Content uploaded by Scherzer Daniel
Author content
All content in this area was uploaded by Scherzer Daniel on Sep 16, 2018
Content may be subject to copyright.
www.disastermedicinejournal.com 75
DM
Abstract
Introduction: Disaster triage training for emer-
gency medical service (EMS) providers is not standard-
ized. Simulation training is costly and time-consuming.
In contrast, educational video games enable low-cost
and more time-efficient standardized training. We
hypothesized that players of the video game “60 Seconds
to Survival” (60S) would have greater improvements in
disaster triage accuracy compared to control subjects
who did not play 60S.
Methods: Participants recorded their demograph-
ics and highest EMS training level and were rand-
omized to play 60S (intervention) or serve as controls. At
baseline, all participants completed a live school-shoot-
ing simulation in which manikins and standardized
patients depicted 10 adult and pediatric victims. The
intervention group then played 60S at least three times
over the course of 13 weeks (time 2). Players triaged 12
patients in three scenarios (school shooting, house fire,
tornado), and received in-game performance feedback.
At time 2, the same live simulation was conducted for
all participants. Controls had no disaster training dur-
ing the study. The main outcome was improvement in
triage accuracy in live simulations from baseline to
time 2. Physicians and EMS providers predetermined
expected triage level (RED/YELLOW/GREEN/BLACK)
via modified Delphi method.
Results: There were 26 participants in the inter-
vention group and 21 in the control group. There was
no difference in gender, level of training, or years of
EMS experience (median 5.5 years intervention, 3.5
years control, p = 0.49) between the groups. At baseline,
both groups demonstrated median triage accuracy of
80 percent (IQR 70-90 percent, p = 0.457). At time 2, the
intervention group had a significant improvement from
baseline (median accuracy = 90 percent [IQR: 80-90
percent], p = 0.005), while the control group did not
(median accuracy = 80 percent [IQR:80-95], p = 0.174).
However, the mean improvement from baseline was not
significant between the two groups (difference = 6.5,
p = 0.335).
Conclusion: The intervention demonstrated a
significant improvement in accuracy from baseline to
time 2 while the control did not. However, there was
no significant difference in the improvement between
the intervention and control groups. These results may
be due to small sample size. Future directions include
assessment of the game's effect on triage accuracy with
a larger, multisite site cohort and iterative development
to improve 60S.
Key words: serious video games, disaster triage,
pediatrics, paramedics, emergency medical techni-
cians, curriculum evaluation
Introduction
In mass casualty incidents (MCIs), prehospital
care providers, specifically paramedics and emergency
medical technicians (EMTs), are called upon to perform
disaster triage.1,2 These incidents are infrequent and
thus most EMTs have little experience with disaster
ORIGINAL ARTICLE
60 seconds to survival: A pilot study of a disaster triage video game
for prehospital providers
Mark X. Cicero, MD; Travis Whitfill, MPH; Kevin Munjal, MD; Manu Madhok, MD, MPH; Maria Carmen G. Diaz, MD;
Daniel J. Scherzer, MD; Barbara M. Walsh, MD; Angela Bowen, RN, BSN, CPEN, NREMT-P;
Michael Redlener, MD; Scott A. Goldberg, MD, MPH; Nadine Symons, MD; James Burkett, DHSc, PA-C, DFAAPA;
Joseph C. Santos, MPH, NREMT; David Kessler, MD, MSc, RDMS; Ryan N. Barnicle, MD, MEd;
Geno Paesano, NREMT-P; Marc A. Auerbach, MD, MSc
DOI:10.5055/ajdm.2017.0263
01-AJDM_Cicero_170011.indd 75 19/10/17 6:15 PM
76 American Journal of Disaster Medicine, Vol. 12, No. 2
triage. Triage impacts outcomes for the individual
patient, outcomes for the whole population of victims,
and the communities in which the MCIs occur.3-5
Regardless of what triage strategy prehospital
care providers use,1,6,7 errors in triage are possible.
The consequences of triage errors can include delayed
field treatment for critically ill or injured patients,
inefficient use of field resources, and use of hospital
resources best used for more critical patients for lower
priority patients instead. Triage errors may be divided
into over- and under-triage.
