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Technology, Computing, and Simulation: Original Clinical Research Report

A Simulation Study to Evaluate Improvements in Anesthesia Work Environment Contamination After Implementation of an Infection Prevention Bundle

Porteous, Grete H. MD*; Bean, Helen A. DO*; Woodward, Crystal M. MD*; Beecher, Ryan P. CRNA*; Bernstein, Jennifer R. BA; Wilkerson, Sarah RN; Porteous, Ian PhD§; Hsiung, Robert L. MD*

Author Information
doi: 10.1213/ANE.0000000000002764



  • Question: Can an infection prevention bundle that includes double gloving, confining airway equipment to a single area, and increased hand hygiene reduce anesthesia work environment contamination in a simulated operating room?
  • Findings: Using ultraviolet fluorescent tracers in simulation scenarios, we found that overall anesthesia work environment contamination was reduced by 27% after implementation of the bundle, with significant improvements seen in medication preparation and administration areas.
  • Meaning: Modest changes in anesthesia provider behavior have the potential to decrease workspace contamination and the risk of hospital-acquired infections.

Microbiological contamination of the anesthesia work environment (AWE) is a potential source of health care–associated infections.1,2 Intravenous (IV) lines, stopcocks, medication syringes, laryngoscope handles, keyboards, anesthesia machines, and many other areas are routinely contaminated during anesthetics.3–8 Although hand contamination is the major mechanism by which bacteria are transferred between patient and environmental reservoirs, anesthesia provider compliance with hand hygiene recommendations is poor.9,10 In 1 study, intraoperative adherence to the World Health Organization recommendations, “5 Moments for Hand Hygiene,” was only 3% (Supplemental Digital Content 1, Appendix 1, In another study, only 13 hand hygiene events were witnessed during 8 hours of observation of 19 anesthesiologists who collectively contacted their work environment more than 1000 times.6 Furthermore, bacterial reservoirs in the operating room (OR) may persist because of inadequate cleaning protocols.11

The problem of AWE bacterial contamination is complex and has defied a simple solution. We considered that if an evidence-based bundle of care could be successfully applied to reduce central line–associated bloodstream infections, then perhaps a bundle could be helpful here as well.12,13 Preliminary observations at our institution confirmed variable clinician practices regarding gloving, hand hygiene, handling of medications, and placement of used airway equipment. Our primary objective was to determine whether an evidence-based bundled intervention (Figure 1) to change anesthesia provider behavior during a simulated case would reduce the extent of AWE contamination by ultraviolet (UV) fluorescent tracers.14–16

Figure 1.:
The Anesthesia Work Environment Simulation (AWESim) bundle of interventions to reduce workspace contamination.

We chose a simulation-based approach rather than live observations in the OR for several reasons. First, we wished to control for variability in case type, duration, and difficulty. Comparing degree of work environment contamination between 2 similar general anesthetic cases could easily be confounded by such variables as multiple attempts at an intubation, the number of anesthesia providers involved, the experience level of providers, and whether the patient required any other hands-on-intervention after intubation. A simulated environment allowed the creation of scenarios where subjects were forced to manage realistic situations under real-life time pressures, but standardized the case difficulty, number of providers, and type and timing of interventions required after taping the endotracheal tube. Furthermore, UV tracers are validated in a simulation setting, but not approved for use in live patient care. Finally, we wanted to use the powerful visual evidence provided by photographs of the UV tracers in the AWE as a tool for education and awareness for the subjects.


This nonrandomized simulation scenario, crossover design study was reviewed and exempted by the institutional review board of the Benaroya Research Institute at Virginia Mason Medical Center. Subjects were volunteers from the Department of Anesthesiology at Virginia Mason, including anesthesiology residents (postgraduate years 3 and 4), attending anesthesiologists, and certified registered nurse anesthetists (CRNAs) in equal proportions. Prospective subjects were told that they would participate in a simulation study about safety in the OR environment, but were not informed that infection prevention was the focus. All subjects had participated in medical simulations and were familiar with the Laerdal SimMan® manikin in our simulation center. To minimize the possibility of subjects hearing in advance about the nature of the study, all data collection occurred between September 7 and September 29, 2016, and subjects were given strict instructions to keep details of the simulations confidential. Verbal consent was obtained from all subjects before enrollment, per institutional review board requirements. All subject data were deidentified.

