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imulation and education
se of an electronic decision support tool improves management ofimulated in-hospital cardiac arrest�
arry C. Fielda,∗, Matthew D. McEvoyb, Jeremy C. Smalleyc, Carlee A. Clarka,ichael B. McEvoya, Horst Riekea, Paul J. Nietertd, Cory M. Fursea
Department of Anesthesia & Perioperative Medicine, Medical University of South Carolina, 167 Ashley Avenue, Suite 301, Charleston, SC 29425,nited StatesDepartment of Anesthesiology, Vanderbilt University, 2301 Vanderbilt University Hospital, Nashville, TN 37232-7237, United StatesDepartment of Orthopedics, Medical University of South Carolina, 167 Ashley Avenue, Suite 301, Charleston, SC 29425, United StatesDepartment of Medicine, Division of Biostatistics and Epidemiology, Medical University of South Carolina, 135 Cannon Street, Room 303J, Charleston, SC9425, United States
r t i c l e i n f o
rticle history:eceived 5 April 2013eceived in revised form 9 August 2013ccepted 4 September 2013
eywords:dvanced cardiac life supportlectronic decision support toolardiopulmonary arrestimulation
a b s t r a c t
Introduction: Adherence to advanced cardiac life support (ACLS) guidelines during in-hospital cardiacarrest (IHCA) is associated with improved outcomes, but current evidence shows that sub-optimal care iscommon. Successful execution of such protocols during IHCA requires rapid patient assessment and theperformance of a number of ordered, time-sensitive interventions. Accordingly, we sought to determinewhether the use of an electronic decision support tool (DST) improves performance during high-fidelitysimulations of IHCA.Methods: After IRB approval and written informed consent was obtained, 47 senior medical students wereenrolled. All participants were ACLS certified and within one month of graduation. Each participant wasissued an iPod Touch device with a DST installed that contained all ACLS management algorithms. Partic-ipants managed two scenarios of IHCA and were allowed to use the DST in one scenario and prohibitedfrom using it in the other. All participants managed the same scenarios. Simulation sessions were video
recorded and graded by trained raters according to previously validated checklists.Results: Performance of correct protocol steps was significantly greater with the DST than without (84.7%v 73.8%, p < 0.001) and participants committed significantly fewer additional errors when using the DST(2.5 errors vs. 3.8 errors, p < 0.012).Conclusion: Use of an electronic DST provided a significant improvement in the management of simulatedIHCA by senior medical students as measured by adherence to published guidelines.
. Introduction
The practice of hospital-based and perioperative medicineequires the knowledge and application of many diverse acuteare skills, including the management of in-hospital cardiac arrest
IHCA). The presence of advanced cardiac life support (ACLS)rained providers and adherence to published guidelines forhe management of cardiac arrest are associated with improved
� A Spanish translated version of the abstract of this article appears as Appendixn the final online version at http://dx.doi.org/10.1016/j.resuscitation.2013.09.013.∗ Corresponding author.
outcomes.1–6 However, management skills and appropriate appli-cation of ACLS guidelines have been shown to quickly fadeafter training.7–9 Furthermore, current evidence shows that sub-optimal resuscitation is common in both medical and surgicalpatients.3,4,10–13
During rapidly evolving or deteriorating patient conditions,there is often insufficient time for physicians to re-familiarizethemselves with current guidelines. Although both paper andelectronic aids may facilitate guideline adherence during the man-agement of acute patient instabilities or cardiac arrest,14–16 thereis also evidence that cognitive aids can negatively affect the way inwhich providers deliver care during cardiac arrest.17,18 Therefore,we sought to determine whether the use of an electronic deci-
sion support tool (DST) that dynamically guides a provider throughAmerican Heart Association (AHA) ACLS protocols would improveperformance during high-fidelity simulations that require the man-agement of acute dysrhythmias and IHCA.
