Unpacking Situational Awareness in Emergency Medical Services: An Eye-Tracking Study of Visual Attention

Abstract

Situational awareness (SA) is critical for Emergency Medical Services (EMS) providers as they operate in high-stakes, dynamic environments requiring rapid information processing and decision-making. While prior research has explored SA challenges in EMS, little is known about how visual attention patterns influence SA and clinical performance. This study employs eye-tracking technology to objectively assess how EMS providers allocate their visual attention during simulated pediatric emergency scenarios in urban and rural settings. We investigate variations in visual attention across experience levels, team structures, and task roles and examine differences between high- and low-performing teams. Results reveal that high-performing teams demonstrate more frequent and evenly distributed visual scanning, whereas lower-performing teams exhibit a narrowed focus, increasing the risk of missing critical cues. Our findings underscore the need for training interventions and technology solutions to enhance SA and optimize EMS performance.

INTRODUCTION

Situational awareness (SA) is a cognitive framework, originated in aviation safety, that describes an individual’s ability to gather information, interpret its meaning, and anticipate future events.¹ This framework has been widely applied to high-stakes medical settings, where maintaining SA is critical for effective decision-making, teamwork, and patient safety.^2,3 This is especially important in emergency care environments, such as Emergency Medical Services (EMS), where care providers (e.g., paramedics, emergency medical technicians, etc.) operate in dynamic and often chaotic out-of-hospital settings, rapidly assessing patient conditions, interpreting multiple information sources, and coordinating care within a condensed timeframe (e.g., minutes).^2,4 These unique work practices and challenges in EMS impose an exceptionally high cognitive load on providers, potentially impacting their SA and clinical decision-making.

Given the importance of SA in EMS, prior studies have explored how EMS providers develop and maintain SA. These studies have identified key skills and strategies for enhancing SA, such as frequent monitoring of diverse cues, avoiding tunnel vision, and verbalizing observed trends to team members.⁴ However, SA is an internal cognitive construct that is inherently difficult to measure.⁵ To date, existing studies primarily relied on retrospective video analysis by expert observers (e.g., rating participants’ performance and identifying SA-related issues) or participant self-reports (e.g., pausing a simulation and querying participants about their awareness).^6-9 While these methods offer valuable insights, they present several notable limitations; for instance, they are often subjective, prone to observer biases, and can disrupt the natural workflow in EMS simulations.¹⁰

Eye-tracking technology has been shown to offer a real-time, objective approach for assessing attentional focus without disrupting workflow, while also offering deeper insights into how individuals process and integrate visual information.¹¹ Unlike traditional SA assessment methods (e.g., video review or self-reporting), eye-tracking enables a moment-to-moment, quantitative analysis of how care providers allocate their visual attention, revealing what they notice, what they overlook, and how they distribute attention across various visual cues.¹² Prior work has suggested a strong link between visual attention and cognition,¹³ making eye-tracking a valuable tool for measuring SA.10 Despite its potential, the application of eye-tracking in EMS research remains limited.^10,14

Moreover, existing research has paid little attention to variations in EMS SA. Team structure (e.g., the number of providers in a team), experience level (less experienced versus more experienced providers), and division of labor (e.g., hands-on versus hands-off providers) likely influence how EMS providers distribute attention, coordinate tasks, and process information. However, these aspects remain underexplored in the literature.¹⁵ Additionally, limited research has examined the relationship between EMS providers’ visual attention patterns and their clinical task performance. Understanding how visual attention differs between high- and low-performing EMS teams can provide valuable insights into attention distribution patterns linked to optimal care performance. These findings can inform training interventions and best practices to enhance SA and improve clinical decision-making in the dynamic EMS settings.

