The Role of Transactive Memory in ICU Team Resuscitation
The intensive care unit (ICU) is branded as a complex environment. Evolving situations, rapid changes to patient condition and frequent interruptions challenge multidisciplinary teams’ ability to work effectively. The interdependent, often ad-hoc nature of ICU teams makes them vulnerable to teamwork skill deficiencies. Particularly, issues in non-technical skills such as communication and teamwork during a crisis situation that can affect patient care. Team members are often juggling individual goals in conjunction with team goals (e.g., getting the task done effectively and efficiently). As such, sharing information does not equate to effective communication. Team communication is prone to misinterpretation leading to error, particularly in multidisciplinary teams.
In the past two decades, team cognition has emerged as an overarching perspective from which to develop theories of team performance. Mental model theory posits that shared knowledge among members facilitates teamwork, but overlooks the knowledge differences inherent to multidisciplinary teams. A more differentiated view of team cognition is captured in transactive memory theory, which is concerned with the prediction of group behaviours through an understanding of the manner in which individuals differentially process and structure information within the group. While it has been studied extensively in dyads and small work groups, and somewhat in virtual teams and organizations, the role of Transactive Memory in high-risk domains has been largely ignored.
Critical care teams (such as ICU and OR) tend have inconsistent membership, lack team training, and are prone to communication issues. These conditions are not ideal for the development TMS, yet the strong cultural norms of healthcare team roles offer an interesting juxtaposition. Despite the lack of team training, ICU teams still manage to perform successfully. Standardized team roles can allow people who have never met to function effectively in safety-critical domains. In aviation, it is often the case that the pilots, co-pilots and flight attendants meet each other for the first time at the start of each flight and proceed to seamlessly execute hundreds of flights a year. Nevertheless, planes still crash and medical errors still occur, so the need to study variation in team performance is still relevant. In the ICU, a team’s structure can be a blend of assigned and assumed roles associated with professional titles, cultural hierarchies and the needs of the situation. An inconsistent team structure would seem problematic for effective teamwork in complex environments. Transactive memory can provide a framework to explain successful team performance. Teams can compensate for shifting roles if members are aware of each other’s knowledge and expertise. This knowledge builds implicit coordinating mechanism that in turn facilitates team processes such as communication and coordination, and allow teams to deal with crisis situations.
The goal of this project is to answer four research questions: 1) What is the importance and applicability of transactive memory in the high-risk healthcare domain? 2) What is the relationship, if any, between transactive memory and non-technical skills (e.g. communication), and between transactive memory and technical skills (i.e. performance)? 3) What is the validity and reliability of the Transactive Memory Systems Scale and a behavioural markers checklist for measuring TMS in ICU teams during simulated crisis situations? and 4) What is the validity of the proposed conceptual framework describing the relationship between TMS, technical, non-technical skills in regards to team performance?
To answer the first question, an extensive literature review of the published literature covers the topics of teamwork, healthcare complexity, and team cognition. This review will be coupled with an ethnographic approach of interviews and questionnaires with subject matter experts in the ICU domain (Study 1). The triangulation of these efforts will be used to formulate behavioural markers within specific context of ICU teams. In Studies 2a and 2b, questions two and three will be answered through an empirical analysis of videos of high-fidelity ICU simulation, using the behavioural markers derived from study 1. The fourth question will be answered in a detailed cross-analysis of the results of the two empirical sub-studies in order to validate the conceptual framework of TMS and performance in ICU teams.
1.1 Medical Context: Resuscitation
Cardiac arrest is internationally defined as the “cessation of cardiac mechanical activity, confirmed by the absence of a detectable pulse, unresponsiveness, and apnea” (Zaritsky et al., 1995). When the heart ceases to provide sufficient blood flow and oxygen to the brain, the victim collapses, and will appear lifeless in the absence of vital signs (Vaillancourt & Stiell, 2004). In the absence of cardiopulmonary resuscitation (CPR) and/or electrical defibrillation, such electrical cardiac activity disappears (i.e., asystole), followed by death in a matter of minutes. When cardiac arrest occurs, the immediate and skilled action of first responders is critical. Once the resuscitation team arrives, a coordinated rapid and efficient exchange of information, along with continuous hands-on CPR measures, is essential. The 2010 North American Heart Association Guidelines dictate CPR as part of the “Chain of Survival” algorithm for cardiac arrest response: 1) immediate recognition of cardiac arrest and activation of the emergency response system; 2) Early CPR with an emphasis on chest compressions; 3) Rapid defibrillation 4) Effective advanced life support and 5) Integrated post-cardiac arrest care (American Heart Association, 2010).
