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Acta Cirurgica Brasileira

Print version ISSN 0102-8650On-line version ISSN 1678-2674

Acta Cir. Bras. vol.33 no.12 São Paulo Dec. 2018 



Response time in the emergency services. Systematic review1

Eric Lucas dos Santos CabralI 

Wilkson Ricardo Silva CastroI 

Davidson Rogério de Medeiros FlorentinoI 

Danylo de Araújo VianaI 

João Florêncio da Costa JuniorI 

Ricardo Pires de SouzaII 

Amália Cinthia Meneses RêgoIII 

Irami Araújo-FilhoIV 

Aldo Cunha MedeirosV 

IFellow Master degree, Postgraduate Program in Production Engineering, Universidade Federal do Rio Grande do Norte (UFRN), Natal-RN, Brazil. Acquisition, interpretation and analysis of data; manuscript writing.

IIFellow Master degree, Postgraduate Program in Production Engineering, UFRN, Natal-RN, Brazil. Acquisition, interpretation and analysis of data; critical revision.

IIIPhD, Health Sciences, Natal-RN, Brazil. Design of the study, interpretation and analysis of data, manuscript writing, critical revision.

IVFull Professor, Department of Surgery, UFRN and Universidade Potiguar (UnP), Natal-RN, Brazil. Design of the study, interpretation and analysis of data, manuscript writing, critical revision.

VPhD, Full Professor, Department of Surgery, UFRN, Natal-RN, Brazil. Design of the study, interpretation and analysis of data, manuscript writing, critical revision.


The growth of the urban population raises concern about municipal public managers in the sense of providing emergency medical services (EMS) that are aligned with the needs of prehospital emergency medical care demanded by the population. The literature review aims at presenting the response time of emergency medical services in several parts of the world and discussing some factors that interfere in the result of this indicator such as GDP (Gross Domestic Product) percentage spent on health and life expectancy of countries. The study will also show that in some of the consulted articles, authors suggest to EMS recommendations for decreasing the response time using simulations, heuristics and metaheuristics. Response time is a basic indicator of emergency medical services, in such a way that researchers use the descriptive statistics to evaluate this parameter. Europe and the USA outstand in the publication of studies that present this information. Some articles use stochastic and mathematical methods to suggest models that simulate scenarios of response time reduction and suggest such proposals to the local EMS. Countries in which the response time was identified have a high index of human development and life expectancy between 74.7 and 83.7 years.

Key words: Emergency Medical Services; Ambulances; Emergency Responders


Growth of the urban population raises the concern of municipal public managers in the sense of providing emergency medical care services that are aligned with the needs of prehospital emergency medical care demanded by the population. There are estimates indicating that by 2050, urban areas should have a contingent of 6.29 billion people, equivalent to 69% of the world’s total population1. With population growth in large cities, it is natural that there is a significant increase in the number of traffic accidents and other serious occurrences such as heart attack, drowning, drilling by firearm and disasters (floods, landslides, earthquakes, for example).

This increase in such occurrences is reflected in the proportional growth in costs of health systems worldwide. In 2010 prognoses show that this value reached 17.6%. Among these health expenditures, 5 to 10% are with emergencies2-3. Furthermore, the increasing number of inhabitants in an urban area will challenge the resources already in place. The traffic density in many metropolitan areas of the world causes severe car congestion4.

The World Health Organization has defined provision of basic life support to all risk situations involving people and goods as a main objective of Emergency Medical Services (EMS)5. EMS is included in this discussion as they are responsible for the provision of acute prehospital care for patients with illnesses and injuries and have a key role to providing quality services to people, minimizing the degree of lesions and even the death toll6. The response time of EMS is a fundamental factor for prehospital care to be successful and, therefore, must be controlled in order to increase the chances of survival7.

Response time is the main indicator of this service. It is defined as the time between notification of an occurrence and the ambulance arrival at the scene. According to the WHO, an ideal response time is equivalent to less than 8 minutes5. Regarding the definition of response time, Lawner et al.7 consider secondary outcomes, which include changes in other main ambulance time metrics such as average ambulance response interval (time from ambulance dispatch to arrival at the scene) and overall out-of-service interval (the amount of time that an ambulance is not available to respond to another incident). Vile et al.8 further stresses that response times are one of WEST’s Key Performance Indicators (KPIs) since they are believed to provide a good indication of the quality and timeliness of care provided by the service.

Worldwide, this parameter is quantified because of its relevance in the evaluation of quality of service. Examples identified in cities with more than one million inhabitants in Brazil and in the world explain the average response time for urgent care: in the UK, it should be a maximum of 8 minutes in 75% of calls and 19 minutes in serious, but not urgent, calls.

