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Translational simulation revisited: an evolving conceptual model for the contribution of simulation to healthcare quality and safety


The simulation community has effectively responded to calls for a more direct contribution by simulation to healthcare quality and safety, and clearer alignment with health service priorities, but the conceptual framing of this contribution has been vague. The term ‘translational simulation’ was proposed in 2017 as a “functional term for how simulation may be connected directly with health service priorities and patient outcomes, through interventional and diagnostic functions” (Brazil V. Adv Simul. 2:20, 2017). Six years later, this conceptual framing is clearer. Translational simulation has been applied in diverse contexts, affording insights into its strengths and limitations. Three core concepts are identifiable in recently published translational simulation studies: a clear identification of simulation purpose, an articulation of the simulation process, and an engagement with the conceptual foundations of translational simulation practice. In this article, we reflect on current translational simulation practice and scholarship, especially with respect to these three core concepts, and offer a further elaborated conceptual model based on its use to date.


The healthcare simulation community was quick to step into a role in healthcare quality and patient safety in the late 1990s. Simulation offers a safe place to practice procedural skills, decision-making and teamwork without placing patients at risk. Education can be ‘on demand,’ scheduled to suit learners and teachers, without relying on opportunistic clinical encounters. Simulation can afford high-volume practice with feedback. Exemplar simulation programs have demonstrated measurable impacts on safety and quality outcomes [1,2,3]. Simulation designed and delivered within this educational paradigm continues to have an important role in supporting healthcare quality and safety.

However, reliance on educational paradigms may fail to realise the full potential of simulation to contribute to quality and safety in healthcare. Healthcare operates as a complex adaptive system, with rich interdependencies between providers, structural elements and social systems [4]. Performance—healthcare that is safe, effective, timely, patient-centred, efficient and equitable [5]—is reliant on more than the knowledge and skills of individuals or teams. For example, training nurses on the dangers of rapid intravenous potassium administration may not be enough to reduce adverse events until concentrated potassium ampoules are removed from wards and replaced with 500 ml bags of diluted potassium [6]. Knowing which blood products are required for a trauma patient will not be enough if there are ineffective systems for ordering and delivering those blood products to the trauma bay [7, 8]. Teaching healthcare practitioners to ‘speak up for safety’ will be ineffective if toxic hierarchies and cultural norms go unaddressed in healthcare environments [9]. Hence, an educational approach to improving patient safety is necessary, but insufficient, to fulfil the potential for simulation to contribute to improved healthcare outcomes [10].

In the article "Translational Simulation—not where but why" [11], terminology was suggested to reflect a revised conceptual framing for the contribution of simulation to healthcare quality and safety. Drawing on Berwick’s ‘Plan Do Study Act’ framework for quality improvement activities [12], this conceptual model illustrated an expanded range of opportunities that simulation practitioners should embrace for the purpose of improving patient care and health systems. Many of these opportunities had not been considered within the remit of healthcare simulation, which had previously focused largely on the education and training of health professionals. A structured approach to how health service outcomes might be achieved through simulation was described: simulation can be used to explore performance, and to test and embed improvements in systems and processes. Through these diagnostic and interventional functions, the scope of simulation in improving health service performance could be widened to include outcomes such as timeliness of care, cost-effectiveness, patient experience, efficiency of care, effectiveness of clinical pathways, team culture and adequacy of the physical environment [11].

The term ‘translational simulation’ drew upon language from the biological sciences research context, where ‘translational’ refers to basic science research evidence being applied to real-world practice. Framing the “bench-to-bedside enterprise of harnessing knowledge from basic sciences to produce new drugs, devices, and treatment options for patients” [13] as a translational activity has focused biomedical researchers on their ultimate purpose—the health of patients and populations.

“All models are wrong, but some are useful” [14]. With this aphorism in mind, we might ask: Why do we (or why does the field) need a conceptual framing for how simulation contributes to healthcare quality and safety, and its ongoing evolution? We suggest that a foundational framework would provide numerous benefits for health service leaders, healthcare simulation practitioners, and scholars. For health services, explicitly framing how healthcare simulation contributes to quality and safety can guide health service resource allocation and optimise the use of simulation capacity. Clearer framing may help health service leaders revise the view of simulation-based education as a financial burden on healthcare institutions, and instead recognise the value of simulation to address organisation and system objectives [15]. For simulation practitioners, a conceptual framing may provide a common language, and guide programs seeking to articulate their mission, vision and scope [15]. Emphasizing the ‘why’ of translational simulation activities—healthcare improvement—may encourage reflection on the methods and tools used in simulation design and delivery [16]. For scholars, a clear conceptual model supports voices arguing for explicit integration of theory as a “conceptual and framing device” within simulation research [17].

We propose that the conceptual framing for translational simulation has evolved since 2017 [11]. In the subsequent sections of this article, we will explore the application of translational simulation in practice, while critiquing the strengths and limitations of this emerging conceptual framing. We will consider alternative terminology and framing presented in the literature: in situ simulation (ISS), non-pedagogical simulation, simulation-based clinical systems testing, and systems integration simulation. We then present a revised conceptual model, representing our synthesis and critical review of scholarship in this area. The model is comprised of three elements—purpose, process and conceptual foundations—that we have identified in reflecting on translational simulation scholarship and practice. Finally, we reflect on directions for future practice and research.

Exploring translational simulation in practice

An analysis of the literature citing Brazil’s translational simulation article [11] demonstrates diverse applications of the concept. These include translational simulation to prepare health services for the COVID-19 pandemic [18,19,20,21,22,23,24,25,26,27,28,29,30], for hospital relocation or physical space testing [31], for clinical pathway or process testing [32,33,34,35,36,37,38,39], and for shaping culture and teamwork in healthcare settings [35, 40,41,42,43,44,45,46]. Other citing literature has included conceptual discussions and review articles [47,48,49,50,51], as well as efforts to develop methods and tools for ‘translational simulation in action’ [16, 32, 52]. We now consider and critique these examples and provide a summary in Table 1.

Table 1 Exploring translational simulation in practice-examples

Responding to COVID-19

Responding to COVID-19 offered an exemplar of how translational simulation could contribute to healthcare process re-design, especially under conditions that were fast-paced and high stakes [18, 22, 27, 29]. A clear lesson, from experience around the world, was that health services should have a strategy and capacity to use translational simulation to enable rapid responsiveness to a crisis [29]. Dube et al. described a large-scale effort to use simulation to prepare for COVID-19 across the province of Alberta, Canada, illustrating the role of simulation in organizational learning [18]. Numerous specific examples of how simulation was used to develop and adapt clinical care for COVID-19 were published, including the development of modified guidelines for cardiac arrest [53], airway management [54], maternity care [25], and patient triage when healthcare resources may be overwhelmed [55].