Two decades ago, resuscitation researchers dismissed the in-hospital cardiac arrest population as unsuitable for resuscitation research because it was composed mostly of patients whose cardiac arrest was the terminal event of a fatal illness (Jastremski, 1993). A standardized solution, the Utstein style of reporting arose out of concern that resuscitation endeavors in different countries (and within countries) could not be compared meaningfully (Cummins et al., 1997). The “In-Hospital Utstein-Style Template” was developed to summarize the critical data elements essential for documenting in-hospital cardiac arrest (IHCA): time of event onset, time of cardio-pulmonary resuscitation (CPR) started/stopped, time to first defibrillation, time to advance airway management, time to administration of first resuscitation medications and time of sustained returned of spontaneous circulation (ROSC) (Cummins et al., 1997; Zaritsky et al., 1995). Since the creation of the Utstein style, a surge of epidemiologically consistent literature has significantly advanced our understanding of the factors involved with IHCA. Unfortunately, we have learned that survival rates of IHCA are low, and have been for some time. The overall survival rate (usually 10 to 20%), from cardiac arrest is calculated by the number of patients discharged from a hospital after successful CPR, divided by the total number of patients in whom CPR was attempted (Skogvoll, Isern, Sangolt, & Gisvold, 1999). In spite of efforts to prevent arrest or enhance resuscitation care before, during and after IHCA, survival rates have not improved in the past four decades (Abella et al., 2012). In this complex healthcare environment, despite training and standardization to improve team performance, errors and inefficiencies persist even among experienced teams.
Resuscitation from cardiac arrest is a frequent intensive care unit (ICU) emergency that requires both technical (e.g. medical knowledge) and non-technical team skills. The majority of Canadian and U.S. hospitals have organized teams (rather than individual healthcare workers) to respond to in-hospital cardiac arrests (Hunziker et al., 2011). Despite substantial efforts to make cardiopulmonary resuscitation (CPR) algorithms known to healthcare workers, resuscitation teams often deviate from CPR algorithms (Mattei, McKay, Lepper, & Soar, 2002; Peberdy, Silver, & Ornato, 2009). Resuscitation guidelines provide a logical, sequential algorithmic approach, but they have mainly emphasized technical tasks performed by individual rescuers and have not addressed issues of adapting to the complex nature of most actual resuscitations. There is mounting evidence that errors in the teamwork associated with patient care may be contributing to poor outcomes (Ornato, Peberdy, Reid, Feeser, & Dhindsa, 2012; Peberdy et al., 2012). Even with the high degree of technical knowledge required, errors that occur in high-stakes medical environments (such as during a resuscitation) often result from a problem with team functioning, rather than from a lack of clinical knowledge (Baker, Day, & Salas, 2006).
The challenging nature of healthcare teams necessitates the ongoing study and refinement of the factors that contribute to teamwork effectiveness, as it ultimately makes up each patient’s level of care. Team skills are inherently cyclical; what serves as an outcome of one variable may serve as an input to another (Salas, 2005). Teams may actively modify and adjust their behaviours accordingly (Frese & Zapf, 1994) Empirical research on the underlying cognitive structures propelling teamwork cycles will improve our ability to identify and train the skills necessary to improve performance in complex, safety-critical domains. The execution of these processes help shape an overarching team cognition (Gorman, Cooke, & Kiekel, 2004), which will serve as an overarching theme of this prospectus. The following sections provide a review of the relevant team cognitive factors, including teams, teamwork and team performance in the complex healthcare environment.
Over the past 40 years, a “golden age” of interest of team research has yielded over 130 models of team, teamwork and team performance (Salas, Cooke, & Rosen, 2008, p. 541). The word “team” typically refers to a group of two or more people that work together towards a common goal in a coordinated and coherent fashion (Paris, Salas, & Cannon-Bowers, 2000). Athletic teams exemplify this classic definition: team members all have a common goal of winning and practice together regularly (even multiple times a day); individuals typically have one clearly designated role or position that is known by other members; team members often develop some type of shared-language to aid communication (e.g. plays, call-signs); and the team typically is assigned an official team leader or captain (Fiedor, Hunt, & Devita, 2011). Team sports are not played or won by individual members. To succeed, they must function effectively and efficiently.