In Vienna, Austria, the average response time recorded in 2015 was 15 minutes considering a 10% dispersion of this value to more or less due to the restrictions of intense traffic of vehicles and climatic conditions. In Brazil, in Belo Horizonte, the average response time in 2010 was approximately 21 minutes9. In the city of São Paulo, in 2007, the best response time found for emergency medical services was 27 minutes10.

Takeda et al.11 discussed in their article the US EMS Act, which sets some standards: 95% of the emergency requests should be served within 10 min in urban areas and within 30 min in rural areas. Similar regulations are found in other parts of the world; for example, in London and Montreal, the regulation states that 95% of the requests should be served within 14 to 10 min, and 50% and 70% of the requests should be served within 8 to7 min, respectively6-8. Throughout the time, there was a tendency of growth of the publications that addressed in its scope the time of response of EMS. Figure 1 represents the continents with a larger number of publication of the selected studies for data extraction12.

Figure 1 Continents that outstand in the publication of response times and EMS. 

It is clear that America and Europe outstand in this classification. It is necessary to point out that the figure in discussion refers to the articles that were selected by this review for presenting the information initially discussed by the authors.

The keywords identified in the articles consulted in this literature review are interrelated and allowed the meeting of technical materials that were aligned with the objective described in this review. In Figure 1, the diameter of each node representing these words is proportional to the number of articles located regarding them. The representativeness of nodes emergency medical services, prehospital emergency care, ambulances, emergency responders, reaction time and emergency health service are significant in the contribution to studies that deepen the discussion of this review.

In order to quantify the response time of emergency medical services and, in some cases, to contribute with suggestions to reduce this indicator, the articles consulted were based on Statistical Methods and Operational Research. The objective of statistical methods is to make processes efficient as much as possible12. Statistical methods are used to understand variability. By variability, it is understood that successive observations of a system or phenomenon do not produce exactly the same result. In the search for this goal, statistic uses some tools: descriptive statistics, statistical inference, regression, correlation and multivariate analysis of the data.

Descriptive statistics, measures of central tendency; dispersion measures; measures of asymmetry and flatness; and measures of association and graphical representation of results. Also, according to, statistical inferences cover some functions of probability density that are used more frequently in statistical analysis13. The functions are Normal distribution; Distribution of the Chi-square (x²); Student’s t-distribution; F Distribution and Binomial Distribution12,13.

Regression is a valid method to understand the influence that one or more predictive variables have in relation to a regressive variable. Through it, it is possible to construct an equation that accurately predicts the future behavior that one wishes to evaluate, if parameters such as correlation coefficient between variables, F-test and p-value test, are satisfactory13.

Operational Research also contributes to the fact that the models developed in the articles consulted for this review reach their objectives, since one of the strategies used by EMS management to reduce response time is to use algorithms, heuristics and metaheuristics to the definition of the routes of the vehicles involved in the provision of this service10-12.

Operational Research attributed to the military services early in World War II. Due to of the war effort, there was urgent need to allocate scarce resources to the various military operations. Another characteristic of operations research is its broad viewpoint, encompassing Linear Programming, Dynamic Programming and Queuing Theory14.

Inserted in this context, this literature review aims to present the response time of emergency medical services (EMS) in several parts of the world and to discuss some factors that interfere in the result of this indicator such as GDP percentage spent with health as well as life expectancy of Countries. Our study will also show that in some of the articles consulted the authors suggest to the EMS recommendations to decreasing the response time using simulations, heuristics and metaheuristics.


Scopus/Embase, NCBI (PubMed) and Science Direct were searched on 5 May 2017 for English-language papers from 2007 to 2017. The following search terms were used: “Emergency Medical Services” and “Response Time”. Titles and abstracts were reviewed independently by four reviewers. The time interval considered for the evaluation of articles was from 2007 to 2017. The Lilacs database was not queried because it is indexed in SciELO, which in turn is indexed in PubMed. Google Scholar was not considered due to the preference of the review authors for the use of peer reviewed databases. It used in this research is showed in Figure 2. Disagreements between reviewers were resolved in a meeting with the rest of the research team. During the full text review, studies were excluded if they did not meet the criteria, response time availability and the referring cities. Studies were not excluded on the basis of methodological quality. The final sample size for the analysis was 205 articles, these came from 864 studies. A recent bibliometric review of scheduling routing presented relevant information regarding response time in emergency medical services with a broader number of selected articles, the mentioned review though, approached the applied methodology aiming at improving the response time. While the present study focus on the descriptive statistical analysis of the response time around the world. The reason of which the aforementioned review differs in searched articles, can be explained by the requirement of the proposition mathematical methods, demand of which is not applied in the present study.

Figure 2 Keyword found in database search. 