The global COVID-19 pandemic offered a unique context for the application of translational simulation, with lessons that extended beyond that context. For many health services and simulation practitioners it was the first time that simulation has been so closely applied to health service priorities and used for rapid adaptation of systems and processes. While educationally-focused simulation programs were closing their doors due to COVID restrictions, translational simulation activities ramped up, with unprecedented volumes of activity [18, 22, 25, 29, 30].

When viewed from the perspective of our conceptual model for translational simulation, our reflections on these published examples of experience during the COVID-19 pandemic are twofold. First, the disciplined focus on purpose remains well placed in the model—aligning with present and emerging health service priorities. Second, a gap in guidance on the process can be identified. Many simulation programs struggled to adapt their simulation design, delivery and debriefing to the novel purpose of system and process testing. Drawing on methods from educationally focused simulations did not always provide the guidance needed to achieve optimal process re-design [22]. By contrast, successful initiatives were reported in programs that had established methods and tools drawn from quality improvement, systems engineering and process redesign [18, 19, 22, 30]. The conceptual foundations of these practice fields were rarely explicitly integrated or articulated in the haste of simulation practice and publication during the COVID-19 pandemic.

Testing clinical processes

The efficiency, safety and effectiveness of patient care journeys have provided fertile ground for healthcare improvement using translational simulation. Examples include: introducing ward-level high-flow oxygen care for infants with bronchiolitis [39], optimization of paediatric airway emergency carts to improve response times in emergencies [56], improved time to intervention in acute stroke [57], improved multidisciplinary response to anaphylaxis in the paediatric emergency department [58], improving rapid transfer to the operating theatre for critically unwell trauma patients at a tertiary referral hospital [59] and reducing the ‘door to needle’ time for patients suffering a myocardial infarction who required safe and fast transfer from the emergency department to cardiac catheter suite [60].

Our reflection on these published examples highlights that most are context-specific case studies [60,61,62,63,64], with simulation methods drawn from educational contexts or reliant on local resources and capacity. This further highlights a limitation of translational simulation: while it represents a broad conceptual reframing of how simulation can contribute to healthcare improvement, it lacks a clear process by which these aims are achieved.

Designing physical infrastructure

Testing the adequacy of physical infrastructure using simulation is not a new concept, but has been surprisingly underutilised in the design and building of healthcare facilities [47, 65]. Guidance has been offered on optimal simulation design for practitioners seeking to test physical infrastructure. For example, in reporting experience in testing new building designs, Barlow et al. offer a documentation framework for healthcare simulation quality improvement activities [66]. Their framework draws upon established methods for quality improvement, including the Failure Modes Effects Analysis (FMEA) approach [67] to collecting and analysing data from translational simulation activities. Seeking to develop a standardised approach to systems testing, Colman et. al. presented a ‘Simulation-based clinical systems testing’ (SbCST) framework, including documentation and evaluation tools [68]. Drawing upon a different conceptual framework, Petrosoniak et al. describe a ‘design thinking-informed’ simulation framework to test, evaluate, and modify new clinical infrastructure [69]. This included the key features (and language) of design thinking [70]: end-user engagement, rapid prototyping and testing, and an experimentation mindset. Kaba et al. offer lessons learned from using process-orientated simulations to test the opening of a new 300-bed healthcare facility [71]. Although offering diverse methodological approaches, these conversations shared a common stance—that the incorporation of simulation and human factors into hospital design is essential [65].

Our reflection on these applications of translational simulation principles identifies an additional gap in the original framing of translational simulation. Broader conceptual foundations—from design theory, human factors and ergonomics, change management, and systems engineering—are necessary to develop the methods and tools for this use of simulation to be effective, and should be explicitly articulated in a revised conceptual framing.

Building teams, shaping culture and relationships

Improvements in healthcare teamwork have been demonstrated in many simulation activities. Less frequent has been a deliberate, systematic focus on teamwork, relationships and culture within intact teams in healthcare institutions. Translational simulation embraces these outcomes as central to improving health service performance. Published examples include shaping relationships and culture in major trauma care [40, 42], maternity emergencies [44], neonatology trainees ‘boot camp’ training [72], and operating theatre teams [73], and in exploring ‘latent social threats’ in a labour and delivery unit [35]. This work illustrates both the exploration and improvement functions with the translational simulation framing. The examples draw upon broader theoretical and conceptual foundations than captured in the original translational simulation framing, including anthropology, relational coordination, and wider methodological approaches such as institutional ethnography [35] and participatory action research [44].

Our reflection on these published examples relates to the cultural signalling from our simulation design and delivery choices [41]. Perhaps the most important impact of integrating simulation into healthcare improvement strategies has been to send a powerful signal of commitment to constant improvement, based on developing a deep understanding of how work is done by frontline clinicians.

Supporting healthcare improvement

There are conflicting conceptualisations in the healthcare improvement practitioner community as to whether simulation is a method [74], a technique, a research ‘test bed’ [75, 76], or an intersecting field of practice with healthcare improvement [77]. Conversations about these varied conceptualisations have been prompted by reports of simulation-based approaches to healthcare improvement, appearing in journals such as BMJ Quality and Safety [78, 79]. The methodologies described in these reports have been diverse, inconsistent, and variably cognisant of accepted quality improvement (QI) methodologies. These conversations appear (to us) to be surprisingly disconnected from parallel conversations in the healthcare simulation community [80].

The conceptual basis on which simulation is employed for quality improvement is evolving, reflecting developments in quality improvement practice. Examples such as improved time to intervention in acute stroke using a simulation-based intervention [57] are typical of a linear approach to quality improvement. In a linear approach, simulation is conceptualised as an intervention, with pre- and post-measures of performance used to measure intervention effectiveness, and researchers providing proof that simulation ‘works’ as an improvement tool. This conceptualisation, with its positivist paradigm, may be appropriate for some QI initiatives and for some research questions. The time to intervention in stroke markedly improved in the example given [57]. However, linear approaches often fail to reproduce successful outcomes when interventions are introduced into new contexts, as illustrated by the extensive literature pertaining to safety checklists in healthcare [81].

There are alternate conceptualisations by which healthcare simulation activities could be a cause, association, or even outcome of improvements in healthcare quality (82). This approach embraces an emerging ‘context logic’ in healthcare improvement practice and research: “identifying the features of particular environments (such as organisational structures, processes, behaviours, practices, and values) that contribute to safety” [82]. The notion of context logic is well-aligned with a core element of our conceptual stance, that translational simulation encompasses exploring work environments and the people in them and shaping the structural elements and social systems that affect performance in complex healthcare environments.

We suggest that the contribution of healthcare simulation to improving quality in healthcare goes well beyond simple technique and instead aligns with the conceptual framing offered in the 2017 translational simulation article [11], i.e. as a complex intervention [83]. This can encompass a plurality of potential conceptualisations for simulation - a method, a technique or a research test bed—appropriate to the context in which it is employed.

