Home Article
International Journal of Healthcare Simulation
Transformative forms of simulation in health care – the seven simulation-based ‘I’s: a concept taxonomy review of the literature

Article Type: Original Research, Article History


Simulation for non-pedagogical purposes has begun to emerge. Examples include quality improvement initiatives, testing and evaluating of new interventions, the co-designing of new models of care, the exploration of human and organizational behaviour, comparing of different sectors and the identification of latent safety threats. However, the literature related to these types of simulation is scattered across different disciplines and has many different associated terms, thus making it difficult to advance the field in both recognition and understanding. This paper, therefore, aims to enhance and formalize this growing field by generating a clear set of terms and definitions through a concept taxonomy of the literature.


Due to the lack of alignment in terminology, a combination of pearl growing, snowballing and citation searching approach was taken. The search was conducted between November 2020 and March 2023. Data were extracted and coded from the included papers according to seven Simulation-Based I’s (SBIs; Innovation, Improvement, Intervention, Involvement, Identification, Inclusion and Influence).


Eighty-three papers were identified from around the world, published from 2008 to 2023. Just over half were published in healthcare simulation journals. There were 68 different terms used to describe this form of simulation. Papers were categorized according to a primary and secondary Simulation-Based ‘I’. The most common primary SBI was Simulation-Based Identification. Selected categorized papers formed a descriptive narrative for each SBI.


This review and taxonomy has revealed the breadth of an emerging and distinct field within healthcare simulation. It has identified the rate at which this field is growing, and how widespread it is geographically. It has highlighted confusion in terminology used to describe it, as well as a lack of consistency in how it is presented throughout the literature. This taxonomy has created a grounding and step change for this work which is embedded in the literature, providing a rich and varied resource of how it is being utilized globally.

Weldon, Buttery, Spearpoint, and Kneebone: Transformative forms of simulation in health care – the seven simulation-based ‘I’s: a concept taxonomy review of the literature

What this study adds

    A literature-based identification of an emerging area of healthcare simulation.

    A concept taxonomy with examples from around the world.

    A descriptive narrative of each Simulation-Based ‘I’.

    Recommendations for future work within the field.


Simulation as a tool for non-pedagogical purposes has begun to emerge over the past decade; however, its objectives and design are often confused with simulation-based education methods, or focused on individual institutions’ needs and uses that have developed organically. There is, therefore, a gap in the literature in terms of a common understanding in how this is defined, described and conducted. With more and more papers emerging, more terms and possible applications being generated, confusion is mounting and no one approach captures all that it does and could encompass. It is, therefore, imperative that this new and exciting development within healthcare simulation has a guiding framework that the simulation community can adopt and evolve. Drawing on the growing global literature to do this is the fairest and most logical approach, as it recognizes the breadth of work that has already been conducted and does not favour one approach. However, this approach is not definitive and will ultimately require further development and refinement by those in the field as further knowledge and understanding develops.

Although slow to get started from a practice and research perspective, the concept of simulation for non-pedagogical purposes is not new and in fact was predicated by Gaba almost 20 years ago [1, p.i2] when he stated his ‘vision of how fully integrating simulation into the structures and processes of healthcare can be used to revolutionize patient care and patient safety; and to provide an overview of the driving forces and implementation mechanisms by which different entities may, or may not, promulgate simulation over the next 20 years’. We are now seeing that it is increasingly used for the study of systems, quality improvement initiatives, testing and evaluating of new interventions, policies and procedures, co-designing new models of care, exploring human and organizational behaviour, workforce development, comparing different sectors, and identifying system glitches, safety threats and misunderstandings, with consequent benefits for a more safety focussed, lateral thinking and cohesive workforce [28].

Additionally, national bodies are recognizing the opportunities of this shift in focus. For example, England’s Health Education body (Health Education England) has developed a strategic vision that identifies simulation to support ‘key national policy and strategic developments that are influencing the current and future workforce requirements across the health and care system’ [9]. They term this use of simulation as ‘simulation-based interventions’ and describe a few case studies where this has been achieved. In addition, they stipulate key national policies that experienced commentators believe ‘effective simulation-based interventions’ could support. They describe the application of simulation as a tool to address system-wide challenges that are being faced by health and care providers, including supporting workforce transformation as part of the process of system redesign, and to support key national policies and strategic developments. However, they stop short of clearly categorizing and describing how simulation of this type can be designed, delivered, translated into practice and reported. Although comprehensive and refreshingly forward-thinking in its vision, and providing a good foundation upon which to develop this growing area, it lacks the bigger picture across all these forms of simulation, and their potential applications and affordances. From another angle, the Health Quality Council of Alberta [10] in Canada have provided evaluation simulation guidelines to optimize the return on investment when evaluating healthcare facility designs for quality and patient safety. They state that ‘conducting simulation-based evaluations (regardless of the type) is perceived by non-participant stakeholders to produce findings that are useful for future projects. Moreover, the process engaged end-users to the extent that they felt they were able to effectively evaluate the design of the room and make meaningful contributions to improve the design’. Although specific guidance is provided, it is not sufficiently generic to be of universal value.

Ultimately, this growing body of work and area of interest need an umbrella term and clear specific definitions to guide and develop the field both in practice and research. Therefore, the authors of this paper have 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. This is an important step-change within the field as it aims to provide clarity within a complex area that is at risk of not advancing in quality and structure due to the ad hoc and opportunistic approach that is often taken due to a lack of a guiding framework. There is also a more serious issue at stake in that simulation conducted of this type without a recognition of what it is ultimately trying to achieve, risks unearthing issues that are not able to be addressed which could generate ethical dilemmas for those with good intentions.

This paper, therefore, aims to enhance and formalize the growing field of ‘transformative’ types of simulation by reviewing the existing literature and drawing on the authors’ extensive experience in this area, their networks, and engagement within the wider simulation community, to provide a more comprehensive understanding of the field, its application, utility and gaps. In addition, it aims to generate clear terms and definitions that the simulation community can use for clarity and purpose – ultimately developing a framework from which advancements can be made to the benefit of all.


The principal issue in developing this use of simulation, both in recognition, understanding and advancement, is that the literature related to these types of simulation activities is scattered across different disciplines and typically focuses on a single aspect of the simulation activity under study. For example, as simulation of this sort spans organizational, policy, engagement and patient safety fields, it can be found in any of these associated journals and more. Additionally, because there is no defined terminology, descriptions and terminology used are diverse and reflect meeting publication criteria as much, if not more than, outcomes. It is, therefore, impossible to draw on traditional literature-reviewing methods and instead a pearl growing, snowballing review of the literature approach was taken to generate a taxonomy of what this non-pedagogical form of simulation encompasses. This approach is not only the most pragmatic way to pursue this aim but it is anticipated that the resulting taxonomy will provide an evidenced-based framework and coherent terminology that future authors can draw on to situate their work for appropriate recognition.


In its basic definition, a taxonomy is a structured set of names and descriptions used to organize information and documents in a consistent way [11]. A ‘knowledge taxonomy’, focuses on enabling the efficient retrieval and sharing of knowledge, information and data across a discipline by building the taxonomy around the knowledge need in an intuitive structure [11,12]. Taxonomies are crucial for the management of complex issues. Pincher [13] argues that, without a taxonomy designed for categorization and management, or one that supports better searching, understanding and clarification can be lost.

Taxonomies can contribute to making explicit knowledge embedded in the literature by mapping and categorizing tacit knowledge embedded in existing expertise. They promote collaboration and sharing between individuals and groups by mapping and coordinating the collegiate enterprise [11,12]. Taxonomies help putting knowledge into practice by making sense of the knowledge of the subject and creating a common vocabulary and a common way of working. They have, therefore, to be treated as an essential part of the knowledge management strategy of an emerging field to ensure appropriate advancement [13].