To address these research gaps, we conducted simulated pediatric emergency scenarios with EMS providers in two regions of the United States and leveraged eye-tracking technology to examine patterns and variations in EMS providers’ visual attention. This study aims to answer the following research questions: RQ1): How do EMS providers distribute their visual attention to maintain SA? RQ2): What variations exist in EMS providers’ visual attention? RQ3) How do EMS visual attention patterns differ between high- and low-performing teams in clinical tasks? By addressing these questions, this study contributes to a more in-depth understanding of how EMS providers maintain SA in high-pressure environments, informing strategies, training, and design implications to improve SA and enhance decision-making in emergency medical care.

METHODS

Data Collection

This study is part of a larger research effort investigating EMS workflows. The dataset consisted of 15 simulation sessions conducted with EMS providers from two agencies in the United States: a fire-based agency in a rural Mountain West region and a hospital-based agency in an urban area on the East Coast. The simulations took place in a mobile simulation lab resembling the back of an ambulance for the mountain region and in a high-tech simulation room for the urban site. A total of 34 unique EMS providers participated, including 24 paramedics (70.59%) and 10 emergency medical technicians (EMTs) (29.41%). Participants had a broad range of experience, from 2 months to 33 years, with 21 providers (61.76%) having over 10 years of experience. In the mountain region, all participants were assigned to three-member teams, reflecting their typical team structure, whereas urban-area teams consisted of at most two providers per simulation (with two teams having only one provider due to the late arrival of the other provider). Each team was randomly assigned to either a pediatric asthma or an anaphylaxis scenario, ensuring that an approximately equal number of teams participated in each scenario. Expected treatments and interventions were similar across both scenarios. The diverse participant backgrounds and EMS agency characteristics enhanced the generalizability of our findings.

The simulations were high-fidelity and included a professional simulation mannequin (e.g., Pediatric HAL® S3005) capable of reacting to administered treatments. A simulation expert controlled the vital signs monitor, dynamically adjusting vital signs based on the performed care. EMS providers had access to a full set of medical equipment and medications, identical to what they use in their ambulances. All simulation sessions were audio- and video-recorded.

Before the start of each simulation, participants were oriented to the mannequin. At the start of the simulation, the EMS providers were given a dispatch report and began pre-arrival planning, including a discussion and determination regarding the division of labor, such as who would perform specific tasks, who would handle hands-on treatments and patient examinations, and who would assume the role of team leader. They also decided who would wear the eye tracker, which was typically chosen by the provider assuming the team leader role (i.e., only one team member wore the device). The eye tracker used in this study was the Pupil Core model, manufactured by Pupil Labs (Berlin, Germany), which could be mounted onto any head-worn glasses. The eye tracker featured two cameras recording eye movement and one camera capturing the first-person view of the wearer, with all recordings synchronized. Due to natural facial variations among participants, a calibration was conducted before each session to minimize any offset between the detected visual attention area and the actual focal point of the wearer. The eye tracker also included a built-in microphone, allowing for synchronized audio and video recordings. All participants voluntarily participated in the study and received compensation. This study was approved by the Pace University IRB with reliance agreements established with the local IRB where the simulations were conducted.

Data Processing and Analysis

After completing all simulations, the recorded sessions—including eyeball movement data captured by two cameras and the first-person view of the wearer captured by a third camera on the eye tracker—were uploaded to Pupil Cloud, the data processing and analysis platform provided by Pupil Labs. The recordings were then automatically synthesized into a video, displaying the first-person perspective of the eye tracker wearer, with a synchronous eye-focusing area overlay. The eye-focusing area was represented as a circle, with its diameter inversely proportional to the confidence level of gaze detection. Specifically, a higher confidence level in detecting the wearer’s focal attention resulted in a smaller diameter, as determined by real-time inference from the software. This step helped us identify the focused object at a specific time.

