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The urban Emergency Medical Services (uEMS) is a service sector that reacts to common medical emergencies and is placed at the disposal of a single facility. Thus, natural disaster relief and particular emergencies are out of coverage within the organization. Moreover, the notion of urban does not only mean the sector that provides customary medical care. Urban areas are also under the patronage of this organization as an ambulance.
An urban area, within the study, is a metropolitan area, a city or a district where many people, visitors, and passengers live, and where high demand roads are located. Such areas imply the development of the city and define it as an urban area where permanent changes are taking place. The development trend stimulates transformation in short periods of hours or days. Thus, the population, visitors, and people who travel a certain distance to work on a regular basis are not a uniform flow across the city territory or throughout the entire time spectrum despite cyclical nature. According to the temporal variables, the conditions of motion change under the action of the proposed cyclic scheme.
Dealing with the subject of working-out and making decisions, stakeholders in the uEMS use a strategic approach in planning the transportation system. Maneuvering ensures decision-making, the consequences of which are expressed in the long term perspective, for instance, a storage facility for large-sized EMS objects. The purpose of this paper is to study the factors that mainly affect the uEMS transport system decisions and are associated with demography assuming an increasing demand for medical calls at a strategic level. The central aspect of the study is given to the plan of the transport system when it deals with the placement of objects and their distribution of objects during response time.
The following paragraph describes the general emergency response plan. The main aspect will be centered on the fact that the service consists of various types of emergencies and relevant institutions that ensure their elimination. The next stage we focus on is the Emergency Medical Service. Within the system, it responds to a call for assistance from the appealing moment to the national/international emergency number and until the service sends the vehicle.
The Emergency Services System
Any human all over the world can appeal to the emergency service (ES) using a particular phone number. For example, in Saudi Arabia, this is 993, which is part of the Global System for Mobile Communications (GMS) standard. All GSM-enabled telephones are available for calling the number, even in case the phone is locked, or SIM card is absent, depending on the technology of the country.
The unique telephone number forwards the incoming caller to the public emergency number by means of the GMS protocol in some countries. For example, a similar system is emergency service 911 in the USA. When a call comes into the emergency service, the operator answers it and reassigns the incoming caller to the appropriate emergency department. It can be considered a police or fire department, or Emergency Medical Service (EMS).
Some countries support the division of the system into two departments, including both a police and a fire unit. In this regard, emergency medical care is provided by the fire department. The resources and equipment of the facility, and, accordingly, the medical subdivision and personnel may be subordinate to the city, regional, or public unit. Likewise, the owner and manager of the appropriate department can be a private person. This distribution corresponds to the fact that there is no direct control of the unit’s overall ambulance transport resources. Moreover, the EMS department in charge has the authority to use any vehicle that is idle, whether public or private, to organize assistance to victims. It should be borne in mind that not all EMS systems can develop strategic and tactical plans independently. In this study, we will assume that uEMS is autonomous, and the department possesses all accessible resources.
The Medical Emergency Service System
An incoming emergency call is redirected to the appropriate specialized department. In Saudi Arabia, as previously indicated, the police, ambulance, and fire services respond to emergency calls. A qualified operator processes the call using software, which consists of a predetermined request, where questions are asked to the caller according to a sequential list. Answers are recorded as a basic algorithm, which gives an automatic assessment of emergency medical care and, if necessary, activates a request for the corresponding car. The use of such an algorithm makes it possible to evaluate the medical care service by using quantitative metrics adequately.
During an emergency call, the operator instructs on possible methods of providing medical care to improve the condition of the victim. Moreover, if the algorithm marks a high level of emergency (if urgent professional medical attention is necessary), the request is transferred to another operator. Moving critical information to another operator means that he can request the system about the availability, accessibility, and location of the response vehicle. It should be noted that ambulances are not equipped with GPS and transmit their coordinates in real-time. The operator sees only the area of free cars located at the initial facility.
Survival and Medical Response
The medical response time and factors affecting the survival of the victim are critical determinants in organizing and planning uEMS. Most of the studies on the relationship between survival and response time are devoted to the problem of cardiac arrest (Erkut et al., 2008). However, survival in traffic accidents (or any type of emergency medical care) is also paid special attention. The issues of their implementation in uEMS are considered, taking into account the problems of victims of traffic accidents (Kepaptsoglou et al., 2012). Eisenberg et al. (1990) evaluate survival rates for cardiac arrest in people being outside the medical institution, emphasizing the expansion of survival functions in this state of affairs. Hypothetical survival curves suggest that resuscitation ability depends on the time, type, and sequence of therapy.
