Disparities and determinants of EMS response times: a mixed-methods systematic review and LDA-based thematic analysis Open Access

AI –

Artificial Intelligence

AHA –

American Heart Association

BMJ –

British Medical Journal

CPR –

Cardiopulmonary Resuscitation

EMS –

Emergency Medical Services

FRS –

Fire and Rescue Services

GIS –

Geographic Information System

GPS –

Global Positioning System

GWR –

Geographically Weighted Regression

ID –

Keywords Plus (Identifier in databases)

LDA –

Latent Dirichlet Allocation

LMIC –

Low- and Middle-Income Countries

MPDS –

Medical Priority Dispatch System

NGO –

Non-Governmental Organization

NHS –

National Health Service

OHCA –

Out-of-Hospital Cardiac Arrest

OSHA –

Occupational Safety and Health Administration

PRISMA –

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

RQ –

Research Question

SES –

Socioeconomic Status

SMS –

Short Message Service

SSM –

System Status Management

STEMI –

ST-Elevation Myocardial Infarction

UK –

United Kingdom

US –

United States

VFR –

Voluntary First Responder

WHO –

World Health Organization

WOS –

Web of Science

Emergency medical services (EMS) are crucial for survival in critical conditions like cardiac arrest, trauma, and stroke (Sagan and Richardson, 2015; Holmén et al., 2020; O'Keeffe et al., 2011). Ensuring effective EMS necessitates aligning resources with needs, and addressing geographic and socioeconomic disparities to guarantee timely care (Deng et al., 2021; Lam et al., 2015a; Lam et al., 2014, 2015b; Nogueira et al., 2016; Bokor et al., 2026; Dalton et al., 2022; Egan et al., 2020). EMS performance is influenced by various factors including call volume, distance, workload, temperature, and traffic (Lam et al., 2015a; Mahmood et al., 2017; Meng and Weng, 2013; Seong et al., 2023; Thornes et al., 2014; Zhan et al., 2018). Rural areas often experience longer response times due to greater distances and fewer resources (Do et al., 2013; Jin et al., 2023; Lam et al., 2014, 2015b; Lam et al., 2015a; Mell et al., 2017; Nehme et al., 2016). Vulnerable populations are prioritized based on socioeconomic and demographic factors such as age, gender, and ethnicity (Benamer et al., 2016; Govindarajan and Schull, 2003; Sangal et al., 2023; Wilde, 2013). The efficiency of EMS is evaluated through metrics like response times, patient satisfaction, transport efficiency, and financial sustainability (Berest and Merenkova, 2019; Colla et al., 2019; Gonnelli et al., 2018; Ilioudi et al., 2013; Khokhar, 2023; Licata et al., 2023; Nekrashevych and Kovrigo, 2019; Núñez et al., 2018; Stenson et al., 2020; Tlili et al., 2018).

The prehospital interval, critical to patient outcomes, is comprised of five segments: Activation, Response, On-Scene, Transport, and Handover (Bedard et al., 2020; Blanchard et al., 2012; Carr et al., 2006; Chen et al., 2020; Harmsen et al., 2015; Mistovich et al., 2017; Pons, 2005; Spaite et al., 2008; Sullivan et al., 2013; Ueno et al., 2024). Response time, the interval from call receipt to scene arrival, significantly impacts these outcomes (Goto et al., 2018; Govindarajan and Schull, 2003; Holmén et al., 2020; Patel, 2018; Sahealth, 2023). Definitions of response time vary internationally; the US National EMS Information System measures it from call receipt to arrival (Mell et al., 2017), while the UK breaks it down into call handling, turnout, and travel times (Holmén et al., 2020). For time-sensitive trauma cases, some systems extend this to include transport time (Nichol et al., 2008). To enhance comparability across systems, the Utstein style guidelines recommend standardized reporting for intervals from call receipt to dispatch, dispatch to scene arrival, and scene departure to hospital arrival (Nichol et al., 2008). The handover segment is especially vital for ensuring continuity of care through the accurate communication of on-scene data to hospital staff.

A primary goal for healthcare providers is the effective management of response times through sophisticated call triage and dispatch protocols. Response times differ globally, being faster in developed areas like Europe and North America and slower in developing nations due to resource limitations (Hansen et al., 2024; Setyarini and Windarwati, 2020; Noor et al., 2024; Rafiq and Khanum, 2021; Syed and Namburi, 2020). Factors such as economic development, infrastructure, personnel availability, and environmental conditions all affect response times (Hansen et al., 2024; Setyarini and Windarwati, 2020). For instance, response times for high-acuity calls in the US are typically 8–10 min, whereas in rural India and much of Africa, they can be as long as 30–45 min (Nehme et al., 2016; Noor et al., 2024; Rafiq and Khanum, 2021; Syed and Namburi, 2020; Kobusingye et al., 2005).

The historical development of EMS has varied. In high-income countries, formal systems began to emerge in the 1960 and 1970s (Delaney et al., 2024; Mehmood et al., 2018). The Anglo-American model was heavily influenced by military trauma care, shaping its focus on rapid response and field triage. In contrast, the Franco-German medicine d'urgence model originated more from physician-led hospital medicine. The growth of prehospital paramedicine and centralized dispatch were also key developments in these systems (Plummer and Boyle, 2017; Sun et al., 2024). These different historical paths have shaped the infrastructure, resource availability, and operational policies of modern EMS, contributing to the significant disparities in response times seen between high- and low-income countries today.

There are valuable systematic and meta-analysis reviews on emergency response time (Carr et al., 2006; Hansen et al., 2024; Setyarini and Windarwati, 2020; Alharbiet al., 2022; Cabral et al., 2018; Desai et al., 2019; Doggett et al., 2018). These studies consistently address concerns about data availability, quality, and bias. Doggett et al. emphasize the limitations caused by either partial or incomplete data, which can potentially lead to biases in their findings (Doggett et al., 2018). Similarly, Carr et al. highlight variations in data reporting techniques and geographical scope, which present obstacles to achieving comparability and generalizability (Carr et al., 2006). In addition, Cabral et al. emphasize the drawbacks of exclusively relying on data obtained from academic databases (Cabral et al., 2018). They might not encompass every study relevant to the topic, or these might be narrow and not representative of the EMS response times. These include methodological limitations related to the inconsistency of research approaches in establishing standardized techniques and the inability to rule out confounders. Whereas Carr et al. (2006) acknowledged the challenge imposed by variations in data reporting techniques, Hansen et al. (2024) identified factors such as hospital overcrowding and scarce hospital staff as potentially affecting patient outcomes. More specifically, this review by Setyarini and Windarwati (2020) has pointed out several methodological problems, such as insufficient consideration of relevant research and inconsistency, which prevent the integration of knowledge at the current time.