To address RQ1 (EMS providers’ distribution of visual attention), we used MS Excel to record the focused objects and the duration of attention for each gaze. Based on the duration of each gaze, visual attention was classified as either fixation (lasting more than 3 seconds) or saccade (less than 3 seconds), following the dispersion-based I-DT method for complex cognitive tasks.^16,17 A 3-second threshold was applied to differentiate fixations from saccades, aligning with prior research.¹⁸ Additionally, we adopted six visual attention metrics from previous research to quantify visual patterns,^19-21 including fixation duration (the time spent fixating on an object (>3 seconds)), fixation frequency (the number of times a fixation occurred), fixation average (fixation duration divided by fixation frequency), saccade duration (the time spent in a saccade (<3 seconds)), saccade frequency (the number of times a saccade occurred), and total duration (the combined time spent on fixations and saccades). Beyond these analyses, we visualized fixation and saccade patterns—identifying which objects were fixated on or glanced at and for how long—along the simulation timeline to assess temporal trends (e.g., which objects were attended to first and what followed). Examples of the visualizations are shown in Figures 1, 4, and 5.

To address RQ2 (variations in EMS situational awareness), we investigated several factors that could potentially influence visual attention patterns, including team structure (e.g., the number of providers in a team), experience level (e.g., less experienced vs. more experienced providers), and division of labor (e.g., hands-on vs. hands-off providers). To analyze these variations, we separated the dataset (e.g., focused objects and their visual attention data) into groups based on the examined factors (e.g., hands-on vs. hands-off providers), using fixation duration as the primary metric, as it is the most representative measure of cognitive attention. We then applied Chi-square tests to assess whether the ranking of most fixated object categories (e.g., patient, vital signs monitor) differed significantly across groups. For groups with significant differences, we conducted Mann-Whitney U Test to further analyze the differences across six visual attention metrics for the focused objects.

Finally, to address RQ3 (visual attention comparison among teams with varying levels of care performance), we first asked a group of expert raters to evaluate each team’s care performance based on the completeness of 18 predefined, cross-scenario comparable critical tasks, following a structured evaluation protocol. Each team’s performance was evaluated by two expert raters to minimize subjective biases. Example tasks included assessing airway, checking breathing, measuring vital signs, and administering medications. Each task was classified as either complete or incomplete, and the total number of completed tasks was used as a metric to evaluate each team’s overall performance. We then selected the four highest-performing teams (who completed >95% of the 18 critical tasks) and the four lowest-performing teams (who completed only a small fraction of the 18 critical tasks) to compare their visual attention patterns.

RESULTS

Visual Patterns of EMS Providers

Through our analysis of focused objects across all simulations, we identified more than 40 distinct objects that were focused by EMS providers. We then grouped them into 7 high-level categories: the patient, vital signs monitor, patient’s family, teammates, medical equipment and treatment activities, cognitive aids (e.g., protocols, etc.), and other (e.g., telemedicine platform, simulation coordinators).

By analyzing the temporal pattern of focused objects along the simulation timeline, we found that most EMS teams initially focused their attention on the patient, while only a few teams began by fixating on the vital signs monitor. However, after this initial focus, the sequence of focused objects did not follow a specific order. This observation is illustrated in Figure 1, which presents visualizations for four teams with similar characteristics (e.g., team structure, division of labor, etc.).

Figure 1.

Visualization of fixation patterns for four EMS teams throughout the simulation timeline

The distribution of EMS providers’ visual attention is summarized in Table 1. As expected, the patient received the most attention across all visual attention metrics, with an average total duration of 77.5 seconds per simulation. Beyond patient monitoring, EMS providers also devoted significant attention to treatment equipment, spending an average of 36.2 seconds per simulation, particularly when preparing and administering medications, intravenous therapy, and airway management devices. The third most frequently observed visual focus was the vital signs monitor, with an average duration of 35.1 seconds per simulation. Notably, vital signs monitoring was often interspersed with patient observation, as EMS providers alternated their attention between the vital signs monitor and the patient (Figure 1).

Table 1.

