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Forecasting Models Of Emergency Department Crowding.

Lisa M. Schweigler, Jeffrey S. Desmond, Melissa Lee McCarthy, Kyle Bukowski, Edward L. Ionides, John G. Younger
Published 2009 · Medicine
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OBJECTIVES The authors investigated whether models using time series methods can generate accurate short-term forecasts of emergency department (ED) bed occupancy, using traditional historical averages models as comparison. METHODS From July 2005 through June 2006, retrospective hourly ED bed occupancy values were collected from three tertiary care hospitals. Three models of ED bed occupancy were developed for each site: 1) hourly historical average, 2) seasonal autoregressive integrated moving average (ARIMA), and 3) sinusoidal with an autoregression (AR)-structured error term. Goodness of fits were compared using log likelihood and Akaike's Information Criterion (AIC). The accuracies of 4- and 12-hour forecasts were evaluated by comparing model forecasts to actual observed bed occupancy with root mean square (RMS) error. Sensitivity of prediction errors to model training time was evaluated, as well. RESULTS The seasonal ARIMA outperformed the historical average in complexity adjusted goodness of fit (AIC). Both AR-based models had significantly better forecast accuracy for the 4- and the 12-hour forecasts of ED bed occupancy (analysis of variance [ANOVA] p < 0.01), compared to the historical average. The AR-based models did not differ significantly from each other in their performance. Model prediction errors did not show appreciable sensitivity to model training times greater than 7 days. CONCLUSIONS Both a sinusoidal model with AR-structured error term and a seasonal ARIMA model were found to robustly forecast ED bed occupancy 4 and 12 hours in advance at three different EDs, without needing data input beyond bed occupancy in the preceding hours.
This paper references
144: Electronic Dashboard and a Multidisciplinary Hospital-Wide Team Decrease Patient Throughput Intervals and Reduce Number of Admitted Patients Held in the Emergency Department
Mark Spektor (2008)
The challenge of predicting demand for emergency department services.
Melissa Lee McCarthy (2008)
Relationship between the National ED Overcrowding Scale and the number of patients who leave without being seen in an academic ED.
S J Weiss (2005)
Care in the emergency department: how crowded is overcrowded?
Ula Hwang (2004)
Forecasting the demand on accident and emergency departments in health districts in the Trent region.
Philip Milner (1988)
Increased health care costs associated with ED overcrowding.
P Krochmal (1994)
Development and validation of a new index to measure emergency department crowding.
Steven L Bernstein (2003)
Emergency department overcrowding: analysis of the factors of renege rate.
Phillip V. Asaro (2007)
Time series forecasts of emergency department patient volume, length of stay, and acuity.
Dan Tandberg (1994)
Hospitalization in the Program of All-Inclusive Care for the Elderly (PACE): rates, concomitants, and predictors.
D. Wieland (2000)
Forecasting Demand of Emergency Care
Simon Jones (2002)
National Hospital Ambulatory Medical Care Survey: 2002 emergency department summary.
L. McCaig (2004)
Advanced statistics: developing a formal model of emergency department census and defining operational efficiency.
Thomas J. Flottemesch (2007)
Annals of Emergency Medicine Journal Club. Emergency department crowding is associated with poor care for patients with severe pain.
T. Barrett (2008)
Effect of emergency department crowding on time to antibiotics in patients admitted with community-acquired pneumonia.
C. Fee (2007)
Measuring and forecasting emergency department crowding in real time.
Nathan R. Hoot (2007)
A physiologically-based early warning score for ward patients: the association between score and outcome.
David R. Goldhill (2005)
A Proof for the Queuing Formula: L = λW
John D. C. Little (1961)
Toward an epidemiology and natural history of SIRS (systemic inflammatory response syndrome)
R. Bone (1992)
Ten-year follow-up of ARIMA forecasts of attendances at accident and emergency departments in the Trent region.
Philip Milner (1997)
Forecasting Emergency Department Crowding by Discrete Event Simulation
Nathan R. Hoot (2008)
A proof of the queueing formula: L=λW
S. Zhi (2001)
Computer Modeling of Patient Flow in a Pediatric Emergency Department Using Discrete Event Simulation
Geoffrey R Hung (2007)
Emergency departments and crowding in United States teaching hospitals.
D. Andrulis (1991)
The financial burden of emergency department congestion and hospital crowding for chest pain patients awaiting admission.
Matthew D Bayley (2005)
Emergency department crowding is associated with poor care for patients with severe pain.
J. Pines (2008)
Challenges in enrollment of minority, pediatric, and geriatric patients in emergency and acute care clinical research.
Seth W. Glickman (2008)
Emergency department crowding and thrombolysis delays in acute myocardial infarction.
