Introduction
Mass casualty incidents (MCIs) are sudden-onset events in which the number of casualties exceeds the capacity of the local health care system to respond with its available resources.Reference Rifino and Mahon1 These incidents can disrupt communities, resulting in loss of life, economic damage, and environmental degradation.2 One of the most critical aspects of MCIs and sudden-onset disaster response is casualty management, which seeks to minimize mortality and disability while maximizing survival through efficient resource allocation and prioritized patient transport to health care facilities.3 Effective casualty management requires a coordinated, multi-agency approach,Reference Farahani, Lotfi, Baghaian, Ruiz and Rezapour4 with Emergency Medical Services (EMS) playing a critical role by often being the first responders (FRs), declaring an MCI, and ensuring rapid resource deployment.Reference Rifino and Mahon1 In an MCI, EMS must prioritize the greatest good for the most people, moving from individual care to a system that evaluates both clinical condition and survival chances of victims through mass casualty triage. In this regard, several triage algorithms have been developedReference Farahani, Lotfi, Baghaian, Ruiz and Rezapour4, Reference Frykberg5 with the most used being Simple Triage and Rapid Treatment (START),Reference Gebhart and Pence6 Triage Sieve,Reference Hodgetts, Hanlan and Newey7 Sacco Triage Method,Reference Sacco, Navin, Fiedler, Waddell, Long and Buckman8 and Sort, Assess, Life-saving interventions, Treatment and/or Transport (SALT).Reference Lerner, Schwartz and Coule9 FRs use these algorithms to assign MCI victims to priority groups, marking them with colored triage tags.Reference Iserson and Moskop10 Traditionally, MCI codes have been assigned to patients using paper tags, bracelets,Reference Galant, Corcostegui and Marrache11 or marker pens.Reference Lerner, Schwartz, Reilly and Markenson12 However, such solutions present several limitations: they are not weather-resistant, can be damaged easily, and require over a minute to record vital signs manually, ultimately slowing down the process.Reference Sakanushi, Hieda and Shiraishi13–Reference Plischke, Wolf, Lison and Pretschner15 Additionally, the tags allow only manual updates by first responders and do not provide interactive or monitoring functions, which means they cannot capture changes in patients’ vital signs or evolving conditions that may influence casualty prioritization and transport decisions.Reference Sakanushi, Hieda and Shiraishi13–Reference Plischke, Wolf, Lison and Pretschner15 Moreover, current mass casualty triage methods lack patient tracking capabilities, which can hinder timely reassessment and prioritization, resulting in challenges in casualty management.Reference Chan, Killeen, Griswold and Lenert16, Reference Pate17
To address the aforementioned limitations of traditional patient management and the need for continuous reassessment and patient tracking during MCIs, wearable electronic devices have been developed to better support casualty management.Reference Lenert, Kirsh and Griswold18 Available e-triage tags can monitor vital signs, such as oxygen saturation (SpO2), respiratory rate (RR), and heart rate (HR), notifying the assigned priority code through colored lights, and wirelessly transmit data to a server every minute.Reference Sakanushi, Hieda and Shiraishi13, Reference Gao, Greenspan, Welsh, Juang and Alm19 Systems like Advanced Health and Disaster Aid Network (AID-N) also monitor blood pressure (BP), perform a 2-lead electrocardiogram, and track patient location using GPS.Reference Gao, Massey and Selavo20 Some e-triage tags feature algorithms for automatic prioritization based on vital signs,Reference Park21 while other systems generate survival curves to assist transport decisions.Reference Tian, Zhou, Wang, Zhang and Li22 These technologies enable continuous monitoring, real-time detection of clinical changes, and reassessment of transport priorities, offering significant potential to improve casualty management during MCIs and sudden-onset disasters.Reference Sakanushi, Hieda and Shiraishi13, Reference Chan, Killeen, Griswold and Lenert16, Reference Park21, Reference Tian, Zhou, Wang, Zhang and Li22 To successfully implement these wearable devices in MCI response, it is crucial to assess user acceptance, as it directly impacts the effectiveness of technology integration and determines how well the system is adopted and used in real-world situations. Indeed, if the technology does not align with users’ needs, expectations, and workflows, it is unlikely to be effectively adopted, regardless of its technical capabilities. As theorized by the Technology Acceptance Model (TAM), factors such as users’ behavioral intention (BI), perceived usefulness (PU), and perceived ease of use (PEU) can greatly assist in this assessment.Reference Ubaidillah, Baharuddin, Kasil and Ismail23 Despite the recent focus on the development and integration of technology into prehospital MCI response and casualty management, a critical gap exists in the literature regarding the perceptions of first responders, which might