Regional disparities in emergency medical services in Poland: a preliminary study

Abstract

Introduction

Equitable access to emergency medical services (EMS) is a core principle of health system performance. However, in territorially heterogeneous countries, resource allocation often fails to reflect interacting demographic and spatial determinants. In Poland, systematic nationwide evidence integrating demographic, spatial, and operational determinants of EMS utilisation remains limited. This study examines whether EMS allocation aligns with actual demand patterns and identifies structural imbalances across regions.

Methods

A nationwide cross-sectional analysis was conducted using official EMS dispatch data covering all 16 voivodeships over a 31-day period (16 February–18 March 2025). Indicators were calculated per 100,000 inhabitants and per km² using national demographic statistics. Associations between population size, density, territorial extent, EMS team allocation and workload were assessed using correlation and regression analyses.

Results

Daily call volume averaged 13,106 nationwide, with 71.5% resulting in ambulance dispatch. Population size strongly predicted absolute demand (r = 0.96, p < 0.001), explaining most regional variance (R² = 0.964). However, population-standardized and area-adjusted indicators revealed substantial structural discrepancies. Population density was positively associated with spatial call intensity (r = 0.88, p < 0.001) but negatively associated with per-capita rates (r = − 0.62, p = 0.008). Workload per EMS team varied significantly across regions (p < 0.001), indicating that resource allocation does not fully correspond to operational burden.

Conclusions

EMS demand in Poland is not solely a function of population size. Territorial accessibility and density shape operational pressure in distinct ways, exposing structural allocation imbalances. Sustainable EMS planning requires integrated, multidimensional allocation models that simultaneously account for demographic scale, spatial dispersion and workload intensity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12873-026-01586-9.

Introduction

Ensuring equitable access to emergency medical services (EMS) is a fundamental requirement of resilient health systems. Although the principles of universal coverage are widely endorsed, operational resource allocation often reflects demographic scale alone, overlooking spatial accessibility and territorial dispersion. Such an approach may generate systemic inequities, particularly in countries characterized by pronounced regional heterogeneity, such as Poland [1–3].

Poland’s internal structure illustrates this diversity clearly. The country is administratively divided into 16 voivodeships that differ substantially in population size, density, urbanization level, and geographic extent. Highly urbanized regions generate large absolute volumes of emergency demand due to population concentration and mobility patterns. In contrast, territorially extensive and sparsely populated regions face spatial barriers that challenge timely access to care, even when overall demand per capita appears lower. These contrasting pressures raise a central policy question: does the current allocation of EMS resources adequately reflect multidimensional demand patterns that combine demographic intensity with territorial dispersion?

Regional disparities in EMS utilization in Poland have long been observed. Differences between urban and rural areas, and across voivodeships, are reflected in response times, patient condition severity at EMS arrival, and patterns of emergency types. Rural areas consistently experience longer response times and higher proportions of severe presentations, including trauma and cardiac arrest [4–9]. Analyses adjusted for both population and geographic area indicate that these disparities are shaped by demographic trends, the spatial distribution of hospital emergency departments, EMS team composition, and organizational features of the national system [10, 11].

Operational differences further compound these inequalities. Helicopter Emergency Medical Services (HEMS) are more frequently deployed in rural settings, where longer transport distances and more severe clinical presentations are common [4–9]. Moreover, variation in the regional implementation of evidence-based interventions, such as ECG teletransmission or dual antiplatelet therapy in myocardial infarction, contributes to unequal treatment pathways and outcomes [12, 13]. Simulation models and spatial accessibility studies highlight how demographic change and EMS resource configuration influence both system workload and access to care, underscoring the need for evidence-informed policy adjustments [10, 13–16]. Collectively, the literature demonstrates that both population density and territorial size must be considered when assessing regional EMS performance in Poland.

