Improving Preparedness for Mass Casualty Incidents in Hospitals: Insights from a Large-Scale Simulation Exercise with Geotracking and Validated Questionnaires

Improving Preparedness for Mass Casualty Incidents in Hospitals: Insights from a Large-Scale Simulation Exercise with Geotracking and Validated Questionnaires | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Improving Preparedness for Mass Casualty Incidents in Hospitals: Insights from a Large-Scale Simulation Exercise with Geotracking and Validated Questionnaires Maik von der Forst, Hanne Schäfer, Stefan Mohr, Hannes Goetz Kenngott, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7859560/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Mass casualty incidents (MCIs) rapidly exceed routine hospital capacity. Full-scale exercises are essential for preparedness, but systematic, multidimensional evaluations remain scarce. This study aimed to evaluate overall team performance in triage accuracy, workflow, and individual workload throughout a MCI exercise. Methods: In a prospective observational study at Heidelberg university hospital (Germany), healthcare professionals managed 91 simulated casualties using a two-stage triage process. Patients and staff carried location tags enabling continuous spatiotemporal tracking. Objective outcomes included triage accuracy, triage duration, patient flow, and staff–patient contact frequency. Subjective workload and teamwork were assessed using the NASA Task Load Index (NASA-TLX) and the Team Emergency Assessment Measure (TEAM), respectively. Results: Overall triage accuracy was 75.4%. Undertriage occurred in 11.6% of category I and 10.1% of category II cases; overtriage was infrequent (2.9%). Mean triage times differed significantly by category: ‘red’ 59 ± 25 s, ‘yellow’ 173 ± 74 s, ‘green’ 205 ± 100 s (p < 0.0001). Geotracking demonstrated consistent patient flow without detectable bottlenecks and a mean of 7.1 ± 5.7 patient contacts per staff member. NASA-TLX scores indicated high temporal demand but low frustration with an overall workload of 66.7 ± 16; specialists and staff with greater professional experience reported significantly lower workload (p < 0.05). TEAM ratings were homogeneously high across all participants (79.8%). Conclusions: This study provides reproducible benchmark data on hospital MCI response. The integration of geotracking with subjective measures, enables a comprehensive evaluation of hospital disaster preparedness. Moreover, the ability to compare different exercises and collect reliable longitudinal data may further enhance hospital disaster response. Mass Casualty Incident Disaster Medicine Hospital Disaster Planning Critical Infrastructure Leadership Communication Incident Command Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Mass casualty incidents (MCIs) challenge healthcare systems.( 1 , 2 ) A sudden influx of numerous injured patients can quickly exceed normal operational capacities of hospitals. Past events suggest that regular practice leads to improved patient care in real-life situations.( 3 – 7 ) Exercises designed to train for MCIs have been carried out for many decades.( 8 , 9 ) Full-scale simulation exercises are recognized as a vital component in enhancing hospital preparedness for such events.( 2 ) Such exercises allow healthcare professionals to practice and refine their response protocols in a controlled, yet realistic environment. Thereby potential weaknesses can be identified and addressed before an actual event occurs.( 10 ) Evidence highlights the value of simulation exercises in improving various aspects of hospital preparedness, including the coordination and decision-making abilities of medical teams under stress, as well as the overall effectiveness of emergency response protocols.( 11 , 12 ) Previous research has shown that regular simulation training can enhance the situational awareness and communication skills of healthcare providers, leading to more efficient and effective patient care during emergencies.( 13 ) It seems imperative to maximize the output from each simulation exercise. Structured evaluation and collection of metric data during MCI training allows for the generation of evidence that can inform long-term improvements in emergency response strategies and healthcare system resilience.( 2 , 14 ) However, the organization and execution of full-scale MCI simulations are fraught with difficulties.( 1 ) Organizational, budgetary, workload and resource constraints cause infrequent execution.( 1 , 15 , 16 ) Therefore, there is a notable lack of comprehensive studies that integrate both objective and subjective data to provide a holistic view of hospital performance during MCI simulations.( 2 ) To address this gap, this study conducted a full-scale MCI simulation designed to obtain a multidimensional evaluation of the operational processes. By integrating objective and subjective assessment methods, the present study aimed to achieve a comprehensive analysis of interdisciplinary treatment teams' performance during MCIs. Methods Study design This is a prospective observational study approved by the ethics committee of the medical faculty of the University of Heidelberg (S-556/2023) and conducted in accordance with the current version of the Declaration of Helsinki. Patients and/or the public were not involved in the design, conduct, reporting or dissemination plans of this research. Employees of Heidelberg University Hospital that participated in the hospital MCI simulation exercise represent the study population. After written informed consent to participate in the study was provided, each participant was registered anonymously using a unique code (Fig. 1 B). In total 75 participants were additionally equipped with a location tag, that was registered under the same code. Immediately after finishing the simulation exercise each participant was asked to answer two standardized questionnaires as well as complete demographic and baseline data registered to the unique code. Data from all modalities were merged using the unique code, which was immediately deleted afterwards to prohibit de-anonymization. Participants received no additional training prior to the MCI simulation exercise and were aware only on the date of the full-scale exercise. Missing data were not imputed. STROBE criteria were adhered to in design, conduct, and reporting of this observational study.( 17 ) Full-scale MCI simulation exercise The exercise was conducted at Heidelberg university hospital (Germany) and involved 91 simulated casualties. The triage at the hospital was performed in a two-stage process. At a first stage patients were classified at “first look”, without further algorithm or diagnostic tools by a triage nurse and separated into two groups Immediate life-threatening condition or serious injuries (‘red’ and ‘yellow’) minor injuries (‘green’). This first triage point was set up at the driveway to the trauma center’s emergency department. The scope was directing vehicles with category I and II (‘red’ and ‘yellow’) patients to the emergency department entry and vehicles with category III (‘green’) patients to the ground level entrance. At a second stage two identically equipped triage points were set up at each respective entrance (Fig. 1 C) and a more sophisticated triage was performed by a tandem of general/trauma surgeon and anesthesiologist. A proprietary triage algorithm was used according to the hospital`s alarm and operation plan, classifying the patients in the known categories: Immediate life-threatening condition (category I, ‘red’), serious injuries, but no immediate needs (category II, ‘yellow’) or minor injuries such as abrasions and smaller lacerations (category III, ‘green’) (Supplemental Fig. 1). After triage, patients were assigned to treatment teams of at least three healthcare professionals (including at least one physician). The teams were formed spontaneously upon arrival and registration at the trauma center. The presentation of injuries followed pre-defined case vignettes (Fig. 1 D). The teams performed initial treatment or ordered further diagnostic measures (ultrasound, x-ray, computed tomography) or emergency surgery according to their knowledge and skills. The simulation was stopped before transfer to a respective ward or after arrival at an assigned operating theatre. Once the treatment simulation for one patient was completed, the teams were ready for their next case assignment. Real-time location system (RTLS) Locators and tags were provided by Favendo GmbH (Bamberg, Germany). Triage areas as well as category I and category II treatment areas and transport areas (Fig. 1 A) were equipped with locators. Participants and patients were equipped with tags (Fig. 1 B). Based on RTLS, triage time was measured and defined as the time from arrival at the second triage area until transport out of the triage area. Further, triage accuracy including analysis of potential over- und undertriage was analyzed. Data preparation was performed using a customized python script (Python Software Foundation, Python Language Reference, Version 3.10.). For visualization, the quupa player (Quuppa Data Player, QDP) was used for each respective floor and respective tags were annotated with regard to the pre-defined triage category (‘red’/’yellow’/’green’) or respective professional group of the study participants. A video illustrating real-time tracking of the emergency department level (Category I and Category II patients) is provided in the supplement to this manuscript (Supplemental Video 1). The spatiotemporal resolution rate corresponded to a sampling process every 500 ms. Patient contact was defined as the proximity of an employee tracker and a patient tracker of < 1 m for a period of at least 1 second. Demographic and baseline data : Participants were asked to provide the following data linked to their unique code: age (in years), gender (‘male’/’female’/’diverse’), professional group (‘doctor’/’nurse’/’nurse trainee’/’other’), professional experience (in years). Doctors were further asked whether they have completed their respective specialist training. National Aeronautics and Space Administration-Task Load Index (NASA-TLX) The NASA-TLX was completed immediately after the exercise. It is a subjective, multidimensional assessment tool for recording perceived workload with six subscales (mental demands, physical demands, time demands, personal performance, effort and frustration), each of which is rated on a scale of 1 to 20, high numbers indicating a high workload. The NASA-TLX is an established tool to measure perceived workload when performing a task. ( 18 – 20 ) Team Emergency Assessment Measure (TEAM) scale : The TEAM comprises 11 items rated on a five-point Likert scale, ranging from ‘0’ (Never/hardly ever) to ‘4’ (Always/nearly always). These items assess three key dimensions: ‘Leadership’ (2 items), ‘Teamwork’ (7 items), which includes aspects of situational awareness, and ‘Task Management’ (2 items). The TEAM total score was calculated as a sum of the 11 eleven section items with a maximum score of 44 as reported previously.( 21 ) Additionally, a 12th item provides a global performance rating on a scale from 1 to 10.( 22 ) The German version of TEAM was used for this study.( 23 ) Traditionally, rating is performed for the whole team by one observer.( 21 , 22 ) Here, the TEAM rating was performed immediately after the exercise by the participants themselves. Statistical analysis Data analysis and statistical testing was done using GraphPad Prism version 10.6.0 for Windows (GraphPad Software, San Diego, California, USA). Missing data was considered missing completely at random and no imputation was performed. Normality of the data was assessed using both the Shapiro–Wilk test and the Kolmogorov–Smirnov test with Lilliefors adaptation. Data that followed normal distribution were tested for statistically significant differences using the two-tailed t-test for comparison of two groups and the one-way ANOVA for comparisons involving three and more groups. An F-Test was conducted and if necessary, Welch’s correction was applied. Data that did not follow normal distribution were tested using the non-parametric two-tailed Mann-Whitney test for comparisons of two groups and the non-parametric Kruskal-Wallis test for comparisons of three and more groups. A p-value < 0.05 was considered statistically significant. Effect sizes were calculated using Hedge’s g, which provides a bias-corrected standardized mean difference suitable for small sample sizes. 