significantly impact the potential adoption and practical utility of these technologies in real-world settings. Although much of the existing literature has focused on the adoption of telemedicine for MCI management and preparedness,Reference Simmons, Murphy, Blanarovich, Workman, Rosenthal and Carbone24–Reference Litvak, Miller and Boyle26 or has examined the acceptance or feasibility of portable data collection systems,Reference Chang, Hsu, Tzeng, Hou and Sang27 disaster management systems,Reference Brar, Shah, Singh, Ali and Kwak28 and virtual reality for MCI education,Reference McCoy, Alrabah and Weichmann29, Reference Chow, Hung, Chu and Lam30 wearable technologies have received comparatively little attention. Therefore, this study seeks to address this gap by exploring the perceptions of Italian EMS professionals regarding the use of a hypothetical wearable technology designed to assist in casualty management during MCIs. By understanding these perceptions, this study aims to support the development and implementation of wearable technology in emergency medical practices, ultimately enhancing MCI management and improving patient outcomes.
Methods
Study Design and Population
This was an observational study based on a semistructured questionnaire (Supplementary Table 1) to assess the perceptions of FRs regarding wearable technology for casualty management during MCIs. The target population for this study was EMS professionals (i.e., medical doctors and nurses) involved in MCI response. Individuals who volunteer or are employed by agencies or organizations that collaborate with EMS but are not licensed health care professionals were excluded from the study. A convenience sampling method was employed to recruit participants from different EMS agencies across Italy. Given the exploratory aim of this survey, no formal calculation of the minimum sample size to ensure adequate statistical power was performed. This approach aligns with the recommendations of Memon et al., who argue that in exploratory research, the primary focus should be on collecting a sample that is representative of the target population, rather than strictly adhering to predetermined sample size thresholds.Reference Memon, Ting, Cheah, Thurasamy, Chuah and Cham31
The questionnaire consisted of 2 sections. Section A included nine demographic questions, including age, gender, work experience, and educational background related to MCI management. Section B began with a paragraph outlining the features of the hypothetical wearable device, ensuring all participants had a consistent understanding of its functionalities (Supplementary Table 2). After reviewing existing technologies, the selected features were deemed relevant to casualty management during MCI and sudden-onset disaster response, including treatment, transport priorities, communication, and tracking:
-
a) Patient identification: each wearable device is assigned a unique identifier (e.g., barcode system) for individual tracking;
-
b) Vital signs monitoring: the wearable device can measure and monitor vital signs (BP, HR, RR, SpO2);
-
c) Triage tag: the triage code is assigned by EMS professionals and displayed via a colored LED light; the triage code can be updated at any time following reassessment;
-
d) Geolocation: an integrated geolocation system (e.g., GPS, Bluetooth tracker, etc.) enables continuous tracking of casualty locations;
-
e) Data collection server: using a wireless network, the device transmits collected data (see other functionalities described above) in real time to a server accessible through an application for mobile phones or other devices (e.g., tablets or computers). The identification code, triage code, vital parameters, and geolocation are then displayed for each victim and can be visualized by EMS professionals.Reference Sakanushi, Hieda and Shiraishi13, Reference Killeen, Chan, Buono, Griswold and Lenert14, Reference Pate17, Reference Gao, Massey and Selavo20–Reference Tian, Zhou, Wang, Zhang and Li22
The second part of the questionnaire, section B, included twenty-four questions based on the TAM framework (Supplementary Table 1). Fourteen questions explored the PU, defined as the individual’s perception that using the IT system will enhance job performance. Six questions examined the PEU, which refers to the perception that using the system will require minimal effort. Four questions investigated the BI, reflecting the individual’s motivation to engage in the target behavior. The questionnaire, provided in Italian, utilized a 7-point linear numeric scale, with responses ranging from “strongly disagree” (1 point) to “strongly agree” (7 points). Prior to national distribution, we conducted a pilot test to establish face and content validity. The draft survey was emailed to EMS physicians and nurses from Azienda Sanitaria Locale Cuneo 1 (Cuneo, Piedmont, Italy), recruited using the same inclusion and exclusion criteria as the main survey (currently employed EMS professionals, excluding students and personnel not directly involved in prehospital emergency care). Participants provided qualitative feedback, which led to the revision of one item in Section A for clarity.