Our reflections on published examples of simulation for quality improvement are threefold. First, to fulfil our aspiration to healthcare simulation as an intersecting field of practice, our scholarship needs to shift from descriptions of project exemplars towards building consensus on theory and principles to guide practice [84]. Second, the evolution of our translational simulation process should align with contemporary and emerging approaches—such as the ‘context logic’ - in quality improvement practice. And third, our evolving conceptual model for translational simulation could better reflect the conceptual intersection with healthcare improvement, and practitioners could benefit from adopting the tools and processes from that field of practice.

Terminology and conceptual overlaps

Other terminologies and conceptual framings have been offered in the academic conversations about what we have termed ‘translational simulation’. These terminologies include In situ simulation, systems testing, systems integration, ‘sim QI’, and transformative simulation [85]. In this section, we offer a brief description of some of these, with particular emphasis on how they may influence our evolving conceptual model.

In situ simulation

In situ simulation (ISS) is delivered within the clinical environment [86]. It has been used to identify latent safety threats and improve health service outcomes where the team and system are closely linked [62, 63, 87]. Similar to the translational simulation literature, a review of published ISS examples reveals little consistency in approach: the contexts and aims are diverse [88]. Problematic in this descriptor is the preoccupation with ‘where’ (the location) of the simulation activity, rather than ‘why’ (its purpose) [11]. However, more recent literature has shifted the academic conversations toward the simulation process and underpinning conceptual foundations. Baxendale et al. generated a conceptual model for ISS in healthcare settings from a scoping review of ISS publications from 2008 to 2018 [89]. The review synthesised various principles, theories and approaches described in ISS literature. The proposed conceptual model consisted of four elements: Understand events, Design and Testing, Practice, and Assess/Evaluate [89]. Baxendale et al. aligned each element with key concepts, including complexity science, systems engineering, complex adaptive systems and knowledge transfer [89]. This model is the most comprehensive integration of purpose, process and conceptual foundations published to date, and informs our revised conceptual framing of translational simulation.

Systems testing and ‘systems-focused simulations

Dube et al. define systems-focused simulations (SFS) by process and purpose: “both routine and high-risk situations are simulated, using real equipment, team members, environments, and processes” [32]. The aim is to “… facilitate the identification of safety threats, inefficiencies, and opportunities for quality improvement at all levels of the system and can aid in highlighting and reinforcing system resilience and organizational learning from simulation” [32]. This terminology has been used in reporting simulations to test new facilities [68], blood transfusion safety and policy [90], and post-cardiac surgery cardiac arrest protocols [91]. Dube’s article clearly articulates the challenges of implementing and integrating simulation within complex healthcare systems, and the need for robust methods of project management, stakeholder engagement, change management, and evaluation metrics [32, 92].

Systems integration

Systems integration is defined in the Society for Simulation in Healthcare (SSH) healthcare simulation dictionary as “a category of simulation program accreditation that recognises programs that demonstrate consistent, planned, collaborative, integrated, and iterative application of simulation-based assessment, research, and teaching activities with systems engineering and risk management principles to achieve excellent bedside clinical care, enhanced patient safety, and improved outcome metrics across the health care system(s)” [93]. This descriptor encompasses both the purpose and process for simulation. The accreditation standards referred to are under the auspices of the US-based SSH [94]. They describe a variety of methods by which these aims may be achieved, drawing heavily on systems engineering principles and tools, including the Systems Engineering Initiative for Patient Safety (SEIPS) model of work system and patient safety [95, 96]. The accreditation standards mandate baseline consistency but do not necessarily reflect the practices of programs engaging in context-relevant approaches or innovations. Reports of simulation to test protocols and systems use this terminology of “systems integration” [91, 97], as do some descriptions of simulation debriefing methods adapted for this purpose [98].

Transformative simulation

There have been attempts to align terminology for simulation that is focused directly on improving healthcare quality and safety. In reviewing the literature on ‘non-pedagogical’ simulation, Weldon and colleagues identified 68 different terms used, and coined the term ‘transformative simulation’—“to describe simulation as a tool to transform health and care through collective understanding, insight and learning, and to distinguish it from the more traditional educational/pedagogical approaches that are more commonly practised, or from specific system-focussed applications only” [85]. The authors developed a taxonomy of transformative simulation, categorising these activities by their objective. They described seven “simulation-based I’s”: identification, influence, improvement, involvement, inclusion, intervention, and innovation [85], but found it difficult to identify only one objective in many published examples. This calls into question the utility of such a granular taxonomy but underlines the need for purpose to remain central to any conceptual framing for healthcare simulation.

The way forward for terminology?

Diverse terminologies and conceptual heterogeneity are no surprise in emerging and evolving fields of practice. This is particularly likely when healthcare simulation draws upon a plethora of theoretical and conceptual foundations. Common to translational simulation, in situ simulation, systems testing, systems integration, ‘sim QI’, transformative simulation and other terminologies is a direct focus on healthcare safety, quality, and systems as purpose. Also common is the struggle to determine how these purposes can be achieved; what is the optimal process for simulation design, delivery and implementation. In searching for these methods, many authors have unveiled conceptual models and connected them with fields of practice that can inform those methods: healthcare improvement, design thinking, systems science, change management, organisational behaviour and many others.

In our use of the term translational simulation, we mean a conceptual framing, rather than a technique, taxonomy or label. We embrace and encourage ongoing work toward consistency in terminology [47, 85, 89, 99], and view that as an important part of an evolving conceptual model.

The developing translational simulation conceptual model: purpose, process and conceptual foundations

The conceptual framing for how healthcare simulation contributes to improving healthcare quality and safety remains incomplete.Translational simulation: not where but why” [11] advocated a refocus on the purpose of simulation activities, against the tide of nomenclature relating to process, location and technique. Subsequent application in practice has underlined the strengths of that stance, while also highlighting limitations. Recent literature reviews have underpinned the need for clear framing, given the emergence and adoption of translational simulation [85]. These final sections of this article are forward-looking; here we propose an evolving conceptual model (Fig. 1) and describe how it supports simulation practitioners to effectively improve healthcare quality and safety.

Fig. 1
figure 1

Translational simulation: purpose, process and conceptual foundations

Purpose remains central to translational simulation; exploring and improving healthcare environments, systems and teams. Translational simulation process is illustrated in two layers—broad frameworks, subsequently expanded to more specific tools and methods. This offers a level of practical detail to guide practitioners toward effective translational simulation design and implementation, while not being limited to a contextually bound ‘prescription’. Expanded theoretical and conceptual foundations on which the conceptual model draws are included safety science, system engineering, complex adaptive systems, team science, experiential learning and implementation science. Education—individual, team and organisational learning—is embraced as an important element of our comprehensive framing for simulation contributing to healthcare improvement.

Descriptions of the purpose, process and conceptual foundations in the model are not intended to be exhaustive or comprehensive. Rather they reflect examples drawn from published literature and from our personal experience working within this community of practice.