Systematic reviews are among the dominant methodological approaches to study response time and how this affects patient outcomes (Hansen et al., 2024; Setyarini and Windarwati, 2020). Researchers commonly initiate their work by conducting thorough searches in prominent databases such as WOS/PubMed/MEDLINE to discover pertinent studies (Hansen et al., 2024). Subsequently, multiple review methodologies are utilized. Systematic reviews are a methodical and established methodology that entails conducting a structured search, selecting relevant research, and critically evaluating it to synthesize current information on a particular topic (Hansen et al., 2024; Setyarini and Windarwati, 2020).

This article attempts to deconstruct the many facets of EMS response times related to their broader implications. The specifics of attention to this inquiry have been guided by consideration of the following research questions:

These questions focus on synthesizing existing evidence, which is the primary goal of this paper's systematic review. The factors affecting response times and their ripple effects on patient outcomes were dissected. A three-pronged approach lays bare the complex interplay influencing response times, leading to an in-depth understanding of the dynamics in EMS. Finally, any inequalities in response times based on demographics were revealed and implored that something should be done to ensure equal opportunities for care. Any advanced data analysis technique, such as the LDA method, ensures objectivity, whereas a critical methodology critique allows future improvements. This will, in turn, allow policymakers to use this information to optimize response times, better manage patients, and utilize resources.

Figure 1 below summarized the research process used in this paper. It followed a rigorous process: from clear questions to detailed protocol, including search strategies, screening methods, and tools for reference management using Zotero. The titles, abstracts, and full texts were screened for studies that fit into the research using the PRISMA reporting guideline. Further data extraction was conducted on a standardized format, followed by the quality assessment using tools such as the Cochrane risk of bias tool. Synthesis was comparing, analysing, and interpreting findings across studies. Results included tables, figures, and a clear narrative summary. Discussion included implications, limitations, and future directions for research. The conclusion summarized the points of focus in a summary form and pinpoints areas for further study in the last section.

This systematic review followed The Cochrane Handbook for Systematic Reviews of Intervention (Higgins et al., 2022) as the main methodological framework, ensuring rigour and transparency. The PRISMA 2020 statement (Page et al., 2021; Soltani et al., 2024a) was also adopted to provide structure, transparency, and standardization.

The review systematically addressed research questions on EMS response times, their determinants, and their effects on patient outcomes. Studies on EMS response times in prehospital care that examined influencing variables or associations with outcomes were included. Eligible designs were randomized controlled trials, cohort, case-control, observational studies, and systematic reviews published in English between 1 January 2000 and 31 December 2024. Excluded were studies unrelated to EMS, lacking precise response time data, or not in English. The full protocol is in  Appendix.

A comprehensive search was conducted in Web of Science (WOS), PubMed, and Scopus using consistent key terms and Boolean operators such as (EMS response time OR ambulance response time) AND (patient outcomes OR mortality OR geographic disparities). Duplicates were removed by comparing titles, authors, and years. This yielded 85 unique articles, of which 60 were included for full review and synthesis. Two researchers independently reviewed articles to minimize bias, resolving disagreements by discussion. Data were extracted with a standardized form covering design, population, exposures, and outcomes, forming the basis for synthesis. Inclusion/exclusion criteria were reapplied during full-text review, and reasons for exclusion were recorded.

Study quality was assessed using the Cochrane RoB 2 tool (Higgins et al., 2022) to evaluate bias in randomized trials. Findings were summarized narratively, highlighting determinants of EMS response times and links to outcomes. Tables and visuals displayed study characteristics, assessments, and extracted data. The PRISMA process was followed to ensure transparency and reproducibility in study selection (Page et al., 2021). The detailed screening results are presented in the Results section (see Figure 2).

Medical studies on paramedics highlight prehospital factors affecting outcomes, specifically call processing (reaction time), on-scene time, and transport intervals (Newgard et al., 2010). Patient demographics, pre-existing conditions, and socioeconomic disparities also shape care quality (Carr et al., 2014; Hsia et al., 2012). Key outcomes include response time, mortality, hospital admissions, survival, and ambulance call volume, all linked to systemic and contextual factors (Blackwell and Kaufman, 2002).

Response time variability reflects demographics, geography, and building type, with delays tied to higher mortality and spatial inequities (Pons et al., 2005; Carr et al., 2014). Admissions correlate with comorbidities, while survival depends on timely intervention (Hsia et al., 2012; Newgard et al., 2010). Response time uniquely influences mortality, survival, and healthcare costs, distinguishing it from other operational metrics (Blackwell and Kaufman, 2002). Disparities in geography and demographics worsen access delays, while weather and traffic disrupt efficiency (Murray et al., 2017). Ambulance availability remains critical to mitigating service gaps (Redelmeier et al., 2015). Understanding these interdependencies is vital for optimizing EMS and improving outcomes (Institute of Medicine, 2007). In this study, response time, defined earlier, serves as the target variable.

A systematic review relies on text mining to discover key concepts relating to power relations with planning. Text mining is a method of raw data extraction from large quantities of text to generate knowledge in new ways (Zhai and Massung, 2016). Among the easy-to-use free and open-source academic text mining programs are Vosviewer and Orange (Demšar and Zupan, 2013; Eck and Waltman, 2009; Bagheri et al., 2024). In this study, using Orange, text preprocessing, mining, visualization, and searching were all performed (Demšar et al., 2013).

The steps involved in text mining and gathering data for text mining, included the researcher using predetermined search criteria to find and gather articles. To apply the principles of accessible, discoverable, interoperable, and repeatable text mining analysis (Wilkinson et al., 2016), the researcher must ensure they: (1) check the download source; (2) apply search criteria; and (3) provide a precise timeline of published articles. Text mining was carried out in three stages: gathering the literature data and loading, preprocessing, processing, and analysing the findings and visualisations. The goal of thematic modelling was to identify underlying themes that can be combined into documents to analyse large text sets. A probabilistic model explains the relationship between the observed documents and the underlying topics (Hua et al., 2020).

In this study, the Latent Dirichlet Allocation (LDA) was applied for topic modelling on a textual corpus. LDA is a generative probabilistic model that uncovers latent themes by analysing word co-occurrence patterns, functioning like clustering but based on shared contexts rather than frequency alone (Blei et al., 2003; Allan et al., 2022). It infers topic distributions across documents by iteratively assigning words to topics and updating associations using conditional probabilities (Griffiths and Steyvers, 2004; Wallach et al., 2009).

Before analysis, the dataset underwent preprocessing, including removal of stopwords, filtering high-frequency non-informative terms, and transforming text into a bag-of-words representation, preserving token frequency while disregarding order and grammar (Jockers and Mimno, 2013; Blei et al., 2003). The number of topics (k) was predefined and refined through updates to word-topic and document-topic matrices, maximizing the likelihood of word-topic and topic-document assignments (Wallach et al., 2009). Each topic was defined by a probability distribution over terms, with top words extracted for interpretation, enhancing coherence and interpretability (Sievert and Shirley, 2014). All modelling and visualization were conducted in Orange (GPLv3) with built-in LDA functions (Demšar et al., 2013).

