Presentation
Simulation Training for Incident Commanders: Enhancing Management and Clinical Skills
DescriptionINTRODUCTION
Mass-casualty incidents (MCIs) are emergencies where the number of casualties overwhelms the available resources for immediate medical treatment and evacuation (Rimstad & Braut, 2015). To effectively handle these situations, paramedics receive training to develop essential Incident Commander (IC) skills, where both management (Perry et al., 2021), decision-making (Perry et al., 2023), information-management (Perry et al., 2021), communication (Rimstad & Braut, 2015) ,and clinical skills (King et al., 2006) are crucial.
The process of skill acquisition can be understood through the skill acquisition theory (Taatgen et al., 2008). This theory outlines three stages of skill acquisition: declarative knowledge, procedural knowledge, and automaticity. In the declarative knowledge stage, trainees acquire the foundational knowledge needed to solve a problem (Logan, 1988). In the procedural knowledge stage, they develop a general problem-solving strategy (Newell & Rosenbloom, 2013). Finally, in the automaticity stage, trainees execute the procedure with greater speed and reduced cognitive effort (Jamshidifarsani et al., 2021).
Current IC training methods deliver declarative knowledge through courses and online courses (Currie et al., 2018), procedural knowledge through field simulations (Gordon et al., 2016), and automaticity through simulation environments, including gaming simulations and virtual reality (VR) (Heinrichs et al., 2010). However, the effect of these simulation environments on incident commanders' skill acquisition remains inconclusive and varies by skill type (Shubeck et al., 2016). To address this, a simulation framework was created to train, assess, evaluate, and provide feedback on IC skills, encompassing both management and clinical skills (Perry et al., 2024a). A study that is using a simulator that is based on the framework was developed to train IC trainees and track their skill acquisition over different MCI scenarios. The goal of this study is to evaluate the simulator's effectiveness in improving these critical skills.
METHODS
Mathematical Formulation
The mathematical model is based on Respiratory rate, Pulse rate, and Motor response (RPM) scores and previous MCI research operation models (Dean & Nair, 2014), and its goal is to assess and measure how management and clinical IC skills affect the casualties’ survivability upon departure from the MCI site. As trainees improve their management and clinical skills, both the time taken to complete the simulation and the average survival probability loss will decrease. The formulation models an MCI site with a predefined set of casualties, categorized into different triage classifications. Each casualty is assigned an RPM score ranges between 0-12, a survival function, and a required treatment time before evacuation. The survival function is a nonlinear, monotonic decreasing function based on the casualty's respiratory, pulse, and motor function scores, reflecting the severity of their injuries.
The emergency response involves a set of medical units, each equipped with specific skills. Each casualty must receive treatment and then be loaded onto an ambulance, which departs for the hospital once full. The total time is composed of the time from the IC arrival at the site until casualty reaches the treatment area, the waiting time for treatment, the treatment time, the waiting time for loading onto an ambulance, the loading time, and the waiting time for the ambulance to depart the site after loading. The IC’s objective is to minimize the utility function, which represents the average survival probability loss of all casualties. As the trainee’s management and clinical skills will improve the both the time to complete the simulation and the average survival probability loss will be reduced.
MCI Simulator Design
To address the research question of this study, an MCI simulator was developed using the GODOT® game engine.
The data for this simulation comprises ten MCI scenarios from a paramedic training course, where students were EMS medics with 2 to 3 years of experience as military medics but no prior experience as ICs. The data was collected based on Perry et al. (2021) data collection method specifically for MCI simulations. This type of simulation is recognized as a high-fidelity, realistic environment for training in incident management (Son et al., 2020).
The simulator consists of three phases: evaluation and triage, treatment and preparation for transportation, and evacuation based on triage classifications (Perry et al., 2024b). In the first phase, the commander evaluates the number of casualties on site. During the second phase, casualties are moved to the treatment area, where the IC must complete various tasks to prepare for their evacuation while additional medical teams arrive. In the third phase, the IC prioritizes casualties for treatment and evacuation according to their triage classifications. To evaluate the management and clinical skills, two metrics were established: the time taken to complete the simulation, evaluating the management skill, and the loss of casualty survival probabilities, evaluating the clinical skill.
Participants and Study Design
A study was conducted involving 22 participants, all of whom were paramedic students training to become an IC. Each participant had completed an MCI course that provided the essential knowledge for the IC role, covering the phases of MCI management, the specific tasks of the IC at each phase, and the Emergency Medical Services (EMS) protocols for MCI response. At the beginning of the study, each participant received an introduction to the simulator from an instructor.
