응급센터 방문 환자 규모 예측 모형의 개발 및 타당성 평가 = Development and Validation of a Prediction Model for the Number of Patients Visiting Emergency Departments

다국어 초록 (Multilingual Abstract)

Purpose: We aimed to develop and validate a prediction model for the number of patients visiting emergency departments.
Methods: Enrolled patients were from eleven regional emergency departments (EDs) (level-1) that inputted information on emergency patients into the National Emergency Department Information System since 2004. We developed the automated regressive integrated moving average (ARIMA)-based prediction model using a dataset covering 2005 to 2007. To validate the prediction model, we performed Bland-Altman plot analysis for a new dataset, that of 2008, calculating the agreement rate.
Results: The total number of enrolled patients was 1,532,294. Of these, 844,802 (55.1%) were male and mean age was 36.5. The ARIMA (1, 1, 1) (1, 1, 1) 7 was selected as the best-fit prediction model. When we tested the validity using Bland-Altman plots, the agreement rate was 96.4%(95% CI, 94.0%~98.1%). Non-agreement dates were national holidays (n=9), and the other weekdays (n=4),respectively.
Conclusion: We developed the ARIMA-based prediction model for emergency patients at regional EDs. The model showed a very high validity.

참고문헌 (Reference)

1 안기옥, "응급센터 과밀화 현상의 정의와 특징분석 - 전국 1 0개 병원 대상으로" 대한응급의학회 15 (15): 261-272, 2004

2 Park JH, "Types and patterns of the emergency patient" 3 : 62-70, 1992

3 Park IC, "Triage of non-emergent patient and guideline for transfer" 6 : 403-410, 1995

4 Chatfield C, "Time series forecasting" Chapman &Hall/CRC 1-3, 2001

5 Box GEP, "Time series analysis: forecasting and control. 4th ed" John Wiley 46-118, 2008

6 Shumway RH, "Time series analysis and its applications: with R examples. 2nd ed" Springer 49-63, 2006

7 Hansagi H, "The urgency of care need and patient satisfaction at a hospital emergency department" 17 : 71-75, 1992

8 Abraham B, "Statistical methods for forecasting. 2nd ed" Wiley-Interscience 281-285, 2005

9 Bland JM, "Statistical method for assessing agreement between two methods of clinical measurement" 1 : 307-310, 1986

10 Theil H, "Some observations on adaptive forecasting" 10 : 198-206, 1964

11 Dagum EB, "Seasonal factor forecasts from ARIMA models" 1975

12 Derlet RW, "Overcrowding in the nation’s emergency departments: complex causes and disturbing effects" 35 : 63-68, 2000

13 Derlet R, "Frequent overcrowding in U.S. emergency departments" 8 : 151-155, 2001

14 Jones SS, "Forecasting daily patient volumes in the emergency department" 15 : 159-170, 2008

15 Sun Y, "Forecasting daily attendances at an emergency department to aid resource planning" 9 : 1-, 2009

16 Bowerman BL, "Forecasting and time series: an applied approach. 4th ed" Brooks/ Cole Publishing 100-180, 2005

17 Montgomery DC, "Forecasting and time series analysis" Mcgraw-Hill 188-236, 1976

18 Olshaker JS, "Emergency department overcrowding and ambulance diversion: the impact and potential solutions of extended boarding of admitted patients in the Emergency Department" 30 : 351-356, 2006

19 Jones SS, "A multivariate time series approach to modeling and forecasting demand in the emergency department" 42 : 123-139, 2009

20 Akaike H, "A Bayesian extension of the minimum AIC procedure of autoregressive model fitting" 66 : 237-242, 1979