L.C. Field et al. / Resuscitation 85 (2014) 138–142 139
Fig. 1. This figure illustrates participant randomization and allocation. Participantswere randomized to one of four pathways, representing an equivalent to random-izing both the order of DST use and the order of Megacode scenario testing. Afterthey managed the first Megacode scenario, they then managed the remaining sce-n[
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Fig. 2. This figure illustrates the difference in performance based upon use of theDST as measured by the percentage of correct actions completed during a simu-
checklist item type revealed that the number of correct manage-
ario with or without the DST, opposite from how they managed the first scenario.DST = decision support tool; A = Megacode scenario A; B = Megacode scenario B].
. Methods
.1. Study design
After IRB approval was granted, written informed consentas obtained from 47 ACLS-certified senior medical students oneonth prior to graduation (26 females, 20 males). Each participantas issued an iPod TouchTM device (Apple Inc., Cupertino, CA) onhich the DST was installed. They were given a brief orientation
∼30 min) to the use of the DST user interface and to the simula-ion environment, the simulator mannequin (SimMan 2G®, Laerdal,nc., Stavanger, Norway), and all equipment to be used during thetudy (defibrillator, monitors, etc.).
Each participant managed two high-fidelity simulation scenar-os involving IHCA in a cross-over design (see Fig. 1). In order toemove bias in performance due to any potential difference in sce-ario difficulty or due to DST order, participants were assigned tone of four combinations as shown in Fig. 1 through a randomumber generator. The scenarios were constructed according toHA testing standards for Megacode scenarios where a participantanages an acute dysrhythmia in a pulsatile state, then a shockable
ulseless state, and then a non-shockable pulseless state. Scenario Aonsisted of the patient presenting with unstable bradycardia pro-eeding to ventricular fibrillation and finally to asystole. Scenarioconsisted of the patient presenting with a narrow complex sta-
le tachycardia proceeding to pulseless monomorphic ventricularachycardia and finally to PEA.
We performed all simulation sessions in a setting that repli-ated a patient room on a general care ward at our institution.he code cart and defibrillator used during these events replicatedhose in clinical use at our institution, as did the available medi-ations, intravenous (IV) fluids, IV lines, medical gases, and airwayevices. Each participant was told to assume the role of the physi-ian team leader responding to a code in the university hospital.ll other roles (chest compressions, airway manager, drug admin-
stration, and defibrillator manager) were played by a standardizedode team that was comprised of simulation center staff trained toespond with consistent scripted responses.
lated IHCA. Of note, the DST did not make a significant difference in assessment ofthe patient, but did significantly improve adherence to the management protocol.[DST = decision support tool].
The DST was programmed for the Apple iOS and deployed oniPodTM devices, as noted above. The Appendix shows screen shotsfrom the DST through which the user would navigate during a briefpatient assessment and then management. The management stepsprogrammed into the DST were based upon the 2005 AHA ACLSupdate.
2.2. Analysis and grading
Each session was video recorded via a multi-camera system andB-line Medical® software (B-line Medical, Washington, DC) andthen graded by two experienced raters according to previously val-idated scoring checklists derived from AHA training manuals.19 Ascore for the percentage of correct actions performed was recorded.Since only errors of omission (i.e. not doing a prescribed action) arecaptured in the percentage of correct steps, errors of commissionwere recorded in a separate error count (e.g. shocking a patient inasystole).
Global performance on each scenario was compared so that anybias due to scenario difficulty could be taken into account. Withno demonstrable difference in global performance between sce-nario A and scenario B, further adjustment of the analysis model toaccount for scenario difficulty was not needed. The averaged cor-rect actions and error count scores for each participant were usedin the analysis, one from the scenario managed with the DST andone from the scenario managed without the DST. Another sensitiv-ity analysis was also performed that took into account whether theDST was used first or second, but no difference was found. There-fore, the percent correct actions and the error counts were analyzedthrough an ANOVA to compare performance with and without theDST. Data are presented as mean ± 95% CI for continuous variablesand p < 0.05 was considered significant.
