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Predicting Ambulance Diverson

Predicting Ambulance Diverson

Abey Kuruvilla, Suraj M. Alexander
Copyright: © 2010 |Volume: 2 |Issue: 1 |Pages: 10
ISSN: 1935-5688|EISSN: 1935-5696|ISSN: 1935-5688|EISBN13: 9781616929497|EISSN: 1935-5696|DOI: 10.4018/jisss.2010093001
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MLA

Kuruvilla, Abey, and Suraj M. Alexander. "Predicting Ambulance Diverson." IJISSS vol.2, no.1 2010: pp.1-10. http://doi.org/10.4018/jisss.2010093001

APA

Kuruvilla, A. & Alexander, S. M. (2010). Predicting Ambulance Diverson. International Journal of Information Systems in the Service Sector (IJISSS), 2(1), 1-10. http://doi.org/10.4018/jisss.2010093001

Chicago

Kuruvilla, Abey, and Suraj M. Alexander. "Predicting Ambulance Diverson," International Journal of Information Systems in the Service Sector (IJISSS) 2, no.1: 1-10. http://doi.org/10.4018/jisss.2010093001

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Abstract

The high utilization level of emergency departments in hospitals across the United States has resulted in the serious and persistent problem of ambulance diversion. This problem is magnified by the cascading effect it has on neighboring hospitals, delays in emergency care, and the potential for patients’ clinical deterioration. We provide a predictive tool that would give advance warning to hospitals of the impending likelihood of diversion. We hope that with a predictive instrument, such as the one described in this article, hospitals can take preventive or mitigating actions. The proposed model, which uses logistic and multinomial regression, is evaluated using real data from the Emergency Management System (EM Systems) and 911 call data from Firstwatch® for the Metropolitan Ambulance Services Trust (MAST) of Kansas City, Missouri. The information in these systems that was significant in predicting diversion includes recent 911 calls, season, day of the week, and time of day. The model illustrates the feasibility of predicting the probability of impending diversion using available information. We strongly recommend that other locations, nationwide and abroad, develop and use similar models for predicting diversion.

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