95%-confidence intervals (95% CI) for Hedge’s g were computed to provide an estimate of the precision of effect size. Results Table 1 shows the characteristics of the exercise participants broken down by occupational group. Table 1 Demographics of MCI Exercise Participants Questionnaire-based data acquisition Professional groups n Percentage [%] Medical staff 46 46.9 Resident 27 58.7 Specialist 19 41.3 Nursing staff 41 41.8 Nursing trainees 6 6.1 Others 5 5.1 Gender n (female) ratio [%] All participants * 52 53.1 Medical staff 16 34.8 Nursing staff 30 73.2 Nursing trainees 4 66.7 Others 2 40.0 Age [y] mean SD median range All participants * 35.7 10.5 32.0 20–66 Medical staff 34.4 6.1 33.0 26–49 Nursing staff 37.5 12.2 31.0 21–66 Nursing trainees 22.0 2.2 21.0 20–25 Others 51.0 10.4 52.0 32–61 Professional experience [y] mean SD median range All participants ** 10.9 11.0 5.0 0–44 Medical staff 7.2 6.0 5.0 0–22 Nursing staff 14.9 13.3 9.0 0.5–44 Nursing trainees 2.3 1.1 3.0 0–3 Others 24.8 10.5 29.0 7–34 * completeness of response: 98.0% ** completeness of response: 97.0% The average age of the participants was 35.7 years and was similar across medical and nursing staff (Mann-Whitney test, not significant). Nevertheless, nursing trainees were significantly younger than nursing staff (excl. trainees) (p < 0.0001) and residents younger than specialists (p < 0.0001). Overall, the nursing staff had a median of 7 years of professional experience and the medical staff a median of 5 years, this difference was not significant (Table 1 ). A similar number of medical staff were recruited as nursing staff. It is striking that although 53.1% of participants were female, the majority of these were from the nursing profession (73.3% female). Triage accuracy and duration Table 2 Results of Patient Triage and Patient Contacts per Participant Triage Triage accuracy n = 69 ratio [%] Triage point 1, "first look" 53 76.8 Triage point 2, "algorithm-based" 52 75.4 Undertriage 15 21.7 Missed cat. I, 'red' 8 11.6 Missed cat. II, 'yellow' 7 10.1 Overtriage 2 2.9 False cat. I, 'red' 2 2.9 Number of patient contacts per person n mean SD All tracked staff 75 7.1 5.7 Tracked medical staff 41 7.9 6.1 Tracked nursing staff 28 6.5 5.6 Tracked nursing trainees 4 6.3 1.3 Patient contacts: Mann-Whitney test P Medical staff vs. nursing staff (incl. trainees) 0.1310 ns Resident vs. specialist 0.9841 ns Nursing staff vs. trainees 0.2800 ns Female vs. male 0.7346 ns Young vs. old 0.6902 ns Professional experience 0.2997 ns Real-time location tracking of 69 patients was achieved. “First look” triage, led by two emergency department nurses, was conducted at triage point 1 in front of the hospital. The accuracy according to predefined case vignettes was 76.8%. An “algorithm based” second triage at triage point 2 (Fig. 1 C) was led by physicians with an overall triage accuracy of 75.4%. At step two 15 patients (21.7%) were undertriaged (eight ‘red’ vignettes triaged as ‘yellow’, seven ‘yellow’ vignettes triaged as ‘green’; Table 2 ). The ‘red’ vignettes triaged as yellow show no specific commonality, two patients had a leading abdominal trauma, four serious burn injuries, two major amputations with hemorrhagic shock and one a thorax trauma with respiratory distress. Instead, going into further detail for undertriaged yellow vignettes reveals that 85.7% of patients falsely triaged as ‘green’ were blast trauma patients, which had deafness and bleeding from the ear as major symptoms. In total, only 2 patients (2.4%) were overtriaged (two ‘yellow’ vignettes triaged as ‘red’; Table 2 ). Altogether n = 536 patient contacts were logged during the exercise. A comparison of the number of patient contacts between the different exercise participants revealed a homogeneous distribution. Nurses had approximately 6.5 patient contacts, while physicians had approximately 7.9 patient contacts; however, this difference was not significant. On average, participants had 7.1 patient contacts. Furthermore, there were no significant differences in patient contacts within the professional groups in terms of age, gender, or professional experience (Table 2 ). The mean triage time was 159 ± 98s overall, analysis based on the actual triage category assignment revealed a mean triage time of 59 ± 25s for patients triaged as ‘red’, 173 ± 74s for patients triaged as ‘yellow’ and 205 ± 100s for patients triaged as ‘green’. The triage of ‘red’ patients was therefore significantly faster (p < 0.0001) compared to both other categories (Fig. 2 ). Workload and Team Performance Perception The NASA-TLX had an overall high response rate of 99%. The emergency medical teams consisted of different professional groups and usually contained at least one nurse and one physician, further also nursing trainees were part of the teams (Supplemental Table 1). Regarding all participants, the items’ performance (13.9) and temporal demand (13.0) in the mean were rated highest, while physical demand (9.2) and frustration (7.2) where perceived less challenging (Supplemental Table 1). The individual items of the NASA-TLX show that medical staff overall reported significantly larger “effort” than nursing staff (p < 0.05; g Hedge = 0.50, [0.072–0.931]). Participants with less professional experience also reported significantly greater “effort” (p < 0.05; g Hedge = 0.38, [-0.02-0.79]), higher “physical demand” (p < 0.05, g Hedge = 0.41, [0.01–0.82]), and even higher “frustration” (p < 0.01, g Hedge = 0.64, [0.29–1.05]) (Supplemental Table 1). Although “ frustration ” was rated relatively low overall as an item, this is consistent with the fact that “frustration” was higher among younger participants and resident physicians than among specialists. The same applies to nursing trainees, although the difference was not significant (p = 0.05; Supplemental Table 1). A comparison of the overall workload shows that the perceived level is very similar across the different occupational groups. Nursing trainees appear to feel a higher overall workload (75.2 ± 16.9), although this result is not significant with n = 6. Consistent with this, among physicians, specialist status is associated with a significantly lower overall workload compared to the residents (63.7 ± 13.0 vs. 71.1 ± 11.0, p < 0.05, g Hedge = 0.62, [0.02–1.22]; Fig. 3 A). A similar result is seen when professional experience is considered independently of the occupational group, with the more experienced half of the participants also perceiving a significantly lower overall workload (63.8 ± 14.9 vs. 70.3 ± 16.1, p < 0.05, g Hedge = 0.42, [0.01–0.82]; Fig. 3 B). Gender and age, on the other hand, had no significant influence on the perceived overall workload (Fig. 3 B). The results of the TEAM questionnaire show a relatively homogeneous picture in the assessment by the members of the individual professional groups. Regarding all participants the lowest scores were given to the two items relating to the team leader (2.8 ± 1.2 and 2.8 ± 1.3), while all other items were rated at an average of more than 3 out of 4 points. The items “ Team morale was positive” (3.6 ± 0.7) and “ Team worked together to complete the tasks in a timely manner” scored highest overall (3.5 ± 0.7; Fig. 4 , Supplemental Table 2). Discussion Main results of the present study show that overall triage accuracy was 75.4%, with category I patients being seen significantly faster. Geotracking revealed a consistent flow of patients without any bottlenecks, with an average of 7.1 ± 5.7 patient contacts per employee, and high TEAM ratings of 79.8%, despite an overall high workload as measured by the NASA-TLX (66.7 ± 16). In context of pre-hospital MCI responses key performance indicators for evaluation have already been investigated.( 24 ) However, there is no such definition for hospital MCI responses yet. There is limited evidence regarding effectiveness of MCI training for hospital staff. Several studies indicate that disaster drills can help train hospital staff effectively.( 25 – 27 ) However, there is a need for scientific evaluation metrics of such training activities to better assess their true impact.( 14 ) This study provides objectively measured benchmark data to enable evidence-based hospital emergency planning. The present study demonstrated that, in addition to the conventional observer-centered qualitative evaluation of MCI exercises, a methodology based on standardizable methods is also feasible. Different studies conducted in both preclinical and clinical settings have previously demonstrated the feasibility of GPS data loggers in accurately capturing patient trajectory data. These studies have been conducted in the context of an MCI simulation, and their findings have consistently shown that the use of GPS data loggers does not compromise the realism of the simulation.( 28 , 29 ) Conversely, the incorporation of additional uninvolved persons could potentially serve to enhance the realism of the exercise.( 28 ) Triage Previous studies considered duration and accuracy for evaluation of triage systems.( 30 ) Hospital-based MCI triage has a different approach with patient flow being a major concern. Triage, even when carried out by experienced trauma physicians, can be unreliable in an MCI.( 31 ) Based on a full-scale simulation exercise, to our knowledge, this was the first time that the evaluation of screening quality and duration could be conducted entirely on the basis of data ascertained via geolocators, as evidenced by a full-scale simulation exercise. The screening duration and the accuracy of the process, including over- and under-triage, were both determined based on the expected screening category and the actual assignment to a range. In this MCI simulation exercise the proprietary triage algorithm of Heidelberg University Hospital was applied (Supplemental Fig. 1). In the literature, a hospital triage accuracy of 63.3% for single triage points and 70.0% for two-stage triage points has been reported.( 30 ) In this study, a two-stage triage resulted in a triage accuracy of 75.4%. category I (‘red’) patients could be identified significantly faster than other triage categories. The total triage time of 59 (± 25 SD) s for patients classified as ‘red’, 173 (± 74 SD) s for patients classified as ‘yellow’, and 205 (± 100 SD) s for patients classified as ‘green’ is comparable to data from the literature. Carles et al. reported that mean triage time was 147 (± 105 SD) s in a real MCI response toward the terrorist attack in Nice in 2016.( 32 ) The reported data are based on 25 category I (‘red’) and 44 category II (‘yellow’) patients.( 32 ) However, a significant proportion of undertriage was also observed (11.6% in category I). In comparison with the publication on the validation of the Berlin triage algorithm, on which the algorithm used is largely based, slightly better triage results were achieved.( 33 ) The use of geolocators made it possible to identify falsely triaged case vignette. For instance, there was a recurrent under-triaging of case vignettes involving blast trauma with blood flow from the ear (n = 6 out of 7), such that this could be explicitly trained after the exercise and incorporated into the algorithm. It is important to consider other potential factors that may contribute to the observed phenomenon, including but not limited to spontaneous deviations from the established algorithm or the performance of the patient actors. This assumption is reinforced by the finding that the Berlin triage algorithm delivered superior results in a study with standardized case vignettes than could be demonstrated in full-scale exercises.( 34 ) Further evaluation of this issue is recommended for future consideration, with the potential utilisation of geolocators as a useful instrument in this regard. The proportion of undertriage identified in this study is consistent with the findings of analyses conducted using other algorithms.( 35 ) Another study by Vargas et al.( 36 ) described the results of two triage groups. The initial accuracy was reported as 45.76% in the low experience group, 45.84% in the overtriage group, and 8.38% in the undertriage group. In the high experience group, the initial accuracy was 53.80%, overtriage 37.66%, and undertriage 8.57%. Subsequent training of both groups resulted in a significant enhancement of these results.( 36 ) In this particular analysis, overtriage was found to be a negligible issue, with a rate of approximately 2%. The results of the simulation can serve as a benchmark against which other hospitals can measure their own emergency exercises. Workload and Perceived Stress In accordance with the findings of preceding studies, the NASA-TLX may be utilised as a metric to evaluate the pedagogical adequacy of scenarios, with the capacity to accurately reflect the stress levels experienced by participants.( 37 ) In the present study, the overall workload of all participants was already in the upper stress level, with a mean score of 66.7 (± 16 SD). A meta-analysis of over 200 studies revealed that the 25th and 75th percentiles ranged from 39 to 61.( 38 ) Interestingly, work experience and not profession or gender has been described to be related with increased self-reported perceived preparedness for MCIs.( 3 ) Our data support this finding, by demonstrating that specialists and participants with a higher professional experience report less perceived overall workload in the NASA-TLX (Fig. 3 A and B). Another finding shows that although "frustration" was rated relatively low, it was higher among younger participants and resident physicians than among specialists (see Supplemental Table 1). These two findings demonstrate the efficacy of the NASA-TLX survey in identifying vulnerable groups within the staff population. It is recommended that these groups be given special attention in the preparation and follow-up of training and real-life scenarios. Another potential application of NASA-TLX is in full-scale simulations, with the purpose of providing a data set for the purpose of benchmarking the development and the evaluation of MCI training scenarios. Additionally, it could be used to monitor the effects of introducing new technologies or instruments in one's own hospital during follow-up exercises.( 39 , 40 ) In a simulation study on MCI management, for example, a comparison of two groups showed that the group with AI support had a significantly lower workload in the NASA-TLX score compared to the control group.( 40 ) Team Performance It has been demonstrated by preceding studies that non-technical skills are conducive to effective medical performance and can be enhanced through training.( 21 ) In this MCI simulation, interprofessional and interdisciplinary teams of three participants including a minimum of one physician were formed. The TEAM was thus employed as a reliable and validated instrument for the assessment of non-technical skills, with these skills being based on leadership qualities, teamwork, and task management.( 21 , 41 ) The findings of the present study demonstrate that, in the face of a challenging environment and a considerable workload, the healthcare professionals exhibited above-average team performance, as evidenced by the NASA-TLX results. The mean TEAM item score was 3.2 (± 0.26) and the total TEAM score was 79.8%. A total of five simulation-based studies of professional teams reported a TEAM score ranging from 71.6% to 89.0%, with an average of 79%.( 21 ) A previous study suggests that a total TEAM score ≤ 33 equates ‘poor’, 34–39 good and ≥ 40 excellent performance.( 42 ) TEAM is a well-established instrument for the evaluation of emergency teams in acute situations, with documented improvements in scores following training sessions.( 43 – 45 ) A survey of the literature reveals no precedent for the utilization of the TEAM questionnaire in MCI scenarios or simulations. Consequently, this study offers preliminary findings that can serve as a benchmark for future simulations and exercises in this domain. A study published from Norway in 2025 suggests that many hospitals provide little education (15%) or training (26%) for major incidents involving a mass casualty situation.