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Forecasting Daily Patient Volumes In The Emergency Department.

Spencer S. Jones, Alun Merlyn Thomas, Robert Scott Evans, Shari J. Welch, Peter J. Haug, Gregory L. Snow
Published 2008 · Medicine
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BACKGROUND Shifts in the supply of and demand for emergency department (ED) resources make the efficient allocation of ED resources increasingly important. Forecasting is a vital activity that guides decision-making in many areas of economic, industrial, and scientific planning, but has gained little traction in the health care industry. There are few studies that explore the use of forecasting methods to predict patient volumes in the ED. OBJECTIVES The goals of this study are to explore and evaluate the use of several statistical forecasting methods to predict daily ED patient volumes at three diverse hospital EDs and to compare the accuracy of these methods to the accuracy of a previously proposed forecasting method. METHODS Daily patient arrivals at three hospital EDs were collected for the period January 1, 2005, through March 31, 2007. The authors evaluated the use of seasonal autoregressive integrated moving average, time series regression, exponential smoothing, and artificial neural network models to forecast daily patient volumes at each facility. Forecasts were made for horizons ranging from 1 to 30 days in advance. The forecast accuracy achieved by the various forecasting methods was compared to the forecast accuracy achieved when using a benchmark forecasting method already available in the emergency medicine literature. RESULTS All time series methods considered in this analysis provided improved in-sample model goodness of fit. However, post-sample analysis revealed that time series regression models that augment linear regression models by accounting for serial autocorrelation offered only small improvements in terms of post-sample forecast accuracy, relative to multiple linear regression models, while seasonal autoregressive integrated moving average, exponential smoothing, and artificial neural network forecasting models did not provide consistently accurate forecasts of daily ED volumes. CONCLUSIONS This study confirms the widely held belief that daily demand for ED services is characterized by seasonal and weekly patterns. The authors compared several time series forecasting methods to a benchmark multiple linear regression model. The results suggest that the existing methodology proposed in the literature, multiple linear regression based on calendar variables, is a reasonable approach to forecasting daily patient volumes in the ED. However, the authors conclude that regression-based models that incorporate calendar variables, account for site-specific special-day effects, and allow for residual autocorrelation provide a more appropriate, informative, and consistently accurate approach to forecasting daily ED patient volumes.



This paper is referenced by
10.1016/j.future.2018.03.040
Predicting host CPU utilization in the cloud using evolutionary neural networks
Karl Mason (2018)
10.1186/s12879-014-0634-9
Forecasting ESKAPE infections through a time-varying auto-adaptive algorithm using laboratory-based surveillance data
Antonio Ballarin (2014)
10.1177/1460458220901889
Emergency patient flow forecasting in the radiology department.
Yumeng Zhang (2020)
10.1177/0972063415625575
Using Statistical Forecasting to Optimize Staff Scheduling in Healthcare Organizations
Anirban Ganguly (2016)
10.1001/jamanetworkopen.2018.4087
Assessment of Time-Series Machine Learning Methods for Forecasting Hospital Discharge Volume
Thomas H. McCoy (2018)
Machine learning in healthcare : an investigation into model stability
S. Gopakumar (2017)
A Framework for Quantifying and Managing Overcrowding in Healthcare Facilities
Abdulrahman Albar (2016)
An ILS algorithm to solve a real Home Health Care Routing Problem
Politecnico di Milano (2015)
10.2196/13659
Artificial Intelligence and the Implementation Challenge
James Shaw (2019)
10.1007/s00484-012-0584-0
Forecasting peak asthma admissions in London: an application of quantile regression models
Ireneous N. Soyiri (2012)
10.1111/1742-6723.13245
Planning for the future: Modelling daily emergency department presentations in an Australian capital city.
Fern J McAllan (2019)
10.1080/09603123.2011.605876
Seven-days-ahead forecasting of childhood asthma admissions using artificial neural networks in Athens, Greece
K. Moustris (2012)
Advances in evolutionary neural networks with applications in energy systems and the environment
Karl Mason (2018)
A data mining approach for estimating patient demand for health services
Ionuţ Ţăranu (2016)
Magnetic Storms and an Emergency Ambulance Call Pattern in Petrozavodsk
B. Z. Belashev (2020)
10.1186/1472-6947-13-132
Detecting and diagnosing hotspots for the enhanced management of hospital emergency departments in Queensland, Australia
Sarah Bolt (2013)
Conception et réalisation d'un système d'aide à la gestion des tensions dans les services d'urgences pédiatriques : vers des nouvelles approches d'évaluation, de quantification et d'anticipation
Wided Chandoul (2015)
10.1097/MEJ.0000000000000451
No more winter crisis? Forecasting daily bed requirements for emergency department admissions to hospital
Mathias Wargon (2018)
Gestion des ressources humaines d'un service d'urgence en période épidémique
Omar El Rifai Sierra (2015)
10.1186/s12873-019-0256-z
Real-time forecasting of emergency department arrivals using prehospital data
A. Asheim (2019)
10.1080/20476965.2018.1561161
A decision support system for demand and capacity modelling of an accident and emergency department.
Muhammed Ordu (2020)
10.1016/j.annemergmed.2010.08.040
Emergency department operational metrics, measures and definitions: results of the Second Performance Measures and Benchmarking Summit.
Shari J. Welch (2011)
10.1093/jamia/ocv106
Real-time prediction of inpatient length of stay for discharge prioritization
Sean L. Barnes (2016)
10.4018/978-1-5225-7071-4.CH012
A Forecasting Model for Patient Arrivals of an Emergency Department in Healthcare Management Systems
Melih Yucesan (2019)
10.1111/j.1553-2712.2009.00356.x
Forecasting models of emergency department crowding.
Lisa M. Schweigler (2009)
10.1109/WSC40007.2019.9004862
A Hybrid Modelling Approach Using Forecasting and Real-Time Simulation to Prevent Emergency Department Overcrowding
Alison Harper (2019)
10.1111/1742-6723.13481
Forecasting daily counts of patient presentations in Australian emergency departments using statistical models with time-varying predictors.
Kalpani I Duwalage (2020)
10.1080/24725579.2019.1584132
A resource-constrained, multi-unit hospital model for operational strategies evaluation under routine and surge demand scenarios
Mersedeh Tariverdi (2019)
10.1007/s10916-018-1080-9
Maximizing Patient Coverage Through Optimal Allocation of Residents and Scribes to Shifts in an Emergency Department
Phichet Wutthisirisart (2018)
10.21427/D7N597
An Optimisation-based Framework for Complex Business Process: Healthcare Application
Waleed Abohamad (2011)
10.1111/j.1553-2712.2010.00897.x
Emergency medicine quality improvement and patient safety curriculum.
John Kelly (2010)
10.1016/J.ORHC.2019.01.002
Forecasting arrivals and occupancy levels in an emergency department
Ward Whitt (2019)
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