patient arrivals generally used over a period ranging from 1- to 10-year data (usually 3 years) [8]. Different techniques used for the prediction of patient arrival rate are the ANN [7, 9, 11, 14], the autoregressive integrated moving average (ARIMA) [4, 8, 12–14], the linear regression (LR) [14, 18], the exponential smoothing [14, 15, 18], the logistic regression [5, 10, 11, 19], the decision tree (DT) [1, 10], the gradient boosted machines (GBM) [10], the Poisson regression model [16], the random forest (RF), the AdaBoost (AB), the support vector machine (SVM) [5], and the LSTM model [20].…”

Section: Introductionmentioning

confidence: 99%

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Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach

Nas

Koyuncu

2019

Computational and Mathematical Methods in Medicine

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Emergency departments (EDs) play a vital role in the whole healthcare system as they are the first point of care in hospitals for urgent and critically ill patients. Therefore, effective management of hospital's ED is crucial in improving the quality of the healthcare service. The effectiveness depends on how efficiently the hospital resources are used, particularly under budget constraints. Simulation modeling is one of the best methods to optimize resources and needs inputs such as patients' arrival time, patient's length of stay (LOS), and the route of patients in the ED. This study develops a simulation model to determine the optimum number of beds in an ED by minimizing the patients' LOS. The hospital data are analyzed, and patients' LOS and the route of patients in the ED are determined. To determine patients' arrival times, the features associated with patients' arrivals at ED are identified. Mean arrival rate is used as a feature in addition to climatic and temporal variables. The exhaustive feature-selection method has been used to determine the best subset of the features, and the mean arrival rate is determined as one of the most significant features. This study is executed using the one-year ED arrival data together with five-year (43.824 study hours) ED arrival data to improve the accuracy of predictions. Furthermore, ten different machine learning (ML) algorithms are used utilizing the same best subset of these features. After a tenfold cross-validation experiment, based on mean absolute percentage error (MAPE), the stateful long short-term memory (LSTM) model performed better than other models with an accuracy of 47%, followed by the decision tree and random forest methods. Using the simulation method, the LOS has been minimized by 7% and the number of beds at the ED has been optimized.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach

Nas

Koyuncu

2019

Computational and Mathematical Methods in Medicine

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“…There exists a large literature on forecasting ED arrivals [6], but much of the variation in ED arrivals remain unaccounted for. This is more pronounced on short time spans: The forecasted daily number of arrivals at a typical ED show a mean absolute percentage error (MAPE) of 10% [7, 8], while for hourly admissions this typically lies around 50% [7].…”

Section: Introductionmentioning

confidence: 99%

Real-time forecasting of emergency department arrivals using prehospital data

et al. 2019

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Background Crowding in emergency departments (EDs) is a challenge globally. To counteract crowding in day-to-day operations, better tools to improve monitoring of the patient flow in the ED is needed. The objective of this study was the development of a continuously updated monitoring system to forecast emergency department (ED) arrivals on a short time-horizon incorporating data from prehospital services. Methods Time of notification and ED arrival was obtained for all 191,939 arrivals at the ED of a Norwegian university hospital from 2010 to 2018. An arrival notification was an automatically captured time stamp which indicated the first time the ED was notified of an arriving patient, typically by a call from an ambulance to the emergency service communication center. A Poisson time-series regression model for forecasting the number of arrivals on a 1-, 2- and 3-h horizon with continuous weekly and yearly cyclic effects was implemented. We incorporated time of arrival notification by modelling time to arrival as a time varying hazard function. We validated the model on the last full year of data. Results In our data, 20% of the arrivals had been notified more than 1 hour prior to arrival. By incorporating time of notification into the forecasting model, we saw a substantial improvement in forecasting accuracy, especially on a one-hour horizon. In terms of mean absolute prediction error, we observed around a six percentage-point decrease compared to a simplified prediction model. The increase in accuracy was particularly large for periods with large inflow. Conclusions The proposed model shows increased predictability in ED patient inflow when incorporating data on patient notifications. This approach to forecasting arrivals can be a valuable tool for logistic, decision making and ED resource management. Electronic supplementary material The online version of this article (10.1186/s12873-019-0256-z) contains supplementary material, which is available to authorized users.

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Predicting hospital emergency department visits accurately: A systematic review

Silva

Pereira

Vieira

et al. 2023

Health Planning & Management

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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.

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Assessment of forecasting models for patients arrival at Emergency Department

Cited by 54 publications

References 15 publications

Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach

Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach

Real-time forecasting of emergency department arrivals using prehospital data

Predicting hospital emergency department visits accurately: A systematic review

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