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Forecasting Daily Attendances At An Emergency Department To Aid Resource Planning

Yan Sun, Bee Hoon Heng, Yian Tay Seow, Eillyne Seow
Published 2009 · Medicine
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BackgroundAccurate forecasting of emergency department (ED) attendances can be a valuable tool for micro and macro level planning.MethodsData for analysis was the counts of daily patient attendances at the ED of an acute care regional general hospital from July 2005 to Mar 2008. Patients were stratified into three acuity categories; i.e. P1, P2 and P3, with P1 being the most acute and P3 being the least acute. The autoregressive integrated moving average (ARIMA) method was separately applied to each of the three acuity categories and total patient attendances. Independent variables included in the model were public holiday (yes or no), ambient air quality measured by pollution standard index (PSI), daily ambient average temperature and daily relative humidity. The seasonal components of weekly and yearly periodicities in the time series of daily attendances were also studied. Univariate analysis by t-tests and multivariate time series analysis were carried out in SPSS version 15.ResultsBy time series analyses, P1 attendances did not show any weekly or yearly periodicity and was only predicted by ambient air quality of PSI > 50. P2 and total attendances showed weekly periodicities, and were also significantly predicted by public holiday. P3 attendances were significantly correlated with day of the week, month of the year, public holiday, and ambient air quality of PSI > 50.After applying the developed models to validate the forecast, the MAPE of prediction by the models were 16.8%, 6.7%, 8.6% and 4.8% for P1, P2, P3 and total attendances, respectively. The models were able to account for most of the significant autocorrelations present in the data.ConclusionTime series analysis has been shown to provide a useful, readily available tool for predicting emergency department workload that can be used to plan staff roster and resource planning.
This paper references
The Analysis of Time Series: An Introduction
C. B. Netter eld (1989)
10.1007/978-0-387-73003-5_813
Time Series
M. G. Kendall (1973)
10.1023/A:1020390425029
Forecasting Demand of Emergency Care
Simon Jones (2002)
10.1136/jech.44.4.307
Models for forecasting hospital bed requirements in the acute sector.
R. D. Farmer (1990)
10.1109/TAC.1972.1099963
Time series analysis, forecasting and control
P. Young (1972)
10.1057/jors.1971.52
Time Series Analysis Forecasting and Control
David J. Bartholomew (1971)
10.1002/(SICI)1097-0258(19970930)16:18<2117::AID-SIM649>3.0.CO;2-E
Ten-year follow-up of ARIMA forecasts of attendances at accident and emergency departments in the Trent region.
P. Milner (1997)
10.1146/annurev.pu.15.050194.000543
Acute respiratory effects of particulate air pollution.
Douglas W. Dockery (1994)
10.1089/109350703322682559
Evaluating disease management program effectiveness: an introduction to time-series analysis.
Ariel Linden (2003)
Models for forecasting bed requirements in the acute sector
RDT Farmer (1990)
10.1002/sim.4780071007
Forecasting the demand on accident and emergency departments in health districts in the Trent region.
Philip Milner (1988)
10.1016/S0169-2607(98)00032-7
Modeling and forecasting monthly patient volume at a primary health care clinic using univariate time-series analysis.
Reem Abdelaal (1998)
SPSS: SPSS trends 15.0
(2006)
Modern methods improve hospital forecasting.
Sterk We (1987)
10.1080/00039896.1995.9940893
Air pollution and mortality in elderly people: a time-series study in Sao Paulo, Brazil.
P. H. Saldiva (1995)
10.1016/S0735-6757(97)90166-2
The dynamics of patient visits to a public hospital ED: a statistical model.
Z. Rotstein (1997)
Ten-year follow-up of ARIMA forecasts for attendances at accident and emergency department in the Trent region
PC Milner (1997)
10.1007/BF02642481
Predicting daily visits to a waik-in clinic and emergency department using calendar and weather data
Donald R. Jr Holleman (2007)
10.1183/09031936.03.00402203
Particulate air pollution and hospital admissions for cardiorespiratory diseases: are the elderly at greater risk?
H. Anderson (2003)
Modern methods improve hospital forecasting.
William Edward Sterk (1987)
10.1016/S0196-0644(94)70044-3
Time series forecasts of emergency department patient volume, length of stay, and acuity.