Taxonomy development

Between November 2019 and March 2020 and based on the available literature at the time, Weldon (SW) identified categories relating to five specific objectives/focuses that became apparent from reviewing the literature. After the COVID-19 pandemic hit, it became apparent that there was an increase in publications related to this type of simulation and Weldon (SW) subsequently engaged with Buttery (AB), Spearpoint (KS) and Kneebone (RK) to develop the field and understand it further. This resulted in meetings with stakeholders (different healthcare professionals conducting transformative types of simulation within healthcare organizations in the UK), Twitter Fests (pre-set and advertised dates and times via a simulation society for a discussion on a topic via a series of questions), and workshops with the simulation community through communities of practice (healthcare simulation conferences and networking events) and an international simulation society (Association for Simulation Practice in Healthcare – ASPiH). In addition, a Specialist Interest Group (SIG) was set up in 2021 via ASPiH that was accessible to members and non-members. This 2-year consultation with the simulation community resulted in a further two categorizations making seven categories under the umbrella term of ‘transformative simulation’, this created a Simulation-Based ‘I’ (SBI) rainbow (Figure 1). There was also further recognition that most forms of transformative simulation activities met more than one category (Table 1). With this clearer understanding, the authors conducted a literature search during July 2021.

Table 1:
SBI categories and definitions
Innovation Introduction of novelties; the alteration of what is established.
Improvement Making something better; becoming better; an addition or alteration to make a change for the better.
Intervention The action of intervening or interfering in any affair to affect its course or issue.
Involvement The process of involving; being involved; being implicated, entangled or engaged.
Identification The act of identifying; what or who; discovery and recognition.
Inclusion The action or an act of including something or someone; the fact or condition of being included.
Influence To exert influence; to work influentially on, upon person(s) or thing.
SBI rainbow.
Figure 1:

SBI rainbow.

Literature review methods

A combination of ‘pearl growing’ (using potentially relevant search terms/keywords to identify associated literature), ‘snowballing’ (tracking down relevant references in key papers), and citation searching (identifying who has cited a relevant paper since its publication and checking to see if it is relevant) approach was taken [14]. The authors’ own awareness, networks and memberships were also utilised.

Search period and process

Due to the unconventional approach required for this type of review, searches informally commenced in May 2017 once the change in application was identified as beginning to emerge as a field. By November 2020, due to the COVID-19 pandemic and a recognized increase in the literature being published on this subject, the search strategy was formally commenced (November 2020–March 2023). Drawing on literature identified and filed between 2017 and 2020, potentially relevant terms/keywords were identified and used for further searches. Searches of the following databases was conducted: Scopus, Medline, PsycInfo and CINAHL. Followed by pearl growing, snowballing and citation searching. When new papers were identified, the process began again until no further papers were identified (Figure 2).

Search process.
Figure 2:

Search process.

Inclusion and exclusion criteria

All papers that described the use of health and social care simulation beyond a pedagogical structure (such as skills acquisition and assessment), even if this was also described, were included. No date, country or language restrictions were applied; however, literature reviews, opinion pieces and editorials were excluded, as they did not provide primary evidence of the approaches direct use or consideration.


Data from all identified papers were extracted into an Excel spreadsheet. Categories for extraction included journal, publication type, location, simulation type/definition, simulation study/description, objectives and goals, target participants, outcomes, impact, terms used to describe the simulation activity, other relevant terms, primary SBI, and secondary SBI.

Weldon (SW) then coded each category according to the definitions related to each SBI, generating primary and secondary SBIs for each paper. Spearpoint (KS) and Buttery (AB) then checked the categorization according to the definitions, and discussions were held where a consensus was not agreed. This process enabled the preliminary categories to be tested whilst identifying if there were any categories missing or where categories could be combined/added.

As this is a taxonomy, the information extracted from the studies included was in relation to the type of transformative simulation being presented and not any evaluative or research methods as it is beyond the scope of this paper to assess each type. Rather our aim was to combine and categorize. Information relating to publication/study type was extracted to generate an overview of how the data are communicated in the literature and to identify gaps in the research so that recommendations for future advancement for the field can be made.



Eighty-three papers were identified and included in this review with a publication date range from 2008 to 2023 (Supplement 1). Thirty-one papers were excluded after a detailed review revealed they did not meet the inclusion criteria (Supplement 2 provides the excluded papers and their reason for rejection). Figure 3 illustrates the increasing rate of publications on this type of simulation year by year. Twenty-six were from the UK, 22 from the USA, 16 from Canada, 6 from Australia, 4 from Denmark, 2 from Spain, 1 from Qatar, 1 from Lebanon, 1 from Brazil, 3 were multi-country (Australia and Canada; Norway, Denmark and UK; Hong Kong and UK), and 1 was unknown. Just over half the publications (42) were published in simulation-specific journals (International Journal of Healthcare Simulation (IJoHS); Simulation in Healthcare; Advances in Simulation; BMJ Simulation & Technology Enhanced Learning (STEL); Clinical Simulation in Nursing), the remaining were published in a mixture of specialist and general medicine (14), quality and patient safety (10), environment research and design (4), education (5), health services and management (2), communication (2), history (2), military (1), and science (1) journals.

Number of publications per year.
Figure 3:

Number of publications per year.

Across the papers, 68 different terms were used to describe the simulation activity (Table 2). The terms used appeared to focus on different aspects of simulation, for example, some were described by the location (e.g. in situ simulation), or the ‘realism’ of the simulation (e.g. high-fidelity simulation). Others focused on what the simulation was about (e.g. clinical or system focused). Some were more related to the design (e.g. sequential simulation), and others the participants (e.g. multidisciplinary simulation). This highlights the many ways simulation can be described, not just for transformative forms but also more generally too, and thus the need for a universal taxonomy.

Table 2:
Simulation terminology used, number of times and grouped into themes
Terminology used No. of times used
 Location/contextual/realism focused
In situ simulation 26
In situ simulation training 1
Immersive full-environment in situ simulation 1
Immersive realistic in situ simulation 1
Immersive simulation 2
Inter-professional in situ simulation 1
On-site simulation 1
Centre-based simulation 1
High-fidelity simulation 3
 Design/resource focused
Sequential simulation 6
Distributed simulation 7
Simulation-based user-centred design 1
Tabletop simulation 2
Clinical simulation scenario 1
Technology-enhanced simulation 1
 Outcome focused
Translational simulation 3
Simulation-based research 1
Simulation-based intervention 1
 Participant focused
Simulation-based inter-professional education 1
Multidisciplinary simulation 1
Behavioural simulation 2
Participatory behavioural simulation 1
Simulation with standardized patients 1
 System focused
Process-orientated simulation 1
System integration simulation 1
Simulation for systems integration 1
Patient safety and system integration simulation 1
System-focused simulation 2
System-focused simulation-based approach 1
System-based investigation 1
System-based clinical systems testing 2
Simulation-based systems testing 1
Macro-systems simulation 1
Simulation-based quality improvement observation tool design 1
Systems-based in situ simulation 1
The patient environment simulation for systems integration 1
Code silver exercise/in situ CSE/virtual CSE 1
Simulated complex systems 1
Simulation-based intervention 1
 Clinical focused
Trauma simulation 1
Simulated inter-operative clinical contexts 1
 Engagement focused
Immersive engagement 1
Engagement through simulation 2
Simulation-based engagement 1
Simulation 11
Live simulation 1
Live medical simulation 1
Simulation-based mock-up 2
Simulation-based training 2
Simulation training 1
Simulation-based enactment 1
Simulation-based re-enactment 1
Scenario-based simulation 1
Simulation-based activities 1
Simulation-based techniques 1
Simulation health economy 1
La-based simulation 1
Lateral play 1

Publication types were highly varied and ranged from case studies, reflections and evaluations, to research, theoretical papers, frameworks and ‘how-to’ strategies. Due to the complexity of these types of simulation, the many potential areas of focus, and a lack of an umbrella term and guiding framework, the papers’ objectives and focus were also highly varied and frequently lacked clarity in expressing ultimate purpose, often with several goals and objectives intertwined but not clearly defined. This meant that extracting data on the papers’ goals and objectives for the use of categorization required careful analysis of the paper to identify what they were – and, where there were multiple objectives, which were pertinent to transformative forms of simulation. There was also confusion in the reporting which we contend is due to trying to fit transformative types of simulation into a simulation-based education pedagogic reporting framework, further reflecting the need for clarification.