Data Collection and Statistical Analysis
The questionnaire was distributed to all 67 Italian EMS Dispatch Centres by contacting EMS chiefs or secretariats across each Italian region via email, requesting them to forward it to the nurses and physicians working in their respective services. The survey was administered electronically through Google Forms in September 2024, with a 30-day period for participants to complete it. Due to platform limitations, it was not possible to ensure that each participant submitted only one response, as Google Forms does not provide a mechanism to simultaneously preserve respondent anonymity and restrict multiple entries. Descriptive statistics were used to summarize participants’ demographic characteristics, EMS work experience, and education and training in MCI management. Section B of the questionnaire, based on the TAM constructs, was analyzed using descriptive statistics. Principal Component Analysis (PCA) was conducted on the PU, PEU, and BI areas to summarize the data into key components that capture the main trends in participants’ perceptions. The results are presented by evaluating the contribution of each variable to these components, categorized as high, moderate, or low. In this context, “contribution” refers to how much each variable (survey question) influences the overall variation in participants’ responses, reflecting the diversity of opinions in how the features were rated. Additionally, inverse associations were used to identify situations where an increase in the rating of one variable corresponded to a decrease in the rating of another (e.g., when 2 features were perceived as opposing or mutually exclusive in some way).
Ethical Considerations
The study was conducted in accordance with the principles outlined in the Declaration of Helsinki and received approval from the Ethics Committee of the “Azienda Ospedaliero-Universitaria Maggiore della Carità” in Novara (protocol number 686/CE). Participation in the survey was voluntary and anonymous, with informed consent obtained from all participants before completing the survey. Data was collected anonymously through Google Forms, which did not record email addresses or any user identifiers. Responses were accessible only to the study authors and not shared with third parties, thereby ensuring data security and confidentiality. Additionally, participants were free to skip any questions they chose, as responses were not mandatory.
Results
The participants’ characteristics are detailed in Table 1. A total of 141 individuals completed the questionnaire, of which 85 (60.3%) were male. The median age of the participants was 45 years (range 25-66). Professionally, the majority of respondents were nurses (66.7%), with 63.1% having over ten years of experience in EMS. Additionally, 71.6% had worked in an EMS dispatch center, and 51.8% had been involved in MCI responses. Regarding MCI education, 87.9% of participants had attended MCI-related classes, 49% had attended university lectures on MCI, and 77.3% had participated in MCI training sessions. Figure 1 provides a graphical representation of the 7-point linear numeric scale responses to the questions in Section B. Since participation in the questions was voluntary, there are missing responses across the three areas. In the first area (PU), approximately 80% of participants assigned high scores (6-7) to the patient identification (A2) and geolocation (A7, A8) features. Items related to the data collection server (A9, A10) received high scores from 72.4% and 68.8% of participants, respectively. The ability to continuously monitor vital signs (A3-A5) garnered high scores from around 60% of participants, while the LED light feature for triage tags (A6) received high scores from 57.5% of respondents. Notably, about 15.6% of participants gave low scores (1-3) to question A4, which assessed whether vital signs monitoring capability of the wearable would expedite patient triage during MCI response. Questions concerning the device as a whole (A11-A14) received high scores from 52.5% to 62.4% of participants. In the second area (PEU), approximately 63% of participants agreed that the wearable’s features would be easy to learn (B1) and that they would be able to master them quickly (B3). Data provided by the device was considered easy to interpret by 56.1% respondents, while 33.3% of them gave medium scores (4-5) to this item (B4). Around 33.3% of participants agreed that minimal effort is required to use the device during the MCI response (B2) and 37.6% agreed that the device could easily integrate into current workflows (B5). Nonetheless, these latter statements received low scores from approximately 20% of respondents. While 47.5% of respondents disagreed that using the device could slow down life-saving procedures (B6), 22% agreed with this statement. In the third area (BI), around 37% of the respondents agreed on the intention to use the wearable technology if available during MCIs response (C1), 40% would recommend its use to colleagues (C2), and around 43% would use all the functions of the wearable technology in future MCI responses. Results showed an excellent internal reliability of the overall questionnaire (α = 0.96), for the PU (α = 0.95), and for the BI (α = 0.91). PEU section internal reliability was acceptable (α = 0.76).
