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Ann Emerg Med. 2015 Apr;65(4):436-442.e1. doi: 10.1016/j.annemergmed.2014.10.008. Epub 2014 Dec 5.

Forecasting emergency department visits using internet data.

Author information

1
Department of Clinical Science and Education, Södersjukhuset, Section of Emergency Medicine, Karolinska Institutet, Stockholm, Sweden; Department of Emergency Medicine, Södersjukhuset, Stockholm, Sweden.
2
Department of Clinical Science and Education, Södersjukhuset, Section of Emergency Medicine, Karolinska Institutet, Stockholm, Sweden.
3
Department of Clinical Science and Education, Södersjukhuset, Section of Emergency Medicine, Karolinska Institutet, Stockholm, Sweden; Department of Emergency Medicine, Södersjukhuset, Stockholm, Sweden. Electronic address: martin.nordberg@sodersjukhuset.se.

Abstract

STUDY OBJECTIVE:

Using Internet data to forecast emergency department (ED) visits might enable a model that reflects behavioral trends and thereby be a valid tool for health care providers with which to allocate resources and prevent crowding. The aim of this study is to investigate whether Web site visits to a regional medical Web site, the Stockholm Health Care Guide, a proxy for the general public's concern of their health, could be used to predict the ED attendance for the coming day.

METHODS:

In a retrospective, observational, cross-sectional study, a model for forecasting the daily number of ED visits was derived and validated. The model was derived through regression analysis, using visits to the Stockholm Health Care Guide Web site between 6 pm and midnight and day of the week as independent variables. Web site visits were measured with Google Analytics. The number of visits to the ED within the region was retrieved from the Stockholm County Council administrative database. All types of ED visits (including adult, pediatric, and gynecologic) were included. The period of August 13, 2011, to August 12, 2012, was used as a training set for the model. The hourly variation of visits was analyzed for both Web site and the ED visits to determine the interval of hours to be used for the prediction. The model was validated with mean absolute percentage error for August 13, 2012, to October 31, 2012.

RESULTS:

The correlation between the number of Web site visits between 6 pm and midnight and ED visits the coming day was significant (r=0.77; P<.001). The best forecasting results for ED visits were achieved for the entire county, with a mean absolute percentage error of 4.8%. The result for the individual hospitals ranged between mean absolute percentage error 5.2% and 13.1%.

CONCLUSION:

Web site visits may be used in this fashion to predict attendance to the ED. The model works both for the entire region and for individual hospitals. The possibility of using Internet data to predict ED visits is promising.

[Indexed for MEDLINE]

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