The research was done in the following order: the country and city where the study was carried out, the methods used to provide the reduction of the response time of the EMS, the journal that published it, year of publication of the article, country’s GDP, public health expenditure percent to GDP and nationality life expectancy. Based on these data, an analysis of the elements of descriptive statistics was developed and the graphical representations of this evaluation were positioned within the scope of this study, in such a way that the conclusions expressed in it are evaluated numerically.


According to the articles listed, an international concern is observed in the measurement and optimization of response time in attending emergency occurrences that require the presence of qualified medical staff15-17. Technical work has been found in this sense in all the habitable continents with emphasis on America and Europe. The largest portion of the articles comprise statistical analysis of the data provided by the medical emergency management agencies of the region aiming at presenting indicators of these services18,19.

In general, local organizations that manage the emergency medical service provide the information necessary for research centers to make this assessment20. Other study will not only make basic statistical descriptions related to the EMS, but also make recommendations that will influence the performance of routine operations impacting, in particular, the service response time21-23. Figure 3 summarizes that a significant portion of the studies dealing with problems related to emergency medical services in order to contribute to decreased response time, resources, coverage and routes, for example, make use of simulation, algorithms and data analysis through descriptive statistics24,25.

Figure 3 Evolution of publication related to responses times and EMS. 

The use of algorithms and simulations allow scenarios to be developed and evaluated. Hence, it is possible to make changes in the characteristics of the scenarios, such as resources, variables and planning horizons, for example26-28. Non-significant methods for usability in articles related to Figure 4 (Queue Theory, for example) are more complex in the construction of scenarios and are descriptive, which makes their recurrent use unfeasible in the articles consulted29.

Figure 4 Methods used in the article searched. 

Data evaluation shows that the localities found, positioned mainly in Europe and North America, present a response time in accordance with what is expected for this type of service, having an estimated time of approximately 8 minutes according the WHO2,30-33.

Locations in Asia, Latin America and Africa showed response time higher than 8 minutes. Economic development and access to health influence these numbers as well as the operational conditions of the emergency medical services of those countries of the aforementioned places34-36. Table 1 shows that the localities with the shortest response time in emergency medical services were Salt Lake City (United States) and Taoyuan (Taiwan). In these cities, the use of information technology (mobile applications), significant GDP percentage spent on public health by the country and high level of schooling of the population (which could provide prehospital medical care before the arrival of ambulances), ensure that the indicator is lower than that of other countries and cities37. The locations with the longest response time were Athens and São Paulo10-12.

Table 1 Information listed in the articles evaluated. 

Year of publication Countries City. Region and State Response Time (Minutes) HDI Life expectancy (Years) Public Health Expenditure (% to GDP)
2014 Taiwan Taoyuan15 5 0.738 76.0 3.1
2016 United States Salt Lake City16 5 0.920 79.2 8.3
2017 Republic of Korea Seoul 17 7 0.901 82.1 7.4
2015 United Kingdom Wiltshire,Glouceste-rshire and Avon in Southwest England18 6 0.909 80.8 7.6
2016 United States Seattle19 6.1 0.920 79.2 8.3
2015 Singapore Singapore20 7.25 0.925 83.2 2.1
2015 Sweden Stockholm21 7.8 0.913 82.3 10.0
2012 Australia Melbourne22 8 0.939 82.5 6.3
2016 Netherlands Nijmegen, Gelderland Zuid23 8 0.924 81.7 9.5
2015 Netherlands Amsterdam 24 15 0.924 81.7 9.5
2015 Finland Southern and Eastern Finland25 8 0.895 81.0 7.3
2016 United States Charlotte26 8.59 0.920 79.2 8.3
2011 Portugal Lisboa27 9 0.843 81.2 6.2
2012 Canada Edmonton28 9 0.920 82.2 7.4
2014 United States Hanover29 9 0.920 79.2 8.3
2016 United States Chicago30,31 10 0.920 79.2 8.3
2015 Norway Bergen32 11 0.949 81.7 9.7
2013 Switzerland Berna33 11.5 0.939 83.1 7.7
2017 Mexico Tijuana34 14 0.762 77.0 3.3
2013 United States Alabama35 6.4 0.920 79.2 8.3
2016 Netherlands Flevoland36 15 0.924 81.7 9.5
2014 Spain Galícia37 15 0.884 82.8 6.4
2014 Iran (Islamic Republic of) Tabriz38 17.87 0.774 75.6 2.8
2016 Ghana Kumasi, Accra and Tamale39 19.5 0.579 62.5 3.6
2016 Brazil Belo Horizonte5 21 0.754 74.7 3.8
2014 United States Dallas40 24 0.920 79.2 8.3
2016 United States Baltimore41 25.1 0.920 79.2 7.8
2013 Spain Navarre42 26 0.884 82.8 6.4
2015 Brazil São Paulo10 27 0.754 74.7 3.8
2007 Brazil Campinas11 13 0.754 74.7 3.8
2014 Turkey Eskisehir43 6.5 0.767 75.5 4.2
2015 Iran (Islamic Republic of) Shiraz4 8.83 0.774 75.6 2.8
2016 Japan All of Japan 44,45 7.5 0.903 83.7 10.2
2013 United States Durham46 6.8 0.920 79.2 8.3
2015 Taiwan Taipei47 6.5 0.738 76.0 3.1
2015 United States Colonial Heigths48 4.09 0.920 79.2 8.3
2016 Finland Turku49 11.6 0.895 81.0 9.7
2016 Austria Styria, Tyrol49 8.2 0.893 81.6 11.2
2016 Croatia Slavonski Brod, Zadar, Zagreb49 26.9 0.827 77.5 7.8
2016 Croatia Koprivinica, Rijeka, Split49 11.1 0.827 77.5 7.8
2016 Czech Republic Hredec Kralove49 8.4 0.878 78.8 7.4
2016 Greece Athens49 28.9 0.866 81.1 8.1
2016 Poland Cracow. Poznan49 16.5 0.855 77.6 6.3
2016 Serbia Novi Sad49 9.2 0.78 75.0 10.4

