Reducing Ambulance Response Times Using Geospatial–Time Analysis of Ambulance Deployment

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 v 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.ACADEMIC EMERGENCY MEDICINE 2010; 17:951-957 ª 2010 by the Society for Academic Emergency MedicineKeywords: geographic information systems, heart arrest, emergency medical services G eographic information systems (GIS) are computer-based systems for the integration and analysis of geographic data. GIS is a multilayering mapping software that is able to portray multiple geographic-time information in an easy-to-read, graphical manner. ''Geographic data'' are spatial data that result from observation and measurement of phenomena referenced to their locations on the earth's surface.

Factors affecting the ambulance response times of trauma incidents in Singapore

“…"Ambulance dispatch" is defined as the event when the ambulance leaves its base station whereas "arrival at the incident location" is defined as the event when the ambulance arrives on scene. This definition of the ART is one of the key performance indicator used by Singapore's national EMS provider (Ong et al, 2010;Lam et al, 2013Lam et al, , 2015Goh et al, 2014).…”

“…The International Standards Organization (ISO) has thus adopted EMS response times as a key performance indicator of fire and emergency services, providing a comparable and verifiable measure of EMS performance across diverse cities (I.S.O., 2014). For cities seeking to improve their EMS systems, it is imperative to examine the factors that can impact EMS response times in order to identify effective interventions (Ong et al, 2010;Lam et al, 2013Lam et al, , 2015Goh et al, 2014).…”

“…This study builds on the existing literature which dealt with specifically out-of-hospital cardiac arrests based on primarily geospatial factors (Ong et al, 2009(Ong et al, , 2010 to investigate generic risk factors affecting the ambulance response times (ART) for trauma incidents, so as to derive interventional measures that can potentially improve the ART.…”

Role of Emergency Medical Services in Disaster Response: Resource Document for the National Association of EMS Physicians Position Statement

“…13 Traditional EMS performance measures have focused on response times, [14][15][16][17][18] appropriate patient selection for prehospital rapidsequence intubation (RSI) 19 and air versus ground transportation, 20 and out-of-hospital cardiac arrest survival. 21,22 Newer efforts are expanding EMS benchmarks for various clinical entities encountered by EMS systems, such as ST-segment elevation myocardial infarction (STEMI), [23][24][25] acute stroke, 26 pulmonary edema, asthma, and seizures.…”

Designing a model to minimize inequities in hemodialysis facilities distribution

“…Another study described a geospatial model to estimate potential catchment areas around health facilities based on access travel time (Hernandez-Avila et al, 2010). An ambulance deployment strategy based on geospatial-time analysis was able to reduce ambulance response times for out-of-hospital cardiac arrests in an urban emergency medical services system (Ong et al, 2010). Several studies have presented models based on geographical information systems (GIS) to identify the best localization of hemodialysis facilities based on the number of patients currently undergoing hemodialysis and the distance travelled for treatment (MacGregor et al, 2005;White et al, 2006).…”