The literature reviewed by Weldon, Buttery and Spearpoint was categorized according to the below seven SBIs. However, many papers’ objectives and goals reflected more than one SBI and therefore they were further categorized to reflect primary and secondary objectives (Supplement 1).

All SBIs were utilized, the most common SBI used overall was identification (38), the frequency of primary objective alone was identification (26), followed by then influence (14), improvement (13), involvement (12), inclusion (7), intervention (5) and innovation (4). No further SBIs were identified from this review (Table 3).

Table 3:
SBI categorization by objective
SBI Primary objective Secondary objective
Innovation 4 3
Improvement 13 20
Intervention 7 5
Involvement 12 1
Identification 26 12
Inclusion 7 5
Influence 14 7

SBI category descriptive narrative

The following narrative provides examples of some of the included papers’ objectives and outcomes of the studies categorized to each SBI. Although many studies had primary and secondary SBIs, this narrative focuses purely on the primary objective and has therefore excluded objectives that have been defined as secondary. This is not to detract from transformative forms of simulation having more than one objective but to ensure clarity at this early stage of understanding. This develops the transformative simulation categories from a dictionary definition (before the review and taxonomy) to a data- and community-driven definition, and helps to build a picture of how each category is being employed in practice as well as its potential.

Simulation-Based Innovation

The Simulation-Based Innovation category is concerned with the introduction of something new or a new way of doing things. As an example, Blanks [21] used an innovation approach to evaluate the efficacy of simulation-based techniques to assess developing polices prior to implementation. They found that using simulation in this way enabled for the safe evaluation of new policies before publication to ensure they are appropriate for front-line use. Alternatively, Madani [44] explored the role of simulation as a potential testbed for diminishing the risks, pitfalls and resource demands associated with the development and implementation of medical innovations, and more specifically the product development pathway. They found that simulation had the strongest role for early prototyping, testing for safety and product quality, and testing for product effectiveness and ergonomics.

Simulation-Based Improvement

The Simulation-Based Improvement category is concerned with using simulation to make something that already exists better and in line with best practice. In their cohort study, Whitfill [18] aimed to compare the US National Paediatric Readiness Project score before and after an in situ simulation-based quality improvement programme across Connecticut hospitals. They concluded that participation in a simulation-based quality improvement collaborative was associated with improvements in paediatric readiness. Similarly, Rojo [39] used simulation to understand care processes reorganization to improve efficiency whilst maintaining patient safety. They concluded that simulation could promote and facilitate change in patient care and organizational re-engineering.

Simulation-Based Intervention

The Simulation-Based Intervention category is concerned with changing a situation or way of doing things. In their paper, Dube [42] described the implementation of a central simulation COVID-19 response team by integrating new components such as novel workflows, protocols and cognitive aids, with rapid changes to practice and care delivery. They concluded that the programme was highly coordinated and enabled sharing across the largest single health authority in Canada.

Simulation-Based Involvement

The Simulation-Based Involvement category is concerned with inviting and engaging otherwise excluded individuals or groups with the purpose of generating new experience and perspectives and to ultimately bridge understandings. In their qualitative study, Korkiakangas [6] used simulation based on prior observations to enable the general public to experience a re-creation of care in the dining and healthcare sectors, followed by discussions of the experiences. Using simulation, they were able to focus on the relational moments of care, unpacking the differences and similarities between dining and clinical care, and asking what participants liked or disliked in the simulated environments. Resulting themes indicated how the simulation participants felt about the care they received in real time and provided recommendations for improved clinical practice. They concluded that simulation provides a new kind of opportunity to bring professionals and patients together for focused discussions, prompted by immersive experiences of care and communication. Weldon [50] used simulation as a tool to engage future healthcare managers in the complexities of redesigning a care pathway process giving particular consideration to local initiatives and cost implications. The approach demonstrated that simulation has wider potential and can be particularly useful in the bringing together of many minds to speak the same language. Similarly, Pillay [73] developed a simulation that focussed on a non-hierarchical, equal partnership between neonatal teams from different unit designations. They concluded that the simulation was an acceptable method of promoting multidirectional understanding within neonatal teams of differing designations.

Simulation-Based Identification

The Simulation-Based Identification category is concerned with the use of simulation to identify, discover or recognize what is happening in a given situation or set of circumstances. This is illustrated through Adler [8] who describes the use of simulation as a method to test systems to identify and reduce latent safety threats present at the opening of a new hospital. They concluded that approximately 641 unique issues were identified through the use of simulation prior to the hospital opening. A similar approach was taken by Colman [53] when opening an outpatient subspecialty clinic and with similar outcomes, and Jafri [78] for recommendations on safety checks before initiating a new programme.

Nielsen [56] explored whether higher numbers of failure modes, causes and effects, in a healthcare process, could be identified when a group of process experts actively simulate the process, as compared with brainstorming on this question. They concluded that the use of simulation enhanced a traditional healthcare failure mode and effects analysis.

Simulation-Based Inclusion

The Simulation-Based Inclusion category is concerned with including key stakeholders to share, empower and enable. For example, Weldon [4], in their mixed-methods study, used simulation to inform, design and operationalize integrated care within health jurisdictions from a ‘bottom-up’ approach in order to fully engage and involve all stakeholders. They concluded that the systematic data collection from the diverse ideas generated through the simulation facilitated a much-needed ‘ear’ to those providing the solutions, as well as a legitimate and balanced perspective. Geis [33] used simulation for an investigative pilot with a range of objectives including defining optimal healthcare team roles and responsibilities, and refining the scope of practice of healthcare professionals. The simulations revealed the need to modify provider responsibilities by demonstrating that the medication nurse had the greatest workload during resuscitations and modifying medication delivery was deemed critical.

Simulation-Based Influence

The Simulation-Based Influence category is concerned with exerting influence on someone or something. In their qualitative study, Brazil [58] used simulation to understand how an established trauma simulation programme was perceived by trauma care providers to influence their relationships with others and to identify those aspects of the simulation experience contributing to relational outcomes. Their findings suggested that simulation can have a profound influence on the relational aspects of care and the development of a collaborative culture, with perceived tangible impacts on teamwork behaviours and institutional systems and processes.

In their mixed-methods study, Weldon [49] designed a simulation of a series of short scenes built up from a collection of real patient healthcare journeys to illustrate to GP receptionists the importance of their role within the patients’ journey and influence how they practised. The simulations were designed to highlight the consequences of disjointed care, aiming to show that individuals in the pathway often act in silos, focusing on their own short interaction, without understanding the impacts of their action throughout the care pathway. In 2013, Tang [46] set out to ‘heighten students’ aspirations in medical science by using simulation to give the taste and feel of what it could be like for them’. They believe this approach has major potential for making the closed world of surgery accessible to young people who are considering a career in health care.