Dan Tandberg (1994)
The analysis of time series: an introduction. 6th edition
C Chatfield (2004)



This paper is referenced by
10.1016/B978-0-12-415783-5.00013-X
Issues of Interferences in Therapeutic Drug Monitoring
Gwendolyn Mcmillin (2013)
10.1287/msom.2015.0573
Using Future Information to Reduce Waiting Times in the Emergency Department via Diversion
Kuang Xu (2016)
10.1016/j.cmpb.2017.11.003
A universal deep learning approach for modeling the flow of patients under different severities
Shancheng Jiang (2018)
10.1590/s1679-45082013000100024
Disease management with ARIMA model in time series
Renato Cesar Sato (2013)
10.1097/01.NPR.0000465117.19783.ee
Fall prevention in older adults.
Scott J. Saccomano (2015)
10.1292/jvms.14-0675
Real time detection of farm-level swine mycobacteriosis outbreak using time series modeling of the number of condemned intestines in abattoirs
Yasumoto Adachi (2015)
10.1016/j.ajem.2017.08.023
The relationship between emergency department volume and patient complexity
Barry Hahn (2018)
10.12968/JPAR.2010.2.9.78627
Human factors within paramedic practice:the forgotten paradigm
Andy Summers (2010)
10.1111/j.1553-2712.2011.01226.x
Emergency medicine: an operations management view.
Olan A. Soremekun (2011)
Emergency department forecasting in a hospital : a hybrid model between the Box-Jenkins methods and ANN
Joao Chang (2016)
Previsão do volume diário de atendimentos no serviço de pronto socorro de um hospital geral: comparação de diferentes métodos
São Paulo. (2013)
10.4328/JCAM.6113
Time series analysis of the admission to the emergency department due to respiratory and cardiovascular diseases between 2010 and 2014 in Kırklareli, Turkey
Yeliz Mercan (2019)
10.1016/j.crad.2016.06.117
Emergency department imaging: are weather and calendar factors associated with imaging volume?
K. Burns (2016)
10.4338/ACI-2015-09-RA-0127
Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit.
Muge Capan (2016)
10.1177/1460458220901889
Emergency patient flow forecasting in the radiology department.
Yumeng Zhang (2020)
10.1016/j.ijmedinf.2020.104143
An Integrated Approach of Machine Learning and Systems Thinking for Waiting Time Prediction in an Emergency Department
Yong-Hong Kuo (2020)
10.1007/s12199-012-0294-6
An overview of health forecasting
Ireneous N. Soyiri (2012)
10.1016/J.IFACOL.2016.07.859
Emergency department flow: A new practical patients classification and forecasting daily attendance
Mohamed Afilal (2016)
10.1007/s10916-016-0527-0
Forecasting the Emergency Department Patients Flow
Mohamed Afilal (2016)
10.1016/j.jen.2011.10.002
Effect of weather on medical patient volume at Kansas Speedway mass gatherings.
Brian Selig (2013)
10.1021/ac5043297
A microfluidic technique for quantification of steroids in core needle biopsies.
Jihye Kim (2015)
10.1016/j.neucom.2015.10.009
Seasonal ARMA-based SPC charts for anomaly detection: Application to emergency department systems
Farid Kadri (2016)
Hourly Forecasting of Emergency Department Arrivals : Time Series Analysis.
Avishek Choudhury (2019)
10.1007/s11356-019-04781-3
Impact of air pollution on hospital admissions with a focus on respiratory diseases: a time-series multi-city analysis
Alessandro Slama (2019)
10.1109/ISDA.2015.7489202
Enhanced monitoring of abnormal emergency department demands
Fouzi Harrou (2015)
10.1136/bmjopen-2017-018628
Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan
Wang-Chuan Juang (2017)
10.1155/2019/4359719
Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach
Serkan Nas (2019)
10.12968/bjhc.2019.0067
Forecasting hourly emergency department arrival using time series analysis
Avishek Choudhury (2020)
10.1186/1472-6920-14-172
Have “new” methods in medical education reached German-speaking Central Europe: a survey
M. Fandler (2014)
The Effects of Weekday, Season, Federal Holidays, and Severe Weather Conditions on Emergency Department Volume in Montgomery County, Ohio
Kiran A. Faryar (2013)
10.1111/1742-6723.12201
Improvement in emergency department length of stay using a nurse-led 'emergency journey coordinator': a before/after study.
Stephen Edward Asha (2014)
10.1109/ACCESS.2019.2936550
Combining Deep Neural Networks and Classical Time Series Regression Models for Forecasting Patient Flows in Hong Kong
Shancheng Jiang (2019)
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