1. Introduction
Trauma injuries are responsible for 9% of global mortality and are considered a risk all around the globe. These injuries result from traffic collisions, drowning, poisoning, falls or burns and violence, causing more than five million deaths worldwide annually [
1]. Moreover, a large number of those injuries cause temporary or permanent disabilities, incurring important consequences on the patients’ lives. Therefore, a fast identification and management of trauma injuries is of great importance. To do that, a systematic and rapid approach should be applied [
2].
The Advanced Trauma Life Support (ATLS) course was created in 1978 by the American College of Surgeons, and it is currently taught in over 60 countries [
3]. This course has used a variety of simulation modalities to teach trauma management and, since then, other trauma management courses have arisen [
4,
5,
6,
7]. Additionally, regarding pre-hospital trauma management, the two courses mainly referenced being the Pre-Hospital Trauma Life Support (PHTLS) course [
8,
9,
10] and the International Trauma Life Support (ITLS) course [
11,
12], which focus on education for first responders and also use simulations, as well as classroom sessions. Trauma training focuses on several aspects that can be classified into technical skills and non-technical skills. Technical skills refer to the application of a correct triage, primary and secondary surveys, including the techniques and treatments needed to achieve that. Non-technical skills focus on communication, leadership, management of situations and decision making. Even though both types of skills are intrinsically related, some training focuses only on technical skills, others in non-technical skills and others on both.
In this context, it is important to highlight the role of clinical simulations. Clinical simulations started to support clinical training by taking into account patient safety [
13,
14,
15,
16], but it also offers some other benefits, such as the opportunity to repeat a simulation as many times as needed, or to train a great variety of technical and non-technical skills [
17,
18,
19,
20]. Nevertheless, there is still limited evidence on the impact of simulation-based training on the performance in trauma management [
14,
16] and on the long-term knowledge retention of such training [
16,
21]. Clinical simulators are classified according to the concept of fidelity, with the simulated model’s relation to its closeness to reality being the main classification: low-, medium- and high-fidelity simulators. Low-fidelity simulators are anatomical representations of a part of the body to train simple tasks and to acquire basic motor skills to be able to develop those tasks. It is generally composed of low technology. Medium-fidelity simulators integrate low-complexity software programs that allow manipulating physiological variables to assess knowledge during decision making in environments such as cardiopulmonary resuscitation. Finally, high-fidelity simulators are life-size mannequins that integrate mechanical devices and computer technology to train advanced techniques and skills in handling critical situations. In principle, high-fidelity simulations are the best option; however, according to [
22,
23,
24], there is no important difference with respect to knowledge and skill improvements of high-fidelity compared to low-fidelity simulators. In [
22], the skill performance evolution comparing low-fidelity and high-fidelity simulations is studied in a systematic review. This study shows that, in the short-term, the use of high-fidelity simulators provides a moderate benefit compared to low-fidelity; however, in the long term, no benefits are obtained. Additionally, in [
23], a study in simulated neonatal resuscitations is presented. It shows no differences after training with a low-fidelity or a high-fidelity simulator. Finally, a randomized control trial with more than 100 undergraduate students was conducted using low- and a high-fidelity simulators [
24]. The conclusion was that the improvement obtained was similar after both training courses.
Therefore, a review has been accomplished to analyze the current practice in teaching trauma management using simulations with the aim of summarizing them, identifying gaps and providing a critical overview on what has been achieved in terms of trauma training.
2. Clinical Practice of Trauma Management Training
A search was performed on the Web of Science website. The initial search provided 1617 publications including on this review, finally, 35 articles. These articles focused on trauma training and provided studies on how different simulation training techniques could impact trauma management training. The article selection process is shown in Figure 1.
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)
The main topics analyzed within this review are: the target audience of the trauma trainings, the simulation methods used, the types of skills acquired after the training, the evaluation methods used and the context in which the simulations take place. To analyze all the articles, the following information was gathered: authors, publication type, year of publication, total number of citations, population of training, simulation method used, skills trained, evaluation type used for the simulation method presented in the paper, if skills improved after the training and the context in which the simulation took place. The main takeaways are summarized below.
Taking a look at the results obtained for the target audience of the trauma trainings, it stood out that there was scarcity in trauma management training courses for medical students. Moreover, according to [
16,
57], the best simulation method and procedure to teach trauma management to medical students have not yet been established. This is a field that needs further development, as medical students should be trained on trauma management skills considering that they are soon-to-be residents. As residents, they are going to be the first attendants in the hospital; therefore, having specific trauma training would allow for better treatments for patients [
20,
30,
58]. Additionally, trauma training supports clinical reasoning learning. This is key for clinical practice, and could be obtained with trauma management training [
48].