Prestudy Testing and Simulation Design

We adapted a method of simulating bacterial contamination in the OR using UV fluorescent tracer.17 As we were not able to obtain the specific fluorescent gel reported by Birnbach et al,17 we selected a combination of 2 products that approximated their results. The tongue and oropharynx of the patient manikin were coated with 2 mL of UV fluorescent gel (GloGerm gel; Glo Germ Company, Inc, Moab, UT), and the cheeks and mandible of the manikin were dusted with UV fluorescent powder (GloGerm). Both products are invisible to casual inspection under normal light conditions. Hand hygiene with alcohol-based hand sanitizer decreases the transmission of both gel and powder from hands to other surfaces. After several months of simulation design and testing, we arrived at a list of 20 frequently contaminated sites in the AWE (Figure 2). These were adapted from the 40 sites identified in Birnbach et al17 to focus attention on medication preparation areas and syringe and IV line handling. We also included surgical scrub jackets as a separate item for analysis. A site was considered contaminated, or “positive,” if any UV tracer was present.

Figure 2.:
Distribution of ultraviolet tracer contamination within the anesthesia work environment. Each bar reflects a site-specific contamination score expressed as a percentage of 25 possible opportunities for contamination during either baseline or intervention simulation scenarios. In sites identified as “high impact,” the intervention bundle was associated with a statistically significant decrease in site-specific contamination score (*P < .05). APL indicates adjustable pressure limiting; ECG, electrocardiogram; ETT, endotracheal tube; IV, intravenous.

As induction and airway management have been identified as particularly high-risk events for AWE contamination, 2 scenarios about the start of a general anesthetic were developed. Although the scenarios feature different clinical prompts, both were crafted to elicit the same physical interactions between the subject and the work environment. By prompting the subject to touch the manikin contamination source several times, we generated a simulated AWE with a reproducibly high degree of contamination, especially in medication preparation and administration areas. This was important so that the intervention tested might show an effect that was both statistically and clinically significant. We did not include computer keyboards and phones, areas known to be highly contaminated during anesthetic care,17,18 because we felt that charting in the electronic medical record or making a phone call would not be realistic in our short scenarios in which hypotension and hypoxemia developed quickly and required hands-on intervention. Details about simulation design and programming are provided in Figure 3. To account for subtle differences in degree of AWE contamination inherent in each scenario design, subjects were randomized to start with scenario A or B based on a coin toss. We chose a 2-scenario approach to minimize favorable bias on the results based on familiarity and practice with a single scenario executed twice.

Figure 3.:
Simulation scenario design. BP indicates blood pressure; Etco 2, end-tidal carbon dioxide; ETT, endotracheal tube; HR, heart rate; IV, intravenous; MDI, metered dose inhaler; PIP, peak inspiratory pressure; RR, respiratory rate; Spo 2, peripheral oxygen saturation.

During their first (“baseline”) simulated case, subjects conducted usual clinical care without the bundle. During their second (“intervention”) simulated case, the work environment was altered and subjects were given instructions to execute the Anesthesia Work Environment Simulation bundle. Subjects performed the scenarios in the order “baseline,” then “intervention.” This order was not randomized as we felt that subjects who started with the intervention would be unblinded to the nature of this infection prevention study, and their subsequent performance would be affected by this knowledge. All subjects were oriented to the simulation scenarios and given instructions by the same investigator (G.H.P.) reading a script. Subjects were excluded from the data analysis if they spontaneously donned a double pair of gloves during their baseline scenario, as removing an outer pair of gloves has previously been identified as a key step in decreasing AWE contamination.14 Interactions between subjects and investigators after the initial orientation were limited to reminders to remove gloves or perform hand hygiene in the intervention scenario, when appropriate.

Room Set-up and Cleaning

The AWE in the simulation lab replicated actual OR conditions at our institution, including a Draeger Fabius GS® anesthesia machine, IV fluids and tubing, airway equipment, and faux medication syringes. A hand sanitizer dispenser was permanently mounted to the anesthesia cart. Before the start of each simulation, the AWE was carefully inspected with a forensic-quality UV light source (HammerHead UV 395 nm ALS; FoxFury Lighting Solutions®, Oceanside, CA) for the presence or absence of any UV tracer. The environment was thoroughly cleaned by gloved personnel using germicidal wipes. Spurious fluorescence in any area due to permanent defects (eg, chipped or discolored paint) was photographed and documented. New disposable equipment used for each simulation included an anesthesia circuit, laryngoscope blade, IV tubing, medications, endotracheal tube with stylet, tape, and oropharyngeal airway. Nondisposable items such as the manikin, anesthesia machine and cart, stethoscope and electrocardiography leads were wiped clean and linens were replaced. The entire AWE was inspected after cleaning with the UV light source. Before the intervention scenario, a second hand sanitizer dispenser was placed on the anesthesia machine upper shelf, and signs reminding subjects to perform hand hygiene were placed in front of both hand sanitizer dispensers. In addition, subjects were given a clean scrub jacket and instructed to wash their hands to remove any residual tracer before the intervention scenario.