EMS visual attention distribution

Major Visual Cues	Metrics
	Fixation Duration	Fixation Frequency	Fixation Average	Saccade Duration	Saccade Frequency	Total Duration
Patient	55.5s	10	5.6s	22.0s	10	77.5s
Vital signs monitor	24.9s	4	6.1s	10.2s	4	35.1s
Patient’s family	7.3s	1	7.8s	3.3s	2	10.5s
Teammates	10.3s	2	5.5s	7.4s	4	17.7s
Medical equipment & treatment activities	26.8s	6	4.9s	9.4s	5	36.2s
Cognitive aids	11.9s	2	7.2s	7.4s	3	19.3s
Other	36.0s	6	6.0s	15.6s	8	51.6s

Another interesting finding was that EMS providers monitored their teammates’ tasks for an average of 17.7 seconds per simulation. For example, EMS providers frequently shifted their visual attention to teammates while deciding or administering treatments. EMS providers also spent a notable amount of time using cognitive aids. As shown in Figure 2, they referred to both a paper-based medication sheet (Figure 2, left) and a mobile application (Figure 2, right) to verify the correct medication dosage. On average, 19.3 seconds per simulation was spent using cognitive aids—longer than the time spent speaking with the patient’s family or monitoring teammates’ tasks.

Figure 2.

Cognitive aids used by EMS providers during simulations. (Left: a paper-based medication sheet for medication dosage calculation; Right: a mobile application for medication dosage calculation.)

Finally, a notable finding was that beyond these visual cues, EMS providers also allocated time to other tasks, averaging a total duration of 51.6 seconds per simulation. For example, in this study, providers were instructed to contact a remote physician via a telemedicine system to obtain approval for medication administration and treatments beyond their protocol. On average, they spent 49.6 seconds on this task, with 8.2 seconds dedicated to operating the telemedicine tool and 41.4 seconds focused on viewing the telemedicine screen while communicating with the physician. This finding suggests a potential shift in visual attention patterns as telemedicine systems become more integrated into EMS care workflows.

Differences in EMS Visual Attention

Regarding the key differences in visual attention among EMS providers, we examined whether significant differences existed in EMS visual patterns based on team structure (e.g., the number of providers in a team), experience level (e.g., less experienced vs. more experienced providers, EMTs vs. paramedics), and division of labor (e.g., hands-on vs. hands-off providers).

We did not find a significant difference in visual patterns between EMTs and paramedics. However, we observed a significant difference in fixation duration between experienced providers (>1 year of experience, median fixation duration = 28s) and novice providers (<1 year of experience, median fixation duration = 23s) (p<0.05). As shown in Figure 3A, experienced providers spent less time fixating on almost all visual cues except for medical equipment and treatment activities, where they had a significantly longer fixation duration compared to novice providers (32 seconds versus 5 seconds).

Figure 3.

Differences in visual attention to major focused objects based on three factors: experience level (A), team structure (B), and division of labor (C, D). A significant difference is marked with an asterisk.

We also found a notable difference between EMS teams in the East Coast urban area, which had at most two providers (median fixation duration = 28s), and those in the rural mountain region, which had three providers (median fixation duration = 25s). Specifically, this difference was observed in saccade frequency related to the patient and vital signs monitor (Figure 3B)—providers in the urban area glanced at the patient (11 times per session) and the vital signs monitor (9 times per session) more frequently than their rural counterparts (8 and 2 times per session, respectively) (p<0.05).

Regarding the visual attention differences between EMS providers engaged in hands-on patient care (median fixation duration = 28s) and those who were hands-off (median fixation duration = 26s), we found that hands-on providers spent significantly more time focusing on medical equipment and treatment activities across three key metrics (p<0.05): fixation duration (41.0s versus 11.5s; Figure 3C), average fixation duration (24.0s versus 6.1s), and total duration (72.0s versus 19.0s; Figure 3D). These differences are also evident in the visualization of their visual patterns, as shown in Figure 4: Hands-on providers (Figure 4 Left) concentrated most of their attention on medical equipment and the patient, while devoting less time to other elements such as vital signs monitors and cognitive aids. In contrast, hands-off providers (Figure 4 Right) distributed their attention more evenly across multiple visual cues, frequently glancing at their teammates, vital signs monitor, and other elements.