Over-triage can occur in two ways and leads to
the allocation of more resources than are necessary or
indicated for a particular patient. It can occur as the
erroneous assignment of a higher triage category (eg,
Red) to a patient who is not critically ill or injured.8
When a physiologically stable patient with minor
injuries consumes healthcare resources for which a
more acute patient has a greater need (eg, field stabi-
lization, transport to a hospital, the operating room),
outcomes for the more injured individual will worsen.9
Over-triage also occurs when deceased patients and
patients whose injuries or illness are incompat-
ible with survival are triaged a category other than
deceased or expectant (eg, Black).4,10
Under-triage, in contrast, is the failure to rec-
ognize a nonambulatory (Yellow), or critically ill or
injured (Red) patient as needing delayed or immedi-
ate medical care.11,12 Consequences of under-triage
can include delayed treatment, worsening of clinical
status, or patient demise.13
Disaster triage occurs in a hectic and time-con-
strained environment. It is a difficult task, particu-
larly when children are among the victims14: children
are more vulnerable than able-bodied adults, may not
communicate with EMS providers, and the manage-
ment of their illness and injuries are less familiar to
EMS providers. Despite this, training for disaster tri-
age is not standardized, and often brief or absent.
Training modalities such as live simulation,15,16
and tabletop exercises17,18 have been shown to improve
triage accuracy. There are several advantages to live
simulation with manikins and standardized patients.
These advantages include opportunities for hands-on
skill practice (eg, airway repositioning and tourniquet
application), debriefing with a facilitator,19,20 and
opportunities to master skills and knowledge with mul-
tiple simulations over time.21,22 There are limitations
to simulation-based disaster training, however, includ-
ing potential cost of the equipment and instructor time,
and schedule constraints for busy EMS providers.
In the aviation industry and in other healthcare
disciplines, serious video games overcome these limi-
tations23 while standardizing training24 and improv-
ing learner performance.25 Though previous work has
examined and supported video game education in
healthcare,26-28 the use of video games in disaster edu-
cation, especially for paramedics and EMTs, has been
the subject of infrequent rigorous investigation.29
In this work, we aimed to assess the efficacy of
the serious video game “60 Seconds to Survival” (60S)
for EMS disaster triage learning. We hypothesized
that participants who played 60S would demonstrate
greater improvement in triage accuracy over time
than EMS providers who had no formal disaster tri-
age training during the study period. Secondary out-
comes included associations between the game and
decreased instances of over- and under-triage for 60S
playing EMS providers.
Methods
Participants
Participants were EMS providers including para-
medics, paramedic students, and EMTs at a single EMS
training facility affiliated with a medical school. All
participants completed a survey of their demographics,
highest level of EMS training, and years of EMS expe-
rience. The institutional review board of the sponsoring
medical school determined this study was exempt from
formal review, as it occurred in an educational context.
Video game design and rationale
The video game 60S was iteratively developed
with input from EMS providers and educators and
emphasizes the triage of pediatric MCI victims. A
team of software designers and graphic designers
worked with the investigators to develop 60S.
In the game, players completed a practice level to
gain familiarity with play. After the practice level, game
play was divided into three levels, each portraying a
01-AJDM_Cicero_170011.indd 76 19/10/17 6:15 PM
www.disastermedicinejournal.com 77
different kind of MCI (school shooting, multiple-family
house fire, tornado). Each level consisted of three views,
for example, gymnasium, hallway, and classroom for the
school shooting scenario (Figure 1), and each view had
three to five victims, for a total of 12 victims per level.
The victim screens (Figure 2) allowed players to perform
assessments (eg, check pulse, assess mental status),
select appropriate interventions (eg, apply a tourniquet,
Figure 1. In the view screen, players view a portion of the disaster scenario, assess which patients can walk and
move, and decide the order in which to evaluate patients.
Figure 2. In the patient screen, players may make assessments, such as checking pulse or mental status, take
actions, such as placing a tourniquet, and assign triage levels.
01-AJDM_Cicero_170011.indd 77 19/10/17 6:15 PM
78 American Journal of Disaster Medicine, Vol. 12, No. 2
reposition the airway, provide rescue breaths), and finally
assign a color-coded triage tag (Red-Immediate, Yellow-
Delayed, Green-Minor, or Black-Deceased/Expectant).
START/JumpSTART triage is employed in 60S, as it is
the most commonly used pediatric triage system in the
United States7 and has compared favorably to other tri-
age modalities.30
Game elements were included to encourage play
and enhance replay value. These elements included
a countdown clock, a minimum score required to
advance to the next level, a soundtrack selected to
promote a sense of urgency, and a leaderboard that
displayed the 10 highest player scores.