The essence of translational simulation as a conceptual framing is purpose - improving healthcare quality, safety and systems (Fig. 2). Published examples of translational simulation (and those published under similar nomenclatures) strongly underpin this element of the model. We draw upon healthcare improvement in offering two distinct elements of purpose: exploration and improvement. The need to understand (explore) our complex adaptive healthcare systems is integral to improvement, but often neglected [82, 100, 101]. Our encouragement for healthcare simulation practitioners is to be mindful of the need for simulation to be employed to help explore and identify [47, 85, 89, 99] before leaping to fix, intervene or improve.

Fig. 2
figure 2

Translational simulation: purpose

We propose that the shaping of healthcare team culture and relationships is an important mechanism of how simulation improves healthcare quality and safety. This has long been anecdotally experienced by simulation facilitators as a positive outcome of simulation training. Recent scholarly work has, however, embraced culture and relationships as a purpose for simulation, and sought to explore the methods and theoretical frameworks that might guide the process for achieving that aim [35, 40,41,42, 44, 46, 85]. This aligns with conversations in the healthcare improvement literature, namely, that healthcare professionals and teams are mediators of healthcare improvement, not just objects to be manipulated, or simply targets of that improvement [101].

We now see that one unintended consequence of promoting a conceptual framing of simulation toward a quality and safety purpose may have been the creation of a false dichotomy between ‘educational sim’ and ‘translational sim’. Terminology such as ‘non-pedagogical simulation’ [85] exacerbates the problem. In many ways, ironically, this unhelpful distinction, with its rush to label translational simulation as superior, seems to mirror the equally unhelpful debate about what is the superior place for simulation (i.e. in situ or in a sim lab). While such labelling may be necessary in some contexts (e.g. monthly reporting of activity for a simulation program), we argue that this runs counter to the more nuanced conceptual framing that is required, and which translational simulation calls for. Indeed, as a conceptual model, translational simulation holds that healthcare simulation focused on educational outcomes remains a dominant and necessary application within simulation practice, because the overarching outcome is quality, safety and systems: there is no dichotomy between the two. Education and learning contribute to quality and safety outcomes, albeit via the mechanisms of improving individual and team knowledge and skills, or by shaping attitudes and changing culture, and are situated within the purpose element of our conceptual model for translational simulation.


While the term “translational simulation” offers a broad conceptual reframing of how simulation can contribute to healthcare improvement, the methods by which these goals are achieved are not well established or clearly elaborated. “Not where, but why?” may need to complement with “And how?” (Fig. 3). As demonstrated in our exploration of translational simulation in practice, published examples are mostly context-specific case studies [60,61,62,63,64], with simulation methods drawn from educational contexts or reliant on local resources and capacity. Efforts to distil broad principles and practical techniques have been published, but these tend to simply encourage the replication of approaches that may have led to success in one institution. Crystallising best practice is problematic when there is significant diversity in (1) the healthcare improvement targets encompassed by translational simulation, (2) the simulation techniques and professional expertise available, and (3) the contexts in which translational simulation may be applied. Indeed, ‘best practice’ is unlikely to be a worthy goal, given the dynamic and highly contextualised nature of the field.

Fig. 3
figure 3

Translational simulation: process

Attempts have been made to “describe a ‘road map’ for practitioners using translational simulation to address health service and patient-oriented outcomes” [16]. Drawing on existing literature and personal experience, Nickson et al. offer an Input-Process-Output (IPO) framework for practitioners planning translational simulation activities, and hypothetical examples to illustrate how that framework could be applied in different contexts. They offer guiding principles for how translational simulation may be “conceptualised operationally”, including (1) Systems approach, embracing organisational learning principles, (2) Stakeholder engagement and participatory design, promoting engagement of frontline clinicians and healthcare consumers, (3) Strategy, not an event, emphasising that healthcare improvement requires an iterative and embedded approach, (4) Disciplined focus, recognising that “goals are more likely to be achieved if they are narrow, specific, and well communicated to those designing and participating in the translational simulation activities” [16], and (5) Functional task alignment, reflecting the diversity of simulation techniques and design choices, and how they should “align with the objectives of the translational simulation strategy” [16]. Other authors have proposed wide-ranging methodological approaches [32, 67,68,69, 71, 84, 92, 98, 102, 103], toolkits and ‘tips’ [104, 105] for simulation design, delivery and debriefing focused on quality and safety outcomes.

The process issues for translational simulation extend beyond simulation design and delivery. Integrating simulation meaningfully within health services requires attention to stakeholder engagement, change management and implementation (32, 48, 102], among many other factors. Guidance on how to position translational simulation programs (operationally) within healthcare institutions is emerging [15], but much more is to be learned. Safety has become a particular concern when simulation is conducted in clinical spaces, potentially leading to unintended threats to system integrity or patient safety [52, 106, 107]. Faculty development for translational simulation is embryonic, with even established programs taking predominantly informal approaches [108]. Strategies for program evaluation and demonstrating return on investment are diverse [84]. All of these issues, and many more besides, are important for translational simulation practice, and we leave them to others to develop as they further test and implement the translational simulation approach in their own contexts.

Conceptual foundations and intersections

Under-explored in the 2017 description of translational simulation were the conceptual and theoretical underpinnings of this emerging practice. The article [11] offered a model for what to do—diagnose, test, and improve processes and systems—as well as a justification for why to do it, but did not elaborate on intersecting fields of practice, including quality improvement, complex adaptive systems, systems modelling, experiential learning, and organisational learning. Subsequent publications have encouraged more thoughtful use of theoretical and conceptual models [32, 47, 75, 89]. This work is particularly important, as the bias toward action and technique in the healthcare simulation community is powerful. This bias has meant that existing and proven theoretical perspectives are not always thoughtfully integrated into research or practice [17]. Our illustration of some relevant conceptual foundations (Fig. 4) aims to encourage practitioners to reflect on these as they operationalise translational simulation concepts, and to continue to consider other theoretical foundations which may be appropriate.

Fig. 4
figure 4

Translational simulation: conceptual foundations

Exploration of broader theoretical and conceptual foundations by simulation practitioners has led to the intersection with a wider range of communities of practice. Systems engineers [32, 95], architects [71], design experts [69], healthcare improvement specialists [75, 84, 100], safety science experts [103], institutional ethnographers [35, 82], and many others are key collaborators for translational simulation research, practice and faculty development [108]. These communities of practice help connect to wider conceptual and theoretical considerations, consistent with the traditions of the broader healthcare simulation field [49]. As translational simulation continues to spread, undoubtedly so will these connections—which we encourage.


Translational simulation remains an incomplete framing for how simulation contributes to healthcare quality and safety. We have described evolving practice in this area over the last 6 years, illustrating diverse applications and methodologies. We have crystallised this in a graphical representation of this conceptual framing with three core elements: purpose, process and conceptual foundations.