( 46 ) It is reasonable to assume that the situation is similar in other regions. Publications from Germany suggest that exercises were carried out in between a quarter and a third of hospitals.( 47 , 48 ) The authors emphasize that it is crucial to back up each exercise with solid scientific research to gain the most valuable insights. Limitations In this simulation, only adult trauma patients were simulated, which limits the generalizability of our findings to scenarios involving pediatric casualties or patients with other injury pattern. Pediatric patients require distinct triage algorithms and different care structures that are specifically tailored to the physiological and developmental needs of children. The participation in this exercise and the associated study was voluntary, which introduces potential biases that may affect the generalizability of the results. Participants who chose to take part may differ systematically from those who did not, possibly in terms of motivation, experience, or perceived competence in handling crisis situations. This selection bias could lead to an overestimation of the effectiveness of the training, as more motivated or experienced staff might perform better in a simulation setting. As the TEAM is primarily designed as an observer-rated tool, direct comparison is not possible. Thus, TEAM has been successful for its application as a tool for participants rating the non-technical skill perception of their respective team. While our findings provide valuable insights into the dynamics of patient flow and resource allocation, they should be interpreted with caution when applied to facilities with differing infrastructure. Summary The implications of our findings are significant for the evidence-based development of hospital emergency response plans. These insights can help other hospitals analyze their processes and identify potential inefficiencies that may exist regardless of the specific infrastructure. Furthermore, by offering a dataset that combines both objective and subjective metrics, this study strenghtens the evidence base on hospital preparedness for MCIs, facilitating the comparability of exercises across institutions and over time when repeated. These findings can inform analyses of learning curves, highlight areas for improvement, and strengthen hospitals' readiness for future emergency scenarios. Declarations Conflict of Interest All authors declare no financial or non-financial competing interests. Patient and Public Involvement Due to the nature and objective of this study, especially with health care staff acting as participants of this study, there was no involvement of patients or the public in design, conduct, reporting and dissemination of this study. Funding Open Access funding was enabled and organized by Projekt DEAL. Further, this project was part of the European Union’s Horizon 2020 project Medical First Responder Training using a Mixed Reality Approach featuring haptic feedback for enhanced realism (MED1stMR; grant number 101021775). G.A.S. is supported by the Clinician-Scientist program of Medical Faculty, University of Heidelberg. The funders had no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript. Author Contribution Conceptualization and Project administration: MF, HS, SM, EP, GAS; Data collection: FM, HS, SM, CL, AB, MOF, GAS; Formal analysis and Methodology: MF, HS, EF, GAS; Visualization: MF, GAS; Writing – original draft: MF, GAS; Writing – review and editing: All authors; Resources: HGK, MH, ML, MWC, CL, MAW, EP; Supervision: SM, EP. All authors have read and agreed to the published version of the manuscript. Acknowledgement Set up of the RTLS system and technical support during the exercise was provided by Favendo GmbH (Bamberg, Germany). The authors express their gratitude to all employees involved in planning and conduction of the full-scale exercise. Further, the authors express their gratitude to prehospital emergency services and firefighter brigades, who participated in the exercise. Data Availability Further information including geo-tracking video data is available in the supplement to this manuscript (Supplemental Video 1). Impressions of the entire exercise can also be gained from a publicly accessible video: https://www.youtube.com/watch?v=Pfu4D1Xh5CQ. Anonymized data are provided by the corresponding authors upon reasonable request. References Cooke MW, Brace SJ. Training for disaster. Resuscitation. 2010;81(7):788–9. Wynter S, Nash R, Gadd N. Major Incident Hospital Simulations in Hospital Based Health Care: A Scoping Review. Disaster Med Public Health Prep. 2023;17:e477. Walls RM, Zinner MJ. The Boston Marathon Response. JAMA. 2013;309(23):2441. Gates JD, Arabian S, Biddinger P, Blansfield J, Burke P, Chung S, et al. The Initial Response to the Boston Marathon Bombing. Ann Surg. 2014;260(6):960–6. Ghanchi A. Insights Into French Emergency Planning, Response, and Resilience Procedures From a Hospital Managerial Perspective Following the Paris Terrorist Attacks of Friday, November 13, 2015. Disaster Med Public Health Prep. 2016;10(5):789–94. Hirsch M, Carli P, Nizard R, Riou B, Baroudjian B, Baubet T, et al. The medical response to multisite terrorist attacks in Paris. Lancet. 2015;386(10012):2535–8. Hoth P, Roth J, Bieler D, Friemert B, Franke A, Paffrath T, et al. Education and training as a key enabler of successful patient care in mass-casualty terrorist incidents. Eur J Trauma Emerg Surg. 2023;49(2):595–605. Lau PF, Lau CC. A disaster drill in Hong Kong. Accid Emerg Nurs. 1997;5(1):34–8. Hays MB, Stefanki JX, Cheu DH. Planning an airport disaster drill. Aviat Space Environ Med. 1976;47(5):556–60. Goniewicz K, Goniewicz M, Włoszczak-Szubzda A, Burkle FM, Hertelendy AJ, Al-Wathinani A, et al. The importance of pre-training gap analyses and the identification of competencies and skill requirements of medical personnel for mass casualty incidents and disaster training. BMC Public Health. 2021;21(1):114. Tallach R, Schyma B, Robinson M, O’Neill B, Edmonds N, Bird R et al. Refining mass casualty plans with simulation-based iterative learning. Br J Anaesth [Internet]. 2022;128(2):e180–9. Available from: http://dx.doi.org/10.1016/j.bja.2021.10.004 Watkins N, Johnston ANB, McNamee P, Muter N, Huang C, ling Li Y et al. Preparing for Mass Casualties: Improving Staff Preparedness and Hospital Operations through Multidisciplinary Simulation Training in Disaster Management. Prehosp Disaster Med [Internet]. 2019;34(s1):s81–2. Available from: http://dx.doi.org/10.1017/S1049023X19001717 Abualenain J, Alhajaji R, Kamel Alsulimani L. A Systematic Review of the Efficacy of Full-Scale Simulation Exercises in Enhancing Hospital Disaster Preparedness. The Journal of Medicine, Law & Public Health [Internet]. 2024;4(3):419–46. Available from: http://dx.doi.org/10.52609/jmlph.v4i3.133 Hsu EB, Thomas TL, Bass EB, Whyne D, Kelen GD, Green GB. Healthcare worker competencies for disaster training. BMC Med Educ. 2006;6:1–9. Pek JH, Quah LJJ, Valente M, Ragazzoni L, Della Corte F. Use of Simulation in Full-Scale Exercises for Response to Disasters and Mass-Casualty Incidents: A Scoping Review. Prehosp Disaster Med. 2023;38(6):792–806. Schulz F, Nguyen Q, Baetzner A, Sjöberg D, Gyllencreutz L. Exploring medical first responders’ perceptions of mass casualty incident scenario training: a qualitative study on learning conditions and recommendations for improvement. BMJ Open. 2024;14(7):e084925. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol [Internet]. 2008;61(4):344–9. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0895435607004362 Hart SG, Staveland LE. In. Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research. 1988. pp. 139–83. Hart SG. Nasa-Task Load Index (NASA-TLX); 20 Years Later. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 2006;50(9):904–8. Prytz PEG, Rybing MJ, Jonson PCO. Workload differences across command levels and emergency response organizations during a major joint training exercise. J Emerg Manage. 2016;14(4):289–97. Cooper S, Connell C, Cant R. Review article: Use of the Team Emergency Assessment Measure in the rating of emergency teams’ non-technical skills: A mapping review. Emerg Med Australasia. 2023;35(3):375–83. Cooper S, Cant R, Porter J, Sellick K, Somers G, Kinsman L, et al. Rating medical emergency teamwork performance: Development of the Team Emergency Assessment Measure (TEAM). Resuscitation. 2010;81(4):446–52. Freytag J, Stroben F, Hautz WE, Schauber SK, Kämmer JE. Rating the quality of teamwork—a comparison of novice and expert ratings using the Team Emergency Assessment Measure (TEAM) in simulated emergencies. Scand J Trauma Resusc Emerg Med. 2019;27(1):12. Markou-Pappas N, Lamine H, Ragazzoni L, Caviglia M. Key performance indicators in pre-hospital response to disasters and mass casualty incidents: a scoping review. Eur J Trauma Emerg Surg. 2024;50(5):2029–37. Lin CH, Tzeng WC, Chiang LC, Lu MC, Lee MS, Chiang SL. Effectiveness of a Structured Disaster Management Training Program on Nurses’ Disaster Readiness for Response to Emergencies and Disasters: A Randomized Controlled Trial. J Nurs Manag. 2024;2024. Parra Cotanda C, Rebordosa Martínez M, Trenchs, de la Maza S, Luaces Cubells V. C. Impact of a disaster preparedness training programme on health staff. Anales de Pediatría (English Edition) [Internet]. 2016;85(3):149–54. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2341287916300953 Ersöz Genç E. Improving Disaster Preparedness Among Healthcare Professionals: A Comprehensive Approach. Eurasian Journal of Emergency Medicine [Internet]. 2025; Available from: https://eajem.com/articles/improving-disaster-preparedness-among-healthcare-professionals-a-comprehensive-approach/doi/eajem.galenos.2025.51482 Ozella L, Gauvin L, Carenzo L, Quaggiotto M, Ingrassia PL, Tizzoni M, et al. Wearable Proximity Sensors for Monitoring a Mass Casualty Incident Exercise: Feasibility Study. J Med Internet Res. 2019;21(4):e12251. Tolg B, Lorenz J. An analysis of movement patterns in mass casualty incident simulations. Adv Simul. 2020;5(1):27. Açıksarı K, Koçak M, Solakoğlu GA, Bodas M. The effect of multiple triage points on the outcomes (time and accuracy) of hospital triage during mass casualty incidents. Injury. 2024;55(5):111318. Ashkenazi I, Kessel B, Khashan T, Haspel J, Oren M, Olsha O, et al. Precision of In-Hospital Triage in Mass-Casualty Incidents after Terror Attacks. Prehosp Disaster Med. 2006;21(1):20–3. Carles M, Levraut J, Gonzalez JF, Valli F, Bornard L. Mass casualty events and health organisation: terrorist attack in Nice. Lancet. 2016;388(10058):2349–50. Kleber C, Solarek A, Cwojdzinski D, Strehl M, Wrede C, Heinz J, et al. The Berlin mass casualty hospital triage algorithm: Development, implementation and influence on exercise-based triage results. Unfallchirurg. 2020;123(3):187–98. Heller AR, Neidel T, Klotz PJ, Solarek A, Kowalzik B, Juncken K, et al. Validierung innerklinischer Sichtungsalgorithmen für den Massenanfall von Verletzten – eine simulationsbasierte Studie – deutsche Version. Die Anaesthesiologie. 2023;72(7):467–76. Bazyar J, Farrokhi M, Salari A, Safarpour H, Khankeh HR. Accuracy of Triage Systems in Disasters and Mass Casualty Incidents; a Systematic Review. Arch Acad Emerg Med. 2022;10(1):e32. Vargas MMJP, Hubloue MPI, Pinzón MJJ, Duque MAC. The effect of training and experience on mass casualty incident triage performance: Evidence from emergency personnel in a high complexity university hospital. Am J Disaster Med. 2019;14(2):113–9. Favre-Félix J, Dziadzko M, Bauer C, Duclos A, Lehot JJ, Rimmelé T, et al. High-Fidelity Simulation to Assess Task Load Index and Performance: A Prospective Observational Study. Turk J Anaesthesiol Reanim. 2022;50(4):282–7. Grier RA. How High is High? A Meta-Analysis of NASA-TLX Global Workload Scores. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 2015;59(1):1727–31. Lochmannová A. Exploring the role of virtual reality in preparing emergency responders for mass casualty incidents. Isr J Health Policy Res. 2025;14(1):22. Liu Z, Lorenz, Souza WH, Han J, Madani A, Using. AI to Optimize Patient Transfer and Resource Utilization During Mass-Casualty Incidents: A Simulation Platform [Internet]. 2025 Sep [cited 2025 Sep 17]. Available from: https://arxiv.org/abs/2509.08756 Boet S, Etherington C, Larrigan S, Yin L, Khan H, Sullivan K, et al. Measuring the teamwork performance of teams in crisis situations: a systematic review of assessment tools and their measurement properties. BMJ Qual Saf. 2019;28(4):327–37. Cooper S, Cant R, Connell C, Sims L, Porter JE, Symmons M, et al. Measuring teamwork performance: Validity testing of the Team Emergency Assessment Measure (TEAM) with clinical resuscitation teams. Resuscitation. 2016;101:97–101. Cant RP, Porter JE, Cooper SJ, Roberts K, Wilson I, Gartside C. Improving the non-technical skills of hospital medical emergency teams: The Team Emergency Assessment Measure ( TEAM ™). Emerg Med Australasia. 2016;28(6):641–6. Cooper S, Cant R, Porter J, Missen K, Sparkes L, McConnell-Henry T, et al. Managing patient deterioration: assessing teamwork and individual performance. Emerg Med J. 2013;30(5):377–81. Doymaz S, Rizvi M, Giambruno C. Improving the Performance of Residents in Pediatric Resuscitation with Frequent Simulated Codes. Glob Pediatr Health. 2020;7. Ugelvik KS, Thomassen Ø, Braut GS, Geisner T, Sjøvold JE, Montán C. A national study of in-hospital preparedness for Mass Casualty Incidents and disasters. Eur J Trauma Emerg Surg. 2025;51(1):18. Pfenninger EG, Villhauer S, Königsdorfer M. Hospital disaster planning in south-western Germany. A survey of 214 clinics. Notfall und Rettungsmedizin. 2022;1–10. Hoth P, Bieler D, Friemert B, Franke A, Blätzinger M, Achatz G. Sicherheitsaspekte und Vorbereitung zur Notfallvorsorge und Gefahrenabwehr in Kliniken bei MANV/TerrorMANV. Die Unfallchirurgie. 