Photography and Scoring

Figure 4.:
Example photos illustrating ultraviolet tracer contamination in the anesthesia work environment after a simulation scenario. A characteristic “stamp” of tracer transferred to an anesthesia machine shelf by a used facemask is shown in the top left image.

After completion of each simulation scenario, subject jackets were collected and subjects were excused from the room. Twenty predetermined areas in the AWE were examined with the UV light source for the presence of any UV tracer. Yellow-tinted plastic glasses were used to enhance detection of tracer. Two investigators scored each area in real time, and 1 (G.H.P.) took photographs with a Nikon D800 camera with a Nikon AF-S Nikkor 50 mm 1:1.8 G lens (Nikon, Natori, Japan). Each scenario was also video recorded with a Sony Alpha 5000 camera (Sony, Tokyo, Japan) and a camera on the SimMan computer. Photo images were downloaded to Photoshop Lightroom CC 2015 (Adobe, San Jose, CA) and processed by adjusting contrast and light balance and cropping to maximize visibility of tracer. Examples of photos are shown in Figure 4. Each set of photo files was renamed using randomized numbers and shuffled by date before scoring by a blinded observer (C.W.). Video recordings of each scenario were reviewed by an infection prevention nurse outside our department (S.W.) for duration, number of glove removals, number of hand hygiene events, and other spontaneous behaviors related to infection prevention. A hand hygiene event was defined as a depression of the lever of the hand sanitizer dispenser followed by a hand cleansing motion.


At the conclusion of simulation data collection for the study, each subject was given a letter explaining the study in detail, and given access to their own baseline and intervention photo sets. They were then sent a 6-question anonymous online survey regarding their opinions about value of this type of simulation in increasing awareness of AWE contamination, the practicality of the interventions tested, barriers to OR infection prevention, and the value of their simulation experience (Supplemental Digital Content 2, Appendix 2,

Sample Size, End Points, and Statistical Analysis

The primary end point was the total number of AWE sites contaminated by a subject at baseline and after implementation of the intervention bundle, which we refer to as the “subject contamination score.” The maximum subject contamination score possible is 20 sites contaminated out of 20 possible sites. To validate a new photography-based scoring method that allowed us to blind an observer, we collected scoring data live and with photos and compared the results. Using the photo technique, both subjects and scoring observer were blind to test conditions. Secondary end points were site-specific contamination scores (defined below), time needed to complete each scenario, and number of hand hygiene and glove removal events performed by subjects. Statistical analysis was performed using R Version 3.3.2 with the lme4 package (R Foundation for Statistical Computing, Vienna, Austria).19 Poisson regression using a mixed effects model was used to analyze the effect of the bundle on subject contamination score. The regression model included provider type as a fixed effect and the order of scenarios as a random effect. A Gaussian regression mixed effects model was used to analyze total time required with the same effects. Site-specific contamination scores were determined by tallying the number of times each site was contaminated by all subjects over all simulations under each condition (baseline or intervention). Contingency tables of site-specific contamination scores were compared using Fisher exact test. For ease of comparison, site-specific contamination scores are reported as percentages in Figure 2, which are calculated by dividing the score by 25, the total number of simulations conducted under each condition, and multiplying by 100%. For all results, mean values are reported with 95% confidence intervals (CIs) or standard deviation as appropriate. A P value <.05 was considered significant. Survey results are reported using simple descriptive statistics. We estimated sample size based on a paired t test for a “before-after” study, the initial statistical test planned for this study. Assuming a 2-tailed α of .05, a power of 0.8, and an effect size of 0.6 (moderate) based on published data, we calculated a group size of 22.14,20 In addition, we judged that we could recruit 20–25 subjects from our department based on previous simulation studies conducted at our institution.


