Tags

Type your tag names separated by a space and hit enter

Forecasting the Emergency Department Patients Flow.
J Med Syst. 2016 Jul; 40(7):175.JM

Abstract

Emergency department (ED) have become the patient's main point of entrance in modern hospitals causing it frequent overcrowding, thus hospital managers are increasingly paying attention to the ED in order to provide better quality service for patients. One of the key elements for a good management strategy is demand forecasting. In this case, forecasting patients flow, which will help decision makers to optimize human (doctors, nurses…) and material(beds, boxs…) resources allocation. The main interest of this research is forecasting daily attendance at an emergency department. The study was conducted on the Emergency Department of Troyes city hospital center, France, in which we propose a new practical ED patients classification that consolidate the CCMU and GEMSA categories into one category and innovative time-series based models to forecast long and short term daily attendance. The models we developed for this case study shows very good performances (up to 91,24 % for the annual Total flow forecast) and robustness to epidemic periods.

Authors+Show Affiliations

Institut Charles Delaunay, LOSI, Université de Technologie de Troyes UMR 6281, CNRS, 12 Rue Marie Curie, CS 42060, cedex, 10004, Troyes, France. mohamed.afilal@utt.fr.Institut Charles Delaunay, LOSI, Université de Technologie de Troyes UMR 6281, CNRS, 12 Rue Marie Curie, CS 42060, cedex, 10004, Troyes, France.Institut Charles Delaunay, LOSI, Université de Technologie de Troyes UMR 6281, CNRS, 12 Rue Marie Curie, CS 42060, cedex, 10004, Troyes, France.Institut Charles Delaunay, LOSI, Université de Technologie de Troyes UMR 6281, CNRS, 12 Rue Marie Curie, CS 42060, cedex, 10004, Troyes, France.Département d'Information Médicale, Centre Hospitalier de Troyes, 101 Avenue Anatole, 10000, Troyes, France.Département d'Information Médicale, Centre Hospitalier de Troyes, 101 Avenue Anatole, 10000, Troyes, France.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

27272135

Citation

Afilal, Mohamed, et al. "Forecasting the Emergency Department Patients Flow." Journal of Medical Systems, vol. 40, no. 7, 2016, p. 175.
Afilal M, Yalaoui F, Dugardin F, et al. Forecasting the Emergency Department Patients Flow. J Med Syst. 2016;40(7):175.
Afilal, M., Yalaoui, F., Dugardin, F., Amodeo, L., Laplanche, D., & Blua, P. (2016). Forecasting the Emergency Department Patients Flow. Journal of Medical Systems, 40(7), 175. https://doi.org/10.1007/s10916-016-0527-0
Afilal M, et al. Forecasting the Emergency Department Patients Flow. J Med Syst. 2016;40(7):175. PubMed PMID: 27272135.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Forecasting the Emergency Department Patients Flow. AU - Afilal,Mohamed, AU - Yalaoui,Farouk, AU - Dugardin,Frédéric, AU - Amodeo,Lionel, AU - Laplanche,David, AU - Blua,Philippe, Y1 - 2016/06/07/ PY - 2016/03/17/received PY - 2016/05/20/accepted PY - 2016/6/9/entrez PY - 2016/6/9/pubmed PY - 2017/6/28/medline KW - Emergency department flow KW - Forecasting KW - Patient classification KW - Time series SP - 175 EP - 175 JF - Journal of medical systems JO - J Med Syst VL - 40 IS - 7 N2 - Emergency department (ED) have become the patient's main point of entrance in modern hospitals causing it frequent overcrowding, thus hospital managers are increasingly paying attention to the ED in order to provide better quality service for patients. One of the key elements for a good management strategy is demand forecasting. In this case, forecasting patients flow, which will help decision makers to optimize human (doctors, nurses…) and material(beds, boxs…) resources allocation. The main interest of this research is forecasting daily attendance at an emergency department. The study was conducted on the Emergency Department of Troyes city hospital center, France, in which we propose a new practical ED patients classification that consolidate the CCMU and GEMSA categories into one category and innovative time-series based models to forecast long and short term daily attendance. The models we developed for this case study shows very good performances (up to 91,24 % for the annual Total flow forecast) and robustness to epidemic periods. SN - 1573-689X UR - https://www.unboundmedicine.com/medline/citation/27272135/Forecasting_the_Emergency_Department_Patients_Flow_ L2 - https://dx.doi.org/10.1007/s10916-016-0527-0 DB - PRIME DP - Unbound Medicine ER -