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Time series modelling and forecasting of emergency department overcrowding.
J Med Syst. 2014 Sep; 38(9):107.JM

Abstract

Efficient management of patient flow (demand) in emergency departments (EDs) has become an urgent issue for many hospital administrations. Today, more and more attention is being paid to hospital management systems to optimally manage patient flow and to improve management strategies, efficiency and safety in such establishments. To this end, EDs require significant human and material resources, but unfortunately these are limited. Within such a framework, the ability to accurately forecast demand in emergency departments has considerable implications for hospitals to improve resource allocation and strategic planning. The aim of this study was to develop models for forecasting daily attendances at the hospital emergency department in Lille, France. The study demonstrates how time-series analysis can be used to forecast, at least in the short term, demand for emergency services in a hospital emergency department. The forecasts were based on daily patient attendances at the paediatric emergency department in Lille regional hospital centre, France, from January 2012 to December 2012. An autoregressive integrated moving average (ARIMA) method was applied separately to each of the two GEMSA categories and total patient attendances. Time-series analysis was shown to provide a useful, readily available tool for forecasting emergency department demand.

Authors+Show Affiliations

Univ. Lille Nord de France, 59000, Lille, France, farid.kadri@univ-valenciennes.fr.No affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article

Language

eng

PubMed ID

25053208

Citation

Kadri, Farid, et al. "Time Series Modelling and Forecasting of Emergency Department Overcrowding." Journal of Medical Systems, vol. 38, no. 9, 2014, p. 107.
Kadri F, Harrou F, Chaabane S, et al. Time series modelling and forecasting of emergency department overcrowding. J Med Syst. 2014;38(9):107.
Kadri, F., Harrou, F., Chaabane, S., & Tahon, C. (2014). Time series modelling and forecasting of emergency department overcrowding. Journal of Medical Systems, 38(9), 107. https://doi.org/10.1007/s10916-014-0107-0
Kadri F, et al. Time Series Modelling and Forecasting of Emergency Department Overcrowding. J Med Syst. 2014;38(9):107. PubMed PMID: 25053208.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Time series modelling and forecasting of emergency department overcrowding. AU - Kadri,Farid, AU - Harrou,Fouzi, AU - Chaabane,Sondès, AU - Tahon,Christian, Y1 - 2014/07/23/ PY - 2014/01/15/received PY - 2014/07/07/accepted PY - 2014/7/24/entrez PY - 2014/7/24/pubmed PY - 2015/4/14/medline SP - 107 EP - 107 JF - Journal of medical systems JO - J Med Syst VL - 38 IS - 9 N2 - Efficient management of patient flow (demand) in emergency departments (EDs) has become an urgent issue for many hospital administrations. Today, more and more attention is being paid to hospital management systems to optimally manage patient flow and to improve management strategies, efficiency and safety in such establishments. To this end, EDs require significant human and material resources, but unfortunately these are limited. Within such a framework, the ability to accurately forecast demand in emergency departments has considerable implications for hospitals to improve resource allocation and strategic planning. The aim of this study was to develop models for forecasting daily attendances at the hospital emergency department in Lille, France. The study demonstrates how time-series analysis can be used to forecast, at least in the short term, demand for emergency services in a hospital emergency department. The forecasts were based on daily patient attendances at the paediatric emergency department in Lille regional hospital centre, France, from January 2012 to December 2012. An autoregressive integrated moving average (ARIMA) method was applied separately to each of the two GEMSA categories and total patient attendances. Time-series analysis was shown to provide a useful, readily available tool for forecasting emergency department demand. SN - 1573-689X UR - https://www.unboundmedicine.com/medline/citation/25053208/Time_series_modelling_and_forecasting_of_emergency_department_overcrowding_ L2 - https://dx.doi.org/10.1007/s10916-014-0107-0 DB - PRIME DP - Unbound Medicine ER -