2022;125(7):542–52. Additional Declarations No competing interests reported. Supplementary Files STROBEchecklist.pdf SupplementTable1.docx SupplementTable2.docx SupplementFigure1.pdf SupplementVideo1.mp4 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7859560","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":532173711,"identity":"a8054125-7e62-4547-80fe-692ebf5613c2","order_by":0,"name":"Maik von der Forst","email":"","orcid":"","institution":"Heidelberg University","correspondingAuthor":false,"prefix":"","firstName":"Maik","middleName":"von der","lastName":"Forst","suffix":""},{"id":532173712,"identity":"5a531eca-d5bb-4c37-81fb-59a8d55a42be","order_by":1,"name":"Hanne Schäfer","email":"","orcid":"","institution":"Heidelberg University","correspondingAuthor":false,"prefix":"","firstName":"Hanne","middleName":"","lastName":"Schäfer","suffix":""},{"id":532173713,"identity":"9085d4cb-5069-41f5-978a-7761647d72aa","order_by":2,"name":"Stefan Mohr","email":"","orcid":"","institution":"Heidelberg University","correspondingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Mohr","suffix":""},{"id":532173714,"identity":"a1a5362f-eea1-4ab8-bf33-4b2520eb11e2","order_by":3,"name":"Hannes Goetz Kenngott","email":"","orcid":"","institution":"Heidelberg University","correspondingAuthor":false,"prefix":"","firstName":"Hannes","middleName":"Goetz","lastName":"Kenngott","suffix":""},{"id":532173715,"identity":"687fd10b-1b84-4a33-a879-58c940f91131","order_by":4,"name":"Elyes Farjallah","email":"","orcid":"","institution":"Heidelberg University","correspondingAuthor":false,"prefix":"","firstName":"Elyes","middleName":"","lastName":"Farjallah","suffix":""},{"id":532173716,"identity":"8e59970c-de9f-467e-896f-574ce46589e4","order_by":5,"name":"Matthias Huck","email":"","orcid":"","institution":"Heidelberg University","correspondingAuthor":false,"prefix":"","firstName":"Matthias","middleName":"","lastName":"Huck","suffix":""},{"id":532173717,"identity":"b0a07506-96b5-4f36-9713-55b01bf47c83","order_by":6,"name":"Anke S. Baetzner","email":"","orcid":"","institution":"Heidelberg University","correspondingAuthor":false,"prefix":"","firstName":"Anke","middleName":"S.","lastName":"Baetzner","suffix":""},{"id":532173718,"identity":"0f363a62-3ad8-4cb0-b3ea-0218aecc0853","order_by":7,"name":"Marie Ottilie Frenkel","email":"","orcid":"","institution":"Furtwangen University","correspondingAuthor":false,"prefix":"","firstName":"Marie","middleName":"Ottilie","lastName":"Frenkel","suffix":""},{"id":532173719,"identity":"c45f1924-6ba1-44f1-ba39-85f91ca5e87e","order_by":8,"name":"Markus Ries","email":"","orcid":"","institution":"University Hospital Heidelberg","correspondingAuthor":false,"prefix":"","firstName":"Markus","middleName":"","lastName":"Ries","suffix":""},{"id":532173720,"identity":"59b3587f-c7db-46a1-8043-2aff52b19a51","order_by":9,"name":"Martin Loos","email":"","orcid":"","institution":"Heidelberg University","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Loos","suffix":""},{"id":532173721,"identity":"cd1eb7e9-e4f5-43f0-bcb2-487f92020e77","order_by":10,"name":"Christoph W. Michalski","email":"","orcid":"","institution":"Heidelberg University","correspondingAuthor":false,"prefix":"","firstName":"Christoph","middleName":"W.","lastName":"Michalski","suffix":""},{"id":532173722,"identity":"0015951e-23a5-4d64-80a9-11f5b0eddfa2","order_by":11,"name":"Christine Leowardi","email":"","orcid":"","institution":"Heidelberg University","correspondingAuthor":false,"prefix":"","firstName":"Christine","middleName":"","lastName":"Leowardi","suffix":""},{"id":532173723,"identity":"940fcdb5-7b82-4882-8387-6ccb5216a038","order_by":12,"name":"Markus Weigand","email":"","orcid":"","institution":"Heidelberg University","correspondingAuthor":false,"prefix":"","firstName":"Markus","middleName":"","lastName":"Weigand","suffix":""},{"id":532173724,"identity":"37696ca3-4287-476c-b2c3-fea648a86570","order_by":13,"name":"Erik Popp","email":"","orcid":"","institution":"Heidelberg University","correspondingAuthor":false,"prefix":"","firstName":"Erik","middleName":"","lastName":"Popp","suffix":""},{"id":532173725,"identity":"f66c42b4-0829-49c4-85b3-8206d4b82788","order_by":14,"name":"Gabriel Alexander Salg","email":"data:image/png;base64,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","orcid":"","institution":"Heidelberg University","correspondingAuthor":true,"prefix":"","firstName":"Gabriel","middleName":"Alexander","lastName":"Salg","suffix":""}],"badges":[],"createdAt":"2025-10-14 14:08:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7859560/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7859560/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94111689,"identity":"4a388981-3200-4cc6-a895-336f68a05b70","added_by":"auto","created_at":"2025-10-22 13:28:36","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1484288,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptMCI.docx","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/a7a683d4342c5fa12687adad.docx"},{"id":94110710,"identity":"e9133b42-17cb-4c78-b075-8aaedcd858fe","added_by":"auto","created_at":"2025-10-22 13:20:36","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15400,"visible":true,"origin":"","legend":"","description":"","filename":"b4de8e056d2640728c19be21020787e1.json","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/e8b27e4d955dddf4c3f6b93f.json"},{"id":94110717,"identity":"39d9d960-aaa7-4035-a64d-50f677a9c3ec","added_by":"auto","created_at":"2025-10-22 13:20:36","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":132091,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEchecklist.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/36fca267d928057dc2386c2c.pdf"},{"id":94110727,"identity":"c8556f8e-a4f5-4630-91af-342bedf11c09","added_by":"auto","created_at":"2025-10-22 13:20:36","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":303021,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementFigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/fdcc53520f255617b0e2dc97.pdf"},{"id":94111691,"identity":"30c9b4ed-76e1-44f8-ae94-7ce2b2b9ade9","added_by":"auto","created_at":"2025-10-22 13:28:36","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":33045,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/096bb1032b793bb353749051.docx"},{"id":94110721,"identity":"5bbc87cc-74da-4744-994b-27c894774a64","added_by":"auto","created_at":"2025-10-22 13:20:36","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":27224,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/866494b26b2c9b1bc07e1e5c.docx"},{"id":94110737,"identity":"9754ae49-981f-4521-800f-889ff8324bb1","added_by":"auto","created_at":"2025-10-22 13:20:36","extension":"mp4","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7908882,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementVideo1.mp4","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/ae1b327a294aa48c0108862d.mp4"},{"id":94111907,"identity":"97a716c4-5313-4992-a7ec-d0fe918e8845","added_by":"auto","created_at":"2025-10-22 13:36:36","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":140567,"visible":true,"origin":"","legend":"","description":"","filename":"b4de8e056d2640728c19be21020787e11enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/5d462a0041c6dcf6d366c1e8.xml"},{"id":94110731,"identity":"7aabc411-1705-4211-b022-b681fc9866da","added_by":"auto","created_at":"2025-10-22 13:20:36","extension":"emf","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2440924,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.emf","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/708a021e85a39319d4a9846b.emf"},{"id":94110722,"identity":"7f4e4dc4-fbc7-444b-a19b-c455260a3bfb","added_by":"auto","created_at":"2025-10-22 13:20:36","extension":"emf","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":101352,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.emf","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/5020627cd5da83fadcd862fa.emf"},{"id":94111699,"identity":"f3097484-437a-4953-9911-da8e9fe30a24","added_by":"auto","created_at":"2025-10-22 13:28:36","extension":"jpeg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":402045,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/9401b60514cfd65b1a74a5bc.jpeg"},{"id":94110730,"identity":"a6b724ea-ce98-4a03-9a16-ad0adb373c42","added_by":"auto","created_at":"2025-10-22 13:20:36","extension":"jpeg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":873425,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/a97e3c6c61b347a9842e7502.jpeg"},{"id":94111697,"identity":"9eeed63b-3ce2-4dcd-8039-c0e80da6cf21","added_by":"auto","created_at":"2025-10-22 13:28:36","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84131,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/520fe988ae18820273b1a49e.png"},{"id":94110725,"identity":"2bdac209-74a0-47f7-94e3-a6990799d553","added_by":"auto","created_at":"2025-10-22 13:20:36","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15351,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/fb41bf4d76c4a7383a840449.png"},{"id":94111693,"identity":"caa0e769-e5a5-43c1-9bbf-180871efa3ca","added_by":"auto","created_at":"2025-10-22 13:28:36","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":64375,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/3d3e3ab557e98600b7ace157.png"},{"id":94111694,"identity":"29191f71-7b63-4b37-9f16-1007eccf846b","added_by":"auto","created_at":"2025-10-22 13:28:36","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":87255,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/2c8d472e2a3f2ef6ec739fb4.png"},{"id":94110734,"identity":"8de98772-f0c3-43ac-8257-90952bad92e2","added_by":"auto","created_at":"2025-10-22 13:20:36","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":140183,"visible":true,"origin":"","legend":"","description":"","filename":"b4de8e056d2640728c19be21020787e11structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/87f5ebbc5fd10de66b0ea650.xml"},{"id":94110736,"identity":"db2f742f-aafe-4540-a294-321588a49b2b","added_by":"auto","created_at":"2025-10-22 13:20:36","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":155195,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/c8497842cbd6b66566875c6e.html"},{"id":94110711,"identity":"f075c1fe-e7f9-44a8-808c-5f0e141acda9","added_by":"auto","created_at":"2025-10-22 13:20:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":418608,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpressions of the full-scale MCI exercise. A\u003c/strong\u003eGeotracking of patients and participants during the simulation exercise in the different areas. For further insights see also Supplemental Video 1. \u003cstrong\u003eB\u003c/strong\u003ePatient and medical team in the category I (‘red’) treatment area, red circle: unique code for study participation, yellow circle: tags for geo-tracking. \u003cstrong\u003eC\u003c/strong\u003eSet-up of the algorithm-based triage point. \u003cstrong\u003eD\u003c/strong\u003e Simulation patient with realistic makeup injuries.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/fa0c19c9b0197e2cc75a5f77.png"},{"id":94111688,"identity":"0ce03a24-1976-4d92-8cdd-13039f0251ca","added_by":"auto","created_at":"2025-10-22 13:28:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43489,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of Triage times by assigned Category. \u003c/strong\u003e\u003cem\u003eThe figure shows the time required to examine a patient according to the assigned category. The times were automatically recorded based on the length of stay at the triage point using tracking data. Mann-Whitney test, **** p\u0026lt;0.001.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/0f9e90871f557a9b5bfe3830.png"},{"id":94111905,"identity":"fc704c58-fcd6-46ef-b0e5-9996df9e37f5","added_by":"auto","created_at":"2025-10-22 13:36:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":81059,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall workload according to NASA-TLX based on various participant characteristics. \u003c/strong\u003e\u003cem\u003eThe overall workload is shown according to the “Raw TLX” by adding up the individual items. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e shows the comparison based on the different professional groups and levels of training, while \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/em\u003e\u003cem\u003ecompares the overall workload based on gender, age, and professional experience. Unpaired t-test if necessary, with Welch`s correction. * p\u0026lt;0.05.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/bcd0db09ce4fddc734f22373.png"},{"id":94111906,"identity":"82e909b7-31d0-40b7-aca4-9e84f136beee","added_by":"auto","created_at":"2025-10-22 13:36:36","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":873425,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Team Emergency Assessment Measure (TEAM) – Questionnaire. \u003c/strong\u003e\u003cem\u003eThe eleven items (each from 0-4, higher values indicate better performance) of the TEAM questionnaire, divided into professional groups, Means (±SD) are shown. No significant differences were found when comparing the different groups.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/027a94b43ff39fdd941890d5.jpeg"},{"id":94113229,"identity":"ff700f95-01d3-4515-87e8-2ebb50fb72bb","added_by":"auto","created_at":"2025-10-22 13:52:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2421920,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/132859d3-be3f-4bbd-9b87-199fe7b8c18a.pdf"},{"id":94110713,"identity":"b9ffce77-9489-47e1-8169-645471d45fd2","added_by":"auto","created_at":"2025-10-22 13:20:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":132091,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEchecklist.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/276be58c6a846e462aa2bced.pdf"},{"id":94110716,"identity":"c2d306cd-706e-4069-9627-c3257485ecfa","added_by":"auto","created_at":"2025-10-22 13:20:36","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":33045,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/828b72e69e40ee1d0625d108.docx"},{"id":94110720,"identity":"40416cdb-618c-4886-aee8-1c172bd0bac5","added_by":"auto","created_at":"2025-10-22 13:20:36","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":27224,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/fd9fded61ea66f79f8c9d105.docx"},{"id":94111908,"identity":"828c9242-f7f2-42aa-a2c8-4f5844da3b1b","added_by":"auto","created_at":"2025-10-22 13:36:36","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":303021,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementFigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/7dd62b95bb80b2b81632387f.pdf"},{"id":94111909,"identity":"37855216-8f44-457e-ae53-0273c2a5fcda","added_by":"auto","created_at":"2025-10-22 13:36:36","extension":"mp4","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":7908882,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementVideo1.mp4","url":"https://assets-eu.researchsquare.com/files/rs-7859560/v1/913b2626123adec800c6331a.mp4"}],"financialInterests":"No competing interests reported.","formattedTitle":"Improving Preparedness for Mass Casualty Incidents in Hospitals: Insights from a Large-Scale Simulation Exercise with Geotracking and Validated Questionnaires","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMass casualty incidents (MCIs) challenge healthcare systems.