INTRODUCTION

       Approximately 2 billion people are impacted annually as a result of the occurrence of natural disasters.1 Natural disasters and mass casualty (MASCAL) scenarios (i.e., situations when the number of patients requiring treatment exceed avail able resources)2,3 are often unavoidable; however, health care organizations can improve in their preparedness and responsiveness to MASCAL events.2,4–6 MASCAL events are marked by increased patient volumes, uncommon injury types (e.g., trauma), as well as scarcity of readily available resources (e.g., blood, advanced expertise); as such, timeliness of response and decisions in MASCAL are imperative. This, in turn, often requires advanced specialized expertise from non-co-located team members to treat patient needs seamlessly and efficiently.7–9

Teamwork in Health Care

       Health care today is complex, often requiring upwards of 17 or more clinicians to work together to care for a single patient.10,11 Quality care necessitates the seamless coordination between team members;12 however, effective teams require access to the appropriate team member expertise (i.e., clinical knowledge) and requisite attitudes, behaviors, and cognitions (ABCs) to act as a team (i.e., teamwork).13 Teamwork ABCs are required for team members to work effectively together and include factors such as, psychological safety, communication, situation monitoring, and transactive memory.12 Prior research illustrates that enhancements to teamwork ABCs translate to improved team performance14,15—and, in clinical contexts—improved patient outcomes (e.g., reduced mortality)16–18 and clinical performance. 19

       The state of the science of health care teams is largely bounded by co-located, stable teams,16,18,20,17 which are often not possible when providing care to rural or disadvantaged areas (i.e., team members may need to connect or work virtually with other providers through telehealth platforms or solutions).21,22 Despite technological advances permitting connection across geographical space, very little research is dedicated to understanding technology’s influence on critical teamwork ABCs and how they relate to quality of care.

       To this end, we investigated the utility of a telemedical device to a geographically dispersed team. Specifically, we sought to determine the impact of DiMobile Care on a quality metric of particular importance to the complex MASCAL setting: teamwork.23

METHODS

Sample

       We selected a convenience sample of U.S. Army Forward Surgical Teams (FSTs), which are relatively small (approx imately 18-20 clinicians), mobile, and rapidly deployable surgical units. These teams are typically composed of an interprofessional mix of physicians, nurses, medics, technicians, and other ancillary professions (e.g., Operating Room Technician). Participation in the training exercise was mandatory as part of the FST members’ jobs; however, participation in the study (e.g., survey completion) was voluntary. This study was approved by the University of Miami Human Subjects Research Office, and participants gave informed consent.

       We recruited participants before they engaged in a MASCAL simulation drill- a training exercise scheduled to occur during a mandatory 2-week predeployment training rotation at the University of Miami’s Ryder Trauma Center, Army Trauma Training Center. Before engaging in the MASCAL drill, participants underwent TeamSTEPPS teamwork training24 and received a refresher skills course in trauma.

Design and Procedure

       This study was a quantitative, case-control design with the between case factor (use of telemedicine during patient care) and the exposure variable of engagement in MASCAL simulation (before and after).

Introduction to Telemedicine

       Before the MASCAL simulation, FSTs were briefed on how to use an iPad (Apple, Cupertino, CA)-based telemedical device, referred to as DiMobile Care, used to initiate consultation with a third-party clinical expert. Participants were trained on proper use of telemedicine as a potential resource to access expertise external to the current team. Training covered appropriate use of the device and included demonstration(s) of appropriate use.

MASCAL Simulation Training

       The training rotation included a MASCAL training exercise, which served as a vital component of predeployment training to prepare FSTs for the challenges in deployment.25 The purpose of the MASCAL is to better equip FST(s) for unanticipated events experienced in battlefield settings by enhancing teamwork skills. In the MASCAL exercise, a combination of live tissue and simulated patients are presented to the FST, requiring the FST to triage casualties, treat potential survivors, and stabilize patients from sustained trauma injuries, within a MASCAL context.