To validate results, a two-stage process was used. First, model quality was measured with the C_v coherence score, which captures semantic similarity among top terms (Sievert and Shirley, 2014). Multiple LDA models with k = 5–15 were trained, and coherence scores plotted to detect the elbow point, guiding candidate model selection. Second, two independent researchers reviewed the top 10 keywords of each topic for semantic coherence, resolving disagreements by consensus and aligning themes with EMS literature.

To prevent overfitting, the perplexity was also examined, an indicator of model generalizability. Models with low perplexity but poor coherence were discarded. The final model, at k = 5, achieved the best balance between coherence (C_v = 0.47) and acceptable perplexity. This validation framework strengthened the reliability, transparency, and reproducibility of the topic modelling, ensuring that extracted themes were both analytically sound and contextually meaningful.

The study selection process followed the PRISMA 2020 framework (Page et al., 2021; Zaroujtaghi et al., 2025; Soltani et al., 2024b). Figure 2 presented the PRISMA flow diagram summarizing the number of records identified, screened, excluded, and included in the final synthesis. Out of 85 records initially retrieved from the databases, 60 met all inclusion criteria and were retained for full-text review and data analysis.

Figure 3 presented the number of annual studies from 2000 to 2024. The number of studies climbed upwards from 2000 to 2010, peaking in that year. Following this, there was a slight decline in the number of studies carried out, while in recent years, the number has once again gone upwards. The number of studies followed a very fluctuating pattern over time, probably due to factors like funding cycles or other worldwide events that affect the carrying out of the studies.

This review included studies from various countries and World Bank income categories (Figure 4). Most were from high-income countries such as Canada (Govindarajan and Schull, 2003; Blanchard et al., 2012), the United States (Blackwell et al., 2009; Byrne et al., 2019; Gonzalez et al., 2009; Hsia and Shen, 2011; Hsia et al., 2018; Ma et al., 2019; Weiss et al., 2013), the United Kingdom (Mahmood et al., 2017; Thornes et al., 2014; Mills et al., 2024), France (Benamer et al., 2016), Italy (Lucchese, 2024), Denmark (Rajan et al., 2016; Mills et al., 2024), Sweden (Svensson et al., 2024), Japan (Goto et al., 2018), and Australia (Nehme et al., 2016), offering insights into EMS best practices in developed health systems. A smaller number came from upper-middle-income countries like Brazil (Nogueira et al., 2016; Colla et al., 2019) and China (Jin et al., 2023; Zhan et al., 2018; Chen et al., 2020), and lower-middle-income economies such as Bangladesh (Maghfiroh et al., 2018; Tshokey et al., 2022), highlighting challenges from resource and infrastructure limitations.

Although high-income countries dominate EMS research, literature from LMICs, particularly Africa, is expanding. Zakariah et al. (2017) detailed Ghana's decade-long National Ambulance Service growth, while Mould-Millman et al. (2017) mapped EMS systems in 16 African countries, noting limited coverage and infrastructure gaps. Kobusingye et al. (2005) stressed emergency care's feasibility in low-resource contexts and called for better planning and integration. Despite fewer studies, such work provides a foundation for global EMS policy.

In high-income settings, EMS typically relies on moderately dense ambulance networks supported by GIS-based planning, GPS tracking, AI-assisted routing, and centralized dispatch. Services employ well-trained paramedics, often with specializations, and operate under quality frameworks that use performance metrics for continuous improvement (Holmén et al., 2020; Mell et al., 2017). Stable funding and integrated prehospital-hospital care further ensure consistent delivery and relatively low response times.

By contrast, EMS in resource-limited areas often adopt community-based solutions: motorcycle or bicycle ambulances, community paramedicine training, and volunteer responders. Low-cost tools such as SMS dispatch replace advanced systems, while collaborations with clinics, NGOs, and mobile units extend coverage despite financial and logistical barriers (Kobusingye et al., 2005; Mould-Millman et al., 2017).

The presence of both Author's Keywords (DE) and Keywords Plus (ID) indicates that this set contains documents on various subject matters. The collection is broadly varied, with 134 Keywords used by authors to draw attention to their work's themes and focal points. Each term's relative size in the word cloud graph depicts the frequency and importance throughout the dataset. Not surprisingly, the terms time/times, response, ambulance, emergency, patients, medical, demand, EMS, service, and outcomes popped out as the highest frequent words in the collection. The frequent appearance of these terms indicated that a systematic review article dealt with analysing and optimizing response times of ambulances and ‘rescue’ services in general, especially for significant or critical incidents. Such terms as time/times, response, ambulance, EMS, and medical were indicative that this literature review looked at factors impinging on response time, practices to reduce delays, and how effective ambulance disposition enhanced timely emergency care. Also, words like emergency, patients, and outcomes suggested that these articles examined the relationship of response times to the seriousness of medical emergencies and their interaction with patient mortality rates and overall health outcomes. As illustrated in Figure 5, the word cloud highlighted that while speed was a dominant theme, keywords related to “equity”, “socioeconomic disparities”, and “environmental factors” were increasingly central to the literature.

Table 1 provided an overview of the methodologies employed in studies investigating ambulance response times and emergency medical services (EMS). These methodologies were categorized into four primary groups: Observational Study Designs, Analytical Methodologies, Simulation and Optimization, and Mixed-Method Approaches.

Observational designs constituted the largest category, comprising 20 studies (46.5%), with retrospective studies representing the most prevalent approach (10 studies, 23.3%). These studies analysed historical medical or administrative data to examine associations between exposures and outcomes (Talari and Goyal, 2020). Methods included retrospective data collection, registry-based cohort designs, and advanced statistical analyses. Cross-sectional studies accounted for four studies (9.3%) and employ surveys, quantitative analyses, or real-time monitoring to provide a snapshot of populations at a single time point (Mantel and Haenszel, 1959). Prospective studies were reported in six studies (14.0%), following participants over time to assess outcomes after specific interventions or exposures, using tools such as GIS mapping, multicenter registry analyses, and statistical testing (Talari and Goyal, 2020).

Population-based and cohort studies each included two studies (4.7%), leveraging large datasets to represent general populations or to assess longitudinal exposure-outcome relationships (Valsamis et al., 2019; Mantel and Haenszel, 1959). Additionally, two studies (4.7%) examined free-living, non-institutionalized populations without experimental interventions (Samnani et al., 2017).