Demographic characteristics of participants
EMS: Emergency Medical Services; MCI: mass casualty incidents.
Section B responses. BI: Behavioral Intention; PEU: Perceived Ease of Use; PU: Perceived Usefulness. A1-A14: responses to PU questions; B1-B6: responses to PEU questions; BI: responses to BI questions. NA: not applicable, 1-7: numerical scale scores for responses.
Principal Component Analysis
As shown in Figure 2, the PCA of the first area (PU) reveals that the variables associated with data collection server function (A9, A10) and with scene awareness and coordination (A11, A12) had the greatest contribution to the overall variation in participants’ perceptions of the system’s usefulness. In addition, the capability to monitor vital signs, the related improved triage accuracy, and speed (A3, A4, A5), the improved patient care and resource allocation (A13, A14) are variables that gave an intermediate contribution to the PU. Moreover, this PCA suggests a potential inverse association between A3, A4, A5 and A9, A10, A11, A12, indicating that respondents who found scene awareness and coordination more useful perceived the ability to monitor vital signs, along with the associated improvements in triage accuracy and speed, as less useful. Finally, variables related to patient identification (A1, A2) and LED light (A6) contributed to the domain to a lesser extent. The geolocation function (A7, A8) also made a limited contribution.
Principal component analysis of the Perceived Usefulness domain.
As illustrated in Figure 3, the variables associated with ease of learning and rapid time to become proficient in the use of the wearable device (B1, B3) were the most significant for the PEU domain. As demonstrated by the descriptive analysis, the variability of B6 is also reflected in a higher contribution to dimension 2 of the second domain. Indeed, the participants did not express a consensus that the use of the device could slow down life-saving procedures. Finally, the undemanding use of the device (B2) and its easy integration into existing workflows (B5) made an intermediate contribution to the PEU, while the interpretation of the data provided by the device (B4) contributed minimally.
Principal component analysis of the Perceived Ease of Use domain.
The PCA of the BI is shown in Figure 4. The active seeking of opportunities to use the device in MCI response (C3) exhibited a strong contribution to the third area, while the intention to use it in future MCI responses (C1) had an intermediate one. Conversely, the recommendation to use the device (C2) and its comprehensive use in future MCI responses (C4) appeared to exert minimal influence on the dimensions of the third domain.
Principal component analysis of the Behavioral Intention domain.
Limitations
This study has some limitations that should be considered when interpreting the findings. First, the use of Google Forms did not allow us to ensure that each participant submitted only one response, as guaranteeing single entries would have required identity tracking, which would have compromised respondent anonymity. Second, the voluntary nature of the questionnaire responses, combined with lower completion rates for certain questions, introduces the potential for selection bias. This may affect the generalizability of the results, as the sample may not fully represent the broader population of EMS professionals. Third, the study population was limited to experienced and regularly employed EMS physicians and nurses, which may restrict transferability of findings to less-experienced or adjunct providers. Additionally, the device explored in this study is hypothetical, and participants were provided with only descriptive information about its characteristics for use in MCIs. As a result, the study may have only uncovered a portion of the potential challenges associated with its real-world application, as participants did not have hands-on experience with the device. Finally, while the PEU section of the questionnaire demonstrated acceptable reliability, its internal consistency could be improved. This suggests that further refinement of this section is necessary to ensure more robust and reliable measurements in future studies.