We hope that this clarity supports the work of simulation practitioners and scholars, and informs colleagues from intersecting fields of practice. Now, as simulation continues to develop and mature as a field of its own, and as simulation becomes more embedded in the complex systems of healthcare in the myriad of cultural, economic, geographic, and regulatory frameworks, we hope this more elaborated conceptual framing will support colleagues seeking to improve the quality and safety of patient care through simulation. As we look back at what has been done since 2017, and as we seek to frame our own work over the coming years, we found it both necessary and helpful. We look forward to working on translational simulation implementation, faculty development, ethical issues, evaluation and many other important issues for optimising the contribution to healthcare quality and safety.

Availability of data and materials

Not applicable.



In situ simulation


Failure Modes Effects Analysis


Simulation-based clinical systems testing


Quality improvement


Systems focused simulations


Society for Simulation in Healthcare


Systems Engineering Initiative for Patient Safety




  1. Barsuk JH, McGaghie WC, Cohen ER, O’Leary KJ, Wayne DB. Simulation-based mastery learning reduces complications during central venous catheter insertion in a medical intensive care unit. Crit Care Med. 2009;37(10):2697–701.

    PubMed  Google Scholar 

  2. Draycott T, Sibanda T, Owen L, Akande V, Winter C, Reading S, et al. Does training in obstetric emergencies improve neonatal outcome? BJOG : an international journal of obstetrics and gynaecology. 2006;113(2):177–82.

    Article  PubMed  Google Scholar 

  3. McGaghie WC, Draycott TJ, Dunn WF, Lopez CM, Stefanidis D. Evaluating the impact of simulation on translational patient outcomes. Simulation in Healthcare: Journal of the Society for Simulation in Healthcare. 2011;6(Suppl):S42-7.

    Article  PubMed  Google Scholar 

  4. Ratnapalan S, Lang D. Health care organizations as complex adaptive systems. Health Care Manage. 2020;39(1):18–23.

    Article  Google Scholar 

  5. Six Domains of Healthcare Quality. Rockville, MD.: Agency for Healthcare Research and Quality; 2022. Available from:

  6. Reeve J, Allinson Y. High-risk medication alert: intravenous potassium chloride. Aust Prescr. 2005;28:14–6.

    Article  Google Scholar 

  7. Enticott JC, Jeffcott S, Ibrahim JE, Wood EM, Cole-Sinclair M, Fitzgerald M, et al. A review on decision support for massive transfusion: understanding human factors to support the implementation of complex interventions in trauma. Transfusion. 2012;52(12):2692–705.

    Article  PubMed  Google Scholar 

  8. Carrington S, Turner C, Burton J, Woolrich-Burt L. Excellence through Design - Improving the Massive Haemorrhage Protocol. Emergency Medicine Journal. 2015;32(12):981.

    Article  Google Scholar 

  9. Umoren R, Kim S, Gray MM, Best JA, Robins L. Interprofessional model on speaking up behaviour in healthcare professionals: a qualitative study. BMJ Leader. 2022;6(1):15–9.

    Article  PubMed  Google Scholar 

  10. Soong C, Shojania KG. Education as a low-value improvement intervention: often necessary but rarely sufficient. BMJ Quality & Safety. 2019;29:353–7.

    Article  Google Scholar 

  11. Brazil V. Translational simulation: not ‘where?’ but ‘why?’ A functional view of in situ simulation. Adv Simul. 2017;2(1):20.

    Article  Google Scholar 

  12. Berwick DM. A primer on leading the improvement of systems. BMJ. 1996;312(7031):619–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Woolf SH. The meaning of translational research and why it matters. JAMA. 2008;299(2):211–3.

    Article  CAS  PubMed  Google Scholar 

  14. Box GEP. Science and Statistics. J Am Stat Assoc. 1976;71(356):791–9.

    Article  Google Scholar 

  15. Davies E, Montagu A, Brazil V. Recommendations for embedding simulation in health services. Adv Simul. 2023;8(1):23.

    Article  Google Scholar 

  16. Nickson CP, Petrosoniak A, Barwick S, Brazil V. Translational simulation: from description to action. Adv Simul (London, England). 2021;6(1):6.

    Article  PubMed Central  Google Scholar 

  17. Eppich W, Reedy G. Advancing healthcare simulation research: innovations in theory, methodology, and method. Adv Simul. 2022;7(1):23.

    Article  Google Scholar 

  18. Dubé M, Kaba A, Cronin T, Barnes S, Fuselli T, Grant V. COVID-19 pandemic preparation: using simulation for systems-based learning to prepare the largest healthcare workforce and system in Canada. Adv Simul (London, England). 2020;5:22.

    Article  PubMed Central  Google Scholar 

  19. Curtis S, Flower R, Emanuel-Kole L, Nadarajah P. Failure modes and effects analysis to assess COVID-19 protocols in the management of obstetric emergencies. BMJ Simul Technol Enhanced Learn. 2021;7(4):259–61.

    Google Scholar 

  20. Juelsgaard J, Løfgren B, Toxvig N, Eriksen GV, Ebdrup L, Jensen RD. Healthcare professionals’ experience of using in situ simulation training in preparation for the COVID-19 pandemic: a qualitative focus group study from a Danish hospital. BMJ Open. 2022;12(1):e056599.

    Article  PubMed  Google Scholar 

  21. Munzer BW, Bassin BS, Peterson WJ, Tucker RV, Doan J, Harvey C, et al. In-situ simulation use for rapid implementation and process improvement of COVID-19 airway management. West J Emerg Med. 2020;21(6):99–106.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Brydges R, Campbell DM, Beavers L, Khodadoust N, Iantomasi P, Sampson K, et al. Lessons learned in preparing for and responding to the early stages of the COVID-19 pandemic: one simulation’s program experience adapting to the new normal. Adv Simul (London, England). 2020;5:8.

    Article  PubMed Central  Google Scholar 

  23. Puslecki M, Dabrowski M, Baumgart K, Ligowski M, Dabrowska A, Ziemak P, et al. Managing patients on extracorporeal membrane oxygenation support during the COVID-19 pandemic - a proposal for a nursing standard operating procedure. BMC Nurs. 2021;20(1):214.

    Article  PubMed  PubMed Central  Google Scholar 

  24. So EHK, Chia NH, Ng GWY, Chan OPK, Yuen SL, Lung DC, et al. Multidisciplinary simulation training for endotracheal intubation during COVID-19 in one Hong Kong regional hospital: strengthening of existing procedures and preparedness. BMJ Simul Technol Enhanced Learn. 2021;7(6):501–9.

    Article  Google Scholar 

  25. Lowe B, De Araujo V, Haughton H, Schweitzer J, Brazil V. Preparing maternity for COVID-19: A translational simulation approach. Aust New Zealand J Obstetrics & Gynaecology. 2020;60(4):628–32.

    Article  Google Scholar 

  26. Chan AKM, Rudolph JW, Lau VNM, Wong HMK, Wong RSL, Lo TSF, et al. Rapid cycle system improvement for COVID-19 readiness: integrating deliberate practice, psychological safety and vicarious learning. BMJ Simulation & Technology Enhanced Learning. 2021;7(4):199–206.