Figure 4.

Comparison of visual pattern between hands-off (Right) and hands-on (Left) provider

Visual Pattern Comparison between High-Performing and Low-Performing Teams

We compared the four best-performing teams with the four lowest-performing teams to identify differences in their visual attention patterns. Due to space limitation, we present examples of the two best and two worst performing teams in Figure 5. The best-performing teams successfully completed 18, 17, 16, and 15 out of 18 critical tasks, whereas the lowest-performing teams only completed 7, 8, 8, and 8 tasks, respectively.

Figure 5.

Comparison of visual patterns between providers from the best-performing team (A), the second best-performing team (B), the worst-performing team (C), and the second worst-performing team (D)

From this comparison, we observed that the team leader in the high-performing teams consistently monitored and integrated diverse sources of information (Figure 5 A and B). Specifically, they exhibited a more evenly distributed and shorter fixation duration (average 4.76 seconds vs. 5.51 seconds for the lowest-performing teams) across key categories, including the patient, vital signs monitor, teammates’ tasks, and medical equipment. In contrast, the team leader in the lowest-performing teams exhibited imbalanced attention distribution, with excessive fixation on a single object, such as the patient, while neglecting vital signs and teammates’ ongoing activities (Figure 5 C and D). Additionally, the team leader in the high-performing teams demonstrated higher saccade frequency (8.23 times per minute vs. 5.31 times per minute for those in the lowest-performing teams), suggesting more frequent and adaptive shifts in visual attention.

Upon further examination, we found that the four top-performing teams had more team members (two teams with two providers and two teams with three providers) compared to the worst-performing teams (two teams with one provider and two teams with two providers). Additionally, the top-performing teams were led by hands-off providers, whereas three of the four worst-performing teams were led by hands-on providers. Furthermore, the two EMT-led teams were among the worst-performing teams. Our statistical analysis confirmed these observations. Spearman’s rank correlation coefficient revealed a significant positive correlation between team performance and the number of participants (teams with more members demonstrated higher critical action completeness) (r = 0.52, p < 0.05). The Mann-Whitney U test further demonstrated that the teams with hands-off leaders (M = 15) completed significantly more critical care steps than those led by hands-on providers (M = 9) (U = 12, p < 0.05). Finally, a significant difference was observed between EMT-led teams (M = 8) and paramedic-led teams (M = 14) (p < 0.05). Given that there were only two EMT-led teams while the rest were led by paramedics, we conducted an exact Mann-Whitney U test, which remained significant (p < 0.05). The effect size, calculated using Cliff’s Delta, was 0.91 (large effect), indicating that EMT-led teams consistently underperformed compared to paramedic-led teams.

Discussion

Enhancing Situational Awareness Through Technology-Assisted Integration of Visual Cues

Our analysis identified more than 40 distinct visual cues that EMS providers attended to during emergency care, which we categorized into seven high-level groups. This finding underscores the immense cognitive workload required to collect, process, and interpret a vast amount of visual information in real time. A failure to integrate these cues effectively can lead to suboptimal care performance. For example, if an EMS provider does not notice critical changes in a patient’s vital signs, they may miss signs of deterioration, leading to delayed interventions. Prior research has highlighted key challenges in developing and maintaining SA during EMS care, including difficulties in recalling essential information, maintaining awareness of team members’ actions, and recognizing patient needs and symptoms in dynamic environments.^7,22 Given these challenges, there is a critical need for technology solutions that can support EMS providers in managing and synthesizing information efficiently.