       More specifically, during the MASCAL, each team is presented with multiple trauma patients. FST members must perform the aforementioned tasks, as well as transport to combat support hospital(s)—all of which require effective teamwork. The MASCAL is designed to integrate life-like environmental triggers to simulate battlefield-like conditions including the sound of gunfire, helicopters, and explosions that are broadcast over a speaker system. These stressors impose barriers on team communication, as well as demands on team coordination, such as, shifting roles and responsibilities. Demonstrably, roles and responsibilities shift when a member of the FST “dies” during the simulation which can occur, for instance, if a triage member fails to detect a simulated improvised explosive device. Shifting of responsibilities may also occur should a more complex patient be transferred from triage to surgical, requiring more manpower and resources.

       Members of the research team worked with creators of the MASCAL simulation with the purpose of eliciting use of the device by members of the FST. To elicit use of the DiMobile Care telemedical device, our multidisciplinary research team scripted two simulated patient injuries to integrate into the MASCAL simulation (one patient with “head trauma” and another with a “pelvic fracture”). Patient scenarios were chosen based on lack of available expertise and clinical skill of the clinicians who are commonly deployed as members of FSTs (e.g., general surgical skills). See Appendices A and B for the MASCAL Training Research Plan and information on survey(s) used.

Telemedical Device

       Members of the FST were encouraged to utilize DiMobile Care as they deemed appropriate. To facilitate its use, iPads equipped with DiMobile Care were positioned at each of the three areas of patient care (i.e., pre-op, surgical, and post-op). This positioning afforded an opportunity for FST clinicians to initiate the telemedicine application at work stations throughout the three aforementioned points of patient care. As this was a field study, meaning we did not assign or randomize cases as “case” or “control”, rather we observed members naturally engaging in DiMobile Care use as our “cases”, whereas FST clinicians who did not engage in use were viewed as the control group.

Measures

       Participants completed a baseline (Time 1) paper and pencil self- report survey that assessed their comprehension of telemedicine when to use it, among other baseline measures of affective (i.e., attitudinal), skill-based (i.e., behaviors), and knowledge-based (i.e., cognitions) out comes of teamwork (see Supplemental Table S1). The end line (Time 2) survey was administered after completion of the simulation. Unless otherwise noted, all surveys included Likert-type scales with responses ranging from 1 (Strongly Disagree) to 7 (Strongly Agree). Tables provided herein depict our key results, while supplemental information—including definitions of key operational constructs, supplemental results, and further description of study materials—is available online (see Supplemental Tables S1-3).

Attitude-Based

Psychological Safety

       Team psychological safety was assessed by adapting a scale created by Edmonson (1999). Sample items include “If you make a mistake on this team, it is often held against you” and “It is safe to take a risk on this team”.

Behavior-Based

Use/Disuse

       Use of telemedicine was measured with dichotomous scale using observational methods to determine whether telemedicine was initiated or not.

       Teamwork behaviors were measured at a more granular level using self-report metrics. Specifically, to measure improvements and changes to teamwork, we utilized a battery of teamwork measures to evaluate communication, situation monitoring, mutual support, and the team leadership. We will describe the specific metrics for each measure.

Communication

       Communication was assessed with 6 items on a 5-point scale, adapted from Hoegl and Gemuenden.26 Response options ranged from 1 (strongly disagree) to 5 (strongly agree). Sample items include “There is frequent communication within the team” and “the team members communicate directly and personally with each other”.

Situation Monitoring

      Situation monitoring was assessed with 3 items on a 5-point scale, adapted from Hoegl and Gemuenden.26 Response options ranged from 1 (strongly disagree) to 5 (strongly agree). Sample items include “the team monitors changes in the team’s external environment” and “the team notices flaws in task procedures or team outputs”.

Mutual Support

      Mutual support was assessed with 3 items on a 7-point scale, adapted from Hoegl and Gemuenden.26 Sample items include “team members offer assistance when other team members became task overloaded”.

Shared Leadership

       Eleven items adapted from the team leadership questionnaire (TLQ), establishing expectations and goals, a dimension of Morgeson et al.’s measure of team leadership,27 were included. Sample items include “team members define and emphasize team expectations” and “team members maintain clear standards of performance”.

