Author	Affiliation
Nicholas Nacca, MD	SUNY Upstate Medical University, Department of Emergency Medicine, Syracuse, New York
Jordan Holliday	SUNY Upstate Medical University, Department of Emergency Medicine, Syracuse, New York
Paul Y. Ko, MD	SUNY Upstate Medical University, Department of Emergency Medicine, Syracuse, New York

Introduction
Methods
Results
Discussion
Limitations
Conclusion

INTRODUCTION

Simulation has advanced rapidly over the past decade with more innovative and realistic tools being incorporated to educate healthcare providers on a daily basis.^1-4 The body of literature investigating the effectiveness of medical simulation has struggled to keep pace with the developments in simulation technology. Many authors have called for quality research regarding the use of simulation in medical education.^5-8
Advanced cardiac life support (ACLS) is a collection of skills and interventions intended to direct healthcare providers during treatment of cardiac arrest and other life-threatening emergencies. Currently, many medical schools do not require ACLS certification for graduation.^9,10 Many medical school graduates feel underprepared and lack the confidence and skills to effectively participate in resuscitative efforts as interns beginning their residency programs.^10-14

Figure 1. Students with access to computer based simulation did not initiate compressions in the ACLS Megacode faster than students without access.
ACLS, advanced cardiac life support

The necessity for high-quality education of ACLS at the medical student level cannot be overstated. The integration of high-fidelity mannequin-based simulation (HFMBS) into existing ACLS curricula as a supplemental instructional tool has been advocated.^10,15-21 A recent study of medical students using HFMBS in ACLS training showed significantly improved knowledge and psychomotor skills.22 The costs associated with acquiring and maintaining an HFMB simulator may be impeding its use in many ACLS training programs.^3,10,23

The department of emergency medicine at the State University of New York (SUNY) Upstate Medical University uses two different ACLS curricula to train approximately 650 healthcare providers annually. The first is a traditional two-day American Heart Association (AHA) ACLS course, which uses didactic lectures, AHA-approved instructional videos, and low-fidelity simulation in small-group sessions.¹⁰ The second is a three-day HFMBS course, which is used to teach third-year medical students ACLS during their two-week internal medicine rotation. Third-year medical students enrolled in the HFMBS course demonstrated higher proficiency in learning ACLS than students enrolled in the traditional course.10 This is consistent with previous studies, which revealed improved educational outcomes, retention of ACLS skills and knowledge, and adherence to ACLS guidelines.^24-26

Figure 2. Students with access to computer based simulation defibrillated Ventricular Fibrillation in the ACLS Megacode 38 seconds faster than students without access [t(63=3.36, p<0.5].
ACLS, advanced cardiac life support

The majority of the early literature on HFMBS as an educational tool for training healthcare providers stemmed from residency programs, and there was little data on the use of simulation at the medical student level.27 Not until recently have educators studied the utility of HFMBS at the undergraduate medical education level.^10,27-29 In an effort to further increase student proficiency in ACLS, we noted reports of ACLS-specific computer-based simulators dating back to 1991.^30,31 Advances in computer technology have facilitated the development of many high-quality computer-based simulation programs that are efficacious instruction tools.^13,23,32 A more recent study suggested that a computer-based simulation program improved retention of ACLS guidelines as a standalone teaching tool.³³ We hypothesized that a computer-based simulation program used as an adjunct to HFMBS ACLS training would result in improved ACLS proficiency.

METHODS

At SUNY Upstate, each third-year emergency medicine resident serves as a certified AHA ACLS instructor during their administrative month. They train groups of 4-6 third-year medical students in ACLS using HFMBS. During their two-week rotation, the medical students undergo three days of HFMBS ACLS training, which consists of an introduction to ACLS material using partial task trainers, followed by simulated management of five ACLS scenarios including ventricular fibrillation, ventricular tachycardia, symptomatic bradycardia, pulseless electrical activity, and asystole. The details of this curriculum have been previously published.¹⁰

At the conclusion of the course students were assessed by running a “Mega Code” on the high-fidelity mannequin simulator (SimMan®, Laerdal Medical Corporation, Wappingers Falls, NY, USA). The “Mega Code” scenario consisted of a 40-year-old male patient complaining of chest pain who becomes unresponsive after 40 seconds of conversation. The student must first recognize and treat ventricular fibrillation using the appropriate electrical and pharmacologic interventions, in addition to directing team members on proper cardiopulmonary resuscitation. After the third defibrillation attempt, the patient converts to symptomatic sinus bradycardia, which the student must recognize and treat appropriately. Failure to initiate and carry out the proper interventions would result in simulated patient death.

A novel, online, computer-based ACLS simulator (SimCode ACLSTM,Transcension Healthcare LLC) teaches the user to direct nurses, diagnose, and appropriately treat ACLS cases. Student accounts can be monitored by a manager account, with student use and performance assessed and trended over time. Transcension Healthcare LLC, provided student accounts for participants of this study.