This literature review has revealed that transformative forms of simulation have been embraced and implemented successfully, for a range of reasons, globally over the past 14 years. In the UK, examples of its use include to investigate, understand and improve management and policy-making in a healthcare organization, inform, design and operationalize integrated care from a bottom-up approach to engage, identify, compare and contrast the experience of care in a day surgery unit, and encourage primary, secondary and tertiary services to function in a more integrated fashion to name but a few [3,4,6,24].

In Canada and the USA, transformative simulation has been used to test the opening of a new healthcare facility by identifying latent safety threats, and screening for unintended consequences of proposed solutions, as well as testing the systems and preparing staff for the transition to a new hospital [2,7,8,34,38]. In the USA, Norway, Denmark, Lebanon and Australia, it has been used to define optimal healthcare team roles and responsibilities, and refine their scope of practice [5,34], and as a response to different elements of the COVID-19 pandemic [16,23, 28,31].

Although varied examples of transformative simulations exist globally, there is little in the way of robust guidance or models that goes beyond specific applications within individual organizations. In Australia, Brazil [66] described their organization-wide simulation strategy development. They used the COVID-19 pandemic as an example where they were able to deliver more than 250 translational simulations, involving more than 1500 healthcare staff, across multiple hospital departments within a 30-day period (March 2020). Barlow [7] has developed a documentation framework for ‘simulation quality improvement activities’ that evaluate patient care workflows, processes and systems, to capture and report findings of system deficits identified in the simulations to key decision-makers. In Canada, it has been used province wide as a first choice strategy for ensuring individual, team and system readiness of the pandemic. In their paper, Brydges [31] conclude that ‘the pandemic cemented simulation as fundamental for any healthcare organization interested in ensuring its workforce can adapt in times of crisis’. Given the likelihood of the ongoing need for healthcare change post-COVID-19, transformative simulation approaches will remain critical and, therefore, this field has the potential to advance exponentially with the required guidance and evidence in place.

Although implementation of transformative simulation is clearly taking place, few organizations have recognized or incorporated it as a tool into their normal practice. In many organizations, it is common for impassioned practitioners to carry out transformative simulation activities in their own time using existing resources. This involves drawing on simulation-based education resources, then using the outputs to convince their organization of the value of the approach whilst trying to secure more resources to make it sustainable. This undermines the organizations’ ability to fully capitalize on the benefits of transformative simulation. Furthermore, little implementation guidance is available. Only a few general frameworks to guide implementation efforts have been published, all of which are based on personal reflections rather than robust or varied research evidence [31,32,65]. It is, therefore, likely that they do not reflect the unique characteristics of all healthcare systems or capture the detail and flexibility required. In their review of the literature, Cohen [40] concluded most reports they reviewed neither described the simulation methodology in sufficient detail to determine its validity, nor was the process of analysis described sufficiently to be certain that conclusions were balanced and reflective of the proceedings. They stated that a clear description of a transparent, validated method and analysis framework, including triangulation of evidence and assessment scales, could provide reliable evidence upon which policy makers and stakeholders could act. In their commentary back in 2013, Salas [22] situate what is known about simulation in health care at the time, and predicted ‘critical future research and application directions for simulation as a patient safety strategy’ through the promotion, reinforcement and development of attitudes for effective patient care. They state that ‘new insights from neuroscience, organisational, cognitive, human factors and team sciences will help make simulation even more effective, creating a new world in which our imagination will be the limit’.

The presented taxonomy provides practitioners and researchers with an objective-driven description of transformative forms of simulation that enables a more robust, unified format and process. We believe that the current confusion across the literature, and the inability to advance this field beyond individual accounts, could be solved by situating it within this taxonomy of terms, definitions and examples. We would hope that through generating a shared understanding based on existing work conducted within the global simulation community over the past decade, more examples can be added generating further evidence that can be collectively examined, and universal design considerations and guidelines developed.


Due to the methods applied, we are unable to be confident in the comprehensiveness of the search; however, we hope that this pragmatic approach generates further clarity that can help to advance the field. Although no date or language barriers were applied to the search, we recognize that the methods (pearl growing, snowballing and citation searching) would likely not pick up many non-English written papers. As this field grows, we hope that the clarity being created will in time reveal a breadth of non-English written papers that can be added to the growing body of work and understanding.


Based on this review and taxonomy, we recommend that simulation practitioners aim to consider what their primary and secondary (where applicable) SBIs are when conducting transformative types of simulation – for design, outcome and reporting clarity. We suggest that researchers consider the different objectives and uses the taxonomy has described and pose the questions that need to be answered as well as developing the studies that can answer them. We encourage debate around the taxonomy and expect to see it evolve over time as the simulation community make use of this new platform to grapple with this powerful use of simulation that has huge untapped potential and impactful benefits for health care and simulation globally.


This review and taxonomy has revealed the breadth of an emerging and distinct field within healthcare simulation. It has identified the rate at which this field is growing, and how widespread it is geographically. It has highlighted confusion in terminology used to describe similar applications of this form of simulation as well as a lack of consistency in how it is presented throughout the literature. This taxonomy has created a grounding for this work which is embedded in the literature, providing a rich and varied resource of how it is being utilised globally. Just as Bloom’s taxonomy contains multiple terms under each level of complexity, transformative simulation is not absolute or constraining, but descriptive, supporting and evolving.

This paper aspires to create a step change in the understanding, employment and reporting of simulation for health and care, by generating a living, robust but evolving framework from which transformative forms of simulation can be situated, understood, developed and researched.

Supplementary material

Supplementary data are available at The International Journal of Healthcare Simulation online.


Authors’ contributions

None declared.


None declared.

Availability of data and materials

Through corresponding author.

Ethics approval and consent to participate

Not required.

Competing interests

None declared.



Gaba DM. The future vision of simulation in health care. Quality & Safety in Health Care [Internet]. 2004 [cited 2021 Apr 05] (Suppl 1):i210. doi: 10.1136/qhc.13.suppl_1.i2


Kaba A, Barnes S. Commissioning simulations to test new healthcare facilities: a proactive and innovative approach to healthcare system safety. Advanced Simulation [Internet]. 2019 [cited 2021 Apr 05];4:17. doi: 10.1186/s41077-019-0107-8


Lamé G, Simmons RK. From behavioural simulation to computer models: how simulation can be used to improve healthcare management and policy. BMJ Simulation & Technology Enhanced Learning [Internet]. 2018 [cited 2021 Apr 05];6:95102. doi:10.1136/bmjstel-2018-000377


Weldon SM, Kneebone R, Bello F. Collaborative healthcare remodelling through sequential simulation: a patient and front-line staff perspective. BMJ Simulation & Technology Enhanced Learning [Internet]. 2016 [cited 2021 Apr 06];2(3):7886. doi: 10.1136/bmjstel-2016-000113


Purdey E. ICE BLOG. Simulation for enculturation into trauma care: making organizational culture objectives explicit [Internet]. Australia: By ICE BLOG ADMIN. 2021 [cited 2022 Jan 06]. Available from: https://icenetblog.royalcollege.ca/2021/03/23/simulation-for-enculturation-into-trauma-care-making-organizational-culture-objectives-explicit/


Korkiakangas K, Weldon SM, Kneebone R. ‘Let me take care of you’: what can healthcare learn from a high-end restaurant to improve the patient experience? Journal of Community Healthcare [Internet]. 2021 [cited 2021 March 09];14(3):225240. doi: 10.1080/17538068.2021.1877602