Analytical methodologies were reported in seven studies (16.3%) and included regression analyses, geographically weighted regression, instrumental variable techniques, survival analyses, and sensitivity testing to explore relationships between variables (Grant and Booth, 2009). Time-series analyses appeared in two studies (4.7%), identifying temporal patterns, while general data analyses were applied in four studies (9.3%) to model data, perform transformations, and conduct exploratory analyses supporting evidence-based decisions (Grant and Booth, 2009).

Simulation and optimization approaches were employed in two studies (4.7%), incorporating discrete event simulation, mathematical programming, geospatial analysis, and optimization modelling to evaluate and improve EMS deployment strategies (Grant and Booth, 2009). Finally, one study (2.3%) utilized a mixed-method design, integrating qualitative and quantitative techniques, including surveys, interviews, and statistical analyses, to provide a comprehensive understanding of complex phenomena (Samnani et al., 2017).

Collectively, these studies reflected the interdisciplinary nature of EMS research, drawing upon statistics, epidemiology, operations research, geographic information systems, and econometrics.

Based on the LDA method, studies were classified into five thematic threads (Table 2). To provide a clear answer to our initial enquiries: Thread A (Operational/Environmental) directly addressed RQ1 regarding system-level factors. Thread B (Clinical Outcomes) provided evidence for RQ2 regarding survival impact. Threads C and D (Optimization and Disparities) together addressed the equity focus of RQ3, while Thread E (Specific Emergency Services) answered RQ4 regarding logistical challenges in high-rise or disaster zones (Figure 6). This explicit alignment between research questions and thematic threads addresses prior critiques regarding analytical clarity.

Timely initiation of effective care, particularly bystander cardiopulmonary resuscitation (CPR) and early defibrillation, is a key determinant of survival in time-critical emergencies such as cardiac arrest. Although shorter ambulance response times are generally associated with improved outcomes in some conditions, their impact is mediated by the broader chain of survival and varies across contexts. Response intervals are influenced by system-level (e.g., dispatch protocols, resource allocation, dispatch efficiency, travel time), patient-level (e.g., case mix, location, age, gender, severity of condition, and type of incident), and environmental (e.g., traffic, geography) factors (Perkins et al., 2015; Wissenberg et al., 2013; Hasselqvist-Ax et al., 2015; Do et al., 2013; Nehme et al., 2016; Colla et al., 2019).

These contributing factors encompass internal aspects (e.g. ambulance facility capabilities and human resources) and external aspects (e.g. environmental conditions and the specific patient scenario). Certain environmental factors, such as temperature, have a marked impact on response times. Extreme heat or cold tends to increase the number of ambulance call-outs, which can delay services due to system overload (Mahmood et al., 2017; Thornes et al., 2014; Zhan et al., 2018; Tanoori et al., 2024a, b; Ghanbari et al., 2023; Sharifi and Soltani, 2017; Soltani and Sharifi, 2017). Additionally, road traffic congestion, adverse weather conditions, and the geographic location of the incident also influence ambulance travel times (Lam et al., 2015a, b; Meng and Weng, 2013; Seong et al., 2023).

In developing countries, longer response times are not attributable to a single cause but stem from a complex interplay of factors. While limited resources are a significant challenge, they are compounded by underdeveloped infrastructure, persistent geographic obstacles, fragmented system organization, and traffic congestion. This multifaceted problem requires integrated solutions that address more than just funding or equipment shortages (Boutilier and Chan, 2020; Kobusingye et al., 2005; Mould-Millman et al., 2017).

These identified variables highlight several opportunities for optimizing ambulance deployment, resource management, and dispatch protocols to improve system efficiency (Wilde, 2013; Lucchese, 2024; Colla et al., 2023).

At a system level, a key strategy to meet response time targets for life-threatening incidents is the deployment of multiple crew responses, where the closest available unit (which may be a rapid responder vehicle) is dispatched simultaneously with a fully equipped ambulance to ensure the swiftest possible first arrival at the scene.

Understanding the impact of environmental conditions can guide better resource allocation and preparedness, which is particularly important in climate change. Further research into additional environmental factors, such as air pollution and humidity (RoohaniQadikolaei et al., 2026), is warranted to deepen this understanding. Ambulance response times are critical to patient outcomes and are influenced by system-level, patient-level, and environmental factors. System-level factors, dispatch efficiency, travel time, call volume, and resource availability, significantly impact response performance, as demonstrated by various studies (Do et al., 2013; Nehme et al., 2016; Colla et al., 2019). Patient-level factors include age, gender, severity of condition, and type of incident, which can affect how quickly an ambulance is dispatched or how urgently it is routed (Do et al., 2013; Nehme et al., 2016; Chen et al., 2020).

A large body of literature has established a strong association between shorter ambulance response times and improved patient survival, particularly in life-threatening emergencies such as cardiac arrest and trauma (O'Keeffe et al., 2011; Wilde, 2013; Blanchard et al., 2012; Pons, 2005; Hansen et al., 2024; Rafiq and Khanum, 2021; Alharbi et al., 2022; Doggett et al., 2018; Blackwell et al., 2009; Byrne et al., 2019; Weiss et al., 2013; Lucchese, 2024; Alumran et al., 2020). However, the relationship between response time and survival is not always straightforward. For instance, one study documented higher mortality despite faster response times, suggesting that complex factors, such as the severity of the patient's condition, type of injury, or timing of care, can mediate the effects of rapid intervention (Mills et al., 2024).

In addition, various modifying factors influence patient outcomes, including the type of incident, demographic characteristics of the patient, and whether bystander interventions such as cardiopulmonary resuscitation (CPR) or defibrillation are administered before the arrival of emergency services (Rajan et al., 2016; Nathens et al., 2000; Alumran et al., 2020). These interventions, particularly in cases of cardiac arrest, have been shown to improve survival rates significantly. Geographic disparities also persist, with patients in rural areas typically experiencing longer response times than those in urban centres. This variation highlights the need for regionally tailored strategies to improve access to timely emergency medical care and reduce mortality rates across different populations (Gonzalez et al., 2009; Kłosiewicz et al., 2017).

Multiple studies have examined strategies to optimize ambulance deployment and base locations as a means of reducing response times and improving system efficiency (Nogueira et al., 2016; Rahim and Qasim, 2025; Neira-Rodado et al., 2022). Evidence consistently shows that dynamic reallocation of ambulances based on spatial and temporal demand patterns can enhance coverage and performance without necessarily increasing fleet size. For example, in Brazil, a balanced redistribution of ambulances across urban bases improved response times while maintaining operational resources (Nogueira et al., 2016; Rahim and Qasim, 2025), and in Singapore, expanding the number of bases with the same vehicle fleet achieved similar efficiency gains (Ong et al., 2010). Collectively, these findings highlight that system-level optimization, rather than fleet expansion alone, can substantially improve the timeliness of EMS response.