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) A sudden influx of numerous injured patients can quickly exceed normal operational capacities of hospitals. Past events suggest that regular practice leads to improved patient care in real-life situations.(\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) Exercises designed to train for MCIs have been carried out for many decades.(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) Full-scale simulation exercises are recognized as a vital component in enhancing hospital preparedness for such events.(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Such exercises allow healthcare professionals to practice and refine their response protocols in a controlled, yet realistic environment. Thereby potential weaknesses can be identified and addressed before an actual event occurs.(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) Evidence highlights the value of simulation exercises in improving various aspects of hospital preparedness, including the coordination and decision-making abilities of medical teams under stress, as well as the overall effectiveness of emergency response protocols.(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) Previous research has shown that regular simulation training can enhance the situational awareness and communication skills of healthcare providers, leading to more efficient and effective patient care during emergencies.(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) It seems imperative to maximize the output from each simulation exercise. Structured evaluation and collection of metric data during MCI training allows for the generation of evidence that can inform long-term improvements in emergency response strategies and healthcare system resilience.(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) However, the organization and execution of full-scale MCI simulations are fraught with difficulties.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Organizational, budgetary, workload and resource constraints cause infrequent execution.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eTherefore, there is a notable lack of comprehensive studies that integrate both objective and subjective data to provide a holistic view of hospital performance during MCI simulations.(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) To address this gap, this study conducted a full-scale MCI simulation designed to obtain a multidimensional evaluation of the operational processes. By integrating objective and subjective assessment methods, the present study aimed to achieve a comprehensive analysis of interdisciplinary treatment teams' performance during MCIs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003cp\u003e This is a prospective observational study approved by the ethics committee of the medical faculty of the University of Heidelberg (S-556/2023) and conducted in accordance with the current version of the Declaration of Helsinki. Patients and/or the public were not involved in the design, conduct, reporting or dissemination plans of this research. Employees of Heidelberg University Hospital that participated in the hospital MCI simulation exercise represent the study population. After written informed consent to participate in the study was provided, each participant was registered anonymously using a unique code (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). In total 75 participants were additionally equipped with a location tag, that was registered under the same code. Immediately after finishing the simulation exercise each participant was asked to answer two standardized questionnaires as well as complete demographic and baseline data registered to the unique code. Data from all modalities were merged using the unique code, which was immediately deleted afterwards to prohibit de-anonymization. Participants received no additional training prior to the MCI simulation exercise and were aware only on the date of the full-scale exercise. Missing data were not imputed. STROBE criteria were adhered to in design, conduct, and reporting of this observational study.(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFull-scale MCI simulation exercise\u003c/strong\u003e\u003cp\u003eThe exercise was conducted at Heidelberg university hospital (Germany) and involved 91 simulated casualties. The triage at the hospital was performed in a two-stage process. At a first stage patients were classified at \u0026ldquo;first look\u0026rdquo;, without further algorithm or diagnostic tools by a triage nurse and separated into two groups\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eImmediate life-threatening condition or serious injuries (\u0026lsquo;red\u0026rsquo; and \u0026lsquo;yellow\u0026rsquo;)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eminor injuries (\u0026lsquo;green\u0026rsquo;).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThis first triage point was set up at the driveway to the trauma center\u0026rsquo;s emergency department. The scope was directing vehicles with category I and II (\u0026lsquo;red\u0026rsquo; and \u0026lsquo;yellow\u0026rsquo;) patients to the emergency department entry and vehicles with category III (\u0026lsquo;green\u0026rsquo;) patients to the ground level entrance.\u003c/p\u003e\u003cp\u003eAt a second stage two identically equipped triage points were set up at each respective entrance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) and a more sophisticated triage was performed by a tandem of general/trauma surgeon and anesthesiologist. A proprietary triage algorithm was used according to the hospital`s alarm and operation plan, classifying the patients in the known categories: Immediate life-threatening condition (category I, \u0026lsquo;red\u0026rsquo;), serious injuries, but no immediate needs (category II, \u0026lsquo;yellow\u0026rsquo;) or minor injuries such as abrasions and smaller lacerations (category III, \u0026lsquo;green\u0026rsquo;) (Supplemental Fig.\u0026nbsp;1).\u003c/p\u003e\u003cp\u003eAfter triage, patients were assigned to treatment teams of at least three healthcare professionals (including at least one physician). The teams were formed spontaneously upon arrival and registration at the trauma center. The presentation of injuries followed pre-defined case vignettes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). The teams performed initial treatment or ordered further diagnostic measures (ultrasound, x-ray, computed tomography) or emergency surgery according to their knowledge and skills. The simulation was stopped before transfer to a respective ward or after arrival at an assigned operating theatre. Once the treatment simulation for one patient was completed, the teams were ready for their next case assignment.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eReal-time location system (RTLS)\u003c/strong\u003e\u003cp\u003eLocators and tags were provided by Favendo GmbH (Bamberg, Germany). Triage areas as well as category I and category II treatment areas and transport areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) were equipped with locators. Participants and patients were equipped with tags (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Based on RTLS, triage time was measured and defined as the time from arrival at the second triage area until transport out of the triage area. Further, triage accuracy including analysis of potential over- und undertriage was analyzed. Data preparation was performed using a customized python script (Python Software Foundation, Python Language Reference, Version 3.10.). For visualization, the quupa player (Quuppa Data Player, QDP) was used for each respective floor and respective tags were annotated with regard to the pre-defined triage category (\u0026lsquo;red\u0026rsquo;/\u0026rsquo;yellow\u0026rsquo;/\u0026rsquo;green\u0026rsquo;) or respective professional group of the study participants. A video illustrating real-time tracking of the emergency department level (Category I and Category II patients) is provided in the supplement to this manuscript (Supplemental Video 1). The spatiotemporal resolution rate corresponded to a sampling process every 500 ms. Patient contact was defined as the proximity of an employee tracker and a patient tracker of \u0026lt;\u0026thinsp;1 m for a period of at least 1 second.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eDemographic and baseline data\u003c/em\u003e: Participants were asked to provide the following data linked to their unique code: age (in years), gender (\u0026lsquo;male\u0026rsquo;/\u0026rsquo;female\u0026rsquo;/\u0026rsquo;diverse\u0026rsquo;), professional group (\u0026lsquo;doctor\u0026rsquo;/\u0026rsquo;nurse\u0026rsquo;/\u0026rsquo;nurse trainee\u0026rsquo;/\u0026rsquo;other\u0026rsquo;), professional experience (in years). Doctors were further asked whether they have completed their respective specialist training.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNational Aeronautics and Space Administration-Task Load Index (NASA-TLX)\u003c/strong\u003e\u003cp\u003eThe NASA-TLX was completed immediately after the exercise. It is a subjective, multidimensional assessment tool for recording perceived workload with six subscales (mental demands, physical demands, time demands, personal performance, effort and frustration), each of which is rated on a scale of 1 to 20, high numbers indicating a high workload. The NASA-TLX is an established tool to measure perceived workload when performing a task. (\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eTeam Emergency Assessment Measure (TEAM) scale\u003c/em\u003e: The TEAM comprises 11 items rated on a five-point Likert scale, ranging from \u0026lsquo;0\u0026rsquo; (Never/hardly ever) to \u0026lsquo;4\u0026rsquo; (Always/nearly always). These items assess three key dimensions: \u0026lsquo;Leadership\u0026rsquo; (2 items), \u0026lsquo;Teamwork\u0026rsquo; (7 items), which includes aspects of situational awareness, and \u0026lsquo;Task Management\u0026rsquo; (2 items). The TEAM total score was calculated as a sum of the 11 eleven section items with a maximum score of 44 as reported previously.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) Additionally, a 12th item provides a global performance rating on a scale from 1 to 10.(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) The German version of TEAM was used for this study.(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) Traditionally, rating is performed for the whole team by one observer.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) Here, the TEAM rating was performed immediately after the exercise by the participants themselves.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003cp\u003eData analysis and statistical testing was done using GraphPad Prism version 10.6.0 for Windows (GraphPad Software, San Diego, California, USA). Missing data was considered missing completely at random and no imputation was performed. Normality of the data was assessed using both the Shapiro\u0026ndash;Wilk test and the Kolmogorov\u0026ndash;Smirnov test with Lilliefors adaptation. Data that followed normal distribution were tested for statistically significant differences using the two-tailed t-test for comparison of two groups and the one-way ANOVA for comparisons involving three and more groups. An F-Test was conducted and if necessary, Welch\u0026rsquo;s correction was applied. Data that did not follow normal distribution were tested using the non-parametric two-tailed Mann-Whitney test for comparisons of two groups and the non-parametric Kruskal-Wallis test for comparisons of three and more groups. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Effect sizes were calculated using Hedge\u0026rsquo;s g, which provides a bias-corrected standardized mean difference suitable for small sample sizes. 95%-confidence intervals (95% CI) for Hedge\u0026rsquo;s g were computed to provide an estimate of the precision of effect size.\u003c/p\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the characteristics of the exercise participants broken down by occupational group.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographics of MCI Exercise Participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eQuestionnaire-based data acquisition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eProfessional groups\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ePercentage [%]\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedical staff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResident\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e27\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e58.7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecialist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e19\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e41.3\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNursing staff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNursing trainees\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en (female)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eratio [%]\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll participants *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e53.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedical staff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNursing staff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNursing trainees\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e66.