Despite these insights, prior research has typically examined demographic demand or geographic accessibility in isolation. A comprehensive nationwide analysis integrating demographic scale, population density, territorial extent, and operational workload within a single analytical framework has not yet been conducted. This study addresses that gap.

Using dispatch records from February–March 2025 obtained from the National Medical Rescue System – National Center for Monitoring Emergency Medical Services, Command Support System of the State Emergency Medical Services [17], and combining them with official demographic and territorial statistics from Statistics Poland [18, 19], this research provides an integrated assessment of EMS utilization across all 16 voivodeships. The key methodological innovation lies in calculating EMS indicators both per 100,000 inhabitants and per square kilometer. This dual standardization enables identification of regions where EMS systems are disproportionately burdened relative to their demographic weight or geographic scale.

The primary objective of this study is therefore to evaluate regional disparities in EMS utilization in Poland, with particular emphasis on population- and area-adjusted indicators. By identifying structural imbalances in demand and allocation, the findings aim to support evidence-based health policy, optimize EMS resource distribution, and inform future system planning to enhance equity and resilience.

Methods

Study setting and EMS system characteristics in Poland

Poland is a Central European country with a total area of 313,933 km² and a population density of 119 inhabitants per km² as of 1 January 2025. The country is administratively divided into 16 voivodeships, 314 counties, 66 cities with county rights, and 2,479 communes. Approximately 59.4% of the population resides in urban areas and 40.6% in rural areas [18]. Substantial regional variation exists in population size, density, level of urbanization, and territorial extent, creating heterogeneous conditions for the provision of emergency medical services (Table 1).

Table 1.

Demographic characteristics of Polish voivodeships [18]

Voivodeship	Area (km²)	Population (n)	Population density (inhabitants per km²)
Dolnośląskie	19,947	2,868,242	144
Kujawsko-pomorskie	17,971	1,984,479	110
Lubelskie	25,122	1,996,440	79
Lubuskie	13,988	969,819	69
Łódzkie	18,219	2,345,924	129
Małopolskie	15,184	3,429,084	226
Mazowieckie	35,559	5,508,322	155
Opolskie	9,412	930,296	99
Podkarpackie	17,845	2,062,997	116
Podlaskie	20,187	1,132,641	56
Pomorskie	19,547	2,359,493	121
Śląskie	12,334	4,291,441	348
Świętokrzyskie	11,709	1,157,991	99
Warmińsko-mazurskie	24,174	1,349,172	56
Wielkopolskie	29,827	3,479,986	117
Zachodniopomorskie	22,909	1,622,760	71

The Polish Emergency Medical Services (EMS) system operates as a publicly funded, nationwide service coordinated at the national level and implemented regionally. Ground EMS response is provided by two types of ambulance teams: specialist teams (S) and basic teams (B) [19].

Specialist teams include at least one physician (typically trained in emergency medicine, anesthesiology, or intensive care), supported by a paramedic or nurse and a driver. The presence of a physician allows the provision of the full range of advanced prehospital interventions requiring medical authorization.

Basic teams operate without a physician and consist of at least two paramedics or a paramedic and a nurse, with one member usually serving as the driver. Paramedics in Poland complete a standardized three-year university program in emergency medical services and are authorized to perform advanced life-saving procedures within a legally defined scope of practice, including defibrillation, emergency pharmacotherapy, oxygen therapy, and advanced airway management.

At the national level, the EMS system is dominated by basic teams. Of the 1,704 operational ground EMS teams, 1,481 (86.9%) are basic teams and 223 (13.1%) are specialist teams, corresponding to a ratio of approximately 6.64 basic teams per one specialist team [20–37]. This configuration reflects a strategic emphasis on broad territorial coverage combined with selective deployment of physician-led units.

Statutory response time standards are defined by national regulations. The median response time should not exceed 8 min in urban areas (> 10,000 inhabitants) and 15 min in rural areas, while the third quartile should not exceed 12 and 20 min, respectively. These benchmarks provide a formal framework for evaluating accessibility of emergency care.