Previous research has extensively explored the use of visual displays and information dashboards to enhance SA in emergency care settings, such as trauma resuscitation, by integrating and presenting critical patient information in real time.^23,24 While traditional stationary displays may not be directly applicable to EMS settings—given the highly mobile nature of EMS providers—these studies highlight the value of integrating diverse information sources into a cohesive and accessible format. A practical solution for the EMS context could use mobile applications, such as tablet-based decision support systems,²⁵ to alert providers to critical patient conditions in real time. Recent advancements in wearable technologies, such as smart glasses,²⁶ offer another promising avenue. Augmented reality (AR) interfaces can project real-time alerts or patient vitals directly within the provider’s field of vision, minimizing distractions and reducing the need to look away from patients.

To effectively capture and synthesize different types of information in real time, a multimodal technical approach— combining speech recognition, computer vision, and wearable devices—can be a viable solution. More specifically, speech recognition technologies can process verbal communication among team members, transcribing and analyzing discussions to extract key phrases related to medications, procedures, or patient conditions.²⁷ This real-time analysis can provide automatic summaries or generate alerts for critical updates that EMS providers may have missed. Advanced computer vision techniques, integrated with wearable technologies (e.g., smart glasses and body-worn cameras), can facilitate the continuous capture of visual and contextual information (e.g., patient’s skin color, trauma injuries, administered medications). Additionally, biometric sensors can monitor workload and stress levels, alerting providers to cognitive overload that may impair decision-making. Finally, integrating these multimodal data sources into AI-driven decision support systems can enable context-aware recommendations by detecting patterns, predicting patient deterioration, and generating automated alerts. Future research should explore the integration of these multimodal systems to develop adaptive, context-aware tools tailored to the unique challenges of EMS care.

Variability in EMS Visual Attention and Situational Awareness

Our findings revealed significant variations in how EMS providers allocated their visual attention during emergency care scenarios. Notably, we did not observe a consistent temporal pattern in how EMS providers distributed their vision throughout the patient care process. This variability highlights the dynamic nature of EMS workflows, where providers must rapidly assess evolving situations and adapt their attention strategies in real time.²⁸ These findings suggest that the development of SA in EMS may depend more on individual experience, work habits, and adaptive strategies to emerging needs rather than following a standardized, sequential pattern of information gathering.

Additionally, we found that several factors contributed to differences in EMS situational awareness (SA), with experience level being a notable determinant. Experienced providers exhibited shorter fixation durations on nearly all visual cues, except for medical equipment and treatment activities. This observation aligns with previous research on visual attention differences between experienced and novice care providers.^29,30 Specifically, experienced providers had shorter total view durations on key visual cues compared to their novice counterparts. One possible explanation, as suggested in prior literature,^29,30 is that novice clinicians require more time to interpret the meaning and relevance of critical visual cues. Novices may adopt a more deliberate data-gathering approach before making decisions, whereas experienced providers rely on past experiences and pattern recognition to rapidly assess and process relevant information, allowing for more efficient visual scanning and decision-making.³⁰

Another key finding is that hands-on providers focused more on medical equipment and treatment activities, while hands-off providers maintained a broader situational overview by frequently glancing at teammates, vital signs monitors, and other visual cues. This distinction suggests that hands-on providers may experience a narrowed focus due to their engagement in physical tasks, which could limit their real-time SA. Our statistical analysis examining the relationship between division of labor and care performance supported these observations—teams led by hands-on providers demonstrated lower care performance compared to those led by hands-off providers. Therefore, hands-off providers should ideally assume the role of a “team leader” who takes responsibility for integrating all visual cues and making informed decisions. However, some EMS agencies operate in a self-organized manner without a clear division of labor, where all team members share responsibilities and roles without a formally designated leader.^7,31 This study highlights the potential benefit of having a hands-off provider—who only performs hands-on tasks when necessary or at the partner’s request—assume the leadership role, ensuring that at least one team member maintains an overarching awareness of the scene to optimize decision-making and patient care.