Discussion
Technological advancements have become a critical asset in health care, with an increasing number of devices developed in recent decades to enhance the quality of patient data available MCIs.Reference Lenert, Kirsh and Griswold18 Among these, wearable devices designed to support MCI triage have garnered considerable attention in the last years.Reference Sakanushi, Hieda and Shiraishi13, Reference Killeen, Chan, Buono, Griswold and Lenert14, Reference Pate17, Reference Gao, Massey and Selavo20–Reference Tian, Zhou, Wang, Zhang and Li22 In this evolving landscape, where the integration of technology is expected to enhance the efficiency of FRs without disrupting established protocols, our findings should be interpreted as informing and supporting the development of future technologies rather than reflecting responses to an existing device. Understanding how these technologies are perceived by their potential users is fundamental to assess the feasibility of incorporating new technological functions into prehospital MCI response procedures. Thus, the aim of this study was to investigate the views of Italian EMS professionals on the use of a hypothetical wearable device intended to assist in casualty management during MCIs. The device under consideration incorporated a series of functionalities—such as unique patient identification, patient geolocation, continuous monitoring of vital signs, triage tag allocation, and data collection—deemed as important for supporting FRs during prehospital MCI management and addressing gaps identified in the literature.Reference Killeen, Chan, Buono, Griswold and Lenert14, Reference Gross, Coughlin, Cone, Bogucki, Auerbach and Cicero32, Reference Režek and Žvanut33
The strong endorsement of features like data collection server and scene awareness suggests that Italian EMS regard data management and real-time coordination as fundamental to enhancing MCI response. This was further supported by the PCA analysis, which highlighted the data collection server and scene awareness as the primary contributors to the variability in perceived usefulness. The emphasis on these functionalities reflects Italian EMS personnel’s preference for tools that improve situational awareness and facilitate smooth communication and data flow during MCIs. These findings are consistent with what was already stated by O’Brien and colleagues, which emphasizes that establishing and sharing situational awareness is a significant challenge in prehospital MCI response.Reference O’Brien, Read and Salmon34 Indeed, a lack of situational awareness is frequently recognized as a major factor undermining response effectiveness and decision-making during MCIs and sudden onset disasters, largely driven by poor information exchange.Reference O’Brien, Read and Salmon34 As such, prioritizing these features in the design of future devices could enhance their adoption and effectiveness in real-world emergency scenarios. The data collection capabilities could enhance decision-making during response phases while also facilitating post-incident analysis and the implementation of corrective measures. Moreover, they would help establish robust prehospital databases for MCIs, addressing a current gap that significantly limits data-driven analysis.Reference Caviglia and Argyri35 These databases could also play a crucial role in training artificial intelligence algorithms designed to optimize prehospital MCI response, as their development relies on access to large, high-quality datasets.Reference Caviglia and Argyri35
Findings of this study suggest that while the ability to continuously monitor vital signs is generally valued, its perceived usefulness in supporting the triage process varies among respondents. While the majority rated this feature positively, a notable portion expressed skepticism about its direct impact on expediting triage. These variations in perception highlight the need to consider user expectations and operational constraints when integrating monitoring technologies into prehospital care. The PCA further supports these observations. Additionally, current MCI triage algorithmsReference Gebhart and Pence6–Reference Lerner, Schwartz and Coule9 typically rely on a limited set of vital signs, and first responders may not know how to interpret or prioritize the additional data provided by new monitoring technologies. This suggests that the introduction of such technologies must be accompanied by updated guidelines and protocols to ensure their effective use. The reliability of these vital signs also plays a crucial role in their perceived value, as inconsistent or inaccurate readings could further undermine their utility in high-stakes, fast-paced environments.Reference Gao, Greenspan, Welsh, Juang and Alm19 Interestingly, an inverse relationship emerged between the PU of scene awareness and vital signs monitoring, suggesting a divergence in priorities among respondents. This may reflect a tendency to prioritize situational awareness over continuous monitoring, particularly in dynamic and high-stress MCI environments where broader scene management takes precedence over individual patient data, which might be seen as not immediately useful or as a distraction. The importance of vital signs monitoring during MCIs has been highlighted numerous times in the literature, particularly regarding its usefulness for patients who have been triaged and are awaiting ambulance transport.Reference Gao, Greenspan, Welsh, Juang and Alm19, Reference Hogan and Brown36 This monitoring facilitates casualty management by tracking patient status, generating alerts, and enabling patient record review. Therefore, while the findings of this study align with existing literature in suggesting that continuous vital signs monitoring may not necessarily expedite or enhance triage,Reference Cuthbertson, Weinstein and Franc37 it nevertheless highlights how EMS professionals perceive potential benefits and limitations, providing guidance for developers on where such a feature may best support casualty management.