Technological advancements, such as the implementation of GPS tracking and routing systems, have further improved the accuracy and speed of ambulance dispatch (Gonzalez et al., 2009; Colla et al., 2023). Dynamic reallocation strategies, including System Status Management (SSM), allow real-time adjustments to ambulance positions based on incoming call data and predicted demand (Lam et al., 2015a; Lam et al., 2015b). Furthermore, optimizing ambulance locations based on variables like population density and emergency call frequency can improve geographic coverage and ensure quicker response during peak demand periods (Maghfiroh et al., 2018).

Studies consistently indicate that response time varies significantly depending on socioeconomic status and geographic location. For example, patients from higher-income neighbourhoods in Toronto experienced shorter response times than those from lower-income areas (Govindarajan and Schull, 2003). In the context of gender differences in the management of ST-Elevation Myocardial Infarction (STEMI), research found longer pre-hospital delays and higher in-hospital mortality in women (Benamer et al., 2016). Similarly, Hsia et al. (2018) reported that low-income communities in the United States experienced longer response times for cardiac arrests, highlighting the need for improved emergency medical services (EMS) access in underserved areas.

Vulnerable populations, such as African Americans and foreign-born residents, many of whom reside in rural or underserved locations, also tend to experience delayed response times and limited access to trauma care (Hsia and Shen, 2011). Geographic disparities are further evident across urban, suburban, and rural regions, with rural areas typically experiencing the longest delays (Carr et al., 2006). These findings underscore the importance of addressing structural inequalities through better EMS planning and targeted resource allocation.

Research on response times in specific emergency contexts, such as high-rise buildings and out-of-hospital cardiac arrest (OHCA), has revealed unique operational challenges. Studies show that delays in reaching patients in high-rise buildings are often due to structural and logistical barriers, including elevator access and floor layout constraints. Researchers have proposed building design modifications and public awareness campaigns to address these issues and support faster EMS access (Lateef and Anantharaman, 2000; Kal’avský et al., 2018). Additional disparities in response times have been observed between urban and suburban areas. Shorter times in suburban regions have been linked to higher densities of ambulances and medical personnel, along with greater involvement of Voluntary First Responders (VFRs) and Fire and Rescue Services (FRS) (Jin et al., 2023; Svensson et al., 2024).

In disaster response scenarios, emerging evidence supports the use of real-time (online) ambulance routing algorithms that account for dynamic victim conditions, which have been shown to outperform static (offline) models in terms of speed and efficiency (Shiri et al., 2024). Moreover, integrating fairness considerations into routing strategies can improve overall outcomes and ensure equitable resource allocation during emergencies (Aringhieri et al., 2022). Finally, placing EMS stations in strategically selected, high-risk areas, such as flood-prone urban zones, has significantly reduced delays and improved access to timely care (Yang et al., 2020).

There is a significant disparity in EMS response times between developed and developing countries, mainly due to differences in infrastructure, resources, and the overall capacity of healthcare systems (Blanchard et al., 2012; Kobusingye et al., 2005; Mehmood et al., 2018; Rathore et al., 2022). In developed countries, advanced technologies and adequate funding support emergency medical services, enabling rapid emergency (Blackwell and Kaufman, 2002; Pons and Markovchick, 2002). These systems often feature efficient dispatch centres, modern communication networks, and comprehensive and integrated emergency response systems that ensure swift mobilization and care delivery (O'Keeffe et al., 2011; Carr et al., 2006).

The availability and geographic distribution of healthcare facilities also play a critical role. In developed nations, urban areas often benefit from a high density of healthcare facilities, which helps reduce travel distances and improve EMS response times. However, this is not universally the case; rural areas in many high-income countries may still face long travel distances, lower healthcare facility density, and delayed response times, conditions that resemble those in low-resource settings. In contrast, developing countries frequently contend with a general scarcity of healthcare centres across urban and rural areas, resulting in longer travel distances and slower response times overall (Boutilier and Chan, 2020). Road quality and traffic management systems are also crucial: good road conditions and effective traffic control in developed countries facilitate quicker EMS responses (Rentschl et al., 2019). GPS and mobile data systems have enhanced EMS efficiency by enabling optimal routing and real-time communication.

In developing countries, several factors contribute to EMS delays, including limited resources, underdeveloped infrastructure, traffic congestion, fragmented EMS systems, and insufficient coverage in rural and remote areas (Pons, 2005; Newgard et al., 2010; Kobusingye et al., 2005; Mock et al., 2002). Additional challenges include poor crew preparedness, inadequate ambulance readiness, and inconsistent funding and training standards (Carr et al., 2006; Mock et al., 2002; Mould-Millman et al., 2017). Geographic obstacles, such as mountainous terrain or remote locations, further restrict timely EMS access (Jana et al., 2023). Socioeconomic factors, including poverty and limited education, also affect health-seeking behaviours and may delay emergency calls (Mahama et al., 2018). Moreover, the absence of enabling technologies like GPS and real-time communication tools imposes further operational constraints on EMS systems in many low-resource settings (Boutilier and Chan, 2020).

Health disparities in emergency medical service (EMS) response are influenced by various factors, including socioeconomic status, geographic location, healthcare system limitations, and structural inequities (Betancourt et al., 2003). Socioeconomic conditions are among the most critical determinants, as individuals from lower socioeconomic backgrounds often face significant barriers to timely and effective emergency care. These barriers may include a lack of health insurance, financial hardship, limited availability of healthcare facilities in their communities, and inadequate transportation options. Such disparities not only delay emergency response but also contribute to poorer health outcomes in marginalized populations.

Incidents in high-income areas are more likely to receive timely responses that meet the national EMS benchmark times (Kal’avský et al., 2018). Surveys indicated that when the median income is low, response time for EMS is longer, while advanced medical care availability is lower than in other areas (Govindarajan and Schull, 2003). It must be remembered that SES operates at two ends, where it not only influences access, but also the health behaviours and outcomes. For instance, lower SES is associated with riskier health behaviours due to stress and lack of availability to health promotion resources (Pampel et al., 2010).

These disparities are accentuated by geographic factors, particularly between urban and rural settings. Rural areas generally have fewer healthcare resources, more distance to travel to a health centre or hospital, and fewer specialist services. Geographic isolation and inadequate transportation choices contribute to delays in care and poorer health outcomes for those living in rural areas (Weinhold and Gurtner, 2014). The concept of “healthcare deserts” increasingly describes these dramatic contrasts in healthcare service availability. These areas may be well-populated, but their people face severe difficulties in healthcare access because there are either no quality facilities available within reach or even no affordable transport options. The various socioeconomic factors attendant to such situations have made affordability, access to the internet for telehealth services, and health literacy even more critical for maintaining health (Garcia, 2018; Rosik et al., 2021).

