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Forecasting daily attendances at an emergency department to aid resource planning.
BMC Emerg Med. 2009 Jan 29; 9:1.BE

Abstract

BACKGROUND

Accurate forecasting of emergency department (ED) attendances can be a valuable tool for micro and macro level planning.

METHODS

Data for analysis was the counts of daily patient attendances at the ED of an acute care regional general hospital from July 2005 to Mar 2008. Patients were stratified into three acuity categories; i.e. P1, P2 and P3, with P1 being the most acute and P3 being the least acute. The autoregressive integrated moving average (ARIMA) method was separately applied to each of the three acuity categories and total patient attendances. Independent variables included in the model were public holiday (yes or no), ambient air quality measured by pollution standard index (PSI), daily ambient average temperature and daily relative humidity. The seasonal components of weekly and yearly periodicities in the time series of daily attendances were also studied. Univariate analysis by t-tests and multivariate time series analysis were carried out in SPSS version 15.

RESULTS

By time series analyses, P1 attendances did not show any weekly or yearly periodicity and was only predicted by ambient air quality of PSI > 50. P2 and total attendances showed weekly periodicities, and were also significantly predicted by public holiday. P3 attendances were significantly correlated with day of the week, month of the year, public holiday, and ambient air quality of PSI > 50.After applying the developed models to validate the forecast, the MAPE of prediction by the models were 16.8%, 6.7%, 8.6% and 4.8% for P1, P2, P3 and total attendances, respectively. The models were able to account for most of the significant autocorrelations present in the data.

CONCLUSION

Time series analysis has been shown to provide a useful, readily available tool for predicting emergency department workload that can be used to plan staff roster and resource planning.

Authors+Show Affiliations

Health Services & Outcomes Research, National Healthcare Group, Commonwealth Lane, Singapore. yan_sun@nhg.com.sgNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article

Language

eng

PubMed ID

19178716

Citation

Sun, Yan, et al. "Forecasting Daily Attendances at an Emergency Department to Aid Resource Planning." BMC Emergency Medicine, vol. 9, 2009, p. 1.
Sun Y, Heng BH, Seow YT, et al. Forecasting daily attendances at an emergency department to aid resource planning. BMC Emerg Med. 2009;9:1.
Sun, Y., Heng, B. H., Seow, Y. T., & Seow, E. (2009). Forecasting daily attendances at an emergency department to aid resource planning. BMC Emergency Medicine, 9, 1. https://doi.org/10.1186/1471-227X-9-1
Sun Y, et al. Forecasting Daily Attendances at an Emergency Department to Aid Resource Planning. BMC Emerg Med. 2009 Jan 29;9:1. PubMed PMID: 19178716.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Forecasting daily attendances at an emergency department to aid resource planning. AU - Sun,Yan, AU - Heng,Bee Hoon, AU - Seow,Yian Tay, AU - Seow,Eillyne, Y1 - 2009/01/29/ PY - 2008/07/03/received PY - 2009/01/29/accepted PY - 2009/1/31/entrez PY - 2009/1/31/pubmed PY - 2009/5/30/medline SP - 1 EP - 1 JF - BMC emergency medicine JO - BMC Emerg Med VL - 9 N2 - BACKGROUND: Accurate forecasting of emergency department (ED) attendances can be a valuable tool for micro and macro level planning. METHODS: Data for analysis was the counts of daily patient attendances at the ED of an acute care regional general hospital from July 2005 to Mar 2008. Patients were stratified into three acuity categories; i.e. P1, P2 and P3, with P1 being the most acute and P3 being the least acute. The autoregressive integrated moving average (ARIMA) method was separately applied to each of the three acuity categories and total patient attendances. Independent variables included in the model were public holiday (yes or no), ambient air quality measured by pollution standard index (PSI), daily ambient average temperature and daily relative humidity. The seasonal components of weekly and yearly periodicities in the time series of daily attendances were also studied. Univariate analysis by t-tests and multivariate time series analysis were carried out in SPSS version 15. RESULTS: By time series analyses, P1 attendances did not show any weekly or yearly periodicity and was only predicted by ambient air quality of PSI > 50. P2 and total attendances showed weekly periodicities, and were also significantly predicted by public holiday. P3 attendances were significantly correlated with day of the week, month of the year, public holiday, and ambient air quality of PSI > 50.After applying the developed models to validate the forecast, the MAPE of prediction by the models were 16.8%, 6.7%, 8.6% and 4.8% for P1, P2, P3 and total attendances, respectively. The models were able to account for most of the significant autocorrelations present in the data. CONCLUSION: Time series analysis has been shown to provide a useful, readily available tool for predicting emergency department workload that can be used to plan staff roster and resource planning. SN - 1471-227X UR - https://www.unboundmedicine.com/medline/citation/19178716/Forecasting_daily_attendances_at_an_emergency_department_to_aid_resource_planning_ L2 - https://bmcemergmed.biomedcentral.com/articles/10.1186/1471-227X-9-1 DB - PRIME DP - Unbound Medicine ER -