Groups of third-year medical students were randomized. Both groups underwent a two-week ACLS course taught by a third-year emergency medicine resident, which used HFMBS. The intervention group (n=29) each received a two-week access code to the online ACLS simulator as a supplemental resource, whereas the control group (n=36) did not. A run’s test was performed to confirm true randomization. The students in the intervention group were instructed to complete the computer-based tutorial on the first day of their ACLS instruction. They were then asked to complete the online ventricular tachycardia and symptomatic bradycardia cases during their two-week ACLS course. We used the manager account feature to verify qualitative usage of the computer-based ACLS simulator for students in the intervention group. On the last day of the ACLS course, informed consent was obtained from both groups prior to any data collection.

Each student’s simulated “Mega Code” was recorded using audio, video, and Laerdal recording software. Prior to the assessment, students were asked to rate their subjective knowledge and confidence of ACLS material and practice. Following the “Mega Code” assessment, students were again asked to rate their subjective knowledge and confidence. Students in the intervention group were asked various additional questions regarding their use of the computer-based ACLS simulation program.

Data collected by review of video and software recording included the time to initiate chest compressions during cardiopulmonary resuscitation and time to defibrillation, which were calculated for each student from the beginning of the simulation. The time to pace symptomatic bradycardia was calculated from the onset of the rhythm change. We used these times as our primary outcome measures. The post mega code subjective knowledge and confidence surveys were used as secondary outcome measures. We entered the collected data into spreadsheet format, and we used a statistical software package (GraphPad Prism Version 4.0®, GraphPad Software, Inc., San Diego, CA, USA) to run a Student’s t-test for each of the variables. The SUNY Upstate Medical University Institutional Review Board granted exemption status for this study.

RESULTS

We obtained complete data from the recorded “Mega Code” and surveys of 65 third-year medical students. During the study period four groups (three assigned to intervention and one to control) were not consented and therefore data was not collected. This accounted for approximately 24 missed participants. An additional two groups (one assigned to control and one to intervention) were consented, but video recording was unavailable. This accounted for seven consented students that were excluded from analysis for lack of data. Only one student in the intervention group refused participation. Ten students assigned to the intervention group never accessed the online simulator and thus were excluded from analysis.

Figure 3. Students with access to computer based simulation paced Symptomatic Bradycardia in the ACLS Megacode 60 seconds faster than students without access [t(63)=3.71, p<0.5].
ACLS, advanced cardiac life support.

Of the 65 participants with complete data (36 controls and 29 Intervention), there was not a statistically significant difference between the time required to initiate chest compressions on the unresponsive, pulseless high-fidelity mannequin in the control versus the intervention group (Figure 1). A statistically significant difference was noted between the two groups in time to defibrillate ventricular fibrillation, where those students who had used the computer-based simulator defibrillated on average in 112 seconds versus those without access defibrillated on average in 149.9 seconds, on average 38 seconds faster. [t(63)=3.36, p<.05] (Figure 2). Additionally, students in the intervention group paced symptomatic bradycardia in an average time of 95.14 seconds versus the average time of 154.9 seconds in the control group. Thus, the intervention group was on average 60 seconds faster than the control, which was statistically significant [t(63)=3.71, p<.05] (Figure 3).

Figure 4. Students with access to computer based simulation did not assess their knowledge of ACLS guidelines differently than those without access.
ACLS, advanced cardiac life support

Figure 5. Students with access to computer based simulation had significantly lower subjective confidence in running a code than those without access [t(63)=4.19, p<.05].

There was not a statistically significant difference between students’ perceived ACLS knowledge following the Mega Code assessment between the control and intervention groups (Figure 4); however, analysis of the students’ subjective confidence following the Mega Code assessment revealed a statistically significant difference [t(63)=4.19, p<.05], where students in the intervention group reported lower confidence levels on a 5-point scale (Figure 5). Students in the intervention group provided many positive subjective responses indicating satisfaction with the computer-based simulation program. Sixty-six percent of students reporting that the program “Very Much Improved” or “Extremely Improved” their ability to effectively manage an ACLS code. Additionally, 34% reported that they would “Definitely” and 62% reported that they would “Probably” use the online, computer-based simulator in the future.

DISCUSSION

The data demonstrated that the addition of an online computer-based simulation program to an HFMB ACLS training program did not influence the time to initiate chest compressions in a simulated scenario requiring cardiopulmonary resuscitation. It did, however, reduce the time to defibrillate ventricular fibrillation and pace symptomatic bradycardia. Though the primary outcome measurements have not been systematically validated to reflect improved ACLS performance, the authors interpret these as valuable indicators of comfort and fluency in ACLS protocols.