3. Results
Overall, as seen in Fig. 2, the total number of correct check-list items during high-fidelity simulation of IHCA was significantlyimproved with the iOS-based DST compared to management frommemory alone (78.4% vs. 71.8%, p < 0.001). Further analysis by
ment steps was significantly improved with the use of the DST(84.7 vs. 73.8%, p < 0.001), while the number of patient assess-ment steps did not appear significantly improved (73.8 v 70.4%,
140 L.C. Field et al. / Resuscitatio
Fig. 3. This figure illustrates the difference in performance based upon use of theDST as measured by the number of errors committed during a simulated IHCA. ThisooF
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nly involves errors of commission, such as defibrillating when not indicated. Errorsf omission are accounted for in percentage of correct actions completed shown inig. 2 [DST = decision support tool].
= 0.103). In addition to improving the percentage of correct guide-ine actions performed, participants were also less likely to makerrors of commission, such as inappropriate defibrillation or incor-ect medication administration, when the DST was used (2.5 errorss. 3.8 errors, p < 0.012, see Fig. 3). As measured by time to firstefibrillation, which was similar with and without the DST (63.6s. 65.7 s, p = 0.808, respectively), use of the DST did not appear toelay timely delivery of care.
. Discussion
As noted above, adherence to published ACLS guidelines isften suboptimal with respect individual aspects such as toime to defibrillation and quality of CPR during in-hospital car-iac arrest.3,4,10,12,13 Furthermore, we have recently shown that
mproved overall adherence to published ACLS guidelines through-ut the entire resuscitation event is associated a much greaterikelihood of achieving return of spontaneous circulation (ROSC).20
hus, we investigated whether the use of an electronic DST thatynamically guides a provider through published AHA ACLS proto-ols would improve performance during high-fidelity simulationsequiring management of acute dysrhythmias and IHCA. The resultsf this study demonstrate that the DST which we tested sig-ificantly improved adherence to and reduced deviations fromublished guidelines. Our recent findings from a retrospectivenalysis of IHCA at our institution suggest that the 11% absolutemprovement in adherence to guidelines that we report in this sim-lation study would likely be clinically relevant, possibly improvinghe odds of return of spontaneous circulation by approximately0%.20
Additionally, the DST tested in this study advances upon the pre-ious report of a smartphone cognitive aid used in simulations ofHCA,16 as the DST used in this study actively prompts the end-user
ith the proper decision points and actions to consider based uponrior decisions and management steps logged by the end-user (seeppendix for example). It is therefore a dynamic and fluid manage-ent tool for IHCA events. Concerning the use of DSTs in healthcare
oday, those deployed within the electronic medical record haveeen shown to improve adherence to guidelines and, in some
eports, to improve patient outcomes in non-crisis settings.21–24
urthermore, the use of mobile technology (e.g. automated textessaging reminders) has been shown to improve adherence to
uidelines for clinicians and patients in the management of several
n 85 (2014) 138–142
conditions.25–27 Even though these DSTs and mobile technologytools are showing benefit, none of these tools are for use at thebedside in crisis situations. Our report thus advances upon whathas previously been shown concerning the effect of DSTs on adher-ence to guidelines in high-fidelity simulation of crisis situations.Future research needs to test its application in the clinical arena.
Another important consideration with respect to testing andemploying technology is that it can often change the manner inwhich people work, and not always in a beneficial manner. Thenew technology may distract and/or burden the clinician with extrawork, resulting in an impediment to timely IHCA management.Zanner et al. found that a mobile phone application not only failedto improve BLS performance of laypersons, but actually slowed theperformance of those with pre-existing BLS knowledge.17 Nelsonet al. also found that residents using pocket cards were more likelyto choose an incorrect pediatric cardiac arrest treatment algorithm,and suggested that successful cognitive aids should help guide thisdecision-making.18 We, therefore, used a human factors approachto streamline our dynamic DST to assist with rapid decision mak-ing and to reinforce the timely application of crucial managementsteps. We believe this extra attention to the human factors aspectduring application development is largely responsible for our abil-ity to demonstrate improved overall performance without slowingdown IHCA management. In contrast to these previous studies, ourstudents were able to complete a greater number of correct actionsin the same amount of time while using the DST, and time to firstdefibrillation was similar between the groups.