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge [y]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003emean\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003emedian\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003erange\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll participants *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20\u0026ndash;66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedical staff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNursing staff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e31.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21\u0026ndash;66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNursing trainees\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20\u0026ndash;25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e32\u0026ndash;61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eProfessional experience [y]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003emean\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003emedian\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003erange\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll participants **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedical staff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u0026ndash;22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNursing staff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.5\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNursing trainees\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003ecompleteness of response: 98.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003ecompleteness of response: 97.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe average age of the participants was 35.7 years and was similar across medical and nursing staff (Mann-Whitney test, not significant). Nevertheless, nursing trainees were significantly younger than nursing staff (excl. trainees) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and residents younger than specialists (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Overall, the nursing staff had a median of 7 years of professional experience and the medical staff a median of 5 years, this difference was not significant (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A similar number of medical staff were recruited as nursing staff. It is striking that although 53.1% of participants were female, the majority of these were from the nursing profession (73.3% female).\u003c/p\u003e\n\u003ch3\u003eTriage accuracy and duration\u003c/h3\u003e\n\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of Patient Triage and Patient Contacts per Participant\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTriage accuracy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en\u0026thinsp;=\u0026thinsp;69\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eratio [%]\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTriage point 1, \"first look\"\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTriage point 2, \"algorithm-based\"\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUndertriage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMissed cat. I, 'red'\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMissed cat. II, 'yellow'\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOvertriage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFalse cat. I, 'red'\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of patient contacts per person\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003emean\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll tracked staff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTracked medical staff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTracked nursing staff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTracked nursing trainees\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePatient contacts: Mann-Whitney test\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedical staff vs. nursing staff (incl. trainees)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResident vs. specialist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNursing staff vs. trainees\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale vs. male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYoung vs. old\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProfessional experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eReal-time location tracking of 69 patients was achieved. \u0026ldquo;First look\u0026rdquo; triage, led by two emergency department nurses, was conducted at triage point 1 in front of the hospital. The accuracy according to predefined case vignettes was 76.8%. An \u0026ldquo;algorithm based\u0026rdquo; second triage at triage point 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) was led by physicians with an overall triage accuracy of 75.4%. At step two 15 patients (21.7%) were undertriaged (eight \u0026lsquo;red\u0026rsquo; vignettes triaged as \u0026lsquo;yellow\u0026rsquo;, seven \u0026lsquo;yellow\u0026rsquo; vignettes triaged as \u0026lsquo;green\u0026rsquo;; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The \u0026lsquo;red\u0026rsquo; vignettes triaged as yellow show no specific commonality, two patients had a leading abdominal trauma, four serious burn injuries, two major amputations with hemorrhagic shock and one a thorax trauma with respiratory distress. Instead, going into further detail for undertriaged yellow vignettes reveals that 85.7% of patients falsely triaged as \u0026lsquo;green\u0026rsquo; were blast trauma patients, which had deafness and bleeding from the ear as major symptoms. In total, only 2 patients (2.4%) were overtriaged (two \u0026lsquo;yellow\u0026rsquo; vignettes triaged as \u0026lsquo;red\u0026rsquo;; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAltogether n\u0026thinsp;=\u0026thinsp;536 patient contacts were logged during the exercise. A comparison of the number of patient contacts between the different exercise participants revealed a homogeneous distribution. Nurses had approximately 6.5 patient contacts, while physicians had approximately 7.9 patient contacts; however, this difference was not significant. On average, participants had 7.1 patient contacts. Furthermore, there were no significant differences in patient contacts within the professional groups in terms of age, gender, or professional experience (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe mean triage time was 159\u0026thinsp;\u0026plusmn;\u0026thinsp;98s overall, analysis based on the actual triage category assignment revealed a mean triage time of 59\u0026thinsp;\u0026plusmn;\u0026thinsp;25s for patients triaged as \u0026lsquo;red\u0026rsquo;, 173\u0026thinsp;\u0026plusmn;\u0026thinsp;74s for patients triaged as \u0026lsquo;yellow\u0026rsquo; and 205\u0026thinsp;\u0026plusmn;\u0026thinsp;100s for patients triaged as \u0026lsquo;green\u0026rsquo;. The triage of \u0026lsquo;red\u0026rsquo; patients was therefore significantly faster (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) compared to both other categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eWorkload and Team Performance Perception\u003c/h3\u003e\n\u003cp\u003eThe NASA-TLX had an overall high response rate of 99%. The emergency medical teams consisted of different professional groups and usually contained at least one nurse and one physician, further also nursing trainees were part of the teams (Supplemental Table\u0026nbsp;1). Regarding all participants, the items\u0026rsquo; performance (13.9) and temporal demand (13.0) in the mean were rated highest, while physical demand (9.2) and frustration (7.2) where perceived less challenging (Supplemental Table\u0026nbsp;1). The individual items of the NASA-TLX show that medical staff overall reported significantly larger \u003cem\u003e\u0026ldquo;effort\u0026rdquo;\u003c/em\u003e than nursing staff (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; g\u003csub\u003eHedge\u003c/sub\u003e = 0.50, [0.072\u0026ndash;0.931]). Participants with less professional experience also reported significantly greater \u003cem\u003e\u0026ldquo;effort\u0026rdquo;\u003c/em\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; g\u003csub\u003eHedge\u003c/sub\u003e = 0.38, [-0.02-0.79]), higher \u003cem\u003e\u0026ldquo;physical demand\u0026rdquo;\u003c/em\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, g\u003csub\u003eHedge\u003c/sub\u003e = 0.41, [0.01\u0026ndash;0.82]), and even higher \u003cem\u003e\u0026ldquo;frustration\u0026rdquo;\u003c/em\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, g\u003csub\u003eHedge\u003c/sub\u003e = 0.64, [0.29\u0026ndash;1.05]) (Supplemental Table\u0026nbsp;1). Although \u0026ldquo;\u003cem\u003efrustration\u003c/em\u003e\u0026rdquo; was rated relatively low overall as an item, this is consistent with the fact that \u003cem\u003e\u0026ldquo;frustration\u0026rdquo;\u003c/em\u003e was higher among younger participants and resident physicians than among specialists. The same applies to nursing trainees, although the difference was not significant (p\u0026thinsp;=\u0026thinsp;0.05; Supplemental Table\u0026nbsp;1).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA comparison of the overall workload shows that the perceived level is very similar across the different occupational groups. Nursing trainees appear to feel a higher overall workload (75.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16.9), although this result is not significant with n\u0026thinsp;=\u0026thinsp;6. Consistent with this, among physicians, specialist status is associated with a significantly lower overall workload compared to the residents (63.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0 vs. 71.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, g\u003csub\u003eHedge\u003c/sub\u003e = 0.62, [0.02\u0026ndash;1.22]; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). A similar result is seen when professional experience is considered independently of the occupational group, with the more experienced half of the participants also perceiving a significantly lower overall workload (63.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.9 vs. 70.3\u0026thinsp;\u0026plusmn;\u0026thinsp;16.1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, g\u003csub\u003eHedge\u003c/sub\u003e = 0.42, [0.01\u0026ndash;0.82]; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Gender and age, on the other hand, had no significant influence on the perceived overall workload (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe results of the TEAM questionnaire show a relatively homogeneous picture in the assessment by the members of the individual professional groups. Regarding all participants the lowest scores were given to the two items relating to the team leader (2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 and 2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3), while all other items were rated at an average of more than 3 out of 4 points. The items \u0026ldquo;\u003cem\u003eTeam morale was positive\u0026rdquo;\u003c/em\u003e (3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7) and \u0026ldquo;\u003cem\u003eTeam worked together to complete the tasks in a timely manner\u0026rdquo;\u003c/em\u003e scored highest overall (3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Supplemental Table\u0026nbsp;2).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMain results of the present study show that overall triage accuracy was 75.4%, with category I patients being seen significantly faster. Geotracking revealed a consistent flow of patients without any bottlenecks, with an average of 7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7 patient contacts per employee, and high TEAM ratings of 79.8%, despite an overall high workload as measured by the NASA-TLX (66.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16). In context of pre-hospital MCI responses key performance indicators for evaluation have already been investigated.(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) However, there is no such definition for hospital MCI responses yet. There is limited evidence regarding effectiveness of MCI training for hospital staff. Several studies indicate that disaster drills can help train hospital staff effectively.(\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) However, there is a need for scientific evaluation metrics of such training activities to better assess their true impact.(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) This study provides objectively measured benchmark data to enable evidence-based hospital emergency planning. The present study demonstrated that, in addition to the conventional observer-centered qualitative evaluation of MCI exercises, a methodology based on standardizable methods is also feasible. Different studies conducted in both preclinical and clinical settings have previously demonstrated the feasibility of GPS data loggers in accurately capturing patient trajectory data. These studies have been conducted in the context of an MCI simulation, and their findings have consistently shown that the use of GPS data loggers does not compromise the realism of the simulation.(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) Conversely, the incorporation of additional uninvolved persons could potentially serve to enhance the realism of the exercise.(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e\n\u003ch3\u003eTriage\u003c/h3\u003e\n\u003cp\u003ePrevious studies considered duration and accuracy for evaluation of triage systems.(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) Hospital-based MCI triage has a different approach with patient flow being a major concern. Triage, even when carried out by experienced trauma physicians, can be unreliable in an MCI.(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) Based on a full-scale simulation exercise, to our knowledge, this was the first time that the evaluation of screening quality and duration could be conducted entirely on the basis of data ascertained via geolocators, as evidenced by a full-scale simulation exercise. The screening duration and the accuracy of the process, including over- and under-triage, were both determined based on the expected screening category and the actual assignment to a range. In this MCI simulation exercise the proprietary triage algorithm of Heidelberg University Hospital was applied (Supplemental Fig.\u0026nbsp;1). In the literature, a hospital triage accuracy of 63.3% for single triage points and 70.0% for two-stage triage points has been reported.