The 60S feedback system of 60S was personalized
to the participant's performance during each instance
of gameplay and displayed gaps between actual and
ideal triage performance. Participants received a
numerical score, representing percentages of overall
triage accuracy and efficiency (ideal efficiency defined
as triage of an individual victim in <30 seconds,
acceptable as triage in 30-59 seconds, and needing
improvement if triage took 60 + seconds).
Study design
We conducted a randomized controlled study of
the impact of playing 60S on EMS provider's triage
accuracy. At the onset of the study (baseline), all par-
ticipants completed the same live simulation, a school
shooting with 10 victims, six children and four adults.
One of the victims was a nursery caregiver, portrayed
by a standardized patient. The other nine victims were
played by a uniform group of low-, mid-, and high-fidel-
ity manikins. Injuries included minor, serious, and
fatal gunshot wounds and blunt trauma. Participants
individually assessed and triaged the patients.
Facilitators introduced the scenario and directed the
participants to triage each of the victims. Facilitators
used a standardized set of responses and vital signs
that were offered when requested. Facilitators did
not provide any guidance, hints, or feedback to par-
ticipants. Participants were video recorded for later
analysis of triage accuracy, and videos were stored in
a secure, password-protected repository.
Participants were randomized in blocks of three, 2:1
Intervention: Control. Control subjects had no formal
disaster triage training offered for the remainder of the
study. Participants randomized to the intervention group
were encouraged to play the game weekly for 13 weeks
and received weekly email reminders to play. If a par-
ticipant did not play the game for 14 days, they received
personal communication encouraging gameplay.
At the conclusion of the study (time 2), 13 weeks
after the onset, all participants returned to the same
simulation center and completed the same live simu-
lation as at the onset of the study. At the conclusion of
the second live simulation, the expected triage level
of each victim was revealed to the participant, as well
as expected assessments and actions. Control subjects
were granted access to 60S after completing the sec-
ond simulation. All participants who completed the
study were granted a continuing education certificate
and a $50 gift card.
Data management and statistical analysis
A SQL-based relational database was automati-
cally populated from 60S. Captured data included
players’ demographics and registration information,
instances of play, assessments, actions, and triage deci-
sions, and time spent per patient and game level. Data
were extracted from the relational database using
Tableau v. 9.0.2 (Tableau Software, Seattle, WA) and
analyzed using SPSS v. 22 (IBM Inc., Armonk, NY).
Descriptive statistics were conducted with Mann-
Whitney U tests for nonparametric continuous data,
independent, two-sided t tests for normal continuous
data, and chi-square tests for proportions. Wilcoxon
signed-rank tests were conducted to determine any
significant improvements to baseline to the end of the
study. Pearson's correlations were used to examine
any possible relationships between game play and live
simulation performance. Last, a mixed-effects linear
regression controlling for site and modeling improve-
ment was performed to evaluate potential confounders
and their effect on improvement between the control
and intervention groups.
Results
A total of 68 participants were recruited for
the study, and 62 were enrolled and randomized. Of
the 62 that were enrolled, 23 were randomized to the
01-AJDM_Cicero_170011.indd 78 19/10/17 6:15 PM
www.disastermedicinejournal.com 79
control group and 39 were randomized to the inter-
vention group. Of these, two controls were excluded
from analysis because they did not complete the
second session, and 13 were excluded from analyses
because eight did not complete the second session
and five did not play the game at least three times
(Figure 3). There was no difference between the
groups regarding gender (p = 0.53), level of training
(p = 0.55), or years of experience (median 3.5 years
intervention, 5.5 years control, p = 0.49). Participant
characteristics are presented in Table 1.
At the onset of the study, median triage accu-
racy in the live simulation was 80 percent in both
groups (IQR 60-90 percent control, 70-80 percent
intervention). There was no difference between
groups (p = 0.457). At the end of the study, after
the intervention group had played the game for 13
weeks, the intervention group had median triage
accuracy of 90 percent (IQR 80-90 percent) with a
median improvement of 10 percent (IQR:0,20) and
mean improvement of 13 percent (p = 0.005 between
baseline and time 2), while the control group saw
no difference between the start and end of the
study (median improvement = 0 [IRQ: −5,20], mean
improvement = 6 percent; p = 174 between baseline
and time 2). The median difference between the two
groups was 10 percent (p = 0.335) and the mean dif-
ference was 6.5 (p = 0.279). Improvement in both
groups is represented in Figure 4. A mixed-effects
linear regression revealed that a higher number of
years of experience was negatively associated with
simulation improvement (Table 2). There was no
correlation between either number of game plays
or triage accuracy in the game and improvement in
live simulation triage accuracy.
