Construction and application of an ARIMA model for predicting the number of outpatient visits in general hospitals.
- Author:
Qian XIANG
1
;
Ping-yan CHEN
Author Information
- Publication Type:Journal Article
- MeSH: China; Forecasting; Humans; Models, Statistical; Outpatient Clinics, Hospital; statistics & numerical data; Outpatients; statistics & numerical data; Patient Readmission; statistics & numerical data; Seasons
- From: Journal of Southern Medical University 2009;29(5):1076-1078
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVETo investigate the variation patterns of the number of outpatient visits in hospitals to provide references for more effective management of general hospitals.
METHODSThe forecasting model of ARIMA(1,0,1)(0,1,1)12 was established using residual error analysis and least squares method according to the sequence stability, long-term trend and seasonal effect after logarithm transformation and differencing.
RESULTSThe residual sum of squares was 2.790, AIC=-178.126, SBC=-170.080. The relative predictive error of the model for predicting the outpatient visits in a general hospital in the year 2008 was 6.11%, smaller than that of exponential smoothing (8.78%). This model predicted a number of outpatient visits of 1,501,200 in this hospital in the year 2009.
CONCLUSIONSThe ARIMA model provides a means for predicting the number of total outpatient visits, its long-term tendency and seasonal variation. The parameters p,d,q in the ARIMA model may vary between different hospitals, and the ACF and PACF charts of the original sequences are helpful for determining these parameters.