Barlow M, Dickie R, Morse C, Bonney D, Simon R. Documentation framework for healthcare simulation quality improvement activities. Advanced Simulation [Internet]. 2017 [cited 2021 Feb 20];2:19. doi: 10.1186/s41077-017-0053-2


Adler MD, Mobley BL, Eppich WJ, Lappe M, Green M, Mangold K. Use of simulation to test systems and prepare staff for a new hospital transition. Journal of Patient Safety [Internet]. 2018 [cited 2021 Feb 20];14(3):143147. doi: 10.1097/PTS.0000000000000184


Health Education England Technology Enhanced Learning. Enhancing education, clinical practice and staff wellbeing. A national vision for the role of simulation and immersive learning technologies in health and care [Internet]. 2020 [cited 2021 Jan 08]. Available from: https://www.hee.nhs.uk/sites/default/files/documents/National%20Strategic%20Vision%20of%20Sim%20in%20Health%20and%20Care.pdf


Health Quality Council of Alberta. Healthcare facility mock-up evaluation guidelines: using simulation to optimize return on investment for quality and patient safety [Internet]. 2020 [cited 2021 Jan 08]. Available from: https://hqca.ca/wp-content/uploads/2020/10/Healthcare-facility-mock-up-evaluation-guidelines-FINAL.pdf


Lambe P. Organising knowledge: taxonomies, knowledge and organisational effectiveness. Woodhead: Chandos Publishing. 2007. ISBN: 978-1-84334-227-4


Basaruddin S. Haron H. Noordin SA. Taxonomy approach for organizing knowledge in academic institutions. Journal of Organizational Knowledge Management [Internet] 2013 [cited 2020 May 22]. doi: 10.5171/2013.241646


Pincher M. ComputerWeekly.com. A guide to developing taxonomies for effective data management. 2010 [updated 2010 March 08; cited 2020 May 22]. Available at: www.computerweekly.com/Articles/2010/04/06/240539/A-guide-to-developing-taxonomiesfor-effective-data.htm


Papaioannou D, Sutton A, Carroll C, Booth A, Wong R. Literature searching for social science systematic reviews: consideration of a range of search techniques. Health Information & Libraries Journal [Internet]. 2010 [cited 2020 Feb 08];27:114122. doi: 10.1111/j.1471-1842.2009.00863.x


Auerbach M, Kessler DO, Patterson M. The use of in situ simulation to detect latent safety threats in paediatrics: a cross-sectional survey. BMJ Simulation & Technology Enhanced Learning [Internet]. 2015 [cited 2020 Nov 12];1(3):7782. doi: 10.1136/bmjstel-2015-000037.


Dieckmann P, Torgeirsen K, Qvindesland SA, Thomas L, Bushell V, Ersdal HL. 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. Advanced Simulation [Internet]. 2020 [cited 2020 Nov 12];5:3. doi: 10.1186/s41077-020-00121-5


Nickson CP, Petrosoniak A, Barwick S. Brazil V. Translational simulation: from description to action. Advanced Simulation [Internet]. 2021 [cited 2021 Mar 29] 6:6. doi: 10.1186/s41077-021-00160-6


Whitfill T, Gawel M, Auerbach M. A simulation-based quality improvement initiative improves pediatric readiness in community hospitals. Pediatric Emergency Care [Internet]. 2018 [cited 2021 Mar 29];34(6):431435. doi: 10.1097/PEC.0000000000001233. PMID: 28719479.


Imran A, Holden B, Weldon SM, et al ‘How to help your unwell child’: a sequential simulation project. BMJ Simulation & Technology Enhanced Learning [Internet]. 2020 [cited 2021 Mar 29];6:127128. doi: 10.1136/bmjstel-2018-000432


Tribe HC, Harris A. Kneebone R. Life on a knife edge: using simulation to engage young people in issues surrounding knife crime. Advanced Simulation [Internet]. 2018 [cited 2021 Mar 29];3:20. doi: 10.1186/s41077-018-0079-0


Blanks T, Woodier N, Baxendale B, Fores M, Fullerton L. A qualitative evaluation of the role of simulation in policy development for service improvement. BMJ Simulation & Technology Enhanced Learning [Internet]. 2017 [cited 2021 Mar 29];4(1):1922. doi: 10.1136/bmjstel-2017-000219.


Salas E, Paige JT, Rosen MA. Creating new realities in healthcare: the status of simulation-based training as a patient safety improvement strategy. BMJ Quality & Safety [Internet]. 2013 [cited 2021 Nov 23];22(6):449452. doi: 10.1136/bmjqs-2013-002112. PMID: 23704117.


Brazil V, Lowe B, Ryan L, et al Translational simulation for rapid transformation of health services, using the example of the COVID-19 pandemic preparation. Advanced Simulation [Internet]. 2020 [cited 2021 Apr 12];5:9. doi: 10.1186/s41077-020-00127-z


Powell P, Sorefan Z, Hamilton S, Kneebone R, Bello F. Exploring the potential of sequential simulation. The Clinical Teacher [Internet]. 2016 [cited 2020 Apr 02];13(2):112118. doi: 10.1111/tct.12391


Yajamanyam P, Sohi D. In situ simulation as a quality improvement initiative. Archives of Disease in Childhood: Education and Practice Edition [Internet]. 2015 [cited 2020 Apr 02];100:162163. doi:10.1136/archdischild-2014-306939


Hughes J, Hope A, Reid C, Knight P, Baker A. End of life communication and organ donation simulation. Developing a multi-disciplinary faculty and course in order to share and improve practice across hospital trusts. BMJ Quality & Safety [Internet]. 2019 [cited 2021 Apr 02];5:A1. doi:10.1136/bmjstel-2019-heeconf.2


Gormley GJ, Kajamaa A, Conn RL. O’Hare S. Making the invisible visible: a place for utilizing activity theory within in situ simulation to drive healthcare organizational development? Advance Simulation [Internet]. 2020 [cited 2021 Apr 02];5:29. doi: 10.1186/s41077-020-00148-8


Lakissian Z, Sabouneh R, Zeineddine R. Fayad J. Banat R. Sharara-Chami R. In-situ simulations for COVID-19: a safety II approach towards resilient performance. Advanced Simulation [Internet]. 2020 [cited 2021 Jun 24];5:15. doi: 10.1186/s41077-020-00137-x


Kelay T, Ako E, Cook C, et al Physician-patient interactions and communication with conscious patients during simulated cath lab procedures: an exploratory study. BMJ Simulation & Technology Enhanced Learning [Internet]. 2018 [cited 2021 Nov 03];5(1):1521. doi: 10.1136/bmjstel-2017-000249. PMID: 35517907; PMCID: PMC8990186.


Cohen D, Vlaev I, Heitmueller A. Validation of behavioral simulations: a case study on enhancing collaboration between partnership organizations. Journal of Public Health (Berlin) [Internet]. 2019 [cited 2021 Apr 03];27:367378. doi: 10.1007/s10389-018-0948-y


Brydges R, Campbell DM, Beavers M, 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. Advanced Simulation [Internet]. 2020 [cited 2021 Apr 03];5:8. doi: 10.1186/s41077-020-00128-y


Kerner RL Jr, Gallo K, Cassara M, DʼAngelo J, Egan A, Simmons JG. Simulation for operational readiness in a new freestanding emergency department: strategy and tactics. Simulation in Healthcare [Internet]. 2016 [cited 2020 Mar 29];11(5):345356. doi: 10.1097/SIH.0000000000000180.