Helicopter Emergency Medical Services (HEMS) complement ground EMS, particularly in rural and geographically challenging areas [20]. HEMS teams consist of a pilot, a physician, and a paramedic or nurse and are deployed both for primary emergency responses and interhospital transport of critically ill patients. In 2024, the Polish HEMS network comprised 22 helicopter bases and one fixed-wing air medical transport base, distributed across all voivodeships. Six helicopter bases operate 24 h per day, while most others function during daytime hours. One seasonal base operates during the summer period. The fixed-wing base located in Warsaw provides continuous interregional and international medical transport.

Emergency medical dispatch is coordinated through regional dispatch centers. Currently, 23 dispatch centers operate nationwide with 226 operator workstations. According to national reform plans, by 2028 each voivodeship will be served by a single dispatch center, with two centers maintained in the most populous regions (Mazowieckie and Śląskie).

These structural features — including heterogeneous population distribution, mixed urban–rural settlement patterns, and variable territorial accessibility — create diverse operational conditions across voivodeships and form the contextual basis for analyzing regional differences in EMS demand and workload.

Detailed information on EMS team distribution and HEMS infrastructure across voivodeships is provided in Supplementary Tables S1–S3.

Data sources

The primary dataset was compiled from official daily reports published by the National Center for Monitoring Emergency Medical Services [17]. Data were collected for a continuous 31-day period (16 February–18 March 2025). The observation window was selected to ensure methodological consistency and to minimize seasonal bias, as it falls outside major public holidays and periods of extreme seasonal variation.

To reduce potential selection bias, the starting week was determined using simple random sampling from all eligible weeks in the first quarter of 2025.

The analysis included two levels of aggregation:

• National data – daily totals for EMS operational indicators.

• Regional data – corresponding variables disaggregated across the 16 voivodeships.

All data were derived from official administrative records of the Polish Emergency Medical Services system.

Data preparation

Data were extracted from official tabular reports and standardized to ensure consistency across national and regional datasets. Non-analytical summary entries were excluded. Variables were harmonized and converted into numeric formats where necessary.

Regional datasets were structurally reorganized to ensure consistent alignment of variables across voivodeships. Aggregated daily values were used to calculate regional means for the study period.

Variables and indicators

EMS operational measures were defined according to the official dispatch reporting framework. To ensure conceptual clarity, the following absolute daily measures were used:

• All calls – all incoming calls received by emergency medical dispatch centers, regardless of further processing. This category includes interrupted calls, non-medical calls, duplicate calls, and other contacts not qualifying for EMS intervention.

• Accepted calls – calls that met formal and medical criteria and were validated by the dispatcher for further handling within the EMS system. These represent medically eligible incidents requiring operational assessment.

• Ambulance dispatches – the number of ambulance deployments to incidents. A single accepted call could result in more than one dispatch (e.g., deployment of multiple EMS teams to a single event).

• Categorized medical events – accepted calls classified into specific medical categories according to the official dispatch classification system in use.

Distinguishing total calls from accepted calls allowed differentiation between overall dispatch center workload (all incoming communications) and clinically validated EMS demand (eligible medical incidents).

To assess regional disparities, the following derived indicators were calculated:

• Regional averages – mean daily values per voivodeship across the 31-day period.

• Population-adjusted indicators:

  • Calls per 100,000 inhabitants.
  • Accepted calls per 100,000 inhabitants.
  • Ambulance dispatches per 100,000 inhabitants.

• Area-adjusted indicators: Mean daily calls per square kilometer.

• System capacity indicators:

  • Number of EMS teams (Basic and Specialist) per voivodeship.
  • Dispatches per EMS team per day (workload intensity indicator).

Population-standardized indicators were calculated using the formula: (Mean daily value/Population)×100,000.