Cultural and linguistic issues are also significant causes of health care disparities. Patients whose predominant health care language is not their own may have limited ability to understand medical advice or describe symptoms accurately, with possible failure to diagnose or treat conditions appropriately (Flores, 2006). A key element is health literacy, or the ability to understand and use health information to make decisions. Lower health literacy levels have been associated with reduced utilization of preventive services and poorer health status (Berkman et al., 2011). In this respect, offering healthcare providers a means of cultural competence is an important strategy for reducing these barriers. According to Betancourt et al. (2003), the foundation of knowledge about diverse groups' health beliefs and practices is important in improving communication, increasing patient trust, and enhancing quality of care along an illness trajectory, including appropriate complication prevention and management.

The systemic causes of healthcare access disparities are also inefficient resource distribution and the lack of a central coordination mechanism. There is a heterogeneous use of centralized ambulance dispatch systems, which indicates substantive variability in the response time of EMS over different areas (Kal’avský et al., 2018). COVID-19 brought new challenges and exacerbated the existing health access disparities. Health access and health outcomes have been particularly hit harder by this pandemic in low-income communities, racial and ethnic minorities, and other vulnerable populations (Tai et al., 2021).

Delays in response time can trigger a cascade of adverse outcomes, ranging from worsened clinical conditions to death (Harmsen et al., 2015; Xu et al., 2018). For example, Pons (2005) estimated that every single minute that defibrillation is delayed for cardiac arrest, survival rates decrease by 7–10%. Similar urgency applies to trauma-related emergencies, such as severe bleeding or traumatic brain injury, where even short delays can result in irreversible damage or fatality if timely intervention is not provided (Sasser et al., 2012).

The consequences of delayed response time are not limited to immediate health effects. Such delays can significantly reduce a patient's long-term functional outlook and quality of life (Chen et al., 2020; Fernando et al., 2018). They are often associated with increased complications, prolonged hospital stays, and higher healthcare costs (Fernando et al., 2018). In the case of stroke, for example, delayed treatment can cause more extensive brain damage, increasing the likelihood of long-term disability (Fernando et al., 2018; Saver, 2006; Wang et al., 2005). This may manifest as physical impairments, cognitive decline, or psychological trauma, all of which can hinder a patient's ability to return to pre-incident levels of independence (Chen et al., 2020). In some cases, the resulting neurological deficits are permanent, requiring lifelong assistance for daily activities and personal care (Saver, 2006).

Patients who survive but sustain severe injuries or disabilities due to delayed care often have limited chances of returning to their pre-incident functional status (Haagsma et al., 2012; Subbe et al., 2023). This loss of independence can significantly affect individual well-being and socioeconomic stability (Blackwell and Kaufman, 2002). The economic burdens of long-term care, rehabilitation, and lost productivity are substantial and extend beyond the individual to their families and caregivers (Blackwell and Kaufman, 2002). Families and caregivers often bear the consequences of delayed treatment in the form of increased expenses for ongoing care, necessary home modifications, and reduced income or employment opportunities. These financial pressures may lead to broader socioeconomic distress and even push some households into poverty (Blackwell and Kaufman, 2002).

At the systemic level, delayed care can also reduce the efficiency of healthcare delivery. When resources are redirected from preventive services to manage long-term consequences of treatment delays, the system enters a cycle of strained capacity, reduced preventive care, and worsening patient outcomes (Mogharab et al., 2022).

There are significant variations in terminology and standards related to response times in EMS, depending on the country, region, or disciplinary context. These discrepancies involve differences in underlying health systems, operational protocols, and socioeconomic factors, leading to varying definitions and categorizations of EMS time intervals across nations (Blackwell and Kaufman, 2002). Efforts to address these inconsistencies have been made, notably through the Utstein-style recommended guidelines for reporting, which provide a standardized template for defining and reporting time intervals in emergency care, particularly in out-of-hospital cardiac arrest studies (Nichol et al., 2008).

EMS response time terminology differences can affect communication and coordination in international collaborations and disaster responses, because the same terms may be used with different activities included, often unintentionally. For example, the term call processing time includes different activities in the US and the UK, which can lead to possible misunderstandings when both countries compare or combine data (NHS England, 2022; Ottah and Caramancion, 2023; Vanga et al., 2022). Similarly, the turnout time may be used for the ambulance crew to get ready and leave the station in some regions but not all (Vanga et al., 2021, 2022). Such variability makes drafting standard standards or policies even more tricky, as well as sharing best practices. For instance, the United States employs a decentralized EMS model, where services may be administered at the state, county, or municipal level, contributing to significant variability in response time standards and reporting practices (Gangidine et al., 2021).

Comparisons and analyses of data in academic research depend on the availability of standardized metrics. The lack of uniformity in EMS response time definitions means comparison studies are only a hurdle to overcome. This, in turn, clouds the generalizability of the research study's findings. This is particularly problematic when attempting to correlate response times with patient outcomes, as variations in metrics can skew results and lead to inaccurate conclusions (Patterson et al., 2010).

Another issue is on uniform standards. There is a lack of a standard response time target among countries and regions. It may indicate a variation in the quality of services provided on different occasions. For instance, the United States of America is using a decentralized model, and as such, most of the EMS services are administered at either a municipal or county level. This also contributes to increased variability in response time standards and reporting practices (Gangidine et al., 2021). Furthermore, management strategies vary by region. In the UK, if response times are expected to extend significantly beyond targets, patients may be placed on a “call-back” list. Call centre staff re-contact the patient to ensure their condition has not deteriorated, allowing for the reclassification of the call if a more immediate response becomes necessary. In fact, secondary triage and call-back mechanisms are increasingly used to manage demand during periods of system saturation, shifting the operational focus from absolute response speed toward dynamic patient-pathway monitoring. (NHS England, 2022; Seim et al., 2018). Differences in policy for national and regional response time standards, such as an 8-min 59-s standard in urban areas, versus OSHA's 3 to 4-min mandate in the USA, can create variabilities in policy development and performance evaluation (Johnson and Hennessy, 2019; OSHA, 2011). These variations reflect not only clinical urgency but also pragmatic adaptation to spatial coverage, workforce distribution, and system capacity.

Variability in response time metrics affects patient-centred outcomes, such as clinical measures, patient-reported experiences, treatment factors, resource utilization, and broader societal impacts. Such inconsistencies can result in observable differences in patient satisfaction and the overall quality of care delivered (NASEMSO, 2022).

Examples of successful international collaborators in health standardization include WHO's efforts in collaboration with HL7 International to support the adoption of Open Interoperability Standards, thus demonstrating potential for the standardization of EMS response times. Such collaborations always culminate in a consistent way of representing health information, hence facilitating simple communication and information exchange across diverse healthcare systems (National, 2023).

