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Emergency Service Delivery Response Time Optimization Proposal

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Updated: Jul 2nd, 2021

Emergency Medical Demand Distribution

A big percentage of major cities across the globe have Emergency Medical Services (EMS) that comprise pre-hospital medical care and transfer to health amenities like hospitals (Sariyer et al. 2017). The high demand for pre-hospital care has contributed to EMS organizations encountering significant problems attributed to delayed compensation for their services (Ostermayer et al. 2017). A study by the National Emergency Medical Services Information System (NEMSIS) found that EMS services are highly sought after in the United States. As per the report, EMS agencies serve at least 17.4 million people annually (Crowe et al. 2018).

Further, investigations carried out in Victoria, Australia (Lowthian et al. 2011) and New York (Smith et al. 2016) discovered that the demand for EMS services continues to grow significantly. Apart from issues associated with the delayed payment for services delivered (Ostermayer et al. 2017); EMS organizations also encounter problems in recruiting suitable employees and sustaining their operations (Cortez et al. 2017). These compounded challenges have made it difficult for EMS firms to satisfy the ever-growing demand for their services.

Researchers have conducted studies to determine approaches that EMS agencies can utilize to reduce response time. Some investigators have evaluated how leveraging allocation and location can enhance efficiency. Others have the opinion that having clear knowledge of the total population of a given city can aid in improving response time by EMS companies (Maghfiroh, Hossain & Hanaoka 2017; Saba, Noor & Malik 2017; Shavidze et al. 2016).

Systematic appraisal of the existing literature showed that most scholars have not used the time of the day to examine the demand allocation trend. Instead, many researchers have relied on the day of the week as the primary variable for explaining the prevailing pattern of demand distribution (Cantwell et al. 2013; Cantwell et al. 2015). Moreover, limited literature has graphically portrayed the general inclination of the daily demand for EMS services.

Currently, there is scarce data that depicts the graphical distribution of EMS demand by the day of the week as highlighted by Cortez et al. (2017). One of the researchers has used information about the number of calls that an emergency center, situated in a certain area, receives in a specified duration to address the problems attributed to delayed response (Sariyer et al. 2017). Hence, it is imperative to preserve and utilize existing information. This research sought to establish the state of allocation of emergency medical services in Izmir, Turkey. The investigator used various variables, including location features, week, day, and time to classify medical cases (Sariyer et al. 2017).

The study found variations in need of EMS services depending on the day of the week and the time of the day. The investigator found that EMS organizations were busy at night throughout weekends. In the course of the week, the demand for EMS services was high during the daytime. For weekdays, the researcher discovered a striking correlation in the trends of calls made during morning and evening hours. Additionally, an analysis of 30 facilities revealed that the demand for emergency medical care was premised on the location of an agency (Sariyer et al. 2017). The primary limitation of this investigation is that it failed to categorize emergency calls as non-urgent, temperate, and critical. This was despite the fact that an evaluation of case studies from Izmir found that the majority of the calls fell under these classifications.

Presently, no research has been conducted to ascertain the occurrence of medical emergencies in entire Saudi Arabia or its major cities. This gap underscores the need to use location features, week, day, and month to examine the distribution and prevalence of crisis calls (pressing, non-pressing, and temperate) to facilitate efficient planning. Future studies should investigate the state of emergency calls in the 12 districts of Saudi Arabia and compare the findings with those of Riyadh. So far, no research has used chronological patterns, which are pegged on hourly, weekly, and monthly records to examine the demand for EMS in this country.

Therefore, it is hard to group emergencies as urgent, non-critical, or moderate. There is a need to use accurate data to assess the level of demand for EMS in various administrative units across Saudi Arabia. This would aid in recruitment processes, as well as resource allocation both at district and local levels.

Population Aspects on Emergency Medical Demands

Most operations research studies in the sphere of EMS address challenges attributed to creating broad systems that can promote efficient reaction to crisis calls. This essential challenge highlights the importance of considering the location of an agency when making decisions. Today, a lot of studies have examined challenges that arise due to the location of EMS organizations. The analysis of these problems has resulted in the compilation of assorted case studies and the creation of models that can be helpful in resolving them (Aringhieri et al. 2017; Boutilier & Chan 2018; El Sayed 2012; Farahani et al. 2012; Li et al. 2011; McManamny et al. 2015; Vasilyeva et al. 2018; Vincent-Lambert & Mottershaw 2018).

