A Multivariate Time Series Approach to Modeling and Forecasting Demand in the Emergency Department

Published in: Journal of Biomedical Informatics, v. 42, no. 1, Feb. 2009. p. 123-139

Posted on RAND.org on February 01, 2009

by Spencer S. Jones, R. Scott Evans, Todd L. Allen, Alun Thomas, Peter J. Haug, Shari J. Welch, Gregory L. Snow

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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.

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