Despite the recognized benefitsReference Ziegler, Koenig and Schultz38 of patient identification, geolocation, and triage code displaying features were perceived as less useful than those supporting real-time data collection and scene coordination. Respondents appeared to prioritize tools that directly facilitate decision-making and resource allocation, with PCA results further indicating a limited contribution of tracking features to overall system utility. However, previous research highlights their potential value, not only in improving on-scene incident managementReference Chan, Killeen, Griswold and Lenert16 but also in enhancing situational awareness for emergency department staff during MCIs.Reference Gross, Coughlin, Cone, Bogucki, Auerbach and Cicero32
The majority of respondents positively rated the perceived ease of learning and rapid mastery of the device, emphasizing the necessity for intuitive interfaces that facilitate swift adoption in high-pressure environments. The significant contribution of these factors to the PEU domain further underscores their importance in ensuring operational effectiveness. In emergency scenarios where time and cognitive load are limited, minimizing the learning curve could enhance responders’ ability to integrate new technologies seamlessly, ultimately improving overall response efficiency.Reference D’Andrea, Grifoni and Ferri39 However, concerns regarding workflow integration and potential disruptions to life-saving procedures present challenges to adoption. While the device is recognized as valuable, a considerable proportion of respondents expressed reservations about its impact on operational efficiency. The intermediate contribution of these concerns to the PEU domain, along with the variability in perceptions, suggests that apprehensions may derive from the device’s compatibility with existing protocols. Previous research has highlighted the complexity of integrating new technologies with existing systems, emphasizing the challenges related to data interoperability.Reference Režek and Žvanut33 Thus, to facilitate smoother integration of technology in MCI response, addressing these concerns through refined design and improved alignment with established MCI workflows will be essential.
The findings of this study suggest a generally positive outlook on the potential utility of the wearable technology for future MCI responses. Participants expressed a strong intention to use the device and recommend it to colleagues, indicating that they perceive it as a valuable tool for improving emergency medical response. The PCA analysis further supports this, with respondents showing a significant willingness to actively seek opportunities to integrate the device into their practice. Nonetheless, these results reflect the perceptions of experienced and regularly employed EMS professionals, and caution is warranted when extrapolating to less experienced adjunct providers whose perspectives may differ. As the study was based on a hypothetical device, the findings should be seen as informing and supporting the design of future physical technologies, rather than as evidence of user responses to an existing system.
Conclusions
This study underscores the potential value of a hypothetical wearable technology device in enhancing casualty management during MCIs, with Italian EMS personnel viewing it favorably, particularly for improving situational awareness and coordination. As the device was presented only in descriptive form, findings should be interpreted as informing and supporting the design of future technologies rather than reflecting responses to an existing system. Further research is required to validate these perceptions and to assess potential challenges in real-world application, such as possible delays in patient management or disruptions to established EMS workflows.
In a broader context, successful adoption of similar technologies in MCI response will depend on several key factors including ease of use, intuitive design, clear data interpretation, and seamless compatibility with existing protocols.
Supplementary material
The supplementary material for this article can be found at http://doi.org/10.1017/dmp.2025.10256.
Acknowledgments
This manuscript is the result of a study conducted in the framework of the Advanced Master of Science in Disaster Medicine (EMDM—European Master in Disaster Medicine), jointly organized by CRIMEDIM (Center for Research and Training in Disaster Medicine, Humanitarian Aid and Global Health of the Università del Piemonte Orientale [UPO]) and REGEDIM (Research Group on Emergency and Disaster Medicine of the Vrije Universiteit Brussel [VUB]).
Funding statement
The authors did not receive support from any organization for the submitted work.
Competing interests
The authors declare no competing interests.
Ethics standard
This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of “Azienda Ospedaliero-Universitaria Maggiore della Carità” of Novara (protocol number 686/CE).
Open access