Discussion
Between the onset and the conclusion of the study,
there was a significant improvement in triage accu-
racy for the intervention group but not for the control
group. This finding supports our hypothesis; however,
the difference in triage accuracy between the control
and intervention groups at the end of the study was
not significant in unadjusted and adjusted analyses.
This is most likely due to a small sample size, as our
study was under-enrolled due to a number of partici-
pants not completing the study.
Video game based factors are another potential
explanation for the lack of difference in the triage accu-
racy between the intervention and control groups. The
median number of times (8) that players interacted with
the game may have been too few for learners to reach
maximal learning. A game-based barrier to more inter-
actions with the game was the lack of novelty in replay-
ing the game: each patient behaved exactly the same
way each time participants played the game. Previous
work has shown that maintaining novelty31 is associated
with increased video game play to the point of mastery.
Figure 3. Participant flow diagram, including randomization and inclusion in analysis.
01-AJDM_Cicero_170011.indd 79 19/10/17 6:15 PM
80 American Journal of Disaster Medicine, Vol. 12, No. 2
Video games in healthcare education are increas-
ingly recognized as valid and efficacious means for
imparting skills23,26 and knowledge.27 Video games have
faced the same barriers to acceptance in healthcare
education seen at the onset of healthcare simula-
tion.32,33 Recent work by Mohan et al has shown that
video game education can be rigorously designed and
used to evaluate changes in clinical work behaviors.
Table 1. Participant characteristics
Group
p-value
Control
N = 21 (%)
Intervention
N = 26 (%)
Gender 0.526
Male 17 (81) 19 (73)
Female 4 (19) 7 (27)
Profession 0.549
First responder or student 6 (29) 6 (27)
EMT 6 (29) 11 (42)
Paramedic 7 (33) 6 (23)
Other 3 (13) 4 (12)
Race 0.501
White 17 (81) 23 (89)
Other 3 (14) 3 (12)
Median years of experience (IQR) 5.5 (0, 10.5) 3.5 (0, 9.3) 0.486
Median number of game plays —8.0 (2.8, 11.5) —
Median final game-based triage accuracy —50 (50, 100) —
Table 2. Linear regression modeling
improvement from baseline
to second simulation
βSE p-value
Intervention
group vs. control
group
2.3 6.2 0.710
Number of years
of experience −0.8 0.4 0.038
Gender (males
vs. females) −2.6 7.7 0.736
Training (EMT/
Paramedic vs.
others)
−9.1 8.9 0.316
Figure 4. Improvement in triage accuracy for video
game participants and controls.
01-AJDM_Cicero_170011.indd 80 19/10/17 6:15 PM
www.disastermedicinejournal.com 81
Parallels may be drawn between that work and ours,
as both studies involved adherence to algorithms and
guidelines in the triage of trauma victims. Game-based
curricula for clinical reasoning and decision-making for
nursing students34 and medical students35 have further
strengthened the argument that video games are effi-
cacious, serious modes of education.
Participant factors may have contributed to the
lack of difference between the intervention and con-
trol groups. As noted above, there were no significant
differences in the demographics of the two groups.
Additionally, this study was conducted at a single EMS
training facility. During the time of the study, there
was no formal MCI or disaster training offered at the
training site, nor, to our knowledge, was there an MCI
to which our participants responded. No feedback
was given after the first live simulation to any par-
ticipants. However, media coverage of MCI and/or
community events during the study period could have
triggered self-study by EMS providers. It is also pos-
sible that members of the control group independently
studied disaster triage in the interval between the
two live simulations or that the first live simulation
served as an educational experience for the control
group, even without feedback or formal instruction.
The Hawthorne effect, by which the participants knew
they would be observed and therefore altered their
behavior, may also have influenced performance in the
second simulation.