Cognitive-Based

Transactive Memory

       Transactive memory was measured using an adapted questionnaire developed by Lewis.28 Transactive memory systems represent the awareness of the location of knowledge distributed among team members; in essence, transactive memory systems are the knowledge of “who knows what” within the team.29 We utilized the 5-item credibility dimension of the transactive memory measure, which specifically asks about credibility because it was more appropriate to the current sample and tasking. The scale points ranged from 1 (strongly disagree) to 5 (strongly agree). Sample questions of this 5-item survey include “When other members gave information, I wanted to double-check it for myself” and “I was comfortable accepting procedural suggestions from other team members”.

Sharedness of Team-Related Knowledge

      We adapted Johnson et al.’s measure of sharedness of team related knowledge using four items.30 Sample items include “My team had a shared goal for various patient cases” and “My team knew the general process involved in how we will conduct a given task in a patient case”. This was measured at Time 2.

       We leveraged a preexisting battery of measures (e.g.,Teamwork Quality [TWQ]) to examine critical ABCs related to teamwork and telemedicine use, which includes: communication, mutual support, and situation monitoring. We tested measures for reliability and found they met acceptable levels for Cronbach’s alpha (i.e., α > .76;31 α = .80-.96).

       Participants completed a second survey upon completion of the MASCAL simulation. Surveys at Time 2 assessed teamwork demonstration during scenario and perceptions of telemedical device utility and use for patient cases.

Statistical Analysis

       We used IBM SPSS Statistics 26.032 to calculate descriptive statistics (mean, median, standard deviations, and frequency), as well as paired t-test analyses. Missing data were excluded from analyses (i.e., participants included in analyses responded before and after MASCAL simulation; see Fig. 1 for additional information regarding sample size used in analyses). Unless otherwise indicated, t-tests were two-tailed.

RESULTS

       We obtained complete responses from 50 participants (i.e., respondents who completed surveys at both at Time 1 and Time 2). Of all participants who responded both before and after the MASCAL simulation, 14 of the participants were female and 36 were male. Participants included physicians, nurses, medics, techs, and others; see Supplemental Figures 1 and 2 for a breakdown of months of experience in trauma and professions of all participants. Seven participants (19% of our respondents) utilized telemedicine during the study; as this creates unfair comparisons between cases and controls, we report comparisons between teamwork at Time 1 (before the MASCAL simulation) and Time 2 (post-MASCAL simulation, when telemedicine was introduced). We further compare case-control groups at post-MASCAL where differences in pre-post exist.

Attitudes

Psychological Safety

       We conducted a t-test to compare team psychological safety at Time 1 and Time 2. On average, participants experienced significantly decreased psychological safety after the simulation (M = 4.76, SD = 0.94) than before the simulation (M = 5.07, SD = 0.79), t(49) = 2.5, p < .05) (see Table I). Given the meaningful differences that exist between pre post comparisons, we conducted a one-way ANOVA to compare ratings of psychological safety between users and nonusers of telemedicine in our study (see Table II). Results show statistical differences between users and nonusers of telemedicine such that those who utilized the telemedical device reported higher levels of psychological safety postsimulation than individuals who did not use telemedicine, F(1, 48) = 3.03, p = .08, η2 = 0.06.

Team Behaviors

Use/Disuse

       Seven individuals (14%; out of 50) reported utilizing the telemedical device within their team. This number reflects the individuals who directly interacted with the device as representatives within their team who relied on information through use of the device. To garner an in-depth picture as to how telemedicine may have impacted teamwork behaviors within telemedicine, we examined the survey-based collection strategies with both inferential tests as well as descriptive statistics. See Table III for breakdown of teamwork competencies by use/disuse of telemedicine.

Communication

       For communication, there was no significant effect of the simulation on communication from pre- to post-simulation, t(48) = 0.95, p > .05. All pre-post comparisons of team competencies are reported in Table S3.

Mutual Support

      Additionally, for mutual support, there was no significant effect of the simulation on team mutual support, t(47) = −0.14, p > .05.

Situation Monitoring

       Situation monitoring behaviors were also not impacted by participation in the simulation, t(47) = −1.22, p > .05.

Leadership

       For leadership behaviors, there was no significant effect from the simulation, t(47) = −0.06, p > .05; however, it is important to note that leadership and situation monitoring behaviors did increase over time and that this effect approached significance.