Steins, Matinrad, and Granberg (2019) cite population qualities like education, age, income, race, employment, and gender as some of the factors that hinder the capacity of EMS agencies to respond promptly. Others include health, workplace conditions, the day of the week, climate, time, nature of the road, traffic movement, season (holiday), distance from town, and accessibility of ambulance station. These factors influence the operations of EMS centers making it hard for them to effectively attend to emergency calls, thereby resulting in increased response time. Some studies have collated demographic data that could be helpful in improving EMS management. This information has been useful in studying the degree of point demands as per neighborhood (Lowthian et al. 2011; Sariyer et al. 2017).

Some studies argue that these factors affect not only the response time but also demand (Alnemer et al. 2016; Batt, Al-Hajeri & Cummins 2016; Gul et al. 2019; Nassel et al. 2014; Peyravi et al. 2015). The study by Steins, Matinrad, and Granberg (2019) is regarded as one of the modern literature that investigates the impacts of socio-ecological and spatial factors on EMS. These researchers ignore the contribution of the level of education to response time. It is imperative to acknowledge that the intellectual capacity of a caller can determine how an EMS organization reacts. The individual calling a facility should be capable of describing the nature of the emergency.

The majority of the studies that rely on socio-ecological factors use descriptive methods of data analysis (Courtemanche et al. 2019; Kvålseth & Deems 1979; Mahmood et al. 2017; Schuman, Presser & Research 1977). Most researchers have considered gender, income, age, and race in their investigations (Deasy et al. 2012; Gul et al. 2019; Lai & Wong 2015; Mahama et al. 2018; Nagata et al. 2011; Nassel et al. 2014; Sasaki et al. 2010; Uber et al. 2017).

However, a few studies have investigated the role of education and labor force on EMS (Agarwal et al. 2019; Lerner, Fairbanks & Shah 2005). In major American cities, scholars have carried out research on frequent users. They have used varied definitions of “frequent users” to aid in arriving at comprehensive findings (Agarwal et al. 2019). In Canada, some studies have focused on EMS facilities that are located in mid-sized cities with unique health care services models (such as universal health insurance) (Agarwal et al. 2019).

In Saudi Arabia, all people enjoy free ambulance services regardless of their nationality and the nature of the emergency. A study carried out in Riyadh raised the importance of considering patient-related factors (gender and age) for individuals with heart conditions when analyzing EMS issues (Alnemer et al. 2016). It also showed how one can analytically use response time to forecast the distribution of ambulance stations in a region. Scholars are yet to conduct research that merges age, workforce, income, education, and spatial factors.

The application of GIS can allow an investigator to draw insightful information on the nature of demand patterns. In spite of various researches leveraging mapping of avoidable ED use rates to determine the people and regions that are susceptible to such consumptions (Gresenz, Ruder & Lurie 2009; Dulin et al. 2010), only a few scholars have gone further to examine the connection between vulnerability and reduced spatial access to basic health care (Fishman, McLafferty & Galanter 2018).

The findings of these investigations have fuelled the study of social conditions that could be helpful in the development and organization of primary and protective health services. Moreover, it has helped in bridging the gap in the existing literature and opening opportunities for future studies. The current knowledge of the Spatio-temporal variations of emergency services requires further improvement to understand interrelated issues. Broadening the chronological data will help to uncover essential dynamics and trends of emergency services. There exists a need to conduct population-based studies that use socio-demographic data from various censuses to boost the knowledge of EMS.

Although most of the current experimental studies discuss the primary drivers of EMS (the demand perspective), limited literature sheds light on the response times to urgent medical needs (i.e. reaction to emergency calls). Moreover, there is scarce information regarding the link between access to EMS and population distribution (i.e. the correlation between demographic patterns, EMS response times, and low or high rate demands).

Location Planning of EMS

The past researches have highlighted the importance of swift reaction to EMS calls (Goto, Funada & Goto 2018; Nogueira, Pinto & Silva 2016; Reuter-Oppermann, van den Berg & Vile 2017). Moreover, there exist many studies that discuss the various approaches to creating systems for defining EMS delivery such as the site of health facilities (Aringhieri et al. 2017; Baloyi et al. 2017; Erdemir et al. 2010; Li et al. 2011). Others delve into the strategies that EMS organizations can use to optimize their response time, thereby guaranteeing efficiency. Presently, a lot of literature on location planning, implementation of EMS, and developed site models can be accessed from LSCP, BACOP, MCLP, MALP, MEXCLP, and DDSM (Aringhieri et al. 2017; Li et al. 2011).

