A multivariate time series approach to modeling and forecasting demand in the emergency department

Authors:
Spencer S. Jones;R. Scott Evans;Todd L. Allen;Alun Thomas;Peter J. Haug;Shari J. Welch;Gregory L. Snow
Affiliations:
Department of Biomedical Informatics, University of Utah, Health Sciences Education Building, 26 S 2000 E, Salt Lake City, UT 84112-5750, USA;Department of Biomedical Informatics, University of Utah, Health Sciences Education Building, 26 S 2000 E, Salt Lake City, UT 84112-5750, USA and Intermountain Healthcare, Salt Lake City, UT, USA;Department of Emergency Medicine, Intermountain Medical Center, Salt Lake City, UT, USA;Department of Biomedical Informatics, University of Utah, Health Sciences Education Building, 26 S 2000 E, Salt Lake City, UT 84112-5750, USA;Department of Biomedical Informatics, University of Utah, Health Sciences Education Building, 26 S 2000 E, Salt Lake City, UT 84112-5750, USA and Intermountain Healthcare, Salt Lake City, UT, USA;Department of Emergency Medicine, Intermountain Medical Center, Salt Lake City, UT, USA;Intermountain Healthcare, Salt Lake City, UT, USA
Venue:
Journal of Biomedical Informatics
Year:
2009

Citing 1
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Improving the Sipp Approach for Staffing Service Systems That Have Cyclic Demands

Operations Research

HYBRID GREY RELATIONAL ARTIFICIAL NEURAL NETWORK AND AUTO REGRESSIVE INTEGRATED MOVING AVERAGE MODEL FOR FORECASTING TIME-SERIES DATA

Applied Artificial Intelligence
Modeling daily patient arrivals at Emergency Department and quantifying the relative importance of contributing variables using artificial neural network

Decision Support Systems

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Abstract

Study objective: The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. Methods: Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources. Results: Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources. Conclusion: Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.