Adaptation

       Perception of the team’s ability to adapt was assessed as moderately high as compared to before the simulation, M = 4.20(0.57), yet did not differ significantly between telemedicine users (M = 4.26, SD = 0.38) and nonusers (M = 4.19, SD = 0.60).

Reactions to Telemedicine in Relation to Teamwork

       To understand what role telemedicine (rather than just participation in the simulation itself) played in the perceived change(s) in teamwork, we examined clinicians’ reactions to the telemedical device. Overall, the reactions to telemedicine in relation to teamwork were positive. On a 7-point scale, the mean for the sample was 4.93. To assess perceptions toward telemedicine, means and standard deviations were calculated for individual reactions to the telemedicine items. Overall, when asked whether clinicians believed that telemedicine improved teamwork processes in their team, 89.3% said that it at least somewhat improves teamwork and 34.8% indicating it improved teamwork very much. However, 9.1% of the participants indicated that it did not improve teamwork at all. A majority of participants (77.2%) agreed that telemedicine improved leader decision making, while 34.8% indicated that telemedicine improved leader decision-making “very much”, and only 6.1% indicated that telemedicine did not improve it at all.

Team Cognitions

       When asked whether telemedicine influenced the way the team adapted their patient care plan, 27.8% of those who reported telemedicine use during the simulations (n = 18) agreed it influenced it very much, 22.2% indicated it only moderately improved adaptive responses, and 22.2% indicated that it somewhat improved adaptation. However, some (22.2%) indicated that telemedicine did not impact adaptive response at all. Upon further investigation of those who reported utilizing telemedicine to assist with the care of patients, 72.2% indicated it at least somewhat contributed to the way the team adapted its patient care plan and 50.0% said it at least moderately contributed to the adaptive responses of the team when compared to those who didn’t agree with these statements.

Transactive Memory

       On average participants experienced no change in transactive memory after the simulation than before the simulation, t(49) = 0.26, p > .05.

Shared Knowledge

       Overall, participants rated their overall shared knowledge during the simulation as high, M = 5.99(0.91). This value was slightly but not significantly elevated in those who reported using the telemedical device (M = 6.25, SD = 0.43) than those who did not (M = 5.94, SD = 0.96).

DISCUSSION

       A minority of clinicians (45.6%) participating in this study reported strong confidence in the accuracy of the procedures they used to treat scripted trauma patient cases. Although telemedicine did not appear to impact teamwork positively or negatively, increased perceptions of psychological safety were an exception. Psychological safety was found to be higher among clinicians who used the telemedical device than those who did not.33 Findings suggest that teams with high levels of psychological safety may be more likely to initiate telemedicine because they feel safe to take risks on behalf of the patient (e.g., using the device for the patient). However, our results are portrayed at the individual level(s). As users of telemedicine tended to be surgical team leads, this finding may not fully reflect the team’s shared experience(s) when device is used. Further, having few instances during which telemedicine is initiated may reduce the ability to detect statistically significant differences in our outcomes between treatment and control groups.

      Further, results speak to decreases in quality, frequency, and timeliness of team communication from pre- to postsimulation, which perhaps may be a result of the intentionally noisy simulated environment (i.e., environmental stressors, including noise, were used to simulate battlefield conditions in a deployment in the Middle East and may have inhibited the ability to communicate effectively). However, this may also indicate the need for a device which better fits into the team’s workflow so that communication patterns are not disrupted.

Limitations

       This study is not without limitations. First, data were collected during a 2-week training course in which 1 team trained per month. This limited our data to three FSTs and only allowed us to examine teamwork at the individual level, rather than team level due to power considerations. Further, when con sidering our small sample size, the post-MASCAL (Time 2) questionnaire was administered after the 6-hour long simulation, which began at 4 a.m. local time. The length of the simulation coupled with the 4 a.m. start time may induce fatigue, diminishing motivation and will power to complete a “post” simulation questionnaire. Additionally, a smaller sample size limits the generalizability of our findings. However, this weakness is somewhat counterbalanced by the increased external validity provided by using real FSTs.