In the past, researchers used location standards established in 1971 to develop site models. Additionally, they leveraged time as a coverage gauge to address location issues (Toregas et al. 1971). Scholars have compiled multiple studies on location models that consider various factors, among them, time, cost, population, distance, socioeconomic conditions, and climate as their concept of coverage (Dolney & Sheridan 2006; Jezek et al. 2011; Morley et al. 2018; Saba, Noor & Malik 2017).

Even though these researches we geared towards ensuring that communities receive quality EMS through improved response times and optimal contact with demand areas, medical organizations continue to face challenges in handling emergency calls that originate from regions with a high population such as cities (Terzi et al. 2013).

The majority of the current literature on EMS has established that most existing location prototypes were created to address unique challenges like the desire to come up with suitable sites for building emergency medical facilities. Some scholars have cited efficiency and cost as the two critical factors that one has to consider when constructing an EMS center. It is imperative to ensure that medical facilities are built close to people as much as possible to minimize the distance that one has to travel to access services (Afshari, Peng & Management 2014).

An evaluation of the available studies shows that scholars in the medical field use information about location models to ascertain if facilities satisfy the demand for EMS. They use the international standard time to determine if specific medical stations meet the required demand coverage. Little has been done to assess the accessibility of emergency health care facilities regardless of their location and whether they fall within the recommended impedance. In some instances, some emergency medical stations are unable to respond promptly (4 minutes as per international standards) to urgent calls despite patients residing within the boundaries of their assigned service areas.

One of the limitations of the existing research on EMS is that it uses total population instead of density. Moreover, many studies rely on interval time in the analysis of health facilities and ambulance stations that respond to emergency calls. Scholars are yet to conduct research that jointly evaluates the emergency response time (4-8 minutes) for both ambulance stations and health facilities.

Research Gaps

  • The majority of the available studies have examined emergency medical call demand in a sequential model and on small scale. They have concentrated on patients served by ambulances, particularly those with cardiovascular conditions, ignoring other medical cases (critical, non-critical, and moderate). In addition, scholars have not categorized geographical locations according to demand rate and population density.
  • The findings of the past studies have contributed to an increase in the number of scholars who are researching the ways to improve EMS response time by leveraging coverage, efficiency, cost, and other structural aspects like infrastructural development. On the contrary, researchers have overlooked the contribution of economic and social conditions to the success of emergency medical facilities, their accessibility, and patient result. Some studies have evaluated emergency medical call demands based on household income, gender, and age. Others have drawn their attention to the role of education, employment, and household income on the demand for EMS. Many scholars have used statistical approaches to analyzing their data and have found all these factors (age, gender, income, education, and employment among others) to have significant impacts on EMS. However, no studies have used GIS to analyze socio-economic conditions such as gender, age, the labor force (employees and retirees), residents, non-residents, and education. In spite of the massive literature on EMS, there is inadequate knowledge of its Spatio-temporal variations and their influences on response times (for critical, non-critical, and moderate cases). Researchers have not used mapping instruments such as GIS to determine the connection between population density, social features, and regional distribution of emergency medical facilities. Recognition and sketching of chronological blueprints of EMS and response times can aid in site location, especially in regions with high demand for these essential services. The primary objective is to minimize the duration that medical personnel takes to address emergency cases.
  • Evaluating population density in combination with ambulance stations will help in understanding how the two affect response time, thus being able to improve efficiency. Such a study would be helpful in selecting appropriate sites to construct hospitals and EMS stations in the future.
  • This study will endeavor to cater to the existing knowledge gaps through a spatial combination of six unrelated sets of data, (that is emergency call records, urban boundary data, location of the EMS facility, census information (age, employment, unemployment, race, household income and level of education), infrastructural development, number of hospitals and total ambulance stations in a given area). It will boost people’s knowledge of EMS and determine the connection between earnings, spatial-temporal changes, and response time. This study will entail determining how demographic aspects like population density impact the establishment of emergency medical centers. The findings of this research (such as population distribution) will be invaluable in the determination of possible sites for EMS facilities in Riyadh, Saudi Arabia.
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