Fifty simulation scenarios were completed by 25 subjects (9 residents, 8 attending anesthesiologists, and 8 CRNAs). The median number of years of experience giving anesthesia was 5.5 (range, 1.5–30 years). No subjects were excluded because of spontaneously double gloving in the first scenario and all subjects completed the study. Approximately 1300 photos and 6 hours of video were analyzed. There were 3 instances in which photos were inadvertently not taken of a site (2 during baseline scenarios and 1 during an intervention scenario). In these cases, the corresponding “live” score was substituted to complete data sets.

Photo Scoring

Using the photo scoring method, the mixed effects model showed an intervention effect of decreasing subject contamination score by 4.0 (95% CI, 2.2–5.6; P < .001; Table), a 27% reduction in score between baseline and intervention scenarios. The effect of provider type (resident, attending, or CRNA) was not significant (P = .58). There was no (0) variance due to the random effect of whether scenario A or B was performed first. To validate our new photo scoring method, we compared photo score results to subject contamination scores recorded live according to the published method.14 In comparison to photo score results, the mixed effects model showed an intervention effect of decreasing subject contamination score by 3.45 (95% CI, 1.7–5.0; P < .001) when live scoring was used. Photo scores were higher than live scores at each site by 0.16 on average. The photo scoring method was thus slightly more sensitive at detecting UV tracer contamination, but the difference was minor and a significant effect was observed with both methods.

Subject Contamination Scores, HHEs, and GREs During Baseline and Intervention Scenarios

Analysis of site-specific contamination scores (Figure 2) demonstrate that some sites (eg, circuit reservoir bag, laryngoscope handle, stethoscope) are universally contaminated regardless of subject behavior. The bundled intervention had a greater impact on reducing contamination scores at other sites. Site-specific contamination scores of medication syringes decreased after the intervention from 92% to 52% (23/25 to 13/25; P = .004). Anesthesia cart top and front contamination scores decreased from 100% to 25% (25/25 to 6/25; P < .001) and 68% to 32% (17/25 to 8/25; P = .023), respectively. Ventilator control contamination scores decreased from 72% to 36% (18/25 to 9/25; P = .02). Contamination scores of jacket sleeves and torsos decreased from 76% to 36% (19/25 to 9/25; P = .005) and 96% to 64% (24/25 to 16/25; P = .005), respectively.

Video Analysis

Figure 5.:
The association between cumulative hand hygiene events, glove removal events, and subject contamination scores. Bubble size is proportionate to the number of subjects at a data point.

The time needed to complete each of the 50 simulation scenarios ranged from 4:20 to 10:37 (min:s). From the mixed effects model analysis, the intervention effect of time was not significant (−8.8 seconds, 95% CI, −49.7 to 31.6; P = .67). However, the effect of resident as provider on time taken to perform scenarios was significant (+64.0 seconds, 95% CI, 14.6–113.3; P = .01). This is not surprising given the relative level of experience of residents versus attending anesthesiologists and CRNAs. Hand hygiene events and glove removal events by subject are reported in the Table. Four subjects did not don gloves during their baseline scenarios, and all subjects donned 2 pairs of gloves initially during their intervention scenarios. Cumulative hand hygiene events and glove removal events were associated with lower subject contamination scores (Figure 5).


The survey (Supplemental Digital Content 2, Appendix 2, was completed by 92% of subjects (23 of 25). All subjects felt that OR contamination was an important patient safety issue (78.3% “very important,” and 21.7% “somewhat important”). The greatest barriers to infection prevention in the OR reported were “time pressure” 69.6% (16 of 23), “lack of equipment or supplies” 8.7% (2 of 23), “location of equipment or supplies” 4.3% (1 of 23), “prevailing culture and knowledge” 4.3% (1 of 23), and “other” 13% (3 of 23). The least practical intervention reported by 73.9% of subjects (17 of 23) was cleaning hands before touching the anesthesia cart. Subjects felt that double gloving and separating airway equipment were equally practical (11 of 23 or 47.8% of subjects each). When queried if their practice would change after this experience, 21.5% of subjects (5 of 23) reported “definitely,” 52.2% (12 of 23) reported “possibly,” and 26.1% (6 of 23) reported “probably.” The simulation experience was felt to be “extremely valuable” by 52.2% of subjects (12 of 23), “moderately valuable” by 43.5% (10 of 23), and “a little valuable” by 4.3% (1 of 23).