Limitations
Our study is subject to several limitations. It is
possible that the study was underpowered to detect
a significant difference in improvement between
the intervention and control groups. The difference
between the two groups was 6.5 but was not signifi-
cant. Possibly with a larger cohort, a significant dif-
ference would have emerged. Additionally, we did not
evaluate for the association between individual par-
ticipant demographics and interaction with 60S. For
example, an assessment by participant age and self-
reported experience with video game play could assist
in targeting subgroups that are more likely to ben-
efit from 60S as an educational intervention. A fur-
ther limitation is the relative small size of the MCIs
depicted in 60S. Each game level had 12 victims. It
is unknown how performance in 60S would be associ-
ated with triage accuracy in a video game, live simu-
lation, or real MCI with a greater number of victims.
Future directions
This investigation suggests several directions for
future inquiry. First, from participant feedback and
the results of the study, it is likely that variety in
the presentation of each patient (eg, changes in vital
signs, injuries, and responses to triage interventions,
or combination of the assessment and intervention
game interface panels, as suggested by some partici-
pants) will improve player engagement. Next, a com-
parison of learner improvement in which intervention
participants with the lowest initial live simulation
accuracy are compared to intervention participants
with the highest initial accuracy might help target
characteristics of learners most likely to benefit from
the video game as a learning intervention. In other
words, an assessment of players’ baseline accuracy
and amount if improvement could reveal whether the
game is helpful for all learners or just those with the
biggest gaps between actual and ideal performance.
Additionally, comparisons of future participant base-
line triage accuracy and triage accuracy after the
video game intervention by population density (eg,
urban vs. rural), and EMS agency model (fire, free-
standing EMS, hospital, or aeromedical-based) may
guide the design of targeted video game education.
Finally, the linear regression analysis showed a nega-
tive association with improvement in the live simula-
tions. An intervention targeting junior, and possibly
younger, EMS providers could reveal that 60S has the
greatest effect for that group.
Additional future investigations can include a mul-
ticenter study with a greater number of EMS providers
will provide greater power for detecting associations
between 60S game play and improved triage accuracy
and efficiency. Finally, a cost-benefit analysis compar-
ing triage accuracy and efficiency among EMS provid-
ers who participate in a live simulation curriculum to
those who complete a video game such as 60S would be
important for guiding disaster triage training decisions.
A final, possibly most intriguing, future direction is a
01-AJDM_Cicero_170011.indd 81 19/10/17 6:15 PM
82 American Journal of Disaster Medicine, Vol. 12, No. 2
non-inferiority study comparing triage outcomes when
EMS providers learn disaster triage with live-simula-
tion versus a video game educational intervention.
Conclusion
Participants who played 60S demonstrated
improved triage accuracy from the beginning to
the end of the study, and the control group did not.
However, there was no significant difference in tri-
age performance at the end of the study between the
intervention and control groups. These results may
be due to small sample size, the Hawthorne effect or
lack of variation in patient presentation in this itera-
tion of 60S. Future studies can determine why the null
hypothesis was accepted, and result in a more effica-
cious version of the 60S disaster triage video game.
Acknowledgment
This study was supported by the Agency for Healthcare Research
and Quality grant 1R18HS022837-01.
Mark X. Cicero, MD; Departments of Pediatrics and Emergency
Medicine, Yale School of Medicine, New Haven, Connecticut.
Travis Whitfill, MPH, Department of Pediatrics, Yale School of
Medicine, New Haven, Connecticut.
Kevin Munjal, MD, Emergency Medicine, Mount Sinai Medical
Center, New York, New York.
Manu Madhok, MD, MPH, Pediatrics, Children's Minnesota
Minneapolis Hospital, Saint Paul, Minnesota.
Maria Carmen G. Diaz, MD, Pediatrics, A. I. DuPont Hospital for
Children, Wilmington, Delaware.
Daniel J. Scherzer, MD, Pediatrics, Nationwide Children's Hospital,
Columbus, Ohio.
Barbara M. Walsh, MD, Associate Professor of Pediatrics, Division
of Pediatric Emergency Medicine; Director, Pediatric Emergency
Medicine Simulation Program, Hofstra School of Medicine,
Cohen’s Children’s Medical Center, New Hyde Park, New York.
Angela Bowen, RN, BSN, CPEN, NREMT-P, Radiation Emergency
Assistance Center/Training Site (REAC/TS), Oak Ridge, Tennessee.
Michael Redlener, MD, Department of Emergency Medicine,
Mount Sinai St. Luke's, New York, New York.
Scott A. Goldberg, MD, MPH, Emergency Medicine, Brigham and
Women's Hospital, Boston, Massachusetts.