Geis GL, Pio B, Pendergrass TL, Moyer MR, Patterson MD. Simulation to assess the safety of new healthcare teams and new facilities. Simulation in Healthcare [Internet]. 2011 [cited 2021 Nov 20];6(3):12533. doi: 10.1097/SIH.0b013e31820dff30. PMID: 21383646


Ventre KM, Barry JS, Davis D, et al Using in situ simulation to evaluate operational readiness of a children’s hospital-based obstetrics unit. Simulation in Healthcare [Internet]. 2014 [cited 2020 Mar 29];9(2):102111. doi: 10.1097/SIH.0000000000000005.


Fawcett M, Coughlan C, McGeorge E, et al Qualitative evaluation of asthma services for young people: a sequential simulation study. BMJ Simulation & Technology Enhanced Learning [Internet]. 2020 [cited 2021 Mar 29];7(3):134139. doi: 10.1136/bmjstel-2020-000585.


Murphy JI. Using plan do study act to transform a simulation center. Clinical Simulation in Nursing [Internet]. 2012 [cited 2020 Mar 29];9(7). doi: 10.1016/j.ecns.2012.03.002.


Betegon MA, Baladron Gonzalez V, Bejarano Ramırez N, Martınez Arce A, Rodrıguez De Guzman J, Redondo Calvo FJ. Quality management system implementation based on lean principles and ISO 9001:2015 standard in an advanced simulation centre. Clinical Simulation in Nursing [Internet]. 2021 [cited 2021 Nov 12];51(C), 2837. doi: 10.1016/j.ecns.2020.11.002.


Bender GJ. In situ simulation for systems testing in newly constructed perinatal facilities. Seminars in Perinatology [Internet]. 2011 [cited 2021 Nov 12];35(2):8083. doi: 10.1053/j.semperi.2011.01.007


Rojo E, Maestre JM, Díaz-Mendi AR, Ansorena L, Del Moral I. Innovando en procesos asistenciales y seguridad del paciente mediante simulación clínica [Innovation in healthcare processes and patient safety using clinical simulation]. Revista de Calidad Asistencial [Internet]. 2016 [cited 2020 Apr 02];31(5):267278. doi: 10.1016/j.cali.2015.12.008. Epub 2016 Mar 7. PMID: 26965531.


Cohen D, Darzi A, Vlaev I. Behavioural simulations in health care policy: current uses and future developments. Journal of Health Services Research & Policy [Internet]. 2013 [cited 2020 Mar 29];18(2):98106. doi:10.1177/1355819612473591


Kneebone R, Weldon SM, Bello F. Engaging patients and clinicians through simulation: rebalancing the dynamics of care. Advanced Simulation [Internet]. 2016 [cited 2020 Mar 29];1:19. doi: 10.1186/s41077-016-0019-9


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. Advanced Simulation [Internet]. 2020 [cited 2021 Nov 12];5:22. doi: 10.1186/s41077-020-00138-w


Tang JJ, Maroothynaden J, Bello F, Kneebone R. Public engagement through shared immersion: participating in the processes of research. Science Communication [Internet]. 2013 [cited 2020 Mar 29];35(5):654666. doi:10.1177/1075547012466389


Madani A, Gallix B, Pugh CM, et al Evaluating the role of simulation in healthcare innovation: recommendations of the Simnovate Medical Technologies Domain Group. BMJ Simulation & Technology Enhanced Learning [Internet]. 2017 [cited 2020 Mar 29];3(Suppl 1):S8S14. doi: 10.1136/bmjstel-2016-000178


Cohen D, Vlaev I, McMahon L, et al The crucible simulation: behavioral simulation improves clinical leadership skills and understanding of complex health policy change. Health Care Management Review [Internet]. 2019 [cited 2020 Mar 29];44(3):246255. doi: 10.1097/HMR.0000000000000162


Tang JJ, Maroothynaden J, Kneebone R. The role of medical simulation technologies for outreach activities in secondary school education: A workshop for prospective medical students. British Journal of Educational Technology [Internet]. 2013 [cited 2020 Mar 02];44(5):E120E126. doi: 10.1111/j.1467-8535.2012.01345.x


Kneebone R, Woods A. Recapturing the history of surgical practice through simulation-based re-enactment. Medical History [Internet]. 2014 [cited 2020 Mar 02];58(1),106121. doi: 10.1017/mdh.2013.75


Weldon SM, Ralhan S, Paice E, Kneebone R, Bello F. Sequential simulation of a patient journey. The Clinical Teacher [Internet]. 2016 [cited 2020 Mar 02];14(20):9094. doi: 10.1111/tct.12549


Weldon SM, Ralhan S, Paice E, Kneebone R, Bello F. Sequential simulation (SqS): an innovative approach to educating GP receptionists about integrated care via a patient journey – a mixed methods approach. BMC Family Practice [Internet]. 2015 [cited 2020 Mar 02];16:109. doi: 10.1186/s12875-015-0327-5


Weldon SM, Kelay T, Ako E, Cox B, Bello F, Kneebone R. Sequential simulation used as a novel educational tool aimed at healthcare managers: a patient-centred approach. BMJ Simulation & Technology Enhanced Learning [Internet]. 2017 [cited 2020 Mar 02];4(1):1318. doi: 10.1136/bmjstel-2017-000216. PMID: 35517378; PMCID: PMC8936806.


Huddy JR, Weldon S, Ralhan S, et al Sequential simulation (SqS) of clinical pathways: a tool for public and patient engagement in point-of-care diagnostics. BMJ Open [Internet]. 2016 [cited 2020 Mar 02];6:e011043. doi: 10.1136/bmjopen-2016-011043


Posner GD, Clark ML, Grant VJ. Simulation in the clinical setting: towards a standard lexicon. Advanced Simulation [Internet]. 2017 [cited 2020 Mar 02];2:15. doi: 10.1186/s41077-017-0050-5


Colman N, Doughty C, Arnold J, et al Simulation-based clinical systems testing for healthcare spaces: from intake through implementation. Advanced Simulation [Internet]. 2019 [cited 2020 Mar 02];4:19. doi: 10.1186/s41077-019-0108-7


Lutgendorf MA, Spalding C, Drake E, Spence D, Heaton JO, Morocco KV. Multidisciplinary in situ simulation-based training as a postpartum hemorrhage quality improvement project. Military Medicine [Internet]. 2017 [cited 2020 Mar 02];182(3):e1762e1766. doi: 10.7205/MILMED-D-16-00030.


Calhoun AW, Boone MC, Miller KH, Pian-Smith MC. Case and commentary: using simulation to address hierarchy issues during medical crises. Simulation in Healthcare [Internet]. 2013 [cited 2021 Nov 12];8(1):1319. doi: 10.1097/SIH.0b013e318280b202. PMID: 23343842.


Nielsen DS, Dieckmann P, Mohr M, Mitchell AU, Østergaard D. Augmenting health care failure modes and effects analysis with simulation. Simulation in Healthcare [Internet]. 2014 [cited 2020 Mar 02];9(1):4855. doi: 10.1097/SIH.0b013e3182a3defd.