    Google Scholar 

  27. Brazil V, Dubé M. Simulation changing the face of healthcare improvement: a silver lining from the COVID-19 pandemic? CJEM. 2022;24(4):357–8.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Hazwani TR, Al Hassan Z, Al Zahrani A, Al Badawi A. A simulation-based program for preparedness for COVID-19 at a pediatric tertiary hospital in Saudi Arabia. Cureus. 2021;13(2):e13131.

    PubMed  PubMed Central  Google Scholar 

  29. Brazil V, Lowe B, Ryan L, Bourke R, Scott C, Myers S, et al. Translational simulation for rapid transformation of health services, using the example of the COVID-19 pandemic preparation. Advances in Simulation (London, England). 2020;5:9.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Dieckmann P, Torgeirsen K, Qvindesland SA, Thomas L, Bushell V, Langli Ersdal H. The use of simulation to prepare and improve responses to infectious disease outbreaks like COVID-19: practical tips and resources from Norway, Denmark, and the UK. Advances in Simulation (London, England). 2020;5:3.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Schram AL, Lindhard MS, Bie M, Gamborg ML, Toxvig N, Skov G, et al. Using simulation-based training during hospital relocation: a controlled intervention study. Advances in Simulation (London, England). 2022;7(1):41.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Dubé M, Posner G, Stone K, White M, Kaba A, Bajaj K, et al. Building impactful systems-focused simulations: integrating change and project management frameworks into the pre-work phase. Advances in Simulation (London, England). 2021;6(1):16.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Tankard KA, Sharifpour M, Chang MG, Bittner EA. Design and implementation of airway response teams to improve the practice of emergency airway management. J Clin Med. 2022;11(21):6336.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Couto TB, Barreto JKS, Marcon FC, Mafra A, Accorsi TAD. Detecting latent safety threats in an interprofessional training that combines in situ simulation with task training in an emergency department. Advances in Simulation (London, England). 2018;3:23.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Brydges R, Nemoy L, Ng S, Khodadoust N, Léger C, Sampson K, et al. Getting everyone to the table: exploring everyday and everynight work to consider “latent social threats” through interprofessional tabletop simulation. Advances in Simulation (London, England). 2021;6(1):39.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Barni S, Mori F, Giovannini M, de Luca M, Novembre E. In situ simulation in the management of anaphylaxis in a pediatric emergency department. Internal and emergency medicine. 2019;14(1):127–32.

    Article  PubMed  Google Scholar 

  37. Puslecki M, Baumgart K, Ligowski M, Dabrowski M, Stefaniak S, Ladzinska M, et al. Patient safety during ECMO transportation: single center experience and literature review. Emergency medicine international. 2021;2021:6633208.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Colman N, Newman JW, Nishisaki A, Register M, Gillespie SE, Hebbar KB. Translational simulation improves compliance with the NEAR4KIDS airway safety bundle in a single-center PICU. Pediatric quality & safety. 2021;6(3):e409.

    Article  Google Scholar 

  39. Mallett P, Maxwell B, Harte R, McNaughten B, Bourke T, Thompson A, et al. Translational simulation in action: using simulation-based multidisciplinary teaching to introduce ward-level high-flow oxygen care in bronchiolitis. BMJ Simulation & Technology Enhanced Learning. 2020;6(1):52–3.

    Article  Google Scholar 

  40. Purdy EI, McLean D, Alexander C, Scott M, Donohue A, Campbell D, et al. Doing our work better, together: a relationship-based approach to defining the quality improvement agenda in trauma care. BMJ open quality. 2020;9(1):e000749.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Purdy E, Alexander C, Caughley M, Bassett S, Brazil V. Identifying and transmitting the culture of emergency medicine through simulation. AEM Education and Training. 2019;3(2):118–28.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Brazil V, Purdy E, Alexander C, Matulich J. Improving the relational aspects of trauma care through translational simulation. Advances in Simulation (London, England). 2019;4:10.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Pillay T, Clarke L, Abbott L, Surana P, Shenvi A, Deshpande S, et al. Optimising frontline learning and engagement between consultant-led neonatal teams in the West Midlands: a survey on the utility of an augmented simulation training technique. Advances in Simulation (London, England). 2021;6(1):29.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Brazil V, McLean D, Lowe B, Kordich L, Cullen D, De Araujo V, et al. A relational approach to improving interprofessional teamwork in post-partum haemorrhage (PPH). BMC Health Serv Res. 2022;22(1):1108.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Gard J, Duong C, Murtagh K, Gill J, Lambe K, Summers I. Simulation translation differences between craft groups. Advances in Simulation (London, England). 2022;7(1):22.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Purdy E, Borchert L, El-Bitar A, Isaacson W, Bills L, Brazil V. Taking simulation out of its “safe container”-exploring the bidirectional impacts of psychological safety and simulation in an emergency department. Advances in Simulation (London, England). 2022;7(1):5.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Petrosoniak A, Brydges R, Nemoy L, Campbell DM. Adapting form to function: can simulation serve our healthcare system and educational needs? Advances in Simulation (London, England). 2018;3:8.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Eller S, Rudolph J, Barwick S, Janssens S, Bajaj K. Leading change in practice: how “longitudinal prebriefing” nurtures and sustains in situ simulation programs. Advances in Simulation (London, England). 2023;8(1):3.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Park CS, Clark L, Gephardt G, Robertson JM, Miller J, Downing DK, et al. Manifesto for healthcare simulation practice. BMJ simulation & technology enhanced learning. 2020;6(6):365–8.

    Article  Google Scholar 

  50. Graham AC, McAleer S. An overview of realist evaluation for simulation-based education. Advances in Simulation (London, England). 2018;3:13.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Harwayne-Gidansky I, Panesar R, Maa T. Recent Advances in Simulation for Pediatric Critical Care Medicine. Current pediatrics reports. 2020;8(4):147–56.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Brazil V, Scott C, Matulich J, Shanahan B. Developing a simulation safety policy for translational simulation programs in healthcare. Advances in Simulation (London, England). 2022;7(1):4.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Sowan A, Heins J, Dayton C, Scherer E, Tam WS, Saikumar H. Developing and Testing a Protocol for Managing Cardiopulmonary Resuscitation of Patients with Suspected or Confirmed COVID-19. In Situ Simulation Study JMIR Nursing. 2022;5(1):e38044.

    PubMed  Google Scholar 

  54. Chan A. COVID-19 airway management: better care through simulation: Life in The Fast Lane; 2020. Available from:

  55. Mastoras G, Farooki N, Willinsky J, Dharamsi A, Somers A, Gray A, et al. Rapid deployment of a virtual simulation curriculum to prepare for critical care triage during the COVID-19 pandemic. CJEM. 2022;24(4):382–9.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Fleishhacker ZJ, Bennion DM, Manaligod J, Kacmarynski D, Ropp BY, Kanotra S. Quality improvement of pediatric airway emergency carts: standardization, streamlining, and simulation. Cureus. 2023;15(5):e39727.