Following this, each participant completed a trial scenario with an instructor to familiarize themselves with the simulator's mechanics and operation. Each participant then completed three scenarios, each involving 15 casualties with varying RPM scores and three medical units for treatment and evacuation. To introduce variability between scenarios, the number of casualties and the severity of injuries were randomized at the beginning of each scenario. After completing each scenario, participants received feedback, which included the deterioration in survival probability for all casualties and the total time taken to complete the simulation. To assess participants' improvement across scenarios, the Mann-Whitney U-test was used. In total, the data comprised 66 scenarios, with each participant completing three.
RESULTS
In terms of casualties’ survival probabilities loss, there was a significant difference in the decline of the median survival probabilities loss between the first and second scenario (x ̃_1=0.34,x ̃_2=0.25, p<0.001). However, no significant difference was observed between the medians of the second and third scenario (x ̃_2=0.25,x ̃_3=0.24, p=0.15). For the time for simulation completion, there was a significant difference between the median completion time between scenario 1 and scenario 2 (x ̃_1=613 (seconds) ,x ̃_2=484 (seconds), p<0.001). However, no significant difference was observed between the medians of scenario 2 and scenario 3 (x ̃_2=484 (seconds), x ̃_3=449 (seconds), p=0.16).
DISCUSSION
The results indicate that participants demonstrated improvements in both clinical and management skills. Specifically, they completed tasks more quickly, utilized medical units more effectively for treatment and evacuation, and prioritized casualties requiring urgent care. Consequently, our research goal regarding the effectiveness of the simulator to improve the management and clinical skills was achieved.
The current study offers a twofold contribution. Theoretically, the simulator allows trainees to improve their IC skills and shows promise in facilitating trainees' transition from declarative and procedural knowledge to automaticity. Researchers seeking to advance training simulators can leverage the framework and the training simulator design to develop simulator to further improve the IC skills. Practically, EMS can utilize the framework and develop simulators to enhance the training of their IC personnel, aiming to achieve automaticity in skill execution.
CONCLUSION
This study employed the training simulation to enhance the management and clinical skills of IC trainees. These results should encourage researchers and practitioners to utilize the framework and training simulator design to further advance IC skills.
Mass-casualty incidents (MCIs) are emergencies where the number of casualties overwhelms the available resources for immediate medical treatment and evacuation (Rimstad & Braut, 2015). To effectively handle these situations, paramedics receive training to develop essential Incident Commander (IC) skills, where both management (Perry et al., 2021), decision-making (Perry et al., 2023), information-management (Perry et al., 2021), communication (Rimstad & Braut, 2015) ,and clinical skills (King et al., 2006) are crucial.
The process of skill acquisition can be understood through the skill acquisition theory (Taatgen et al., 2008). This theory outlines three stages of skill acquisition: declarative knowledge, procedural knowledge, and automaticity. In the declarative knowledge stage, trainees acquire the foundational knowledge needed to solve a problem (Logan, 1988). In the procedural knowledge stage, they develop a general problem-solving strategy (Newell & Rosenbloom, 2013). Finally, in the automaticity stage, trainees execute the procedure with greater speed and reduced cognitive effort (Jamshidifarsani et al., 2021).
Current IC training methods deliver declarative knowledge through courses and online courses (Currie et al., 2018), procedural knowledge through field simulations (Gordon et al., 2016), and automaticity through simulation environments, including gaming simulations and virtual reality (VR) (Heinrichs et al., 2010). However, the effect of these simulation environments on incident commanders' skill acquisition remains inconclusive and varies by skill type (Shubeck et al., 2016). To address this, a simulation framework was created to train, assess, evaluate, and provide feedback on IC skills, encompassing both management and clinical skills (Perry et al., 2024a). A study that is using a simulator that is based on the framework was developed to train IC trainees and track their skill acquisition over different MCI scenarios. The goal of this study is to evaluate the simulator's effectiveness in improving these critical skills.
METHODS
Mathematical Formulation
The mathematical model is based on Respiratory rate, Pulse rate, and Motor response (RPM) scores and previous MCI research operation models (Dean & Nair, 2014), and its goal is to assess and measure how management and clinical IC skills affect the casualties’ survivability upon departure from the MCI site. As trainees improve their management and clinical skills, both the time taken to complete the simulation and the average survival probability loss will decrease. The formulation models an MCI site with a predefined set of casualties, categorized into different triage classifications. Each casualty is assigned an RPM score ranges between 0-12, a survival function, and a required treatment time before evacuation. The survival function is a nonlinear, monotonic decreasing function based on the casualty's respiratory, pulse, and motor function scores, reflecting the severity of their injuries.
