Forecasting the Patients Flow at Pediatric Emergency Departments.
Emergency department
Flow
Forecasting
Pediatrics
Journal
Journal of medical systems
ISSN: 1573-689X
Titre abrégé: J Med Syst
Pays: United States
ID NLM: 7806056
Informations de publication
Date de publication:
28 Jan 2021
28 Jan 2021
Historique:
received:
17
04
2020
accepted:
20
01
2021
entrez:
28
1
2021
pubmed:
29
1
2021
medline:
29
7
2021
Statut:
epublish
Résumé
Emergency departments (EDs) have a key role in the public health system. They are facing a constant growth of their volume. Forecasting the daily volume is a major tool to adapt the allocation of resources. In this paper, we focus on pediatric EDs. They are specific by their strong seasonal variation, determined by the academic pace. The main contribution of this paper is to integrate the effects of this pace to the annual seasonality. We also tried out to improve the daily forecasting by forecasting the week means of the flow first. We trained and tested these models specifically on the pediatric EDs of Paris university hospital trust. For the eight pediatric EDs gathered, on average for the years 2016 to 2019, we forecasted the daily volume with a Mean Absolute Percentage Error (MAPE) of 6.6% for a 7-days forecasting, 7.1% for a 14-days forecasting and 7.6% for a 28-days forecasting. Account of rhythm allows a performance increase, with results respectively 7%, 10.1% and 8.4% better relatively to a baseline model based on a periodic regression on the weeks.
Identifiants
pubmed: 33506300
doi: 10.1007/s10916-021-01712-z
pii: 10.1007/s10916-021-01712-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
29Références
Y. Sun, B. H. Heng, Y. T. Seow, and E. Seow, ‘Forecasting daily attendances at an emergency department to aid resource planning’, BMC Emerg Med, vol. 9, p. 1, Jan. 2009.
doi: 10.1186/1471-227X-9-1
Ruiz, M. P., Prat, I. R., and Visintin, F., ‘Forecasting patients’ admissions in an ED: The case of the Meyer Hospital’, p. 65.
M. Afilal, F. Yalaoui, F. Dugardin, L. Amodeo, D. Laplanche, and P. Blua, ‘Forecasting the Emergency Department Patients Flow’, J Med Syst, vol. 40, no. 7, p. 175, 2016.
doi: 10.1007/s10916-016-0527-0
S. N. Wood, N. Pya, and B. Säfken, ‘Smoothing Parameter and Model Selection for General Smooth Models’, Journal of the American Statistical Association, vol. 111, no. 516, pp. 1548–1563, Oct. 2016.
doi: 10.1080/01621459.2016.1180986
Brockwell, P. J. and Davis, R. A., Introduction to Time Series and Forecasting, 2nd ed. New York: Springer-Verlag, 2002.
Liboschik, T., Fokianos, K., and Fried, R., ‘tscount : An R Package for Analysis of Count Time Series Following Generalized Linear Models’, J. Stat. Soft., vol. 82, no. 5, 2017.
L. C. Brooks, D. C. Farrow, S. Hyun, R. J. Tibshirani, and R. Rosenfeld, ‘Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions’, PLoS Comput Biol, vol. 14, no. 6, p. e1006134, 2018.
doi: 10.1371/journal.pcbi.1006134