Early cardiopulmonary resuscitation (CPR) contributes to the effective conduct of the required procedures, including defibrillation, taking medication, and intubation (Eisenberg et al., 1990). There is a great advantage in the quick relief of such diseases. A more in-depth analysis of a previous study shows that without any interference, the survival rate for a heart attack drops linearly to zero after 10 minutes. When using CPR methods, the linear slope decreases while maintaining a negative tendency. A stable state of the patient is only possible when paramedics prescribe medication and hold intubation. In the absence of local assistance, stabilization occurs when the patient is admitted to the hospital. Thus, the latter case indicates that the period between cardiac arrest and arrival at the hospital should not exceed 10 minutes, provided that CPR methods are used.
A study by Valenzuela et al. (2000), demonstrating the previously mentioned differences, was conducted in a casino where security officers were trained for CPR and defibrillation. The author concludes that the survival rate was 74 percent for those who were assisted in the form of a defibrillation procedure no later than three minutes after apparent cardiac arrest. Those patients for whom the first defibrillation was delayed more than 3 minutes showed a reduction in survival rate up to 49.
Erkut et al. (2008) identify four studies relevant to the above problem. Further consideration of the issue is associated with the assessment of such survival functions. Larsen et al. (1993) selected 1,667 patients with a diagnosis of cardiac arrest with a high probability of survival. The scientists used the data from a cardiac arrest monitoring system that has existed since 1976 in King County, Washington. Patients with heart disease were held ventricular fibrillation on time, before the arrival of Emergency Medical Services (EMS) personnel.
The authors calculated the survival coefficient s according to the following equation (2.1):
The authors concluded that the correlation between the independent variables was insignificant or absent. Therefore, the addictive equation can be considered as an effective method. Furthermore, Valenzuela et al. (1997) specify the time interval required for Emergency Medical Technicians (EMT) or paramedics to fasten the defibrillator. The procedure for attaching or cleansing the patient from defibrillation during CPR was evaluated at 2 minutes after the arrival of Emergency Medical Technicians or 1 minute after the start of CPR by EMTs. The conclusions made allow researchers to create a Logistic Regression Survival Model. Equation (2.2) shows the calculation of the survival rate:
The authors indicate in their study that the survival function overestimates the probability of survival when the response time is much longer than the acceptable value. Erkut et al. (2008) mention a third study, which belongs to Waalewijn et al. (2001). The scientists investigated cases using a data set of the system in patients with non-traumatic heart failure outside an inpatient facility. The study was conducted with patients older than 17 years from June 1, 1995, to August 1, 1997. The EMS should indicate the time when the stopped hearts were witnessed or were not staff. The authors included a binary variable in their logistic regression to mention the time when the cardiac arrest was attested or not by EMS personnel. The binary variable should be set to zero in the framework of the conducted analysis. According to our assumption, there is a delay between cardiac arrest and the arrival of the uEMS ambulance. Thus, the model created by Waalewijn et al. (2001) is converted into the equation (2.3):
where: 1response is the response time expressed in minutes.
The last model worthy of attention belongs to De Maio et al. (2003). It was developed using stepwise logistic regression to assess survival at different intervals of the response to the defibrillation process. The data on 392 (4.2%) survivors of a total number of 9.273 patients from January 1, 1991, to December 31, 1997, was presented. The scientists constructed a model consisting of several algorithmic steps. The final version of the model includes only the response time as a dependent variable and is shown in equation (2.4):
Gold et al. (2010) conducted a study of uEMS response time to the cardiac arrest call. A review of the data showed that survival was reduced by an average of 3% for each elapsed minute of delay in medical care. Nevertheless, survival was not crucially reduced if the time between the incident and the EMS arrival was 4 minutes or less. It should be borne in mind that the survival index decreased by 5.2% per minute between 5 and 10 minutes. The arrival of paramedics 11-15 minutes after cardiac arrest revealed a less dramatic decrease in survival, about 1.9% per minute.
It is possible to note that Newgard et al. (2010) when considering traumatic incidents, emphasized that there was no substantial relation between time and mortality for any interval by uEMS. There was no connection with activation, reaction, time at the scene of the incident, transportation, or the overall time of uEMS using multivariate analysis of a group of patients with a field physiological abnormality. The data set corresponded to the transported injured by146 emergency medical agencies to 51 Level I and II trauma hospitals in 10 cities across North America from December 1, 2005, to March 31, 2007. The study proves that some types of injuries are not dependent on earlier medical care and treatment as part of the timely arrival of uEMS.