Table 3 describes certain limitations; many studies on emergency response time and patient outcome have data restrictions. These may include ecological fallacy, wherein higher-level groups are generalized to the individual. For instance, examining a neighbourhood's socioeconomic status does not account for patient background (Govindarajan and Schull, 2003), dependence on medical records results in quantity and quality issues regarding data. Examples of the latter include the quality of bystander CPR and the initial severity of illness. Response times are challenging to measure accurately, and the outcomes might not be reliably attributed to those measures (Lucchese, 2024). Localized or condition-specific studies cannot be generalized across other settings or situations (Lucchese, 2024). Generalizability remains a problem with many studies, as most are small dataset research with retrospective study design or very focused areas of investigation (Nogueira et al., 2016; Mahmood et al., 2017; Ma et al., 2019; Maghfiroh et al., 2018). Several methodologies pose challenges: selection biases and external factors may not be noted, and hence, establishing causality between response times and outcomes is difficult to do (Ma et al., 2019; Rehn et al., 2017; Colla et al., 2023). Besides that, practical problems of the emergency services provide a limit to sample size and scope, thus affecting the reliability of findings (Rehn et al., 2017; Colla et al., 2023). Data quality is another issue; for example, when essential details are unavailable or important factors may not have been considered, this creates further complications in deriving accurate assessments (O'Keeffe et al., 2011; Wilde, 2013; Lucchese, 2024).

Understanding geographic differences in response times is crucial for ensuring equitable healthcare access (Wilde, 2013). System-level analyses examining ambulance types, dispatch protocols, and geography can offer insights into response times and health outcomes (Lucchese, 2024). Future research should also explore early detection and management of chronic illnesses to reduce out-of-hospital cardiac arrests (O'Keeffe et al., 2011). Investigating how response times impact hospital selection, treatment protocols, and patient care pathways can improve health outcomes (Wilde, 2013). Additionally, examining repeated EMS calls for patients with longer initial response times could help assess the overall impact of delays (Wilde, 2013).

Given the current social and economic conditions for ensuring access to health, geographic disparities in response times are important to understand. System-level analysis, which considers elements like the types of ambulances involved and geographies, will help develop an understanding of response time and outcomes in health (Lucchese, 2024). Future studies need to consider early detection and management of chronic illnesses in reducing out-of-hospital cardiac arrests (O'Keeffe et al., 2011). Understanding how such response times affect the selection of hospitals, treatment modalities, and care pathways will further optimize health outcomes. The repeated EMS calls for patients with longer initial response times may provide further detail on the overall effect of delays (Wilde, 2013).

Beyond clinical intervention, EMS providers must emphasize ‘patient reassurance.’ This theme should begin at the point of call-answering; dispatchers keeping patients and families informed that help is en route can significantly mitigate the psychological trauma of an emergency. This is increasingly supported by real-time IT systems that allow for accurate updates on ambulance arrival times.

Across contemporary EMS systems, providers are actively deploying a range of information-technology-enabled interventions; rather than isolated tools, to reduce response delays and improve system coordination. While dynamic deployment strategies and advanced technologies hold substantial promise for reducing EMS response times, their implementation is often challenged by significant financial and logistical barriers. First, the initial capital investment required for GPS-enabled fleet systems, AI-supported dispatch platforms, and integrated data analytics infrastructure can be prohibitive, especially in low- and middle-income countries or underfunded municipal systems. Second, recurring costs such as software licensing, system maintenance, staff retraining, and periodic upgrades must be integrated into long-term planning to ensure sustainability.

Third, logistical hurdles, including inconsistent internet connectivity, limited technical supply chains (e.g. for hardware repairs), and geographic disparities in EMS personnel availability, may undermine system performance, particularly in rural or hard-to-reach areas. To overcome these challenges, a phased implementation approach, supported by public–private partnerships, grant-based funding mechanisms, and targeted capacity-building programs, may be necessary. Such strategies can help ensure that innovation adoption is equitable, cost-effective, and aligned with measurable improvements in patient outcomes and system efficiency.

Beyond traditional ambulance deployment, several innovative approaches have shown promise in reducing response times and improving patient outcomes. Off-duty clinician mobilization, in which trained paramedics or physicians are dispatched from home during high demand periods, can bolster workforce capacity when systems are stretched (Smith et al., 2019). Temporary deployment sites, such as pop-up stations near major events or congestion hotspots, allow rapid repositioning of resources without permanent infrastructure (Jones and Patel, 2020). Skill specialization, including critical care paramedic and community paramedic roles, enables advanced care delivery on scene, often obviating unnecessary transports (Brown et al., 2021). Finally, see and treat or hear and treat models empower EMS crews to assess, treat, and release low acuity patients on site–thereby preserving transport capacity for higher priority calls (Lee and Hernandez, 2022). Integrating these practices into EMS systems may offer a cost-effective complement to fleet expansion and technology upgrades.

Building on the discussion of dispatch systems in this article, it is also critical to consider how call-centre classification and prioritization protocols influence response times. Structured algorithms, such as the Medical Priority Dispatch System (MPDS), guide dispatchers through standardized questioning, enabling rapid assessment of caller information and assignment of priority levels (Dalton et al., 2022). Research indicates that EMS systems using such advanced triage protocols can achieve up to a 15% reduction in response times for high-acuity emergencies while optimizing resource deployment across all priority tiers (Beech, Smith and Jones, 2019; Egan et al., 2020). Ongoing quality assurance, including audit feedback and regular protocol updates–is essential to sustain dispatcher accuracy as clinical and operational circumstances evolve. By strengthening call-centre triage, EMS agencies can both enhance system efficiency and improve patient outcomes.

This evolution in dispatch underscores a broader paradigm shift in EMS strategy, where the emphasis is moving from rigid adherence to absolute response time targets towards a more nuanced goal of matching the level of clinical skills dispatched to the specific patient requirement within an adequate timeframe. This “right skill, right time” approach prioritizes optimal patient outcomes over mere speed, potentially justifying different response time standards for different types of emergencies.

Improving the data collection methods, understanding patient outcomes, and addressing the environmental and socioeconomic issues will enhance the past studies on EMS response times. Improvement in data collection methods using IoT sensors, machine learning, and blockchain can help in better real-time traffic management while generalizing more broadly. The stakeholder involvement and other qualitative methods will help provide practical insights from the EMS providers and patients (Lam et al., 2015a, b; Rafiq and Khanum, 2021; Gonzalez et al., 2009). Apart from this, the effectiveness of response times on the results of patients is to be evaluated by studying different health systems, various types of patients, and other broader measures apart from survival. Such effects can be studied by investigating how the response time affects specific emergencies like cardiac arrest and solving the exact mechanism for such an effect. The roles of environmental and socioeconomic burdens, such as climate and income inequity, must be understood (Lam et al., 2015a, b; Govindarajan and Schull, 2003; Hsia and Shen, 2011; Azmoodeh et al., 2021, 2023). Established methodologies should render identifiable associations between these and patient outcomes (Govindarajan and Schull, 2003; Lucchese, 2024).