External data integration

Population data (as of 1 January 2025) were obtained from Statistics Poland, Area and population by territorial division in 2025 [18]. Territorial area (km²) values were derived from official statistical sources and cross-validated against national statistical databases. The present study did not apply a binary administrative classification of regions as urban or rural. Instead, regional comparisons were conducted at the voivodeship level using continuous indicators, including total population, population density (inhabitants per km²), and territorial area. These measures were used to capture variation in urbanization intensity and spatial dispersion without imposing a dichotomous urban–rural categorization. Population density was therefore treated as a proxy indicator of urbanization level, allowing assessment of gradients in settlement structure rather than discrete urban–rural groupings.

Statistical analysis

Descriptive statistics were calculated for national and regional datasets. Comparative analyses were conducted across four dimensions:

• Absolute call volumes,

• Area-adjusted density (per km²),

• Population-adjusted rates (per 100,000 inhabitants),

• EMS capacity (dispatches per team per day).

Associations between demographic and operational variables were assessed using Spearman’s rank correlation (population size and call volume), Pearson or Spearman correlation (population density and calls per km²), and linear regression models for population-adjusted indicators. Regional differences in daily EMS activity between voivodeships were evaluated using the Kruskal–Wallis test due to non-normal distribution of the data. Post-hoc pairwise comparisons were conducted using Dunn’s test with Holm correction for multiple testing. All statistical analyses were performed using Statistica (StatSoft). A two-sided p value < 0.05 (α = 0.05) was considered statistically significant.

Ethical considerations

The study was based exclusively on aggregated administrative data from publicly available official sources. No individual-level or patient-identifiable information was used. According to Polish national regulations and institutional policies, ethical approval was not required.

Results

The analysis was conducted at both national and regional levels to assess disparities in EMS utilization across voivodeships using absolute, population-adjusted, area-adjusted, and system capacity indicators.

National EMS activity

During the 31-day study period, a total of 406,289 emergency medical service (EMS) calls were registered nationwide. The mean daily number of calls was 13,106.10 (SD = 631.73), with a median of 13,009 and a range from 11,369 to 14,125 (Table 2).

Table 2.

National EMS activity indicators during the 31-day study period

Indicator	Minimum	Maximum	Median	Mean (SD)
All calls	11,369	14,125	13,009	13,106.10 (631.73)
Accepted calls	8,180	10,165	9,430	9,367.77 (425.61)
Ambulance dispatches	8,998	12,477	9,724	9,825.42 (614.67)

SD - standard deviation

Of all incoming calls, a mean of 9,367.77 per day (SD = 425.61) were accepted as medically eligible incidents, corresponding to a median of 9,430 (range: 8 180–10 165). This represents 290,401 accepted calls overall, accounting for 71.5% of all incoming communications.

Ambulance teams were dispatched in a mean of 9,825.42 cases per day (SD = 614.67), with a median of 9,724 and a range from 8,998 to 12,477. In total, 300,588 dispatches were recorded during the study period, corresponding to 74.0% of all calls and exceeding the number of accepted incidents, reflecting the deployment of multiple EMS teams to some events.

Distribution of EMS interventions by diagnostic category

The distribution of EMS interventions across selected diagnostic categories is presented in Table 3. Unconscious patients constituted by far the largest component of daily workload, with a mean of 6,867.71 cases per day (SD = 329.78), representing a substantial proportion of all EMS activity. Injuries and fainting episodes formed intermediate-volume categories, with mean daily values of 902.55 (SD = 98.32) and 765.52 (SD = 69.53), respectively. Neurological symptoms were also common, mean 345.97 cases per day (SD = 30.32). Traffic accidents and cardiac arrests occurred less frequently, with means of 156.48 (SD = 35.57) and 114.87 (SD = 11.81) cases per day, respectively. Other events constituted a relatively small and stable category (mean = 214.68, SD = 18.32). Overall, the distribution indicates that EMS workload is dominated by non-traumatic medical conditions, particularly states of impaired consciousness, rather than acute traumatic events.