According to Lewis C. and Veal B. [
21], none of the 29 articles included in the review could demonstrate a significant objective impact on the mortality and morbidity of trauma patients; therefore, there is still more research needed in this field. Nonetheless, there are studies [
29,
30,
59] that support and present statistical improvements in trauma management performance after simulation training. They confirm that, if the correct simulation modality is used, the expected outcome of the patient could be more easily predicted [
2]. Therefore, it is important to know the different simulation modalities and how they should be implemented within trauma management training. In [
2], it was stated that trauma training uses both low- and high-fidelity training modalities. Low-fidelity training allows to reproduce and practice technical skills such as airway management, whereas high-fidelity training offers the possibility to train both technical and non-technical skills. Additionally, standardized patients could also be used to train non-technical skills and, if properly garbed with the appropriate modules, some technical skills could also be practiced. Moreover, virtual reality is currently increasing its presence, as it allows to connect multiple users at multiple locations, increasing availability to centers with limited resources. This simulation modality offers the possibility to immerse learners within authentic clinical scenarios at a low cost.
With respect to the simulation methods used, traditional simulation methods try to imitate real patient simulations. That is the reason why high-fidelity mannequins or actors have been widely used [
27,
60]. Nevertheless, technology allows for the development of other simulation methods that could offer solutions to the limitations that actors and mannequins have. Simulations with actors have limitations, as some techniques cannot be applied. High-fidelity mannequins are expensive models that require specific technical requirements and resources. Virtual reality offers a solution to these limitations, as it allows trainees to immerse themselves in the situation, enabling them to accomplish different trauma scenarios without compromising the patient and allowing institutions to train a large number of trainees [
26,
47,
48,
61]. However, each simulation method has its advantages and disadvantages and, therefore, a further reflection is needed with respect to the selection of simulation method, as stated in [
2,
16]. It is also important to consider, for the selection of the simulation method, which skills to train.
Taking into account the results obtained for the skills trained, there was still a majority of simulation-based training courses that focused on technical skills. The goal of these training courses is to teach complex and specific skills [
53]. Moreover, the number of training courses that consider non-technical skills is increasing [
43,
62,
63]. That is the reason why the number of simulation-based training courses that consider both types of skills is also higher. It is important to highlight that the articles included in this scoping review focused on individual training. Therefore, it makes sense that more articles focused on training technical skills. The training courses that focus on non-technical skills prefer training in groups or teams, as this allows to practice those skills. Consequently, and as previously highlighted, depending on the skills to train, one simulation method can be better than another.
According to [
15], medical training courses that use simulations should be adapted to the level and the type of education. Therefore, this article considered undergraduate teaching, postgraduate teaching, continuing medical education, disaster management and military trauma management. Furthermore, according to [
2], the training courses should focus on the types of skills to train. Therefore, this article proposed to classify training courses into either task-oriented or non-technical-skill-oriented training, independently on the individual level and type of education of the trainee. The main idea behind these studies is identifying the focus of the training and then trying to find a simulation method that fits better with that focus, independent of the name provided to the focus of the training. It is clear that the trend is to incorporate non-technical skills within training courses in order to create a comprehensive trauma program; therefore, this trend is generally perceived in trauma training. Consequently, high-fidelity mannequins seem to be the best option; however, incorporating virtual reality together with low-fidelity mannequins could be another alternative with some advantages, such as the cost of the chosen simulation method used. For trauma training, as technical and non-technical skills need to be trained, simulation methods that combine low-, medium- and high-fidelity training should be considered.
Regarding the evaluation methods currently used in simulation-based training courses, only two of them considered the option of having an automated evaluation method [
31,
51]; however, either this was only partially considered, or the details on how automation was achieved were not explained. Therefore, the majority of the training courses analyzed in this scoping review did not offer an automated evaluation method, showing an important gap. It is true that there is an important discussion about how the evaluation of simulation-based training must be conducted [
18,
20,
27,
28,
38,
39,
44,
48,
51], but it is surprising that the majority of the articles did not even consider the option to include an automated option. Additionally, this was unforeseen, as the advantage of having high-fidelity mannequins or other simulation methods is that they allow for the possibility to gather objective information directly from them. That information would be extremely valuable, as it is entirely objective, which fits with the purpose of using the simulations to provide a more objective evaluation method [
40,
64]. Additionally, the objective information gathered with the simulation methods has a positive impact on trainees, as it provides high-quality feedback, which allows them to see the impact of their actions during the simulation. This supports skill learning and performance [
32,
65,
66]. As the majority of the trainings did not use an automated evaluation method, an analysis on which methods were used was performed. Many of the training courses used written evaluation forms or checklists. They were specifically developed for the trauma training provided, as stated in [
18,
27,
33,
34,
37]. This allowed for the evaluation process to be more objective, though not entirely, as the trainees’ answers to the questionnaires or checklists comprised their opinions on how the simulations occurred. That opinion is valid and necessary after a simulation-based training; however, evaluating the performance of the training only with this information should not be the case. Just four of the training courses used purely subjective evaluation methods that consisted of either personal interviews, written comments or evaluations conducted through direct observations. Finally, most of the articles analyzed in this scoping review focused on training either technical or non-technical skills for traumas that took place in a hospital environment; however, the presence of pre-hospital training is increasing [
17]. This situation highlights the importance of training all professionals involved in trauma scenarios in any of the environments in which the patient could be located, considering that the resources.
This entry is adapted from the peer-reviewed paper 10.3390/ijerph192013546