Nadine Symons, MD, Pediatrics, Le Bonheur Children's Hospital,
Memphis, Tennessee.
James Burkett, DHSc, PA-C, DFAAPA, Director, Advanced
Physician Assistant Degree Program, Arizona School of Health
Sciences, Mesa, Arizona.
Joseph C. Santos, MPH, NREMT, Emergency Medical Services for
Children, Baylor College of Medicine, Houston, Texas.
David Kessler, MD, MSc, RDMS, Pediatrics, Columbia School of
Medicine, New York, New York.
Ryan N. Barnicle, MD, MEd, Medical School, Frank H. Netter MD School
of Medicine, Quinnipiac University, North Haven, Connecticut.
Geno Paesano, NREMT-P, Sponsor Hospital Program, Yale New
Haven Hospital, New Haven, Connecticut.
Marc A. Auerbach, MD, MSc, Departments of Pediatrics and Emer-
gency Medicine, Yale School of Medicine, New Haven, Connecticut.
References
1. Romig L: Pediatric triage. A system to JumpSTART your triage of
young patients at MCIs. JEMS. 2002; 27(7): 52-58, 60-53.
2. Cone D, Serra J, Burns K, et al.: Pilot test of the SALT mass
casualty triage system. Prehosp Emerg Care. 2009; 13(4): 536-540.
3. Cross KP, Cicero MX: Head-to-head comparison of disaster triage
methods in pediatric, adult, and geriatric patients. Ann Emerg Med.
2013; 61(6): 668-676.e667.
4. Toltzis P, Soto-Campos G, Shelton C, et al.: Evidence-based pedi-
atric outcome predictors to guide the allocation of critical care
resources in a mass casualty event. Pediatr Crit Care Med. 2015;
16(7): e207-e216.
5. North CS, Pfefferbaum B: Mental health response to community
disasters: A systematic review. JAMA. 2013; 310(5): 507-518.
6. SALT mass casualty triage: Concept endorsed by the American College
of Emergency Physicians, American College of Surgeons Committee
on Trauma, American Trauma Society, National Association of EMS
Physicians, National Disaster Life Support Education Consortium,
and State and Territorial Injury Prevention Directors Association.
Disaster Med Public Health Prep. 2008; 2(4): 245-246.
7. Nadeau NL, Cicero MX: Pediatric disaster triage system utiliza-
tion across the United States. Pediatr Emerg Care. 2016.
8. Arshad FH, Williams A, Asaeda G, et al.: A modified simple triage
and rapid treatment algorithm from the New York City (USA) fire
department. Prehosp Disaster Med. 2015; 30(2): 199-204.
9. Eyal N, Firth P, Group MDRE.: Repeat triage in disaster relief:
Questions from haiti. PLoS Curr. 2012; 4: e4fbbdec6279ec.
10. Gall C, Wetzel R, Kolker A, et al.: Pediatric triage in a severe
pandemic: Maximizing survival by establishing triage thresholds.
Crit Care Med. 2016; 44(9): 1762-1768.
11. Cone DC, MacMillan DS, Parwani V, et al.: Pilot test of a pro-
posed chemical/biological/radiation/ nuclear-capable mass casualty
triage system. Prehosp Emerg Care. 2008; 12(2): 236-240.
12. Cross KP, Petry MJ, Cicero MX. A better START for low-acuity
victims: Data-driven refinement of mass casualty triage. Prehosp
Emerg Care. 2015; 19(2): 272-278.
13. Kleber C, Cwojdzinski D, Strehl M, et al.: Results of in-hospi-
tal triage in 17 mass casualty trainings: Underestimation of life-
threatening injuries and need for re-triage. Am J Disaster Med.
2013; 8(1): 5-11.
14. Koziel JR, Meckler G, Brown L, et al.: Barriers to pediatric dis-
aster triage: A qualitative investigation. Prehosp Emerg Care. 2015;
19(2): 279-286.
15. Schulz CM, Skrzypczak M, Raith S, et al.: High-fidelity human
patient simulators compared with human actors in an unannounced
mass-casualty exercise. Prehosp Disaster Med. 2014; 29(2): 176-182.
16. Claudius I, Kaji A, Santillanes G, et al.: Comparison of com-
puterized patients versus live moulaged actors for a mass-casualty
drill. Prehosp Disaster Med. 2015; 30(5): 438-442.
