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Practicing the Unpredictable with In Situ Simulation

2025· article· en· W4414831525 on OpenAlexaboutno aff
Sarah Snobelen

Bibliographic record

VenueEmergency Medicine News · 2025
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsProcess (computing)Event (particle physics)Work (physics)TroubleshootingIn situ

Abstract

fetched live from OpenAlex

Effective management of a medical emergency requires the application of clinical knowledge and mastery of technical skills. But in a busy emergency department (ED), the physicians are not managing the situation in isolation; they are working in an often-chaotic environment alongside a team of nurses, respiratory therapists, anesthesiologists, pharmacists, and other health care professionals (HCPs), while also leading, communicating, and managing the logistics inherent to their specific workplace. In this environment, success requires much more than clinical and technical skills. Organized and automated workflows, efficient processes, situational awareness, and communication and leadership skills are all required to successfully navigate a life-threatening scenario in an ED.1 How can the leadership of an ED and individual emergency medicine physicians set themselves up for success in this situation? The principle of learning by observation then repetition has historically been used to teach the skills needed for managing specific clinical scenarios, but this approach is reactive and error-prone, particularly for the high-risk, low-frequency situations that can occur in the ED.2 How can learners prepare for the day when they will be responsible for managing a pediatric resuscitation or cricothyrotomy if they have rarely encountered these situations? How can the entire team of HCPs trust the process, and each other, if they have never managed the scenario with with this particular group of people on any given day? Bringing Simulation to the Frontlines Addressing these gaps in training and resource management in emergency medicine is the goal of in situ simulation (ISS) training.1,2 ISS involves the simulation of an emergency scenario in the actual physical space of the ED, with the team of HCPs who work together day to day, and uses the equipment and resources on hand.3 In lieu of a real patient, task trainers (eg, anatomically correct limbs), manikins, or standardized patients are used, and debriefing and feedback occur at the end of each session, aided in some cases by audio-visual recordings. Dr Nancy Li, an emergency physician in Toronto, Canada, comments, “In situ simulation is an excellent opportunity to learn valuable crisis resource management, communication, and teamwork skills in a safe environment.” It also allows the participants to have the opportunity for immediate reflection and discussion, led by a trained simulation facilitator.1,4 Advancing Care for Providers and Patients Originally used in the aviation and nuclear industries, simulation training has become increasingly popular in emergency medicine because it provides a standardized and reproducible approach to learning, improves knowledge retention, and provides an opportunity to assess the safety and efficiency of workflows in an ED.5-7 This benefits not only the ED team but has also been shown to improve patient outcomes and patient safety.1,5 Specifically, ISS is useful in identifying latent safety threats, which are conditions related to equipment, design, physical space, or communication that may contribute to a delay in care or medical errors.3,5,6 Dr Marcus Jee, Specialist Registrar, Advanced Specialist Training in Emergency Medicine, in Dublin, Ireland, is the lead author of the BEST-ED study, which examined barriers and enablers to ISS training in the ED.7 Dr Jee states, “In situ simulation is not only about the physical setting and team composition, but also about the authenticity of the experience. Beyond practicing clinical skills, in situ simulation also helps teams spot any latent safety issues in their own departments and improve them as they go. Our BEST-ED study, and other recent research, really highlight how valuable this kind of hands-on, team-based learning can be for both staff and patient safety.” “No-Go” Criteria for Safe ISS Running simulated emergency situations in a space where patient care is taking place is not without its challenges. Resource availability such as staffing and hospital equipment must be assessed, and a designated space is needed for the set-up of simulation equipment.6,7 The psychological safety of staff is also a key consideration, as some may fear negative judgment of their performance by their colleagues, or may be emotionally vulnerable if a challenging clinical event has occurred earlier on their shift. A list of “no-go” criteria, tailored to the specific needs and resources of each ED, is recommended to ensure staff have the dedicated time and space to effectively participate in the ISS scenario, and to make sure that staff and patient safety are not compromised.6 Aligning Priorities for Success While ISS has demonstrated benefits to ED learners, staff, and patients, the reality of simulation equipment costs, time away from the bedside, and understaffing can create challenges for management staff in their support of ISS.1,7 However, the potential benefit of identification of latent safety threats and improvement in patient outcomes could offset these costs over time.1 Dr Jee notes, “We all have the patient at heart, and acknowledging different priorities while creating shared value is what makes programs succeed long term. When departments successfully bridge this gap, they create sustainable programs that serve both operational efficiency and clinical excellence.” The Evolving Role of Technology in ISS Given the success of ISS in emergency medicine and advances in artificial intelligence and virtual-reality technology, ISS continues to evolve as a method to develop and maintain clinical and non-technical skills. Interactive virtual reality platforms that immerse the HCP are available, offering simulations in clinical scenarios that provide interaction with patients and family members, dynamic vital signs and laboratory values, and feedback and debriefing when the scenario is over.8 As the technology for simulation training continues to advance, the balance of virtual versus human interaction, and the management of costs, time, and staffing will need to be assessed by the leadership of individual EDs to ensure that they create and implement the ISS program that meets the needs of their specific ED. SARAH SNOBELEN is a clinical pharmacist and freelance writer and editor. She specializes in writing continuing education content for health care professionals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.423
Teacher spread0.371 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
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