Factors contributing to these figures concern the heavy traffic of vehicles on public roads those cities, which hinders the circulation of ambulances and is an impediment to achieving adequate response times as prescribed by the WHO.2 In Brazil, there is no specific regulation that specifies limitations for response times in EMS11-13,38.

Another indicator evaluated by the United Nations Development Programmer is the proportionality of health spending relative to the GDP and the life expectancy of the countries38-40. Evaluating this indicator, it was observed that there is no significant correlation between the GDP percentage allocated to health spending and the response time of the service under discussion (Table 1). That is, the performance of the response time of emergency medical services does not depend on the amount of resource available for health in a country41. The idea that arises the present from this discussion is centered on the management of the resource derived from the GDP, that is, if it is being used to promote the operational efficiency of emergency medical assistance services.

Furthermore, other factors that should influence the response performance of medical teams are demographic density, localities (size), public health policies and traffic conditions in public roads. Figure 5 represents a summary of the median of the indicators presented in Table 1 per continent11,18.

Figure 5 Median of Response Time, Public Health and HDI. 

The choice of the median for the development of this graph was due to sensitivity, which allows the adequate understanding of the indicators from the continental point of view, according to our review42. Figure 5 shows that there is a predominance of response time in significant nonconformity with what is prescribed by the World Health Organization (8 minutes) in places where there is low life expectancy, low public investment in health and follow average human development index (HDI)43.

In locations where response time happens within the WHO’s determined their conformity, can be seen high life expectancy, higher GDP percentage, more public investment in the area, expenditure, very high human development index and high life expectancy were identified42-44.

The exceptional case of Asia presents a median response time lower than that registered in other continents, although the GDP percentage spent on health is the lowest recorded value, which raises the discussion of the public policies used by the governments of the countries of that continent and the distribution of resources for the provision of these policies and their alignment with the efficiency of routine EMS operations45.

Asia has a shorter mean response time (7.3 minutes), followed by Oceania (8.0 minutes). In this continent was identified only one country (Australia) that presented city (Melbourne) whose emergency medical service measures indicator response time46-48. In Africa only one country (Ghana) presented cities (Kumasi, Accra and Tamale) where in emergency medical service measures indicator response time (19 minutes). America and Europe have a median response time of 9 to 11 minutes, respectively49. Due to the representativeness of North American cities in the measurement of the response time of emergency medical services, a median human development index for the Americas was equivalent to 0.920, value corresponding to the US HDI. This does not reflect the HDI reality of the entire American continent50.


In conclusion, this review has shown that, in several countries around the world, there is an interest of health institutions responding urgently and in an emergency to the quantification of response time, and there is a consensus that this indicator is fundamental to understanding routine EMS.

Due to the importance of the topic, it is necessary to stimulate the development of other studies that seek to understand the factors correlated to the response time, which will allow a scientifically validated understanding of what can be done to reduce it.


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Financial source: CAPES

1Research performed at Nucleus of Experimental Surgery, Department of Surgery and the Department of Production Engineering, Universidade Federal do Rio Grande do Norte (UFRN), Natal-RN, Brazil.

Received: August 06, 2018; Revised: October 08, 2018; Accepted: November 03, 2018

Correspondence: Irami Araújo-Filho Hospital Universitário Onofre Lopes - Núcleo de Cirurgia Experimental/UFRN Avenida Nilo Peçanha, 620 59012-300 Natal - RN Brasil Tel.: (55 84)98876-0206

Conflict of interest: none

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