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Forecasting daily emergency department visits using calendar variables and ambient temperature readings.
Acad Emerg Med. 2013 Aug; 20(8):769-77.AE

Abstract

OBJECTIVES

This study aimed to develop different models to forecast the daily number of patients seeking emergency department (ED) care in a general hospital according to calendar variables and ambient temperature readings and to compare the models in terms of forecasting accuracy.

METHODS

The authors developed and tested six different models of ED patient visits using total daily counts of patient visits to an ED in Sao Paulo, Brazil, from January 1, 2008, to December 31, 2010. The first 33 months of the data set were used to develop the ED patient visits forecasting models (the training set), leaving the last 3 months to measure each model's forecasting accuracy by the mean absolute percentage error (MAPE). Forecasting models were developed using three different time-series analysis methods: generalized linear models (GLM), generalized estimating equations (GEE), and seasonal autoregressive integrated moving average (SARIMA). For each method, models were explored with and without the effect of mean daily temperature as a predictive variable.

RESULTS

The daily mean number of ED visits was 389, ranging from 166 to 613. Data showed a weekly seasonal distribution, with highest patient volumes on Mondays and lowest patient volumes on weekends. There was little variation in daily visits by month. GLM and GEE models showed better forecasting accuracy than SARIMA models. For instance, the MAPEs from GLM models and GEE models at the first month of forecasting (October 2012) were 11.5 and 10.8% (models with and without control for the temperature effect, respectively), while the MAPEs from SARIMA models were 12.8 and 11.7%. For all models, controlling for the effect of temperature resulted in worse or similar forecasting ability than models with calendar variables alone, and forecasting accuracy was better for the short-term horizon (7 days in advance) than for the longer term (30 days in advance).

CONCLUSIONS

This study indicates that time-series models can be developed to provide forecasts of daily ED patient visits, and forecasting ability was dependent on the type of model employed and the length of the time horizon being predicted. In this setting, GLM and GEE models showed better accuracy than SARIMA models. Including information about ambient temperature in the models did not improve forecasting accuracy. Forecasting models based on calendar variables alone did in general detect patterns of daily variability in ED volume and thus could be used for developing an automated system for better planning of personnel resources.

Authors+Show Affiliations

Department of Preventive Medicine, University of Sao Paulo, Brazil.No affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

24033619

Citation

Marcilio, Izabel, et al. "Forecasting Daily Emergency Department Visits Using Calendar Variables and Ambient Temperature Readings." Academic Emergency Medicine : Official Journal of the Society for Academic Emergency Medicine, vol. 20, no. 8, 2013, pp. 769-77.
Marcilio I, Hajat S, Gouveia N. Forecasting daily emergency department visits using calendar variables and ambient temperature readings. Acad Emerg Med. 2013;20(8):769-77.
Marcilio, I., Hajat, S., & Gouveia, N. (2013). Forecasting daily emergency department visits using calendar variables and ambient temperature readings. Academic Emergency Medicine : Official Journal of the Society for Academic Emergency Medicine, 20(8), 769-77. https://doi.org/10.1111/acem.12182
Marcilio I, Hajat S, Gouveia N. Forecasting Daily Emergency Department Visits Using Calendar Variables and Ambient Temperature Readings. Acad Emerg Med. 2013;20(8):769-77. PubMed PMID: 24033619.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Forecasting daily emergency department visits using calendar variables and ambient temperature readings. AU - Marcilio,Izabel, AU - Hajat,Shakoor, AU - Gouveia,Nelson, PY - 2012/10/13/received PY - 2013/01/07/revised PY - 2013/02/20/revised PY - 2013/03/17/revised PY - 2013/04/17/accepted PY - 2013/9/17/entrez PY - 2013/9/17/pubmed PY - 2014/4/25/medline SP - 769 EP - 77 JF - Academic emergency medicine : official journal of the Society for Academic Emergency Medicine JO - Acad Emerg Med VL - 20 IS - 8 N2 - OBJECTIVES: This study aimed to develop different models to forecast the daily number of patients seeking emergency department (ED) care in a general hospital according to calendar variables and ambient temperature readings and to compare the models in terms of forecasting accuracy. METHODS: The authors developed and tested six different models of ED patient visits using total daily counts of patient visits to an ED in Sao Paulo, Brazil, from January 1, 2008, to December 31, 2010. The first 33 months of the data set were used to develop the ED patient visits forecasting models (the training set), leaving the last 3 months to measure each model's forecasting accuracy by the mean absolute percentage error (MAPE). Forecasting models were developed using three different time-series analysis methods: generalized linear models (GLM), generalized estimating equations (GEE), and seasonal autoregressive integrated moving average (SARIMA). For each method, models were explored with and without the effect of mean daily temperature as a predictive variable. RESULTS: The daily mean number of ED visits was 389, ranging from 166 to 613. Data showed a weekly seasonal distribution, with highest patient volumes on Mondays and lowest patient volumes on weekends. There was little variation in daily visits by month. GLM and GEE models showed better forecasting accuracy than SARIMA models. For instance, the MAPEs from GLM models and GEE models at the first month of forecasting (October 2012) were 11.5 and 10.8% (models with and without control for the temperature effect, respectively), while the MAPEs from SARIMA models were 12.8 and 11.7%. For all models, controlling for the effect of temperature resulted in worse or similar forecasting ability than models with calendar variables alone, and forecasting accuracy was better for the short-term horizon (7 days in advance) than for the longer term (30 days in advance). CONCLUSIONS: This study indicates that time-series models can be developed to provide forecasts of daily ED patient visits, and forecasting ability was dependent on the type of model employed and the length of the time horizon being predicted. In this setting, GLM and GEE models showed better accuracy than SARIMA models. Including information about ambient temperature in the models did not improve forecasting accuracy. Forecasting models based on calendar variables alone did in general detect patterns of daily variability in ED volume and thus could be used for developing an automated system for better planning of personnel resources. SN - 1553-2712 UR - https://www.unboundmedicine.com/medline/citation/24033619/Forecasting_daily_emergency_department_visits_using_calendar_variables_and_ambient_temperature_readings_ L2 - https://doi.org/10.1111/acem.12182 DB - PRIME DP - Unbound Medicine ER -