    PubMed  PubMed Central  Google Scholar 

  57. Ajmi S, Advani R, Fjetland L, Kurz KD, Lindner T, Qvindesland S, et al. Reducing door-to-needle times in stroke thrombolysis to 13 minutes through protocol revision and simulation training: A quality improvement project in a Norwegian stroke centre. BMJ Qual Saf. 2019;28(11):939–48.

    Article  PubMed  Google Scholar 

  58. Price K, Cincotta DR, Spencer-Keefe FR, O’Donnell SM. Utilising in situ simulation within translational simulation programmes to evaluate and improve multidisciplinary response to anaphylaxis in the paediatric emergency department. Emerg Med Australas. 2023;35(2):246–53.

    Article  PubMed  Google Scholar 

  59. Baldwin M, Brazil V. Raise the Red Blanket: Rapid transfer to theatre for simulated critically ill trauma patients. Melbourne: Paper presented at SimTecT Health conference; 2009.

  60. Cullen L, Brazil V, Dooris M, Baldwin M, Muller H. Stemi-sim: Process of Care Simulation can Help Improve Door to Balloon Times for Patients with ST Elevation Myocardial Infarction. Heart Lung and Circulation. 2012;21:S50.

    Article  Google Scholar 

  61. Long E, Cincotta DR, Grindlay J, Sabato S, Fauteux-Lamarre E, Beckerman D, et al. A quality improvement initiative to increase the safety of pediatric emergency airway management. Pediatr Anesth. 2017;27(12):1271–7.

    Article  Google Scholar 

  62. O’Leary F, McGarvey K, Christoff A, Major J, Lockie F, Chayen G, et al. Identifying incidents of suboptimal care during paediatric emergencies; an observational study utilising in situ and simulation centre scenarios. Resuscitation. 2014;85(3):431–6.

    Article  PubMed  Google Scholar 

  63. Patterson MD, Geis GL, Falcone RA, LeMaster T, Wears RL. In situ simulation: detection of safety threats and teamwork training in a high risk emergency department. BMJ Qual Saf. 2013;22(6):468–77.

    Article  PubMed  Google Scholar 

  64. Steinemann S, Berg B, Skinner A, DiTulio A, Anzelon K, Terada K, et al. In situ, multidisciplinary, simulation-based teamwork training improves early trauma care. J Surg Educ. 2011;68(6):472–7.

    Article  PubMed  Google Scholar 

  65. Dench B, Barwick S, Barlow M. It’s time for the mandatory use of simulation and human factors in hospital design. Aust Health Rev. 2020;44(4):547–9.

    Article  PubMed  Google Scholar 

  66. Barlow M, Dickie R, Morse C, Bonney D, Simon R. Documentation framework for healthcare simulation quality improvement activities. Adv Simul. 2017;2(1):19.

    Article  Google Scholar 

  67. Davis S RW, Gurses AP, Miller K, Hansen H. Failure modes and effects analysis based on in situ simulations: a methodology to improve understanding of risks and failures. In: Henriksen K BJ, Keyes MA, , editor. Advances in patient safety: new directions and alternative approaches (Vol 3: Performance and Tools). Rockville (MD: Agency for Healthcare Research and Quality (US);; 2008.

  68. Colman N, Doughty C, Arnold J, Stone K, Reid J, Dalpiaz A, et al. Simulation-based clinical systems testing for healthcare spaces: from intake through implementation. Adv Simul. 2019;4(1):19.

    Article  Google Scholar 

  69. Petrosoniak A, Hicks C, Barratt L, Gascon D, Kokoski C, Campbell D, et al. Design thinking-informed simulation: an innovative framework to test, evaluate, and modify new clinical infrastructure. Simul Healthc. 2020;15(3):205–13.

    Article  PubMed  Google Scholar 

  70. IDEO U: IDEO. 2022. Cited 2022. Available from:

  71. Kaba A, Barnes S. Commissioning simulations to test new healthcare facilities: a proactive and innovative approach to healthcare system safety. Adv Simul. 2019;4(1):17.

    Article  Google Scholar 

  72. Yang KC, Hu HH, Ades AM. The hidden impact of neonatology boot camp: a qualitative study. Simul Healthc. 2022;17(4):256–63.

    Article  PubMed  Google Scholar 

  73. Escher C, Rystedt H, Creutzfeldt J, Meurling L, Hedman L, Felländer-Tsai L, et al. All professions can benefit — a mixed-methods study on simulation-based teamwork training for operating room teams. Adv Simul. 2023;8(1):18.

    Article  Google Scholar 

  74. Slakey DP, Simms ER, Rennie KV, Garstka ME, Korndorffer JR Jr. Using simulation to improve root cause analysis of adverse surgical outcomes. International journal for quality in health care : journal of the International Society for Quality in Health Care. 2014;26(2):144–50.

    Article  PubMed  Google Scholar 

  75. Lamé G, Dixon-Woods M. Using clinical simulation to study how to improve quality and safety in healthcare. BMJ simulation & technology enhanced learning. 2020;6(2):87–94.

    Article  Google Scholar 

  76. LeBlanc VR, Manser T, Weinger MB, Musson D, Kutzin J, Howard SK. The study of factors affecting human and systems performance in healthcare using simulation. Simul Healthc. 2011;6(Suppl):S24-9.

    Article  PubMed  Google Scholar 

  77. Kneebone R. Simulation, safety and surgery. Quality and Safety in Health Care. 2010;19(Suppl 3):i47–52.

    Article  PubMed  Google Scholar 

  78. Aggarwal R, Mytton OT, Derbrew M, Hananel D, Heydenburg M, Issenberg B, et al. Training and simulation for patient safety. Quality and Safety in Health Care. 2010;19(Suppl 2):i34–43.

    Article  PubMed  Google Scholar 

  79. Gum L, Greenhill J, Dix K. Clinical simulation in maternity (CSiM): interprofessional learning through simulation team training. Quality and Safety in Health Care. 2010;19(5):e19-e.

    Google Scholar 

  80. O’Connor P. ASPiH Conference 2019 keynote paper. Quality improvement through simulation: a missed opportunity? BMJ SimulTechnol Enhanc Learn. 2020;6(4):193–5.

    Article  Google Scholar 

  81. Catchpole K, Russ S. The problem with checklists. BMJ Quality and Safety. 2015;24(9):545–9.

    Article  PubMed  Google Scholar 

  82. Liberati EG, Tarrant C, Willars J, Draycott T, Winter C, Chew S, et al. How to be a very safe maternity unit: An ethnographic study. Social Science & Medicine. 2019;223:64–72.