       Another limitation of our study is related to the context of the study; the patient care environment for the simulation was high-stress and high-stakes. Many of the team members had just met during the initiation of the 2-week training course; thus, they were likely to be unfamiliar with working with one another before the study. In addition, varying experience with trauma care may have interfered with the ability to detect effects of telemedicine (see Supplemental Fig. 2). Specifically, these health care providers may not have dealt with trauma in a while, as trauma injuries are their own unique clinical subspecialty. The culmination of the following may have dissuaded clinicians from using the telemedicine tool. Moreover, some may have never used telemedicine or were not tech savvy and thus were not inclined to initiate a new technology given the imposed time constraints. As a result of these factors, clinicians may have felt that they did not have enough time or experience for telemedicine to be of value, given the time constraints.

CONCLUSIONS

       Results reveal that overall clinicians had positive reactions toward telemedicine. On average, participants experienced significantly decreased psychological safety after training, particularly among those who did not utilize the telemedical device. However, no significant changes were observed in behavioral and cognitive-based teamwork. Further work should investigate the integration of novel technologies within teams such that psychological safety is improved or maintained within the team’s climate. Telemedicine was not found to improve teamwork overall in MASCAL scenarios; however, telemedicine did not negatively impact teamwork, either. This suggests that telemedicine can be utilized in a MASCAL context without concern that it would pose significant harm to teamwork. Telemedicine uptake, utility, and its ultimate use in MASCAL environments need to be further investigated in future applied and basic research settings. Moving forward, research about the impact of telemedicine should be investigated and developed with team workflow in mind for teams with less expertise (e.g., emergency medical technicians) who act as first response in a variety of contexts.

SUPPLEMENTARY MATERIAL

       Supplementary material is available at Military Medicine online.

ACKNOWLEDGMENTS

       The views expressed in this paper are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States Government.

FUNDING

       This project was conducted in conjunction with members of the Ryder Trauma Center and University of Miami and was funded by the Telemedicine and Advanced Technology Research Center (TATRC) Department of Defense (W81XWH-11-2-0211).

CONFLICT OF INTEREST STATEMENT

       None declared.

REFERENCES

1. Cannon T, Schipper L: World Disasters Report 2014: focus on culture and risk. In: Societies IFoRCaRC, ed2014.

2. Hick JL, Hanfling D, Cantrill SV: Allocating scarce resources in disasters: emergency department principles. Ann Emerg Med 2012; 59(3): 177-87.

3. Pesik N, Keim ME, Iserson KV: Terrorism and the ethics of emergency medical care. Ann Emerg Med 2001; 37(6): 642-6.

4. Born CT, Briggs SM, Ciraulo DL, et al: Disasters and mass casualties: I. General principles of response and management. J Am Acad Orthop Surg 2007; 15(7): 338-96.

5. Kaji AH, Lewis RJ: Hospital disaster preparedness in Los Angeles county. Acad Emerg Med 2006; 13(11): 1198-203.

6. Schenker JD, Goldstein S, Braun J, et al: Triage accuracy at a multiple casualty incident disaster drill: the Emergency medical Service, Fire Department of New York City experience. J Burn Care Res 2006; 27(5): 570-5.

7. Lifshitz AE, Goldstein LH, Sharist M, et al: Medication prescribing errors in the prehospital setting and in the ED. Medication Prescribing Errors in the Prehospital Setting and in the ED 2012; 30(5): 726-31.

8. Auf der Heide E: The importance of evidence-based disaster planning. Ann Emerg Med 2006; 47(1): 34-49.

9. Foran M, Levine A, Lippert S, et al: International emergency medicine: a review of the literature from 2009. Acad Emerg Med 2011; 18(1): 86-92.

10. Gawande A: Atul Gawande: how do we heal medicine? [Internet]; 2012 [cited Dec 19, 2016]. Podcast

11. Organizations J: The Joint Commission Guide to Improving Staff Communication. IL, Oakbrook Terrace; 2005.

12. Salas E, Rosen MA, Burke CS, Goodwin GF: The wisdom of collectives in organizations: an update of the teamwork competencies. Team Effectiveness in Complex Organizations: Cross-disciplinary Perspectives and Approaches New York, NY, Taylor & Francis Group, 2009:39-79.