Wilde (2013) found out that an increase in reaction time of one minute leads to a change in survival of 8% within one day after the initial incident. Utah Pre Hospital Incident Data set of 2001 has been used. Wilde (2013) points out that reaction time is crucial for survival from heart failure but less considerable for survival from other diseases. The reason for the fact mentioned above is that most studies focus on cardiac arrest and the results of Newgard et al. (2010), Pons and Markovchick (2002), and Esposito et al. (1995) works. The researchers did not find a connection between response time and survival in other types of ailments.
However, it should be noted that research made by Pons and Markovchick (2002) forms clear evidence about the differences studied. The scientists do not denote essential differences in survival after a traumatic injury when it comes to exceeding the 8-minute criteria for the arrival of an ambulance in real-time. The mortality odds ratio is equal to 0.81, 95%, CI is from 0.43 to 1.52. Considerable differences were not observed in survival when patients were distributed according to the injury severity group. Furthermore, Pons and Markovchick (2002) used a database that includes all types of uEMS calls. The fact is that each ambulance is equipped with an advanced life support (ALS) system, and the victims had significant traumatic requirements.
A study by Pepe et al. (1987) is a confirmation of the point of view mentioned above. Scientists’ results show an example related to the geographically large urban EMS system. The time factor is associated with the management and transportation of victims with a hypotensive penetrating injury directly to the regional trauma center. The element does not seem to be connected with an adverse outcome, at least during the first hour after the injury. The research includes 30 months and 498 consecutive injured people with penetrating traumas.
Jones and Bentham (1995), in their research, operate the police data on severe and fatal traffic accidents from 1987 to 1991. The scientists state that an increased probability of death has been found among older people, pedestrians affected by numerous disasters. Moreover, road accidents with higher speed limits have also been considered. Jones and Bentham emphasize the absence of connection between the outcomes and the estimated time of getting to the victims and delivering them to the hospital, either before or after alignment with other factors.
A simple assessment of the uEMS response by analyzing survival rates from the results in hospitals may affect the perceptions regarding the present paper. The goal is to reduce the social impact of road traffic accidents, which begins with survival at the scene. The question is whether the death could be avoided if an ambulance vehicle arrived faster. Sanches-Mangas et al. (2010) investigated the results of traffic accidents and their relationship to uEMS response time. The findings show that medical response time is a considerable variable explaining the likelihood of death for both types of conventional roads and motorways.
The scientists claim that the partial effect of reducing reaction times by 10 minutes, namely, from 25 to 15, in automobile accidents on highways leads to an increase in survival ration by about 33%. A similar value (32%) is available for conventional roads by dint of calculation. Most authors found a positive relationship between long distances or time to help with traffic accidents and a higher probability of death (Brodsky, 1990, Brodsky, 1992, Brodsky, 1993, Gonzalez et al., 2009, Li et al., 2008, Durkin et al., 2005, Zwerling et al., 2005, Muelleman and Mueller, 1996, Clark and Cushing, 2002, Evanco, 1999).
This paper presents the conclusion that survival in cardiac arrest depends on the transient response of uEMS. The given empirical equations, (2.1), (2.2), (2.3), and (2.4) are widely used to calculate the survival coefficient of victims. Other types of diseases do not have a sufficient list of evidence that is consistent with the survival rate and response time of the urban emergency service. However, it has been demonstrated that traffic accidents and the survivability of those affected have some correlation with the response time of uEMS. Most authors substantiate the latest claims, despite some studies that show the opposite. Another study claims that quick response time is interrelated and is very crucial only in cases of critical heart disease (Barnard et al. 2019). Both the concept and the importance of response time are presented in a graph made by Sund in 2012 as follows:
The graph shows average survival based on the time for cardiac arrest patients (Sund 2012).
Conclusive evidence precisely identifies the importance of early (less than 4 minutes) defibrillation (Association 2015). It can be noted that in the case of primary and advanced life support after 4 and 8 minutes, respectively, survival would decrease greatly (Doumouras et al. 2012; Jánošíková et al. 2019). Consequently, the scientists offer emergency response to basic and advanced life support providers as recommended methodical recommendations. The study was conducted entirely in the framework of obtaining results on heart failure disease. However, recommendations were subsequently summarized regarding the response time to all reactions that occurred and to any condition or injury (Doumouras et al. 2012; Pons et al. 2005).
Reference List
Barnard, ND, Goldman, DM, Loomis, JF, Kahleova, H, Levin, SM, Neabore, S, & Batts, TC 2019, ‘Plant-Based Diets for Cardiovascular Safety and Performance in Endurance Sports’, Nutrients, vol.11, no. 1, p. 130.
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