Our results demonstrated that the greatest effect of the DST wason adherence to management guidelines during simulated IHCArather than patient assessment. This is similar to a recent report byNeal et al. concerning the effect of a cognitive aid on the manage-ment of simulated local anesthetic systemic toxicity.28 In that studyand ours, the checklist used for grading contained steps dealingwith patient assessment and patient management. In both studies,use of the cognitive aid made a significant difference in the man-agement steps, but not in the items related to patient assessmentsuch as systematically checking neurological responsiveness, ade-quacy of airway/breathing, and existence of intravenous access. Themeasured difference in assessment may not have been significantbecause assessment of patients may occur by a mental process thatis more difficult to effect through the use of a cognitive aid. Alter-natively, failure to detect a difference in patient assessment maybe due to challenges in the video observers’ ability to grade theassessment items. Patient assessment items graded during theseacute physiologic disturbances may have been adequately consid-ered, but not verbalized by the participants, therefore limiting thegrading observers’ ability to record any difference in the assess-ment process. Of note, to avoid any grading bias toward the DSTgroup, the graders did not see the assessments that may have beenselected on the electronic DST, but not verbalized. The treatmentsteps require more definitive action by the participant, which mayhave allowed more precise grading and easier demonstration ofimprovement with the DST. While it is unfortunate that we werenot able to detect a significant improvement in patient assessmentwith the DST, it is ultimately the correct patient treatment stepsthat translate into better clinical outcomes.3,4,10
There are several potential limitations of the present study thatmust be acknowledged. First, the study population involved ACLS-certified graduating medical students, which might be of concernwith respect to generalizability of our results to more experiencedclinicians. This has been partially addressed by several studiesreporting ACLS certification as the factor of importance, not train-ing level,1,29 but medical students may also be more comfortable
with the use of smartphone applications than older clinicians. Sec-ond, we isolated the performance of the team leader in this studyfor analysis. It may be of benefit in future studies to test the impact
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f a DST on the performance of the entire team.30,31 While direc-ion given by the team leader is important, it is the whole ofhe actions performed by the entire team that matter most to
linical outcomes. In fact, it may be even more beneficial to have antherwise non-tasked team member solely focusing on navigatinghe DST in context to the real-time events. Third, there was a ceilingffect in this trial, such that performance with the DST did not reach00% compliance even when all of the appropriate managementteps were presented to the team leader. This highlights the needor ongoing human factors assessment of the technology, as thereould be a problem with the user interface that was limiting or it isossible that not enough time was spent on orientation to the DST.uture studies are needed to elucidate how to improve adherence to00% of all recommended protocol items and reduce the numberf errors (i.e. inappropriate deviations from protocol). While werovided orientation regarding the use of the DST prior to study,e did not formally assess the students’ proper use of the DST prior
he study.
. Conclusion
The dynamic decision support tool investigated in this study isffective for increasing adherence to the ACLS protocols withoutausing any delay in treatment during high-fidelity simulation ofHCA. Further research needs to investigate whether this impact ischievable for team performance in the clinical environment ando confirm whether such improved clinical performance translateso improved patient outcomes.
onflict of interest statement
None of the authors have any financial or personal relationshipshat could have any influence on this research or this manuscript.
cknowledgements
Foundation for Anesthesia Education and Research (FAER),esearch in Education Grant (PI: McEvoy) provided funded researchime. FAER was not involved in the study design or data analysis.
South Carolina Clinical & Translational Research Institute, Med-cal University of South Carolina’s CTSA, supported by National
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Institutes of Health/National Center for Research ResourcesGrant Numbers UL1TR000062/UL1RR029882 provided biostatisti-cal resources.
Appendix A.
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