Table 3.

Descriptive statistics of selected EMS intervention categories

Variable	Minimum	Maximum	Median	Mean (SD)
Injuries	758	1,207	890	902.55 (98.32)
Traffic accidents	89	229	151	156.48 (35.57)
Neurological symptoms	294	406	351	345.97 (30.32)
Fainting episodes	616	888	767	765.52 (69.53)
Cardiac arrests	95	137	116	114.87 (11.81)
Unconscious patients	6,008	7,491	6,848	6,867.71 (329.78)
Other events	181	263	214	214.68 (18.32)

SD - standard deviation

Regional differences in EMS activity

Significant regional differences in daily EMS activity were observed across voivodeships at all stages of the response process. The Kruskal–Wallis test demonstrated highly significant variation in mean daily volumes of total calls, accepted calls, and ambulance dispatches (Table 4; all p < 0.001). Effect size estimates indicated very large regional heterogeneity (ε² ≈ 0.95–0.96).

Table 4.

Regional differences in daily EMS activity across voivodeships: Kruskal–Wallis test results

Outcome	N (days × regions)	df	H	p-value
Total calls	496	15	474.11	< 0.001
Accepted calls	496	15	475.36	< 0.001
Ambulance dispatches	496	15	464.94	< 0.001

N − number of observations (days × regions); df − degrees of freedom; H − Kruskal–Wallis test statistic

Post-hoc pairwise comparisons revealed numerous statistically significant differences between voivodeships. The largest contrasts were consistently observed between high-volume regions, particularly Mazowieckie and Śląskie, and smaller or less densely populated voivodeships. Only a limited number of region pairs showed no significant differences after Holm correction, indicating substantial variability in EMS demand and utilization across Poland. The patterns were broadly similar for total calls, accepted calls, and ambulance dispatches, suggesting persistent regional disparities throughout the entire EMS response process (Supplementary Tables S4–S6).

Regional patterns of call volume

Mean daily call volumes varied substantially across voivodeships (Fig. 1). The highest values were observed in Mazowieckie (mean = 1,820.87, SD = 263.89) and Śląskie (mean = 1,579.84, SD = 95.46), both characterized by large populations and high urbanization levels. Intermediate volumes were recorded in Dolnośląskie, Wielkopolskie, and Małopolskie, each generating over 1,000 calls per day on average. In contrast, less populous regions such as Opolskie, Lubuskie, and Podlaskie recorded fewer than 450 calls daily (Supplementary Table S7).

Fig. 1.

Boxplot of daily EMS calls by voivodeship during the study period

Population density varied widely across regions, ranging from 56 inhabitants per km² in Podlaskie and Warmińsko-Mazurskie to 348 inhabitants per km² in Śląskie. Regions with both large populations and high density tended to generate higher absolute call volumes.

After adjustment for population size, less urbanised and territorially extensive regions exhibited higher call rates per 100,000 inhabitants than densely populated regions. Population density was negatively correlated with per-capita call volume (r = − 0.62, p = 0.008). For example, Podlaskie averaged 36.0 calls per 100,000 inhabitants per day compared with 25.1 in Śląskie. A Welch’s t-test confirmed significant differences between high- and low-density regions (t = 2.71, df = 12, p = 0.019). The highest per-capita rates were observed in Lubuskie (42.23), Dolnośląskie (40.90), Zachodniopomorskie (40.76), and Kujawsko-Pomorskie (39.07), whereas lower rates were recorded in Podkarpackie (28.74), Małopolskie (30.50), and Lubelskie (31.66).

Spatial intensity, expressed as calls per km² per day, showed a different pattern. Population density was strongly positively correlated with spatial call intensity (r = 0.88, p < 0.001). Śląskie exhibited the highest spatial concentration of demand (0.13 calls per km² per day), followed by Małopolskie (0.07) and Dolnośląskie (0.06), while the lowest intensities were observed in Warmińsko-Mazurskie and Podlaskie (both approximately 0.02). These findings indicate that densely populated regions concentrate demand geographically without necessarily generating higher utilization per inhabitant.