    Article  Google Scholar 

  83. Pawson R, Greenhalgh T, Harvey G, Walshe K. Realist review–a new method of systematic review designed for complex policy interventions. Journal of health services research & policy. 2005;10(Suppl 1):21–34.

    Article  Google Scholar 

  84. Brazil V, Purdy E, Bajaj K. Simulation as an Improvement Technique. Cambridge: Cambridge University Press; 2023. Available from:

  85. Weldon SM, Buttery AG, Spearpoint K, Kneebone R. Transformative forms of simulation in health care – the seven simulation-based ‘I’s: a concept taxonomy review of the literature. Int J Healthc Simul. 2023:1-13.

  86. Nickson C. In Situ Simulation: Life in the Fast Lane; 2020. Available from:

  87. Rosen MA, Hunt EA, Pronovost PJ, Federowicz MA, Weaver SJ. In situ simulation in continuing education for the health care professions: a systematic review. J Contin Educ Health Prof. 2012;32(4):243–54.

    Article  PubMed  Google Scholar 

  88. Martin A, Cross S, Attoe C. The use of in situ simulation in healthcare education: current perspectives. Adv Med Educ Pract. 2020;11:893–903.

    Article  PubMed  PubMed Central  Google Scholar 

  89. Baxendale B, Evans K, Cowley A, Bramley L, Miles G, Ross A, et al. GENESISS 1—Generating Standards for In-Situ Simulation project: a scoping review and conceptual model. BMC Medical Education. 2022;22(1):479.

    Article  PubMed  PubMed Central  Google Scholar 

  90. McBain T, Oishi K, Enokson R, Dubé M, Raven A. Harnessing system-focused simulation, debriefing and FMEA to inform healthcare blood transfusion safety and policy. Int J Healthc Simul. 2022;2(1):3–11.

    Google Scholar 

  91. Dubé M, Jones B, Kaba A, Cunnington W, France K, Lomas K, et al. Preventing harm: testing and implementing health care protocols using systems integration and learner-focused simulations: a case study of a new postcardiac surgery, Cardiac Arrest Protocol. Clin Simul Nurs. 2020;44:3–11.

    Article  Google Scholar 

  92. Dubé M, Shultz J, Barnes S, Pascal B, Kaba A. Goals, recommendations, and the how-to strategies for developing and facilitating patient safety and system integration simulations. HERD. 2020;13(1):94–105.

    Article  PubMed  Google Scholar 

  93. Lioce L. (Ed.) LJFE, Downing D., Chang T.P., Robertson J.M., Anderson M., Diaz D.A., and Spain A.E. (Assoc. Eds.) and the Terminology and Concepts Working Group Healthcare Simulation Dictionary –Second Edition.2020 Accessed November 4th, 2023. Available from:

  94. Society for Simulation in Healthcare CfAoHSP. Systems integration - standards and measurement criteria: society for simulation in healthcare; 2016 [Available from:

  95. Carayon P, Schoofs Hundt A, Karsh BT, Gurses AP, Alvarado CJ, Smith M, et al. Work system design for patient safety: the SEIPS model. Qual Saf Health Care. 2006;15 Suppl(Suppl 1):50–8.

    Article  Google Scholar 

  96. Dunn W, Deutsch E, Maxworthy J, Gallo K, Dong Y, Manos J, et al. Systems Integration. In: Levine AI, DeMaria S, Schwartz AD, Sim AJ, editors. The comprehensive textbook of healthcare simulation. New York, NY: Springer New York; 2013:121-33.

  97. Reid J, Stone K, Huang L, Deutsch ES. Simulation for systems integration in pediatric emergency medicine. Clin Pediatr Emerg Med. 2016;17(3):193–9.

    Article  Google Scholar 

  98. Dubé MM, Reid J, Kaba A, Cheng A, Eppich W, Grant V, et al. PEARLS for systems integration: a modified PEARLS framework for debriefing systems-focused simulations. Simul Healthc. 2019;14(5):333–42.

    Article  PubMed  Google Scholar 

  99. Posner GD, Clark ML, Grant VJ. Simulation in the clinical setting: towards a standard lexicon. Adv Simul. 2017;2(1):15.

    Article  Google Scholar 

  100. Brazil V, Purdy EI, Bajaj K. Connecting simulation and quality improvement: how can healthcare simulation really improve patient care? BMJ Qual Saf. 2019:bmjqs-2019-009767

  101. Dixon-Woods M. How to improve healthcare improvement—an essay by Mary Dixon-Woods. BMJ. 2019;367:l5514.

    Article  PubMed  PubMed Central  Google Scholar 

  102. Walsh BM, Auerbach MA, Gawel MN, Brown LL, Byrne BJ, Calhoun A, et al. Community-based in situ simulation: bringing simulation to the masses. Adv Simul. 2019;4(1):30.

    Article  Google Scholar 

  103. MacKinnon RJ, Pukk-Härenstam K, Kennedy C, Hollnagel E, Slater D. A novel approach to explore Safety-I and Safety-II perspectives in in situ simulations—the structured what if functional resonance analysis methodology. Adv Simul. 2021;6(1):21.

    Article  Google Scholar 

  104. Spurr J, Gatward J, Joshi N, Carley SD. Top 10 (+1) tips to get started with in situ simulation in emergency and critical care departments. Emerg Med J. 2016;33(7):514–6.

    Article  PubMed  Google Scholar 

  105. Jacques A. Faculty of Intensive Care Medicine 2020. Cited 2023. Available from:

  106. Bajaj K, Minors A, Walker K, Meguerdichian M, Patterson M. “No-go considerations” for in situ simulation safety. Simul Healthc. 2018;13(3):221–4.

    Article  PubMed  Google Scholar 

  107. Raemer D, Hannenberg A, Mullen A. Simulation safety first: an imperative. Adv Simul (London, England). 2018;3:25.

    Article  PubMed Central  Google Scholar 

  108. Brazil V, Purdy E, El Kheir A, Szabo RA. Faculty development for translational simulation: a qualitative study of current practice. Adv Simul. 2023;8(1):25.

    Article  Google Scholar 

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We would like to thank Professor Michelle McLean, Dr. Eve Purdy and Professor Sharon Mickan for their helpful reviews of the manuscript. We would also like to thank Senior Editor Dr. Ryan Brydges and our reviewers for their thoughtful suggestions during the review process.


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VB conceived the manuscript concept and undertook initial drafting. Both authors were involved in manuscript review and revisions and approved the final version of the manuscript.

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VB is employed by Gold Coast Health and Bond University as Director of the Translational Simulation Collaborative. In that latter role, she provides consultancy services and courses in the area of translational simulation. VB is a Senior Editor for Advances in Simulation.

GR is the Editor-in-Chief of Advances in Simulation. All authors read and approved the final manuscript.

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Brazil, V., Reedy, G. Translational simulation revisited: an evolving conceptual model for the contribution of simulation to healthcare quality and safety. Adv Simul 9, 16 (2024).

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