Finally, our findings indicate that EMT-led teams and those with fewer providers tended to have worse care performance. One possible explanation is that EMTs generally have less advanced training and clinical decision-making experience compared to paramedics, which may impact their ability to lead complex emergency scenarios effectively. Additionally, teams with fewer providers may experience higher cognitive and physical workload, as individual team members must manage multiple responsibilities simultaneously. This increased burden could lead to delayed assessments, overlooked visual cues, and suboptimal execution of critical care actions. These findings highlight the importance of considering team size (e.g., having at least two providers) and team structure (e.g., pairing EMTs with a paramedic or a more experienced provider) when optimizing EMS workflows to enhance team collaboration and care performance.

Insights from the Visual Attention Patterns of High-Performing Teams

A key finding of this study was the distinct visual attention patterns exhibited by high- and low-performing teams. Team leaders in high-performing teams demonstrated a balanced and frequent scanning of multiple visual cues, effectively integrating information from the patient, vital signs monitor, teammates, and medical equipment. Their visual attention was characterized by shorter fixation durations, more frequent saccades, and a higher rate of visual shifts, reflecting a dynamic and proactive approach to SA. This adaptive behavior likely enabled them to anticipate changes in patient status, coordinate efficiently with teammates, and optimize decision-making. In contrast, leaders in low-performing teams exhibited a narrowed situational focus, often fixating excessively on a single element (e.g., the patient) while neglecting other critical cues, such as vital signs monitors and teammate activities. This tunnel vision likely contributed to missed situational indicators, increasing the risk of incomplete or delayed care.

From a training perspective, these results highlight the need for SA-focused interventions that cultivate proactive scanning behaviors. Eye-tracking-based training programs could provide objective feedback by allowing trainees to review their visual patterns and gain data-driven insights into areas for improvement. By integrating these insights into training curricula, EMS providers can develop more effective SA strategies, ultimately enhancing teamwork and patient outcomes in high-pressure environments.

Study Limitations

Several limitations of this study should be noted. First, this study was conducted in a controlled simulation setting, which, while beneficial for standardizing variables and ensuring consistency, may not fully replicate the unpredictable, high-stress conditions of actual EMS work. In actual work, EMS providers must navigate environmental distractions, variable patient conditions, and unexpected interruptions, all of which may influence visual attention distribution and decision-making differently than in a controlled environment. Future research should validate these findings in real-world EMS operations to assess how situational stressors, environmental complexity, and multitasking demands affect situational awareness. Second, participants in this study were assigned to teams that may not have included their regular partners. In real-world EMS operations, team dynamics, communication efficiency, and role differentiations are often shaped by familiarity and established working relationships. The lack of pre-existing team cohesion in this study may have impacted task coordination and situational awareness. Future research should examine how team familiarity influences visual attention and decision-making in high-pressure scenarios. Third, the dataset included only 15 simulation sessions and two clinical scenarios, which may limit the generalizability of the findings. A larger sample size and a broader range of emergency scenarios would allow for a more comprehensive understanding of EMS visual attention patterns and their impact on performance. Nevertheless, we observed recurring trends across teams with similar characteristics (e.g., team size, role differentiation), suggesting that the visual behaviors identified are not isolated instances but rather reproducible patterns. While individual differences naturally exist, the overall consistency of these patterns supports the generalizability of our findings despite the study’s limited scope and sample size.

Conclusion

This study examined EMS providers’ visual attention patterns using eye-tracking technology, revealing significant differences in SA based on experience, team structure, and role differentiation. High-performing teams exhibited balanced and dynamic visual scanning, frequently shifting attention between key cues, while low-performing teams demonstrated a narrowed focus, often neglecting vital signs or team coordination. These findings emphasize the need for SA-focused training interventions that promote proactive scanning behaviors and comprehensive situational monitoring. Additionally, the results highlight the potential for technology, such as wearable devices, AI-driven decision support, and real-time data integration, to enhance SA and reduce cognitive overload in EMS practice. Building on these insights, future research should explore training and technological innovations to strengthen EMS providers’ SA and decision-making capabilities—critical non-technical skills that directly impact care performance and patient outcomes.