(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) In this study, a two-stage triage resulted in a triage accuracy of 75.4%. category I (\u0026lsquo;red\u0026rsquo;) patients could be identified significantly faster than other triage categories. The total triage time of 59 (\u0026plusmn;\u0026thinsp;25 SD) s for patients classified as \u0026lsquo;red\u0026rsquo;, 173 (\u0026plusmn;\u0026thinsp;74 SD) s for patients classified as \u0026lsquo;yellow\u0026rsquo;, and 205 (\u0026plusmn;\u0026thinsp;100 SD) s for patients classified as \u0026lsquo;green\u0026rsquo; is comparable to data from the literature. Carles et al. reported that mean triage time was 147 (\u0026plusmn;\u0026thinsp;105 SD) s in a real MCI response toward the terrorist attack in Nice in 2016.(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) The reported data are based on 25 category I (\u0026lsquo;red\u0026rsquo;) and 44 category II (\u0026lsquo;yellow\u0026rsquo;) patients.(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) However, a significant proportion of undertriage was also observed (11.6% in category I). In comparison with the publication on the validation of the Berlin triage algorithm, on which the algorithm used is largely based, slightly better triage results were achieved.(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) The use of geolocators made it possible to identify falsely triaged case vignette. For instance, there was a recurrent under-triaging of case vignettes involving blast trauma with blood flow from the ear (n\u0026thinsp;=\u0026thinsp;6 out of 7), such that this could be explicitly trained after the exercise and incorporated into the algorithm. It is important to consider other potential factors that may contribute to the observed phenomenon, including but not limited to spontaneous deviations from the established algorithm or the performance of the patient actors. This assumption is reinforced by the finding that the Berlin triage algorithm delivered superior results in a study with standardized case vignettes than could be demonstrated in full-scale exercises.(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) Further evaluation of this issue is recommended for future consideration, with the potential utilisation of geolocators as a useful instrument in this regard. The proportion of undertriage identified in this study is consistent with the findings of analyses conducted using other algorithms.(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) Another study by Vargas et al.(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) described the results of two triage groups. The initial accuracy was reported as 45.76% in the low experience group, 45.84% in the overtriage group, and 8.38% in the undertriage group. In the high experience group, the initial accuracy was 53.80%, overtriage 37.66%, and undertriage 8.57%. Subsequent training of both groups resulted in a significant enhancement of these results.(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) In this particular analysis, overtriage was found to be a negligible issue, with a rate of approximately 2%. The results of the simulation can serve as a benchmark against which other hospitals can measure their own emergency exercises.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eWorkload and Perceived Stress\u003c/h2\u003e\u003cp\u003eIn accordance with the findings of preceding studies, the NASA-TLX may be utilised as a metric to evaluate the pedagogical adequacy of scenarios, with the capacity to accurately reflect the stress levels experienced by participants.(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) In the present study, the overall workload of all participants was already in the upper stress level, with a mean score of 66.7 (\u0026plusmn;\u0026thinsp;16 SD). A meta-analysis of over 200 studies revealed that the 25th and 75th percentiles ranged from 39 to 61.(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) Interestingly, work experience and not profession or gender has been described to be related with increased self-reported perceived preparedness for MCIs.(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Our data support this finding, by demonstrating that specialists and participants with a higher professional experience report less perceived overall workload in the NASA-TLX (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and B). Another finding shows that although \"frustration\" was rated relatively low, it was higher among younger participants and resident physicians than among specialists (see Supplemental Table\u0026nbsp;1). These two findings demonstrate the efficacy of the NASA-TLX survey in identifying vulnerable groups within the staff population. It is recommended that these groups be given special attention in the preparation and follow-up of training and real-life scenarios. Another potential application of NASA-TLX is in full-scale simulations, with the purpose of providing a data set for the purpose of benchmarking the development and the evaluation of MCI training scenarios. Additionally, it could be used to monitor the effects of introducing new technologies or instruments in one's own hospital during follow-up exercises.(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) In a simulation study on MCI management, for example, a comparison of two groups showed that the group with AI support had a significantly lower workload in the NASA-TLX score compared to the control group.(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eTeam Performance\u003c/h3\u003e\n\u003cp\u003eIt has been demonstrated by preceding studies that non-technical skills are conducive to effective medical performance and can be enhanced through training.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) In this MCI simulation, interprofessional and interdisciplinary teams of three participants including a minimum of one physician were formed. The TEAM was thus employed as a reliable and validated instrument for the assessment of non-technical skills, with these skills being based on leadership qualities, teamwork, and task management.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) The findings of the present study demonstrate that, in the face of a challenging environment and a considerable workload, the healthcare professionals exhibited above-average team performance, as evidenced by the NASA-TLX results. The mean TEAM item score was 3.2 (\u0026plusmn;\u0026thinsp;0.26) and the total TEAM score was 79.8%. A total of five simulation-based studies of professional teams reported a TEAM score ranging from 71.6% to 89.0%, with an average of 79%.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) A previous study suggests that a total TEAM score\u0026thinsp;\u0026le;\u0026thinsp;33 equates \u0026lsquo;poor\u0026rsquo;, 34\u0026ndash;39 good and \u0026ge;\u0026thinsp;40 excellent performance.(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) TEAM is a well-established instrument for the evaluation of emergency teams in acute situations, with documented improvements in scores following training sessions.(\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) A survey of the literature reveals no precedent for the utilization of the TEAM questionnaire in MCI scenarios or simulations. Consequently, this study offers preliminary findings that can serve as a benchmark for future simulations and exercises in this domain.\u003c/p\u003e\u003cp\u003eA study published from Norway in 2025 suggests that many hospitals provide little education (15%) or training (26%) for major incidents involving a mass casualty situation.(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e) It is reasonable to assume that the situation is similar in other regions. Publications from Germany suggest that exercises were carried out in between a quarter and a third of hospitals.(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e) The authors emphasize that it is crucial to back up each exercise with solid scientific research to gain the most valuable insights.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eIn this simulation, only adult trauma patients were simulated, which limits the generalizability of our findings to scenarios involving pediatric casualties or patients with other injury pattern. Pediatric patients require distinct triage algorithms and different care structures that are specifically tailored to the physiological and developmental needs of children. The participation in this exercise and the associated study was voluntary, which introduces potential biases that may affect the generalizability of the results. Participants who chose to take part may differ systematically from those who did not, possibly in terms of motivation, experience, or perceived competence in handling crisis situations. This selection bias could lead to an overestimation of the effectiveness of the training, as more motivated or experienced staff might perform better in a simulation setting. As the TEAM is primarily designed as an observer-rated tool, direct comparison is not possible. Thus, TEAM has been successful for its application as a tool for participants rating the non-technical skill perception of their respective team. While our findings provide valuable insights into the dynamics of patient flow and resource allocation, they should be interpreted with caution when applied to facilities with differing infrastructure.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSummary\u003c/h2\u003e\u003cp\u003eThe implications of our findings are significant for the evidence-based development of hospital emergency response plans. These insights can help other hospitals analyze their processes and identify potential inefficiencies that may exist regardless of the specific infrastructure. Furthermore, by offering a dataset that combines both objective and subjective metrics, this study strenghtens the evidence base on hospital preparedness for MCIs, facilitating the comparability of exercises across institutions and over time when repeated. These findings can inform analyses of learning curves, highlight areas for improvement, and strengthen hospitals' readiness for future emergency scenarios.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of Interest\u003c/h2\u003e\u003cp\u003eAll authors declare no financial or non-financial competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003ePatient and Public Involvement\u003c/h2\u003e\u003cp\u003eDue to the nature and objective of this study, especially with health care staff acting as participants of this study, there was no involvement of patients or the public in design, conduct, reporting and dissemination of this study.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eOpen Access funding was enabled and organized by Projekt DEAL. Further, this project was part of the European Union\u0026rsquo;s Horizon 2020 project Medical First Responder Training using a Mixed Reality Approach featuring haptic feedback for enhanced realism (MED1stMR; grant number 101021775). G.A.S. is supported by the Clinician-Scientist program of Medical Faculty, University of Heidelberg. The funders had no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization and Project administration: MF, HS, SM, EP, GAS; Data collection: FM, HS, SM, CL, AB, MOF, GAS; Formal analysis and Methodology: MF, HS, EF, GAS; Visualization: MF, GAS; Writing \u0026ndash; original draft: MF, GAS; Writing \u0026ndash; review and editing: All authors; Resources: HGK, MH, ML, MWC, CL, MAW, EP; Supervision: SM, EP. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eSet up of the RTLS system and technical support during the exercise was provided by Favendo GmbH (Bamberg, Germany). The authors express their gratitude to all employees involved in planning and conduction of the full-scale exercise. Further, the authors express their gratitude to prehospital emergency services and firefighter brigades, who participated in the exercise.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eFurther information including geo-tracking video data is available in the supplement to this manuscript (Supplemental Video 1). Impressions of the entire exercise can also be gained from a publicly accessible video: https://www.youtube.com/watch?v=Pfu4D1Xh5CQ. Anonymized data are provided by the corresponding authors upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCooke MW, Brace SJ. Training for disaster. Resuscitation. 2010;81(7):788\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWynter S, Nash R, Gadd N. Major Incident Hospital Simulations in Hospital Based Health Care: A Scoping Review. Disaster Med Public Health Prep. 2023;17:e477.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWalls RM, Zinner MJ. The Boston Marathon Response. JAMA. 2013;309(23):2441.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGates JD, Arabian S, Biddinger P, Blansfield J, Burke P, Chung S, et al. The Initial Response to the Boston Marathon Bombing. Ann Surg. 2014;260(6):960\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGhanchi A. Insights Into French Emergency Planning, Response, and Resilience Procedures From a Hospital Managerial Perspective Following the Paris Terrorist Attacks of Friday, November 13, 2015. Disaster Med Public Health Prep. 2016;10(5):789\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHirsch M, Carli P, Nizard R, Riou B, Baroudjian B, Baubet T, et al. The medical response to multisite terrorist attacks in Paris. Lancet. 2015;386(10012):2535\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoth P, Roth J, Bieler D, Friemert B, Franke A, Paffrath T, et al. Education and training as a key enabler of successful patient care in mass-casualty terrorist incidents. Eur J Trauma Emerg Surg. 2023;49(2):595\u0026ndash;605.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLau PF, Lau CC. A disaster drill in Hong Kong. Accid Emerg Nurs. 1997;5(1):34\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHays MB, Stefanki JX, Cheu DH. Planning an airport disaster drill. Aviat Space Environ Med. 1976;47(5):556\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoniewicz K, Goniewicz M, Włoszczak-Szubzda A, Burkle FM, Hertelendy AJ, Al-Wathinani A, et al. The importance of pre-training gap analyses and the identification of competencies and skill requirements of medical personnel for mass casualty incidents and disaster training. BMC Public Health. 