Accepted calls

Regional variability was also observed for accepted calls (Supplementary Table S8). The highest mean daily numbers were recorded in Mazowieckie (mean = 1,315.52, SD = 190.36) and Śląskie (mean = 1,218.10, SD = 59.39). Elevated values were also noted in Dolnośląskie (mean = 789.13) and Małopolskie (mean = 760.03).

Intermediate levels were observed in Wielkopolskie, Kujawsko-Pomorskie, and Łódzkie, whereas lower values characterized regions with smaller populations and more dispersed settlement patterns, including Lubelskie, Pomorskie, and Podkarpackie. The lowest means were found in Opolskie, Świętokrzyskie, Podlaskie, and Warmińsko-Mazurskie.

Variability was greatest in Mazowieckie (SD = 190.36) and Podlaskie (SD = 42.26), while Opolskie and Świętokrzyskie demonstrated relatively stable daily volumes.

EMS dispatches

Regional patterns of ambulance dispatches largely mirrored those observed for total and accepted calls (Supplementary Table S9). The highest mean daily numbers were recorded in Mazowieckie (mean = 1,371.97, SD = 201.95) and Śląskie (mean = 1,262.48, SD = 63.01). Moderately high dispatch volumes were observed in Dolnośląskie (mean = 839.39), Małopolskie (mean = 789.74), Wielkopolskie (mean = 750.35), and Łódzkie (mean = 599.74), consistent with the presence of large metropolitan centers combined with substantial rural catchment areas. Variability was particularly pronounced in Mazowieckie and Łódzkie, as indicated by relatively large standard deviations.

Intermediate values were recorded in Pomorskie, Kujawsko-Pomorskie, and Lubelskie, where mean daily dispatches ranged between approximately 500 and 580 cases. Lower volumes were characteristic of regions with smaller populations and more dispersed settlement patterns, including Opolskie, Lubuskie, Podlaskie, and Warmińsko-Mazurskie.

Determinants of regional EMS demand and workload

Across voivodeships, population size was strongly associated with absolute EMS demand. Mean daily call volume correlated highly with population (r = 0.96, p < 0.001), and a similar relationship was observed for dispatches (r = 0.95, p < 0.001). The highest volumes were recorded in Mazowieckie, whereas the lowest occurred in Opolskie. In multivariable regression models including population, area, population density, and number of EMS teams, the explained variance in call volume was high (R² = 0.964). Residual analysis indicated that several regions generated higher or lower call volumes than predicted by demographic variables alone, suggesting the influence of additional regional factors.

Standardized indicators revealed contrasting spatial patterns. Population density was positively associated with spatial call intensity per km² (r = 0.88, p < 0.001) but negatively associated with per-capita rates (r = − 0.62, p = 0.008), indicating that dense regions concentrate demand geographically, whereas less dense regions exhibit relatively higher utilization per inhabitant.

System capacity was broadly aligned with demand (dispatches vs. number of EMS teams: r = 0.93, p < 0.001), yet workload per team differed significantly across voivodeships (ANOVA p < 0.001), suggesting that allocation does not fully correspond to operational burden. Calls and dispatches were strongly correlated overall (r = 0.99, p < 0.001), although the call-to-dispatch ratio varied significantly between regions (Kruskal–Wallis p < 0.001), consistent with differences in triage dynamics and case severity.

System performance indicators

The number of EMS teams was strongly correlated with dispatch volume across voivodeships (r = 0.93, p < 0.001). However, workload per team differed significantly between regions (ANOVA F(15,480) = 6.37, p < 0.001; Kruskal–Wallis H = 57.2, p < 0.001), ranging from a mean of 6.21 dispatches per team per day in Mazowieckie to 4.24 in Podlaskie.