In a simulation setting, implementation of the AWEsim bundle of interventions reduced subject contamination scores in the AWE by 27%. The intervention had minimal to no impact on some parts of the environment, such as linens, oxygen masks, and laryngoscope handles. Statistically significant decreases in site-specific contamination scores were, however, seen in several clinically important areas including the top of the anesthesia cart (76% reduction) and medication syringes (43% reduction). A major mechanism of intraoperative pathogen transmission in the AWE occurs through a pathway from clinician hands, to syringe, to IV line, to patient bloodstream.1,2 Our results suggest that the bundle had an impact on the hygiene of medication preparation and administration steps of anesthetic care that merits further investigation.

The extent of contamination of the AWE generated during both baseline and intervention scenarios was impressive, and emphasizes the importance of frequent hand hygiene and thorough cleaning protocols between cases. Many photographs captured the role of used oxygen face masks and laryngoscope handles in spreading UV tracer wherever they were placed (Figure 4), highlighting the importance of separating clean and contaminated areas within the work environment. Jackets and stethoscopes were frequently contaminated as well, indicating that the concept of the AWE should also include clothing and items carried by providers. Appropriate cleaning of reusable equipment, use of disposable equipment, and guidelines regarding removal and changing of scrub jackets are all measures that can reduce cross-contamination in the OR.

We were able to control for several potential confounding factors in our study design. Whether scenario A or B was performed first did not affect our results. The time needed to complete the simulation scenarios was not significantly different before and after the intervention was implemented. This may reassure the majority of subjects who felt that time pressure was the greatest barrier they faced in implementing infection prevention measures. Anesthesia provider type did not affect subject contamination scores, increasing the generalizability of our results. Subjects’ baseline contamination scores did not decrease over the 4 weeks of testing. Had these baseline scores decreased as the study progressed, it could have indicated that subjects who had completed the simulations were revealing the study’s design and purpose to subjects yet to be tested, thus altering their behaviors.

By design, hand hygiene and glove removal events were expected to increase in the intervention scenario. The cumulative frequency of these events is, not surprisingly, associated with lower subject contamination scores (Figure 5). We have observed that anesthesia providers frequently do not perform the infection prevention behaviors during simulation that they would during an actual case, as traditionally our institution has used simulation to practice other skills like crisis management and resuscitation. Indeed, 4 of the subjects did not don gloves for their baseline scenarios. Thus, the very low rate of hand hygiene and glove removals at baseline is not surprising. What is striking is that even when given specific instructions and written and verbal reminders to clean their hands during intervention scenarios, 3 subjects failed to perform hand hygiene at all, and 40% of subjects failed to do so more than once. These results suggest that regular hand hygiene during this phase of the case is not part of the usual routine of some anesthesia providers, an observation consistent with multiple studies6,7,9,10 Adopting new behaviors under stress is difficult, so clinician buy-in and practice in advance of implementation are crucial for success. The demonstration in this study that improvement in AWE contamination is possible with a combination of interventions that includes cleaning one’s hands once or twice and removing a pair of gloves makes the point that risk reduction in the OR is achievable, even if strict adherence to the “5 Moments of Hand Hygiene” of the World Health Organization is sometimes not. Further video analysis of specific behaviors associated with high or low degrees of workspace contamination may also be helpful in identifying best practices.

Conducting an infection prevention study in a simulated environment has advantages and disadvantages. Simulation offers a particular advantage to studying infection prevention practices in anesthesia by allowing for control over confounding variables such as case type, patient factors, and case duration. Other factors that could affect the degree of contamination of the AWE include multiple attempts to secure an airway, clinician experience level, and whether 1 or more clinicians were involved in different anesthesia tasks. Simulation also affords the opportunity to evaluate new techniques rapidly and in a risk-free setting before more definitive study during live patient care. The main disadvantage of a simulated environment is that clinician behavior, including hand hygiene practices, during simulation does not precisely mirror behavior in the real world. Our scenarios were designed to create a standardized and reproducibly contaminated AWE to test an intervention, and not to reflect real-world conditions exactly. Furthermore, inorganic UV tracers may not behave exactly like biologic materials in how they transfer or adhere to surfaces. Our results should be interpreted in the context of these limitations.

Study design was challenging in several ways. First, sample size was limited by the number of potential subjects we could recruit in our department. Second, we recognized that anesthesia providers tend to be, broadly speaking, neat or messy in the OR, which could confound the interpretation of results of a subject in a single scenario randomized to either baseline or intervention conditions. Third, while our crossover study design allowed each subject to serve as his or her own control, we did not randomize subjects to start with the intervention scenario because we felt strongly that this would bias their subsequent behaviors. We were concerned that if subjects were instructed to change how they gloved, handled dirty airway equipment, and performed hand hygiene in the first scenario, our intent to study infection prevention practices would become obvious. This would make it unlikely they would revert to true “baseline” behavior for the second scenario. It is possible that familiarity with the simulation environment, subject suspicion about the true nature of the study or other unknown factors allowed subjects to reduce contamination scores during the second scenario regardless of the effects of the intervention. Due to limitations of our study design and statistical analysis, this possibility cannot be excluded. In retrospect, a parallel group study design would have better controlled for these confounding factors.