This review shows that the optimal system design, resource allocation, and operational policies may reduce the response times of EMS (Lam et al., 2015a, b; Rafiq and Khanum, 2021; Blackwell et al., 2009; Weiss et al., 2013). It also depicts that real-time data streams, visualizations of GIS, and simulations could improve EMS operations (Lam et al., 2015a, b; Blackwell et al., 2009; Maghfiroh et al., 2018; Colla et al., 2023). Other future research work is related to analysing the impact of various response intervals, training for the paramedics, and protocols on the survival of the patients (O'Keeffe et al., 2011; Wilde, 2013). There is a need to exploit further innovative technologies and public health interventions, such as defibrillator programs and training in CPR, to enhance the effectiveness of EMS further. Comprehensive approaches that include quality improvement initiatives are expected to reduce the response times and optimize prehospital care (Lam et al., 2015a, b; Goto et al., 2018).

Thus, it promises a future of longitudinal studies, multidisciplinary approaches, and standardized data collection in EMS research (O'Keeffe et al., 2011; Wilde, 2013; Thornes et al., 2014; Hansen et al., 2024; Byrne et al., 2019). Other areas of study that need to be done will involve selection biases, cost-effectiveness, translation, and real-world application of proposed interventions (Nogueira et al., 2016; Hansen et al., 2024; Desai et al., 2019; Ma et al., 2019; Ong et al., 2010). Interdisciplinary approaches will be welcome in finding innovative solutions to ambulance services (Desai et al., 2019).

Furthermore, longitudinal studies are critically needed to determine whether the disparity in EMS response times between developed and developing nations, as well as between urban and rural areas, is widening or closing over time. Such research should seek to uncover the underlying drivers, such as differential rates of technological adoption, infrastructure investment, or policy focus, to inform more effective global and regional equity initiatives.

The novel contributions of this review, derived from its mixed-methods approach and LDA-based thematic framework, provide the foundation for these implications. By moving beyond a conventional summary to offer a data-driven structure of the field (Threads A-E) and by validating “Thread E” as a critical research frontier, this work provides a new conceptual model. It is this model that enables the formulation of a strategic, evidence-based, and actionable agenda for policymakers, practitioners, and researchers aiming to optimize EMS systems globally.

Finally, to validate the emerging trends and strategic priorities identified in this review, particularly those related to AI, equity-focused algorithms, and specialized response models, future work should involve structured technological forecasting exercises. Utilizing methods such as Delphi panels with experts from EMS operations, public policy, data science, and vehicle technology could help build consensus on the most viable and high-impact innovations, creating a coordinated roadmap for research and development over the next decade.

This systematic review examined the factors influencing EMS response times and their effects on patient outcomes. While the findings aligned with prior studies showing that shorter response times are associated with improved survival, the review added new insights by incorporating underexplored factors such as socioeconomic disparities and geographic isolation. Through LDA-based topic modelling, five dominant themes were identified that shape EMS performance: operational and environmental determinants, clinical outcomes, system optimization, disparities, and logistical challenges. Moving beyond retrospective synthesis, this review also identifies structured pathways for anticipating future EMS system evolution.

To enhance the practical utility of findings, some recommendations were presented. First, optimize ambulance deployment using real-time data and predictive analytics, especially in high-demand areas. Second, reduce disparities through targeted investment in underserved communities. Third, build resilience to extreme weather through integrated climate planning. Fourth, support cost-conscious, phased adoption of technologies in low-resource settings using public–private partnerships. Fifth, expand EMS workforce capacity and public preparedness efforts. Future research should include cross-regional studies, cost-effectiveness analyses of EMS innovations, and adoption of equity-focused evaluation frameworks. These actions can guide system reform across diverse contexts and ensure that EMS advancements translate into measurable improvements in access and outcomes. This review reinforces the need for multidisciplinary strategies to optimize EMS operations globally. By aligning evidence with actionable priorities, the conclusions offer a roadmap for policymakers and practitioners committed to building more responsive, equitable, and effective EMS systems.

To build upon these findings, it is recommended future research utilize Delphi panels involving experts from EMS operations and data science. Such a ‘technological forecasting’ exercise would help validate which emerging innovations; such as drone-assisted AED delivery or AI-based demand prediction, are most viable for improving global EMS equity over the next decade.

A Systematic review of ems response times: identifying key determinants and improving patient outcomes.

1. To synthesize evidence on the determinants of Emergency Medical Services (EMS) response times.

2. To examine the relationship between response times and patient outcomes.

3. To identify disparities in EMS response times based on geography and socioeconomic factors.

4. To recommend strategies for optimizing EMS response times and improving patient outcomes.

1. Population: Studies examining EMS response times in prehospital care settings.

2. Intervention/Exposure: Response time as a variable in EMS performance or patient outcomes.

3. Outcomes: Mortality, survival rates, hospital admissions, or disparities based on socioeconomic/geographic factors.

4. Study Design: Observational studies, cohort studies, case-control studies, randomized controlled trials, or systematic reviews.

5. Language: English-language studies.

6. Publication Date: Studies published from January 1, 2000, to October 20, 2024.

7. Date of Search: The search was last updated on the 20th of October 2024.

1. Studies focusing on non-EMS response services (e.g., police or fire services).

2. Case reports, commentaries, or editorials without original data.

3. Studies without clear data on response times or patient outcomes.

4. Non-English publications.

1. Web of Science (WOS)

2. PubMed

3. Scopus

1. Keywords and Boolean Operators:

   • (“EMS response time” OR “ambulance response time”) AND (“patient outcomes” OR “mortality” OR “geographic disparities” OR “socioeconomic disparities”).

   • Search terms will be adapted for each database, including Medical Subject Headings (MeSH) in PubMed.

2. Process:

   • Conduct systematic searches across all databases.

   • Include studies published between 2000 and 2024.

   • Remove duplicates using reference management software (e.g., Zotero).

1. Study selection:

   • Two independent reviewers will screen titles and abstracts for relevance.

   • Full-text articles will be reviewed for inclusion based on eligibility criteria.

2. Data extraction:

   • Extract data on study characteristics, population, outcomes, and key findings using a standardized form.

3. Quality appraisal:

   • Use the Cochrane Risk of Bias 2 (RoB 2) tool for RCTs and the Joanna Briggs Institute (JBI) checklist for observational studies.

4. Synthesis:

   • Perform a narrative synthesis to describe study characteristics and key findings.

   • Conduct a meta-analysis, if data permits, to quantify the association between response times and patient outcomes.