M. Schull (2004)
A real-time tracking, notification, and web-based enrollment system for emergency department research.
James V. Quinn (2004)
Developing models for patient flow and daily surge capacity research.
B. Asplin (2006)
The emergency department occupancy rate: a simple measure of emergency department crowding?
M. McCarthy (2008)
Ageing and infection.
Gaëtan Gavazzi (2002)
An independent evaluation of four quantitative emergency department crowding scales.
Spencer S. Jones (2006)
Estimating the degree of emergency department overcrowding in academic medical centers: results of the National ED Overcrowding Study (NEDOCS).
S. Weiss (2004)
The association between hospital overcrowding and mortality among patients admitted via Western Australian emergency departments.
P. Sprivulis (2006)
The association between emergency department crowding and hospital performance on antibiotic timing for pneumonia and percutaneous intervention for myocardial infarction.
J. Pines (2006)
An Early Warning System for Overcrowding in the Emergency Department
Nathan R. Hoot (2006)
Forecasting emergency department presentations.
Robert Champion (2007)
Forecasting daily patient volumes in the emergency department.
Spencer S. Jones (2008)
Emergency Department Overcrowding: The Impact of Resource Scarcity on Physician Job Satisfaction
K. Rondeau (2005)

This paper is referenced by
Knowing what to expect, forecasting monthly emergency department visits: A time-series analysis.
Jochen Bergs (2014)
Medical Mondays: ED Utilization for Medicaid Recipients Depends on the Day of the Week, Season, and Holidays.
Jessica Castner (2016)
Forecasting Daily Patient Outflow From a Ward Having No Real-Time Clinical Data
S. Gopakumar (2016)
Early Index for Detection of Pediatric Emergency Department Crowding
Guillaume Bouleux (2015)
Predicting Outpatient Appointment Demand Using Machine Learning and Traditional Methods
Brian Klute (2019)
Meta-modeling of occupancy variables and analysis of their impact on energy outcomes of office buildings
Qinpeng Wang (2016)
Using Future Information to Reduce Waiting Times in the Emergency Department via Diversion
Kuang Xu (2016)
Modified genetic algorithm-based feature selection combined with pre-trained deep neural network for demand forecasting in outpatient department
Shancheng Jiang (2017)
On Quantifying and Forecasting Emergency Department Overcrowding at Sunnybrook Hospital using Statistical Analyses and Artificial Neural Networks
Jonathan Wang (2012)
Forecasting Emergency Department Volumes Using Time Series and Other Techniques
Uchechukwu A. Nwoke (2013)
CrowdED: crowding metrics and data visualization in the emergency department.
Laura S. Greci (2011)
Probability Models for Health Care Operations with Application to Emergency Medicine
Azaz B. Sharif (2016)
Managing Emergency Units Applying Queueing Theory
Salvador Hernández-González (2016)
A Hybrid Approach for Forecasting Patient Visits in Emergency Department
Qinneng Xu (2016)
Design of a model to predict surge capacity bottlenecks for burn mass casualties at a large academic medical center.
Mahshid Abir (2013)
Modeling daily patient arrivals at Emergency Department and quantifying the relative importance of contributing variables using artificial neural network
M. Xu (2013)
Using ambulance diversion status to validate occupancy rate at an academic emergency department in Taipei, Taiwan
Po-Liang Cheng (2012)
From model to forecasting: a multicenter study in emergency departments.
Mathias Wargon (2010)
A universal deep learning approach for modeling the flow of patients under different severities
Shancheng Jiang (2018)
Operations research applications in hospital operations: Part I
Tolu K. Abe (2016)
Imperatives for health sector decision-support modelling
Rick Nunes-Vaz (2019)
A Congestion Game Framework for Emergency Department Overcrowding
Elizabeth A. Verheggen (2015)
The Impact of Non-Emergency Medical Use on the United States Health Care System: A Retrospective Study
Patrick Casimir (2015)
A Framework for Quantifying and Managing Overcrowding in Healthcare Facilities
Abdulrahman Albar (2016)
Modelling and forecasting daily surgical case volume using time series analysis
Nazanin Zinouri (2018)
Using prediction to facilitate patient flow in a health care delivery chain
Jordan Peck (2013)
Semistructured black-box prediction: proposed approach for asthma admissions in London
Ireneous N. Soyiri (2012)
Applying queueing theory to the study of emergency department operations: a survey and a discussion of comparable simulation studies
Xia Hu (2018)
Dane Louis (2017)
Time series forecasting in an outpatient cancer clinic using common-day clustering
David Claudio (2014)
An overview of health forecasting
Ireneous N. Soyiri (2012)
Machine Learning-Based Patient Load Prediction and IoT Integrated Intelligent Patient Transfer Systems
Kambombo Mtonga (2019)
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