Perhaps the most interesting finding was that, despite outperforming students in the control group, students in the intervention group reported feeling less confident. Additionally, the students in the intervention group did not rate their knowledge level higher than did the students in the control group. These findings were unexpected and in stark contrast to much of the available literature that discusses participants’ self-confidence following an educational intervention.^10,24,34 A proposed explanation for this finding is that the online, computer-based simulation program exposed students in the intervention group to many ACLS simulation cases in excess of the five HFMB simulation scenarios in which both groups participated. Due to the structure of the online computer-based simulation program, these additional simulation cases could not have been completely ignored by students in the intervention group, even thought they were only tasked with completing the ventricular fibrillation and symptomatic bradycardia cases. The exposure to additional cases may have added to the complexity of the material and contributed to the lower subjective confidence ratings despite superior performance on the primary outcome measures.

Managing resuscitations and other medical crises, whether simulated or real, elicits emotional stress and anxiety as individual and team performance may be compromised.^35,36 The stress and anxiety encountered during such situations may enhance memory encoding, consolidation, and retrieval, thereby improving retention.^19,37,38 The reported effects of the stress and anxiety induced during participation in simulations are contrasting, with a group of anesthesia trainees reporting enhanced learning and clinical experience and a group of nursing students reporting hindered learning.^19,39,40 Anxiety is characterized as demotivating and detrimental to perceived self-efficacy within the psychological literature.^41,42 We did not include a real-time measure of student anxiety in this study. It could be that students in the intervention group experienced more anxiety in ACLS learning than did students in the control group.

The improvement in performance demonstrated by students in the intervention group during the HFMB “Mega Code” assessment is consistent with previous studies supporting the use of computer-based simulation as an educational tool.^13,23,32,33

LIMITATIONS

Our study has several clear limitations. The most notable limitation was that the third-year emergency medicine resident who taught the HFMB ACLS course was not consistent. At least eleven different instructors participated in the training of students whose performances on the Mega Code assessment were included in this analysis. Thus, the authors were unable to control for this confounding variable in the analysis. In addition, blinding the instructors to the intervention group students was not enforceable. We did not perform a power analysis due to the difficulty in predicting an expected improvement in performance based on the implemented educational intervention. Incomplete data collection resulting from a combination of computer and human error led to the exclusion of several cohorts of third-year medical students enrolled in the ACLS course, comprised of both intervention and control groups, from data analysis. The authors were unable to monitor the amount of self-study time of students in the control group and were thus unable to determine if students in the intervention group may have spent more time with ACLS material in general than did those in the control group. The authors did not record the duration or frequency of use of the program. Students’ usage of the online, computer-based simulation program was not enforceable, which necessitated that students had to be self-motivated to use the program. Given the nature of this curriculum analysis study, the authors were unable to enforce use of the program with course failure or grade demotion. In practical application of this program, such enforcement would be feasible. Several students in the intervention group neglected to use the program and were thus excluded from the subsequent analysis. Additionally, intention to treat analysis was purposely abandoned based on the premise that a course instructor using the computer-based simulator would have the ability to monitor and enforce the use of the program. A post hoc intention to treat analysis yielded persistent statistically significant results. Students did record the number of previously run ACLS codes in the post megacode data collection form. Only one student in the control group had prior experience as a seasoned paramedic, and was thusly excluded from analysis. The authors did not collect any data regarding ultimate career goals. This may be a source of a confounding variable as those students going into acute care specialties may have not been randomly allocated to intervention and control groups. Finally, it is unclear whether the above listed findings are generalizable to other online, computer-based simulation programs.

CONCLUSION

We interpret the data to reflect the validity of the online, computer-based simulator as a valuable adjunctive ACLS teaching tool. Students with access to the simulation program outperformed students without access in both time to defibrillate ventricular fibrillation and time to pace symptomatic bradycardia. Notably, the students in the intervention group felt less confident than did the students in the control group. We speculate that the students in the intervention group may have felt less confident managing resuscitations due to their broadened perspective of ACLS material that was gained from their access to a multitude of ACLS scenarios within the computer-based simulation program. This would require further investigation to be confirmed.

Footnotes

Supervising Section Editor: Eric Snoey, MD
Full text available through open access at http://escholarship.org/uc/uciem_westjem
Address for Correspondence: Paul Y. Ko, MD. Department of Emergency Medicine, SUNY Upstate Medical University, EmSTAT Building, 550 East Genesee Street # 103C, Syracuse, NY 13202 Email: kop@upstate.edu.
Submission history: Submitted October, 30, 2013; Revision received August 11, 2014; Accepted September 8, 2014
Conflicts of Interest: By the WestJEM article submission agreement, all authors are required to disclose all affiliations, funding sources and financial or management relationships that could be perceived as potential sources of bias. The authors disclosed none.

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