2021;21(1):114.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTallach R, Schyma B, Robinson M, O\u0026rsquo;Neill B, Edmonds N, Bird R et al. Refining mass casualty plans with simulation-based iterative learning. Br J Anaesth [Internet]. 2022;128(2):e180\u0026ndash;9. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/j.bja.2021.10.004\u003c/span\u003e\u003cspan address=\"10.1016/j.bja.2021.10.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWatkins N, Johnston ANB, McNamee P, Muter N, Huang C, ling Li Y et al. Preparing for Mass Casualties: Improving Staff Preparedness and Hospital Operations through Multidisciplinary Simulation Training in Disaster Management. Prehosp Disaster Med [Internet]. 2019;34(s1):s81\u0026ndash;2. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1017/S1049023X19001717\u003c/span\u003e\u003cspan address=\"10.1017/S1049023X19001717\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbualenain J, Alhajaji R, Kamel Alsulimani L. A Systematic Review of the Efficacy of Full-Scale Simulation Exercises in Enhancing Hospital Disaster Preparedness. The Journal of Medicine, Law \u0026amp; Public Health [Internet]. 2024;4(3):419\u0026ndash;46. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.52609/jmlph.v4i3.133\u003c/span\u003e\u003cspan address=\"10.52609/jmlph.v4i3.133\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHsu EB, Thomas TL, Bass EB, Whyne D, Kelen GD, Green GB. Healthcare worker competencies for disaster training. BMC Med Educ. 2006;6:1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePek JH, Quah LJJ, Valente M, Ragazzoni L, Della Corte F. Use of Simulation in Full-Scale Exercises for Response to Disasters and Mass-Casualty Incidents: A Scoping Review. Prehosp Disaster Med. 2023;38(6):792\u0026ndash;806.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchulz F, Nguyen Q, Baetzner A, Sj\u0026ouml;berg D, Gyllencreutz L. Exploring medical first responders\u0026rsquo; perceptions of mass casualty incident scenario training: a qualitative study on learning conditions and recommendations for improvement. BMJ Open. 2024;14(7):e084925.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evon Elm E, Altman DG, Egger M, Pocock SJ, G\u0026oslash;tzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol [Internet]. 2008;61(4):344\u0026ndash;9. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://linkinghub.elsevier.com/retrieve/pii/S0895435607004362\u003c/span\u003e\u003cspan address=\"https://linkinghub.elsevier.com/retrieve/pii/S0895435607004362\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHart SG, Staveland LE. In. Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research. 1988. pp. 139\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHart SG. Nasa-Task Load Index (NASA-TLX); 20 Years Later. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 2006;50(9):904\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePrytz PEG, Rybing MJ, Jonson PCO. Workload differences across command levels and emergency response organizations during a major joint training exercise. J Emerg Manage. 2016;14(4):289\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCooper S, Connell C, Cant R. Review article: Use of the Team Emergency Assessment Measure in the rating of emergency teams\u0026rsquo; non-technical skills: A mapping review. Emerg Med Australasia. 2023;35(3):375\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCooper S, Cant R, Porter J, Sellick K, Somers G, Kinsman L, et al. Rating medical emergency teamwork performance: Development of the Team Emergency Assessment Measure (TEAM). Resuscitation. 2010;81(4):446\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFreytag J, Stroben F, Hautz WE, Schauber SK, K\u0026auml;mmer JE. Rating the quality of teamwork\u0026mdash;a comparison of novice and expert ratings using the Team Emergency Assessment Measure (TEAM) in simulated emergencies. Scand J Trauma Resusc Emerg Med. 2019;27(1):12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarkou-Pappas N, Lamine H, Ragazzoni L, Caviglia M. Key performance indicators in pre-hospital response to disasters and mass casualty incidents: a scoping review. Eur J Trauma Emerg Surg. 2024;50(5):2029\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin CH, Tzeng WC, Chiang LC, Lu MC, Lee MS, Chiang SL. Effectiveness of a Structured Disaster Management Training Program on Nurses\u0026rsquo; Disaster Readiness for Response to Emergencies and Disasters: A Randomized Controlled Trial. J Nurs Manag. 2024;2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParra Cotanda C, Rebordosa Mart\u0026iacute;nez M, Trenchs, de la Maza S, Luaces Cubells V. C. Impact of a disaster preparedness training programme on health staff. Anales de Pediatr\u0026iacute;a (English Edition) [Internet]. 2016;85(3):149\u0026ndash;54. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://linkinghub.elsevier.com/retrieve/pii/S2341287916300953\u003c/span\u003e\u003cspan address=\"https://linkinghub.elsevier.com/retrieve/pii/S2341287916300953\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eErs\u0026ouml;z Gen\u0026ccedil; E. Improving Disaster Preparedness Among Healthcare Professionals: A Comprehensive Approach. Eurasian Journal of Emergency Medicine [Internet]. 2025; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://eajem.com/articles/improving-disaster-preparedness-among-healthcare-professionals-a-comprehensive-approach/doi/eajem.galenos.2025.51482\u003c/span\u003e\u003cspan address=\"https://eajem.com/articles/improving-disaster-preparedness-among-healthcare-professionals-a-comprehensive-approach/doi/eajem.galenos.2025.51482\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOzella L, Gauvin L, Carenzo L, Quaggiotto M, Ingrassia PL, Tizzoni M, et al. Wearable Proximity Sensors for Monitoring a Mass Casualty Incident Exercise: Feasibility Study. J Med Internet Res. 2019;21(4):e12251.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTolg B, Lorenz J. An analysis of movement patterns in mass casualty incident simulations. Adv Simul. 2020;5(1):27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eA\u0026ccedil;ıksarı K, Ko\u0026ccedil;ak M, Solakoğlu GA, Bodas M. The effect of multiple triage points on the outcomes (time and accuracy) of hospital triage during mass casualty incidents. Injury. 2024;55(5):111318.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAshkenazi I, Kessel B, Khashan T, Haspel J, Oren M, Olsha O, et al. Precision of In-Hospital Triage in Mass-Casualty Incidents after Terror Attacks. Prehosp Disaster Med. 2006;21(1):20\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarles M, Levraut J, Gonzalez JF, Valli F, Bornard L. Mass casualty events and health organisation: terrorist attack in Nice. Lancet. 2016;388(10058):2349\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKleber C, Solarek A, Cwojdzinski D, Strehl M, Wrede C, Heinz J, et al. The Berlin mass casualty hospital triage algorithm: Development, implementation and influence on exercise-based triage results. Unfallchirurg. 2020;123(3):187\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeller AR, Neidel T, Klotz PJ, Solarek A, Kowalzik B, Juncken K, et al. Validierung innerklinischer Sichtungsalgorithmen f\u0026uuml;r den Massenanfall von Verletzten \u0026ndash; eine simulationsbasierte Studie \u0026ndash; deutsche Version. Die Anaesthesiologie. 2023;72(7):467\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBazyar J, Farrokhi M, Salari A, Safarpour H, Khankeh HR. Accuracy of Triage Systems in Disasters and Mass Casualty Incidents; a Systematic Review. Arch Acad Emerg Med. 2022;10(1):e32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVargas MMJP, Hubloue MPI, Pinz\u0026oacute;n MJJ, Duque MAC. The effect of training and experience on mass casualty incident triage performance: Evidence from emergency personnel in a high complexity university hospital. Am J Disaster Med. 2019;14(2):113\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFavre-F\u0026eacute;lix J, Dziadzko M, Bauer C, Duclos A, Lehot JJ, Rimmel\u0026eacute; T, et al. High-Fidelity Simulation to Assess Task Load Index and Performance: A Prospective Observational Study. Turk J Anaesthesiol Reanim. 2022;50(4):282\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrier RA. How High is High? A Meta-Analysis of NASA-TLX Global Workload Scores. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 2015;59(1):1727\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLochmannov\u0026aacute; A. Exploring the role of virtual reality in preparing emergency responders for mass casualty incidents. Isr J Health Policy Res. 2025;14(1):22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Z, Lorenz, Souza WH, Han J, Madani A, Using. AI to Optimize Patient Transfer and Resource Utilization During Mass-Casualty Incidents: A Simulation Platform [Internet]. 2025 Sep [cited 2025 Sep 17]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://arxiv.org/abs/2509.08756\u003c/span\u003e\u003cspan address=\"https://arxiv.org/abs/2509.08756\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoet S, Etherington C, Larrigan S, Yin L, Khan H, Sullivan K, et al. Measuring the teamwork performance of teams in crisis situations: a systematic review of assessment tools and their measurement properties. BMJ Qual Saf. 2019;28(4):327\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCooper S, Cant R, Connell C, Sims L, Porter JE, Symmons M, et al. Measuring teamwork performance: Validity testing of the Team Emergency Assessment Measure (TEAM) with clinical resuscitation teams. Resuscitation. 2016;101:97\u0026ndash;101.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCant RP, Porter JE, Cooper SJ, Roberts K, Wilson I, Gartside C. Improving the non-technical skills of hospital medical emergency teams: The Team Emergency Assessment Measure (\u003cem\u003eTEAM\u003c/em\u003e \u0026trade;). Emerg Med Australasia. 2016;28(6):641\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCooper S, Cant R, Porter J, Missen K, Sparkes L, McConnell-Henry T, et al. Managing patient deterioration: assessing teamwork and individual performance. Emerg Med J. 2013;30(5):377\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDoymaz S, Rizvi M, Giambruno C. Improving the Performance of Residents in Pediatric Resuscitation with Frequent Simulated Codes. Glob Pediatr Health. 2020;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUgelvik KS, Thomassen \u0026Oslash;, Braut GS, Geisner T, Sj\u0026oslash;vold JE, Mont\u0026aacute;n C. A national study of in-hospital preparedness for Mass Casualty Incidents and disasters. Eur J Trauma Emerg Surg. 2025;51(1):18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePfenninger EG, Villhauer S, K\u0026ouml;nigsdorfer M. Hospital disaster planning in south-western Germany. A survey of 214 clinics. Notfall und Rettungsmedizin. 2022;1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoth P, Bieler D, Friemert B, Franke A, Bl\u0026auml;tzinger M, Achatz G. Sicherheitsaspekte und Vorbereitung zur Notfallvorsorge und Gefahrenabwehr in Kliniken bei MANV/TerrorMANV. Die Unfallchirurgie. 2022;125(7):542\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mass Casualty Incident, Disaster Medicine, Hospital Disaster Planning, Critical Infrastructure, Leadership, Communication, Incident Command","lastPublishedDoi":"10.21203/rs.3.rs-7859560/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7859560/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMass casualty incidents (MCIs) rapidly exceed routine hospital capacity. Full-scale exercises are essential for preparedness, but systematic, multidimensional evaluations remain scarce. This study aimed to evaluate overall team performance in triage accuracy, workflow, and individual workload throughout a MCI exercise.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn a prospective observational study at Heidelberg university hospital (Germany), healthcare professionals managed 91 simulated casualties using a two-stage triage process. Patients and staff carried location tags enabling continuous spatiotemporal tracking. Objective outcomes included triage accuracy, triage duration, patient flow, and staff–patient contact frequency. Subjective workload and teamwork were assessed using the NASA Task Load Index (NASA-TLX) and the Team Emergency Assessment Measure (TEAM), respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall triage accuracy was 75.4%. Undertriage occurred in 11.6% of category I and 10.1% of category II cases; overtriage was infrequent (2.9%). Mean triage times differed significantly by category: ‘red’ 59 ± 25 s, ‘yellow’ 173 ± 74 s, ‘green’ 205 ± 100 s (p \u0026lt; 0.0001). Geotracking demonstrated consistent patient flow without detectable bottlenecks and a mean of 7.1 ± 5.7 patient contacts per staff member. NASA-TLX scores indicated high temporal demand but low frustration with an overall workload of 66.7 ± 16; specialists and staff with greater professional experience reported significantly lower workload (p \u0026lt; 0.05). TEAM ratings were homogeneously high across all participants (79.8%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study provides reproducible benchmark data on hospital MCI response. The integration of geotracking with subjective measures, enables a comprehensive evaluation of hospital disaster preparedness. Moreover, the ability to compare different exercises and collect reliable longitudinal data may further enhance hospital disaster response.\u003c/p\u003e","manuscriptTitle":"Improving Preparedness for Mass Casualty Incidents in Hospitals: Insights from a Large-Scale Simulation Exercise with Geotracking and Validated Questionnaires","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-22 13:20:31","doi":"10.21203/rs.3.rs-7859560/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f1313f7b-3448-4822-bd4d-104e296b71ae","owner":[],"postedDate":"October 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-22T13:20:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-22 13:20:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7859560","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7859560","identity":"rs-7859560","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}