The emergency response involves a set of medical units, each equipped with specific skills. Each casualty must receive treatment and then be loaded onto an ambulance, which departs for the hospital once full. The total time is composed of the time from the IC arrival at the site until casualty reaches the treatment area, the waiting time for treatment, the treatment time, the waiting time for loading onto an ambulance, the loading time, and the waiting time for the ambulance to depart the site after loading. The IC’s objective is to minimize the utility function, which represents the average survival probability loss of all casualties. As the trainee’s management and clinical skills will improve the both the time to complete the simulation and the average survival probability loss will be reduced.
MCI Simulator Design
To address the research question of this study, an MCI simulator was developed using the GODOT® game engine.
The data for this simulation comprises ten MCI scenarios from a paramedic training course, where students were EMS medics with 2 to 3 years of experience as military medics but no prior experience as ICs. The data was collected based on Perry et al. (2021) data collection method specifically for MCI simulations. This type of simulation is recognized as a high-fidelity, realistic environment for training in incident management (Son et al., 2020).
The simulator consists of three phases: evaluation and triage, treatment and preparation for transportation, and evacuation based on triage classifications (Perry et al., 2024b). In the first phase, the commander evaluates the number of casualties on site. During the second phase, casualties are moved to the treatment area, where the IC must complete various tasks to prepare for their evacuation while additional medical teams arrive. In the third phase, the IC prioritizes casualties for treatment and evacuation according to their triage classifications. To evaluate the management and clinical skills, two metrics were established: the time taken to complete the simulation, evaluating the management skill, and the loss of casualty survival probabilities, evaluating the clinical skill.
Participants and Study Design
A study was conducted involving 22 participants, all of whom were paramedic students training to become an IC. Each participant had completed an MCI course that provided the essential knowledge for the IC role, covering the phases of MCI management, the specific tasks of the IC at each phase, and the Emergency Medical Services (EMS) protocols for MCI response. At the beginning of the study, each participant received an introduction to the simulator from an instructor.
Following this, each participant completed a trial scenario with an instructor to familiarize themselves with the simulator's mechanics and operation. Each participant then completed three scenarios, each involving 15 casualties with varying RPM scores and three medical units for treatment and evacuation. To introduce variability between scenarios, the number of casualties and the severity of injuries were randomized at the beginning of each scenario. After completing each scenario, participants received feedback, which included the deterioration in survival probability for all casualties and the total time taken to complete the simulation. To assess participants' improvement across scenarios, the Mann-Whitney U-test was used. In total, the data comprised 66 scenarios, with each participant completing three.
RESULTS
In terms of casualties’ survival probabilities loss, there was a significant difference in the decline of the median survival probabilities loss between the first and second scenario (x ̃_1=0.34,x ̃_2=0.25, p<0.001). However, no significant difference was observed between the medians of the second and third scenario (x ̃_2=0.25,x ̃_3=0.24, p=0.15). For the time for simulation completion, there was a significant difference between the median completion time between scenario 1 and scenario 2 (x ̃_1=613 (seconds) ,x ̃_2=484 (seconds), p<0.001). However, no significant difference was observed between the medians of scenario 2 and scenario 3 (x ̃_2=484 (seconds), x ̃_3=449 (seconds), p=0.16).
DISCUSSION
The results indicate that participants demonstrated improvements in both clinical and management skills. Specifically, they completed tasks more quickly, utilized medical units more effectively for treatment and evacuation, and prioritized casualties requiring urgent care. Consequently, our research goal regarding the effectiveness of the simulator to improve the management and clinical skills was achieved.
The current study offers a twofold contribution. Theoretically, the simulator allows trainees to improve their IC skills and shows promise in facilitating trainees' transition from declarative and procedural knowledge to automaticity. Researchers seeking to advance training simulators can leverage the framework and the training simulator design to develop simulator to further improve the IC skills. Practically, EMS can utilize the framework and develop simulators to enhance the training of their IC personnel, aiming to achieve automaticity in skill execution.
CONCLUSION
This study employed the training simulation to enhance the management and clinical skills of IC trainees. These results should encourage researchers and practitioners to utilize the framework and training simulator design to further advance IC skills.
Event Type
Oral Presentations
TimeWednesday, April 29:30am - 10:00am EDT
LocationPier 9
Simulation and Education (SE)