Maestre JM, Manuel-Palazuelos JC, Moral ID, Simon R. Clinical simulation as a tool to facilitate culture change in healthcare: practical application of advanced learning theory. Colombian Journal of Anesthesiology [Internet], 2014 [cited 2020 Mar 02];42(2):124128. doi: 10.1016/j.rcae.2014.01.005


Brazil V, Purdy E, Alexander C, Matulich J. Improving the relational aspects of trauma care through translational simulation. Advanced Simulation [Internet]. 2019 [cited 2021 Jan 02];4:10. doi: 10.1186/s41077-019-0100-2


Cheung VK, So EH, Ng GW, So SS, Hung JL, Chia NH. Investigating effects of healthcare simulation on personal strengths and organizational impacts for healthcare workers during COVID-19 pandemic: a cross-sectional study. Integrative Medicine Research [Internet]. 2020 [cited 2021 Nov 12];9(3):100476. doi: 10.1016/j.imr.2020.100476


Petersen LF, Madsen MD, Østergaard D, Dieckmann P. Using simulation to help healthcare professionals relaying patient information during telephone conversations. Heliyon [Internet]. 2020 [cited 2020 Nov 12];6(8):e04687. doi: 10.1016/j.heliyon.2020.e04687


Colman N, Saldana C, Craig K, Edwards N, McGough J, Mason C, Hebbar KB. Simulation-based user-centered design: an approach to device development during COVID-19. Pediatric Quality & Safety [Internet]. 2021 [cited 2021 Nov 12];6(4):e427. doi: 10.1097/pq9.0000000000000427


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 Quality & Safety [Internet]. 2013 [cited 2020 Mar 02];22(6):468477. doi: 10.1136/bmjqs-2012-000942


Dubé M, Laberge J, Sigalet E, Schultz J, Vis C, Ball CG. Evaluations for new healthcare environment commissioning and operational decision making using simulation and human factors: a case study of an interventional trauma operating room. HERD [Internet]. 2021 [cited 2022 Feb 02];14(4):442456. doi:10.1177/1937586721999668


Petrosoniak A, Almeida R, Pozzobon LD, et al Tracking workflow during high-stakes resuscitation: the application of a novel clinician movement tracing tool during in situ trauma simulation. BMJ Simulation & Technology Enhanced Learning [Internet]. 2018 [cited 2020 Mar 02];5(2):7884. doi: 10.1136/bmjstel-2017-000300.


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 [Internet]. 2020 [cited 2021 Apr 03];13(1):94105. doi: 10.1177/1937586719846586.


Brazil V, Scott C, Matulich J. Shanahan B. Developing a simulation safety policy for translational simulation programs in healthcare. Adv Simul [Internet]. 2022 [cited 2022 Feb 03];7:4. doi: 10.1186/s41077-022-00200-9


Sadhu NM, Reid L, Khalid F. Reducing risk: simulation-based clinical systems testing to determine new hospital readiness for anaesthesia services. IJOHS [Internet]. 2021 [cited 2022 Feb 03];1(Suppl 1):A62A63. doi 10.54531/PXGQ8394


Brydges R, Nemoy L, Ng, S, et al Getting everyone to the table: exploring everyday and everynight work to consider ‘latent social threats’ through interprofessional tabletop simulation. Advanced Simulation [Internet]. 2021 [cited 2022 Feb 03];6:39. doi: 10.1186/s41077-021-00191-z


Oliver N, Twentyman K, Howie K. What effect does systems integration simulation have on the sense of preparedness of teams moving to a new unit? International Journal of Healthcare Simulation – Advances [Internet]. 2021 [cited 2022 Feb 03];1(Suppl 1):A78A78. doi: 10.54531/BWLE1794


Kim JJ, Howes D, Forristal C, Willmore A. The Code Silver Exercise: a low-cost simulation alternative to prepare hospitals for an active shooter event. Advanced Simulation [Internet]. 2021 [cited 2022 Feb 03];6,37. doi: 10.1186/s41077-021-00190-0


Colman N, Stone K, Arnold J, et al Prevent safety threats in new construction through integration of simulation and FMEA. Pediatric Quality and Safety [Internet]. 2019 [cited 2022 Feb 03];4(4):e189. doi: 10.1097/pq9.0000000000000189


Tallentire VR, Kerins J, McColgan-Smith S, Power A, Stewart F, Mardon J. Exploring transformative learning for trainee pharmacists through interprofessional simulation: a constructivist interview study. Advanced Simulation [Internet]. 2021 [cited 2021 Nov 12];6(1):31. doi: 10.1186/s41077-021-00180-2. PMID: 34493341; PMCID: PMC8422059.


Pillay, T., Clarke, L., Abbott, L, 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. Advanced Simulation [Internet]. 20218 [cited 2021 Nov 12];6,29. doi: 10.1186/s41077-021-00181-1


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. Advanced Simulation [Internet]. 2021 [cited 2021 Nov 12];6:21. doi: 10.1186/s41077-021-00166-0


Dubé M, Posner G, Stone K, et al Building impactful systems-focused simulations: integrating change and project management frameworks into the pre-work phase. Advanced Simulation [Internet]. 2021 [cited 2021 Nov 12];6:16. doi: 10.1186/s41077-021-00169-x


Reid J, Stone K, Huang L, Deutsch ES. Simulation for systems integration in pediatric emergency medicine. Clinical Pediatric Emergency Medicine [Internet]. 2016 [cited 2020 Mar 02];17(3),193199. doi:10.1016/j.cpem.2016.05.006.


Fuselli T, Raven A, Milloy S, Barnes S, Dubé M, Kaba A. Commissioning clinical spaces during a pandemic: merging methodologies of human factors and simulation. HERD [Internet]. 2022 [cited Feb 12];15(2):277292. doi:10.1177/19375867211066933


Jafri FN, Shah S, Yang CJ, et al Safety considerations for in situ simulation in closed SARS-CoV-2 units. Simulation in Healthcare [Internet]. 2022 [cited 2022 Feb 12];17(1):4953. doi: 10.1097/SIH.0000000000000542. PMID: 33273425.


Davis S, Riley W, 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, Battles JB, Keyes MA, Grady ML, editors. Advances in patient safety: new directions and alternative approaches (Vol. 3: Performance and Tools). Rockville (MD): Agency for Healthcare Research and Quality (US). 2008.


Paige JT, Terry Fairbanks RJ, Gaba DM. Priorities related to improving healthcare safety through simulation. Simulation in Healthcare [Internet]. 2018 [cited 2020 Mar 02];13(3S Suppl 1):S41S50. doi: 10.1097/SIH.0000000000000295


Macrae C. Imitating incidents: how simulation can improve safety investigation and learning from adverse events. Simulation in Healthcare [Internet]. 2018 [cited 2020 Mar 02];13(4):227232. doi: 10.1097/SIH.0000000000000315


Lamé G, Dixon-Woods M. Using clinical simulation to study how to improve quality and safety in healthcare. BMJ Simulation & Technology Enhanced Learning [Internet]. 2020 [cited 2020 Nov 12];6(2):8794. doi: 10.1136/bmjstel-2018-000370.


Petrosoniak A, Hicks C, Barratt L, et al. Design thinking-informed simulation: an innovative framework to test, evaluate, and modify new clinical infrastructure. Simulation in Healthcare. 2020;15:205213.


Couto T, Barreto J, Marcon FC, Mafra A, Accorsi T. Detecting latent safety threats in an interprofessional training that combines in situ simulation with task training in an emergency department. Advanced Simulation. 2018;3:23.


Schram A, Paltved, C, Christensen KB, Kjaergaard-Andersen G, Jensen HI, Kristensen S. Patient safety culture improves during an in situ simulation intervention: a repeated cross-sectional intervention study at two hospital sites. BMJ Open Quality. 2021;10:e001183


Paltved C, Bjerregaard AT, Krogh K, Pedersen J, Musaeus P. Designing in situ simulation in the emergency department: evaluating safety attitudes amongst physicians and nurses. Advanced Simulation. 2017;2:4.


Fregene TE, Nadarajah P, Buckley JF, Bigham S, Nangalia V. Use of in situ simulation to evaluate the operational readiness of a high-consequence infectious disease intensive care unit. Anaesthesia 2020;75:733738.


Wheeler D, Geis G, Mack E, LeMaster T, Patterson M. High-reliability emergency response teams in the hospital: improving quality and safety using in situ simulation training. BMJ Quality & Safety. 2013;22:507551.