17. Lee WH, Kuan JT, Shiau YW, et al.: Designation of a new train-
ing model of a local disaster medical system with tabletop exercises.
Chang Gung Med J. 2003; 26(12): 879-888.
01-AJDM_Cicero_170011.indd 82 19/10/17 6:15 PM
www.disastermedicinejournal.com 83
18. Whitney RE, Burke RV, Lehman-Huskamp K, et al.: On shaky
ground: Learner response and confidence after tabletop earthquake
simulation. Pediatr Emerg Care. 2016; 32(8): 520-524.
19. Zapko KA, Ferranto ML, Brady C, et al.: Interdisciplinary dis-
aster drill simulation: Laying the groundwork for further research.
Nurs Educ Perspect. 2015; 36(6): 379-382.
20. Ngo J, Schertzer K, Harter P, et al.: Disaster medicine: A multi-
modality curriculum designed and implemented for emergency medi-
cine residents. Disaster Med Public Health Prep. 2016; 10(4): 611-614.
21. Ahn J, Yashar MD, Novack J, et al.: Mastery learning of video
laryngoscopy using the glidescope in the emergency department.
Simul Healthc. 2016; 11(5): 309-315.
22. Braun L, Sawyer T, Smith K, et al.: Retention of pediatric resus-
citation performance after a simulation-based mastery learning
session: a multicenter randomized trial. Pediatr Crit Care Med.
2015; 16(2): 131-138.
23. Jalink MB, Goris J, Heineman E, et al.: Construct and concur-
rent validity of a Nintendo Wii video game made for training basic
laparoscopic skills. Surg Endosc. 2014; 28(2): 537-542.
24. Fiellin LE, Kyriakides TC, Hieftje KD, et al.: The design and
implementation of a randomized controlled trial of a risk reduction
and human immunodeficiency virus prevention videogame inter-
vention in minority adolescents: Playforward: Elm city stories. Clin
Trials. 2016; 13(4): 400-408.
25. Ferrero NA, Bortsov AV, Arora H, et al.: Simulator training
enhances resident performance in transesophageal echocardiogra-
phy. Anesthesiology. 2014; 120(1): 149-159.
26. Adams BJ, Margaron F, Kaplan BJ: Comparing video games and
laparoscopic simulators in the development of laparoscopic skills in
surgical residents. J Surg Educ. 2012; 69(6): 714-717.
27. Diehl LA, de Souza RM, Gordan PA, et al.: User assessment of
"InsuOnLine," a game to fight clinical inertia in diabetes: A pilot
study. Games Health J. 2015; 4(5): 335-343.
28. Hogle NJ, Widmann WD, Ude AO, et al.: Does training novices
to criteria and does rapid acquisition of skills on laparoscopic simu-
lators have predictive validity or are we just playing video games?
J Surg Educ. 2008; 65(6): 431-435.
29. Knight JF, Carley S, Tregunna B, et al.: Serious gaming technol-
ogy in major incident triage training: A pragmatic controlled trial.
Resuscitation. 2010; 81(9): 1175-1179.
30. Cicero MX, Overly F, Brown L, et al.: Comparing the accuracy of
three pediatric disaster triage strategies: A simulation-based inves-
tigation. Disaster Med Public Health Prep. 2016; 10(2): 253-260.
31. Huang C, Gao Z: Associations between students' situational
interest, #mastery |experiences, and physical activity levels in an
interactive dance game. Psychol Health Med. 2013; 18(2): 233-241.
32. Starkweather AR, Kardong-Edgren S: Diffusion of innovation:
Embedding simulation into nursing curricula. Int J Nurs Educ
Scholarsh. 2008; 5: Article13.
33. Curtin MM, Dupuis MD: Development of human patient simu-
lation programs: achieving big results with a small budget. J Nurs
Educ. 2008; 47(11): 522-523.
34. Johnsen HM, Fossum M, Vivekananda-Schmidt P, et al.:
Teaching clinical reasoning and decision-making skills to nursing
students: Design, development, and usability evaluation of a seri-
ous game. Int J Med Inform. 2016; 94: 39-48.
35. Kleinert R, Heiermann N, Wahba R, et al.: Design, realization,
and first validation of an immersive web-based virtual patient sim-
ulator for training clinical decisions. Surgery. J Surg Educ. 2015;
72(6): 1131-1138.
01-AJDM_Cicero_170011.indd 83 19/10/17 6:15 PM