Calls and dispatches were highly correlated overall (r = 0.99, p < 0.001), yet the relationship between incoming demand and operational response varied regionally. The call-to-dispatch ratio differed significantly across voivodeships (Kruskal–Wallis H = 41.6, p < 0.001), ranging from 0.72 in Warmińsko-Mazurskie to 0.76 in Mazowieckie.

Residual analysis of multivariable regression models indicated that demographic variables alone did not fully explain regional EMS demand.

Discussion

Principal findings

This nationwide analysis reveals profound and multidimensional inequalities in Emergency Medical Services (EMS) utilisation across Poland. Unlike previous studies that examined isolated indicators, this analysis integrates demographic, spatial, and operational dimensions within a single national framework, providing a more comprehensive understanding of inequalities in EMS utilisation. Demand was not only uneven in absolute terms but also diverged markedly when expressed relative to population size, geographic area, and system capacity. Highly urbanised regions generated the largest call volumes, whereas sparsely populated regions experienced disproportionate operational burden when measured per capita or per unit area. These findings demonstrate that EMS workload is governed by distinct demographic and spatial dynamics that cannot be captured by single indicators.

Despite Poland’s moderate average population density, regional variation was substantial. For example, Mazowieckie and Śląskie produced the highest absolute demand, exceeding 1,500–1,800 calls daily, while peripheral regions recorded fewer than 450 calls per day. However, population-adjusted analyses revealed the opposite pattern: low-density regions exhibited higher utilisation per inhabitant, and spatial analyses showed extreme differences in event concentration per km². Together, these results indicate that EMS demand operates simultaneously on demographic and geographic axes, creating structurally different challenges for urban and rural systems.

Comparison with previous studies

The observed rural–urban disparities align with earlier research showing that remote areas experience longer response times, more severe patient conditions at first medical contact, and reduced access to advanced interventions, contributing to poorer outcomes [4, 5, 9, 38]. Simulation studies and spatial analyses have similarly highlighted the necessity of incorporating both population and geographic determinants into emergency care planning [10, 14]. Comparable territorial inequalities have been reported across Europe, including in Sweden, indicating that such disparities represent a widespread structural phenomenon rather than a country-specific issue [39, 40].

Importantly, the present study extends previous work by integrating multiple indicators—absolute volumes, per-capita rates, spatial density, and system capacity—within a single national analysis. This multidimensional approach reveals that different regions may appear adequately resourced when assessed by one metric but severely strained when evaluated by another.

Structural determinants of inequality

Characteristics of the Polish EMS system further amplify these disparities. The dominance of basic teams (86.9% of all units) reflects a strategy prioritising territorial coverage, yet the study demonstrates that resource distribution does not equalise operational burden. Regions with the largest numbers of teams still exhibited high workload per unit, while peripheral regions faced logistical constraints related to extensive coverage areas.

Such patterns underscore the inherent trade-off between demographic efficiency and geographic accessibility. Urban systems must manage high event density and temporal variability, whereas rural systems must overcome distance, travel time, and limited redundancy. Similar challenges have been documented in other decentralised EMS systems [10, 13, 20, 41–45].

Role of HEMS and system design

Helicopter Emergency Medical Services represent a critical mechanism for mitigating geographic inequities. The nationwide network of helicopter bases provides advanced care access across all voivodeships and compensates for long ground transport times in remote areas. Prior studies emphasise the importance of HEMS in rural settings, where it can significantly reduce time to definitive care [4, 5, 12]. The observed distribution of bases reflects a dual strategy: concentration of continuous resources in metropolitan areas and targeted deployment in sparsely populated regions.

Dispatch and operational performance

Dispatch centres play a central role in translating demand into operational response. During the study period, approximately 13,000 calls were handled daily, with over 70% accepted as medically eligible incidents. This stable acceptance rate suggests consistent triage criteria nationwide, yet regional variation in call-to-dispatch ratios indicates differences in operational dynamics, case severity, or accessibility.