Other limitations of the study were that the investigators performing live scoring and taking photographs were not blinded to scenario conditions due to the small number of personnel participating. A single investigator with the necessary technical expertise took and processed all photographs, and despite efforts to do this consistently some bias could have been introduced to the results. Furthermore, photographs clearly demonstrated that the degree of contamination at a particular site tended to be greatly reduced after implementation of the intervention. Since our data was based on the presence and not quantity of fluorescent tracer detected, the magnitude of this improvement was not captured, and the apparent impact of the intervention was reduced. We encountered technical challenges as well. Photographing fluorescent tracer on plastic items with intrinsic fluorescence such as IV tubing and stopcocks was particularly difficult, and reduced our ability to detect contamination at these sites. We found that the UV tracers transferred less to plastic compared to other surfaces, further reducing degree of contamination detected on syringes, stopcocks, IV tubing, and adjustable pressure limiting valves. Conversely, small amounts of UV tracer powder could be transferred by air currents to large, flat surfaces like the top shelf of the anesthesia machine. This mildly increased the apparent contamination score in such areas.

Despite the limitations of the study, we believe that our results support the concept of an evidence-based bundle of interventions to reduce AWE contamination. Anesthesia providers deliver care in a unique environment in which “clean” and “contaminated” tasks are performed rapidly, and often in parallel. Anesthesia training has not traditionally emphasized rigorous hand hygiene and IV line and medication handling practices, and arrangement of items in the anesthesia workspace has largely been left to the discretion of the individual or institution. The problem is that hospital-acquired infections develop silently, separated in time and space from our actions in the OR. Given current understanding of widespread bacterial contamination in the OR and the accelerating rate of development of antibiotic-resistant pathogens, there is an urgent need to develop practical interventions. Linking hand hygiene to specific high-impact tasks such as drawing up and administering medications, clearly designating areas for contaminated equipment, and double gloving before airway management are simple steps that can be implemented rapidly and are compatible with timely patient care. Our study not only increased awareness of infection prevention within our department but has also prompted examination of our AWE layout and supplies, evaluation of OR cleaning protocols, and development of a simulation-based teaching module for new anesthesia residents. Simulation experience with UV fluorescent tracers allows anesthesia providers to see, perhaps for the first time, the otherwise invisible consequences of their practices. For novices and experts alike, seeing is believing.


We are grateful to Detective Don Ledbetter of the Seattle Police Department Crime Scene Investigations Unit for his advice on forensic photography techniques, and Sally Rampersad, MB, FRCA, of Seattle Children’s Hospital and the University of Washington School of Medicine for sharing her experience with infection prevention strategies in the operating room.


Name: Grete H. Porteous, MD.

Contribution: This author helped with background research, study concept, study design, simulation scenario development and execution, photography, data analysis, manuscript and figure preparation, and final manuscript approval.

Name: Helen A. Bean, DO.

Contribution: This author helped with study design, simulation scenario development and execution, manuscript and figure preparation, and final manuscript approval.

Name: Crystal M. Woodward, MD.

Contribution: This author helped with study design, simulation scenario development, photograph scoring, manuscript preparation, and final manuscript approval.

Name: Ryan P. Beecher, CRNA.

Contribution: This author helped with study design, simulation scenario development and execution, manuscript preparation, and final manuscript approval.

Name: Jennifer R. Bernstein, BA.

Contribution: This author helped with study design, simulation scenario development and execution, manuscript preparation, and final manuscript approval.

Name: Sarah Wilkerson, RN.

Contribution: This author helped with study design, simulation scenario development, video analysis, manuscript preparation, and final manuscript approval.

Name: Ian Porteous, PhD.

Contribution: This author helped with study design, photography, statistical analysis, figure preparation, manuscript preparation, and final manuscript approval.

Name: Robert L. Hsiung, MD.

Contribution: This author helped with study design, simulation scenario development and execution, manuscript and figure preparation, and final manuscript approval.

This manuscript was handled by: Maxime Cannesson, MD, PhD.


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Supplemental Digital Content

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