Literature DB >> 20836775

Reducing ambulance response times using geospatial-time analysis of ambulance deployment.

Marcus Eng Hock Ong1, Tut Fu Chiam, Faith Suan Peng Ng, Papia Sultana, Swee Han Lim, Benjamin Sieu-Hon Leong, Victor Yeok Kein Ong, Elaine Ching Ching Tan, Lai Peng Tham, Susan Yap, V Anantharaman.   

Abstract

OBJECTIVES: This study aimed to determine if a deployment strategy based on geospatial-time analysis is able to reduce ambulance response times for out-of-hospital cardiac arrests (OOHCA) in an urban emergency medical services (EMS) system.
METHODS: An observational prospective study examining geographic locations of all OOHCA in Singapore was conducted. Locations of cardiac arrests were spot-mapped using a geographic information system (GIS). A progressive strategy of satellite ambulance deployment was implemented, increasing ambulance bases from 17 to 32 locations. Variation in ambulance deployment according to demand, based on time of day, was also implemented. The total number of ambulances and crews remained constant over the study period. The main outcome measure was ambulance response times.
RESULTS: From October 1, 2001, to October 14, 2004, a total of 2,428 OOHCA patients were enrolled into the study. Mean ± SD age for arrests was 60.6 ± 19.3 years with 68.0% male. The overall return of spontaneous circulation (ROSC) rate was 17.2% and survival to discharge rate was 1.6%. Response time decreased significantly as the number of fire stations/fire posts increased (Pearson χ(2) = 108.70, df = 48, p < 0.001). Response times for OOHCA decreased from a monthly median of 10.1 minutes at the beginning to 7.1 minutes at the end of the study. Similarly, the proportion of cases with response times < 8 minutes increased from 22.3% to 47.3% and < 11 minutes from 57.6% to 77.5% at the end of the study.
CONCLUSIONS: A simple, relatively low-cost ambulance deployment strategy was associated with significantly reduced response times for OOHCA. Geospatial-time analysis can be a useful tool for EMS providers. 2010 by the Society for Academic Emergency Medicine

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Year:  2010        PMID: 20836775     DOI: 10.1111/j.1553-2712.2010.00860.x

Source DB:  PubMed          Journal:  Acad Emerg Med        ISSN: 1069-6563            Impact factor:   3.451


  7 in total

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Authors:  Elisabeth Dowling Root; Louis Gonzales; David E Persse; Paul R Hinchey; Bryan McNally; Comilla Sasson
Journal:  Resuscitation       Date:  2013-01-11       Impact factor: 5.262

2.  Predicting ambulance time of arrival to the emergency department using global positioning system and Google maps.

Authors:  Ross J Fleischman; Mark Lundquist; Jonathan Jui; Craig D Newgard; Craig Warden
Journal:  Prehosp Emerg Care       Date:  2013-07-18       Impact factor: 3.077

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5.  A spatiotemporal data mining study to identify high-risk neighborhoods for out-of-hospital cardiac arrest (OHCA) incidents.

Authors:  Paulina Pui-Yun Wong; Chien-Tat Low; Wenhui Cai; Kelvin Tak-Yiu Leung; Poh-Chin Lai
Journal:  Sci Rep       Date:  2022-03-03       Impact factor: 4.379

6.  Performance of an Emergency Road Ambulance Service in Bhutan: Response Time, Utilization, and Outcomes.

Authors:  Tshokey Tshokey; Ugyen Tshering; Karma Lhazeen; Arpine Abrahamyan; Collins Timire; Bikash Gurung; Devi Charan Subedi; Kencho Wangdi; Victor Del Rio Vilas; Rony Zachariah
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7.  Accuracy of Perceived Estimated Travel Time by EMS to a Trauma Center in San Bernardino County, California.

Authors:  Michael M Neeki; Colin MacNeil; Jake Toy; Fanglong Dong; Richard Vara; Joe Powell; Troy Pennington; Eugene Kwong
Journal:  West J Emerg Med       Date:  2016-06-21
  7 in total

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