High correlation between calls and dispatches demonstrates that demand is rapidly converted into service utilisation, but significant variation in workload per team highlights persistent inequities in operational pressure. These findings suggest that system capacity planning must therefore account not only for average demand but also for variability, surge potential, and geographic accessibility constraints. These findings are likely applicable to other territorially heterogeneous health systems, particularly in countries where population distribution and accessibility constraints create competing pressures on emergency care delivery.

Implications for health systems

The results have important implications for EMS planning both nationally and internationally. First, reliance on absolute call numbers alone risks underestimating burden in sparsely populated regions. Second, population-based metrics fail to capture geographic barriers, while spatial indicators do not reflect demographic demand. Effective planning therefore requires simultaneous consideration of demographic scale, spatial distribution, and operational capacity.

Urban systems should prioritise resilience strategies capable of absorbing large demand fluctuations, including dynamic fleet deployment, advanced dispatch optimisation, and integration of digital health solutions. Peripheral regions require investments that improve accessibility, such as expanded HEMS support, strengthened capabilities of basic teams, and infrastructure improvements.

Limitations

Several limitations should be acknowledged. Potential underreporting in rural areas, lack of detailed socio-economic variables, and the observational nature of registry data may limit causal inference [12, 13]. Additionally, the relatively short observation period may not capture seasonal variation in demand. Nevertheless, the consistency of patterns across multiple indicators suggests that the identified disparities are structural rather than incidental.

Conclusions

This study demonstrates that EMS utilisation in Poland is shaped by complex interactions between population distribution, geography, and system organisation. Urban regions face high absolute demand and variability, whereas rural regions confront accessibility barriers and disproportionate workload relative to population size. Addressing these inequities requires differentiated, region-specific strategies rather than uniform resource allocation models.

More broadly, the findings highlight a fundamental principle of emergency care systems: equity cannot be achieved through demographic planning alone but requires integration of spatial accessibility and operational capacity. As health systems worldwide confront ageing populations, urbanisation, and resource constraints, multidimensional approaches to EMS planning will be increasingly essential.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Conceptualization: Przemysław Żuratyński; Methodology: Przemysław Żuratyński, Marta Janowska-Zientara; Software: Michał Borowczyk, Tomasz Czynszak; Formal analysis: Daniel Ślęzak; Investigation: Przemysław Żuratyński; Resources: Michał Borowczyk, Tomasz Czynszak, Marta Janowska-Zientara; Data curation: Przemysław Żuratyński; Writing – original draft preparation: Przemysław Żuratyński, Michał Borowczyk, Tomasz Czynszak; Writing – review and editing: Daniel Ślęzak, Marta Janowska-Zientara; Visualization: Michał Borowczyk; Supervision: Daniel Ślęzak; Project administration: Marta Janowska-Zientara.All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data availability

The datasets analyzed during the current study are available from the corresponding author upon reasonable request. The data consist solely of aggregated numerical records derived from official EMS databases.

Declarations

Ethics approval and consent to participate

The analysis is based exclusively on official emergency medical service (EMS) records collected for administrative and reporting purposes over a 31-day observation period. The study used aggregated, fully anonymized numerical data and did not involve any direct contact with individuals, patients, or healthcare personnel. As no personal or identifiable data were accessed and no human participants were involved, ethics approval and informed consent were not required. According to the regulations of the Bioethics Committee of the Nicolaus Copernicus University in Toruń, Collegium Medicum in Bydgoszcz (Order No. ZR.215.2023), studies based solely on fully anonymized aggregated data do not require ethics committee approval. Therefore, formal approval from an institutional ethics committee was not required. The study was conducted in accordance with the principles of the Declaration of Helsinki. Informed consent was not required, as no individual-level data were collected and no participants were directly involved in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.