Gardner A, Johnston M, Korndorffer J, Haque I, Paige J. Using simulation to improve systems-based practices. Joint Commission Journal of Quality and Patient Safety. 2017;43:484491.


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. International Journal of Healthcare Simulation.2022;2:311. Available from: https://www.ijohs.com/article/doi/10.54531/jsvm5820


Brazil V, Purdy E, Bajaj K. (2023). Simulation as an improvement technique (elements of improving quality and safety in healthcare). Cambridge: Cambridge University Press. doi:10.1017/9781009338172


Binder C, Elwell D, Ackerman P, Shulman J, Yang C, Jafri F. Interprofessional in situ simulation to identify latent safety threats for quality improvement: a single-center protocol report. Journal of Emergency Nurse. 2023;49(1):5056. doi: 10.1016/j.jen.2022.09.007. Epub 2022 Nov 16. PMID: 36400572.


Lavelle M, Reedy GB, Attoe C, et al Beyond the clinical team: evaluating the human factors-oriented training of non-clinical professionals working in healthcare contexts. Advanced Simulation. 2019;4:11. doi: 10.1186/s41077-019-0101-1


Choi W, Dyens O, Chan T, Schijven M, Lajoie S, Mancini M, Dev P, Fellander-Tsai L, Ferland M, Kato P, Lau J, Montonaro M, Pineau J, Aggarwal R. Engagement and learning in simulation: recommendations of the Simnovate Engaged Learning Domain Group. BMJ STEL, 2017;3(Suppl 1):S23S32.


Streufert, S., Satish, U., & Barach, P. Improving medical care: the use of simulation technology. Simulation & Gaming. 2001;32(2):164174. doi: 10.1177/104687810103200205


Wheeler DS, Geis G, Mack EH, LeMaster T, Patterson MD. High-reliability emergency response teams in the hospital: improving quality and safety using in situ simulation training. BMJ Quality & Safety. 2013;22(6):507514. doi: 10.1136/bmjqs-2012-000931. Epub 2013 Mar 1. PMID: 23457361.


Yang, F., Wang, Y., Yang, C. et al. Improving clinical judgment by simulation: a randomized trial and validation of the Lasater clinical judgment rubric in Chinese. BMC Medical Education. 2019;19:20. doi: 10.1186/s12909-019-1454-9


Kneebone R & Woods A. Bringing surgical history to life. BMJ. 2012;345:e8135. doi: https://doi.org/10.1136/bmj.e8135


Starr M, Sawyer T, Jones M, et al. a simulation-based quality improvement approach to improve pediatric resident competency with required procedures. Cureus. 2017;9(6):e1307. doi:10.7759/cureus.1307


Zhang, C., Grandits, T., Härenstam, K.P. et al A systematic literature review of simulation models for non-technical skill training in healthcare logistics. Advanced Simulation. 2018;3:15. doi: 10.1186/s41077-018-0072-7


Walsh, B.M., Auerbach, M.A., Gawel, M.N. et al. Community-based in situ simulation: bringing simulation to the masses. Advanced Simulation. 2019;4:30. doi: 10.1186/s41077-019-0112-y


Perot JG, Belmin J. Simulation des limitations liées à l’avancée en âge chez les étudiants et professionnels en santé: une scoping review [Aging-simulation experience among students and health professionals: a scoping review]. Gériatrie et Psychologie Neuropsychiatrie du Vieillissement. 2020;18(3):241248. French. doi: 10.1684/pnv.2019.0837. PMID: 32716000.


Hammer M, Fox S & Hampton MD. Use of a therapeutic communication simulation model in pre-licensure psychiatric mental health nursing: enhancing strengths and transforming challenges. Nursing and Health. 2014;2(1): 18. Available from: http://www.hrpub.org. doi: 10.13189/nh.2014.020101


Orenstein EW, Rasooly IR, Mai MV, Dziorny AC, Phillips W, Utidjian L, Luberti A, Posner J, Tenney-Soeiro R, Bonafide CP. Influence of simulation on electronic health record use patterns among pediatric residents. Journal of American Medical Informatics Association. 2018;25(11):15011506. doi: 10.1093/jamia/ocy105. PMID: 30137348; PMCID: PMC6213084


Brazil V, Purdy EI, Bajaj K. Connecting simulation and quality improvement: how can healthcare simulation really improve patient care? BMJ Quality & Safety. 2019;28:862865.


Salas E, Wildman JL, and Piccolo RF. Using simulation-based training to enhance management education. Academy of Management Learning & Education. 2017;8(4). doi: 10.5465/amle.8.4.zqr559


McGaghie WC, Issenberg SB, Cohen ER, Barsuk JH, Wayne DB. Does simulation-based medical education with deliberate practice yield better results than traditional clinical education? A meta-analytic comparative review of the evidence. Academic Medicine. 2011;86(6):706711. doi: 10.1097/ACM.0b013e318217e119. PMID: 21512370; PMCID: PMC3102783.


Thrasher J, Baker J, Ventre KM, Martin SE, Dawson J, Cox R, Moore HM, Brethouwer S, Sables-Baus S, Baker CD. Hospital to home: a quality improvement initiative to implement high-fidelity simulation training for caregivers of children requiring long-term mechanical ventilation. Journal of Pediatric Nursing. 2018;38:114121. doi: 10.1016/j.pedn.2017.08.028. Epub 2017 Sep 22. PMID: 28943140; PMCID: PMC6456906.


Cowperthwait A, Saylor J, Carlsen A, Salam T, Melby MK, Baker SD. Healthcare theatre and simulation: maximizing professional partnerships. Clinical Simulation in Nursing. 2015;11(9): 411420, 10.1016/j.ecns.2015.05.005


Gómez-Pérez V, Escrivá Peiró D, Sancho-Cantus D, Casaña Mohedo J. In situ simulation: a strategy to restore patient safety in intensive care units after the COVID-19 pandemic? Systematic review. Healthcare. 2023;11(2):263. doi: 10.3390/healthcare11020263


Lamé G, Simmons RK. From behavioural simulation to computer models: how simulation can be used to improve healthcare management and policy. BMJ Simulation & Technology Enhanced Learning. 2020;6(2):95102. doi: 10.1136/bmjstel-2018-000377


Guise JM, Mladenovic J. In situ simulation: identification of systems issues. Seminars in Perinatology [Internet]. 2013 [cited 2020 Mar 02];37(3):161165. doi: 10.1053/j.semperi.2013.02.007. PMID: 23721772.


Phrampus PE. Simulation and integration into patient safety systems. Simulation in Healthcare [Internet]. 2018 [cited 2022 Feb 12];13(4):225226. doi: 10.1097/SIH.0000000000000332


Chetcuti S, Bhowmick K. Simulation on the job: an intensive care unit’s report of how in situ simulation has impacted on human factors and ergonomics in the workplace. BMJ Simulation & Technology Enhanced Learning. 2019;6(1):3940. doi: 10.1136/bmjstel-2018-000348. PMID: 35514453; PMCID: PMC8936907.


So HY, Chen PP, Wong GKC, Chan TTN. Simulation in medical education. Journal of the Royal College of Physicians of Edinburg. 2019;49(1):5257. doi: 10.4997/JRCPE.2019.11


Brazil V. Translational simulation: not ‘where?’ But ‘why?’ A functional view of in situ simulation. Advanced Simulation [Internet]. 2017 [cited 2021 Jan 02];2:20. doi: 10.1186/s41077-017-0052-3
Supplementary materials
  • Supplementary-material_S1.docx info     save_alt  
  • Supplementary-material_S2.docx info     save_alt