13. Salas E, Shuffler ML, Thayer AL, Bedwell WL, Lazzara EH:Understanding and improving teamwork in organizations: a scientifically based practical guide. Hum Resourc Manag 2015; 54(4): 599-622.

14. Salas E, Diaz-Granados D, Klein C, et al: Does team training improve team performance? A meta-analysis. Hum Factors 2008; 50(6): 903-33.

15. LePine JA, Piccolo RF, Jackson CL, Mathieu JE, Saul JR: A metaanalysis of teamwork processes: tests of a multidimensional model and relationships with team effectiveness criteria. Pers Psychol 2008; 61(2): 273-307.

16. Hughes AM, Gregory ME, Joseph DL, et al: Saving lives: a metaanalysis of team training in healthcare. J Appl Psychol 2016; 101(9):

17. Marlow SL, Hughes AM, Sonesh SC, et al: A systematic review of team training in health care: ten questions. Jt Commiss J Qual Pat Saf 2017; 43(4): 197-204.

18. Neily J, Mills PD, Young-Xu Y, et al: Association between implementation of a medical team training program and surgical mortality. JAMA 2010; 304(15): 1693-700.

19. Schmutz JB, Meier LL, Manser T: How effective is teamwork really? The relationship between teamwork and performance in healthcare teams: a systematic review and meta-analysis. BMJ Open 2019; 9(9):

20. Knight LJGJM, Earnest KS, Leong KM, Anglemyer A, Franzon D: Improving code team performance and survival outcomes: implementation of pediatric resuscitation team training. Crit Care Med 2014; 42(2): 243-51.

21. Ilgen DR, Hollenbeck JR, Johnson M, Jundt D: Teams in organizations: from input-process-output models to IMOI models. Annu Rev Psychol 2005; 56: 517-43.

22. Salas E, Tannenbaum SI, Kraiger K, Smith-Jentsch KA: The science of training and development in organizations: what matters in practice. Psychol Sci Public Interest 2012; 13(2): 74-101.

23. Stahl K, Garcia GD, Birnbach DJ, Augenstein J: Improving patient safety through teamwork and team training New York, NY, Oxford, 2013; 146-54.

24. King HB, Battles J, Baker DP, et al: TeamSTEPPS: Team strategies and tools to enhance performance and patient safety. Advances in patient safety: New directions and alternative approaches. Rockville, MD, Agency for Healthcare Research and Quality. Available at https://www.ncbi.nlm.nih.gov/books/NBK43686/.2008.

25. Pereira BMT, Ryan ML, Ogilvie MP, et al: Predeployment mass casualty and clinical trauma training for US army forward surgical teams. J Craniofac Surg 2012; 21(4): 982-86..

26. Hoegl M, Gemuenden HG: Teamwork quality and the success of innovative projects: a theoretical concept and empirical evidence. Organ Sci 2001; 12(4): 435-49. 

27. Morgeson FP, DeRue DS, Karam EP: Leadership in teams: A functional approach to understanding leadership structures and processes. J Manag 2010; 36(1): 5-39.

28. Lewis K: Measuring transactive memory systems in the field: scale development and validation. J Appl Psychol 2003; 88(4): 587.

29. Prichard JS, Ashleigh MJ: The effects of team-skills training on transactive memory and performance. Small Group Res 2007; 38(6): 696-726.

30. Johnson TE, Lee Y, Lee M, O’Connor DL, Khalil MK, Huang X: Measuring sharedness of team-related knowledge: design and validation of a shared mental model instrument. Human Resource Develop Int 2007; 10(4): 437-54.

31. Gliem JA, Gliem RR: Calculating, interpreting, and reporting Cronbach’s alpha reliability coefficient for Likert-type scales. Midwest Research-to-Practice Conference in Adult, Continuing, and Community Education. 2003.

32.IBM Corporation. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY, IBM Corp, 2019.

33. Edmondson A: Psychological safety and learning behavior in work teams. Adm Sci Q 1999; 44(2): 350-83.

This article is excerpted from the MILITARY MEDICINE by Wound World.