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A multivariate time series approach to modeling and forecasting demand in the emergency department.
J Biomed Inform. 2009 Feb; 42(1):123-39.JB

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.

Authors+Show Affiliations

Department of Biomedical Informatics, University of Utah, Health Sciences Education Building, 26 S 2000 E, Salt Lake City, UT 84112-5750, USA. spencer.jones@hsc.utah.eduNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural

Language

eng

PubMed ID

18571990

Citation

Jones, Spencer S., et al. "A Multivariate Time Series Approach to Modeling and Forecasting Demand in the Emergency Department." Journal of Biomedical Informatics, vol. 42, no. 1, 2009, pp. 123-39.
Jones SS, Evans RS, Allen TL, et al. A multivariate time series approach to modeling and forecasting demand in the emergency department. J Biomed Inform. 2009;42(1):123-39.
Jones, S. S., Evans, R. S., Allen, T. L., Thomas, A., Haug, P. J., Welch, S. J., & Snow, G. L. (2009). A multivariate time series approach to modeling and forecasting demand in the emergency department. Journal of Biomedical Informatics, 42(1), 123-39. https://doi.org/10.1016/j.jbi.2008.05.003
Jones SS, et al. A Multivariate Time Series Approach to Modeling and Forecasting Demand in the Emergency Department. J Biomed Inform. 2009;42(1):123-39. PubMed PMID: 18571990.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A multivariate time series approach to modeling and forecasting demand in the emergency department. AU - Jones,Spencer S, AU - Evans,R Scott, AU - Allen,Todd L, AU - Thomas,Alun, AU - Haug,Peter J, AU - Welch,Shari J, AU - Snow,Gregory L, Y1 - 2008/05/17/ PY - 2008/02/20/received PY - 2008/05/06/revised PY - 2008/05/12/accepted PY - 2008/6/24/pubmed PY - 2009/8/14/medline PY - 2008/6/24/entrez SP - 123 EP - 39 JF - Journal of biomedical informatics JO - J Biomed Inform VL - 42 IS - 1 N2 - 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. SN - 1532-0480 UR - https://www.unboundmedicine.com/medline/citation/18571990/A_multivariate_time_series_approach_to_modeling_and_forecasting_demand_in_the_emergency_department_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S1532-0464(08)00063-4 DB - PRIME DP - Unbound Medicine ER -