Organizations in high risk domains, such as aviation, nuclear, military and healthcare, also rely on teams to deal with the complex nature of their environments. However, compared to athletic teams, the need to effective performance, and the potential consequences of poor performance are much more severe. This concept has been demonstrated in the famous quick decision making witness during the “Miracle on Hudson”. On January 15, 2009, US Airways Flight 1549 struck a flock a geese following takeoff from LaGuardia Airport in New York. The crew noticed that both engines had lost power and informed air traffic control (ATC). As per standard protocol, the ATC sought the closest alternative runway, which happened to be in New Jersey airstrip (Atkins, 2010). The flight ‘team’, while having the common goal of a safe flight from point A to B, was distributed among the pilots in the cockpit, the flight crew in the cabin, and the ATC in the tower. Through communication via radio headsets, the team was able to coordinate their different perspectives on the immediate situation. The ATC thought Flight 1549 had sufficient time to make it to the alternate landing strip, and followed all of the appropriate communication protocols with the pilots. However, while the various ATC operators were discussing their options, Captain Sullenberger judged the immediate situation and decided to conduct an emergency landing in the Hudson River resulting in all 155 surviving passengers (Eisen & Savel, 2009).
In, complex, fast-paced, dynamic domains, errors can lead to severe consequences when the task complexity exceeds the capacity of an individual; when the task environment is ill-defined, ambiguous, and stressful; when multiple and quick decisions are needed; and when the lives of others depend on the collective insight of individual members (Salas, Cooke, & Rosen, 2008).
Distributed responsibilities allow teams to process massive amounts information, thus reducing the cognitive load on individuals (Hazlehurst, 2004). The distribution of cognitive workload can allow teams to function more effectively than individual members (Fiore et al., 2010). For instance, working in teams (versus working alone) has been shown to mitigate the negative effects of task interruption as demonstrated by reduced individual workload, and faster resumption time to a resource monitoring task (Tremblay, Vachon, Lafond, & Kramer, 2011). The next section discusses how the beneficial impact or potential pitfalls of teamwork are important for the healthcare domain.
2.1.1 Healthcare Teams
Healthcare teams operate in an environment characterized by stress, workload, complexity and high stakes for decision and action errors (Salas, Rosen, & King, 2012). Healthcare complexity stems from the individual nature of patients; the frequency, public exposure, and applied investigation methods with respect to adverse incidents (e.g., Taneva, Plattner, Byer, Higgins, & Easty, 2010) and error (e.g., Wu, Folkman, McPhee, & Lo, 2003); training and evaluation of professional skills (e.g., Nurok, Lipsitz, Satwicz, Kelly, & Frankel, 2010); and the constant coordination and re-coordination of behaviours as each members’ actions can immediately require another team member to react appropriately (Helmreich, 1996).
The dynamic, interdependent, and adaptive interaction of team members toward a common and valued goal (Salas, Dickinson, Converse, & Tannenbaum, 1992) facilitates performance of complex, critical tasks (Cooke, Kiekel, & Helm, 2000). In fact, many medical procedures such as surgery, emergency medicine, and anaesthesia can only be performed by teams. In these group procedures, clinical performance and patient safety are key functions of group coordination (Kolbe, Burtscher, Manser, Kunzle, & Grote, 2011). Multidisciplinary teams play a vital role in healthcare because some level of collaboration between healthcare providers is always necessary as no single discipline or specialty can meet all of a patient’s needs (Ellingson, 2002). A hospitalized patient, for example, may need a physician to provide a diagnosis and treatment plan, a nurse to administer medications, a dietitian to monitor food intake, a physical therapist to aid in muscle strengthening and flexibility, and a social worker to coordinate home care following release (Ellingson, 2002). This is particularly true in dynamic domains of surgery, intensive care, and trauma where effective multidisciplinary teamwork has been shown to be important for safe patient care (Burtscher et al., 2011).
Teams composed of multiple disciplines must consider the coordinated needed among crews of the same profession, as well as coordination between professional groups. Anesthesia teams, for example, are constantly engaged in planning (e.g. planning the correct amount of a certain medication), problem solving (e.g. finding the correct reason for suddenly rising blood pressure), decision making (e.g. deciding on the right time for intubation), and psycho-motor performance (e.g. laryngoscopy) (Burtscher, Kolbe, Wacker, & Manser, 2011; Schulz et al., 2011). These crew-specific demands are further complicated by the need to coordinated with the other professions throughout “mixed-motive” medical procedures (e.g. choosing between the surgeon’s request to proceed with the operation and the need to further stabilize the patient) (Kolbe et al., 2011).
The increasing complexity in healthcare and the demanding nature of healthcare services has made multidisciplinary healthcare teams an integral part in the delivery of patient care (Sutton, Liao, Jimmieson, & Restubog, 2005). However, in reality different professionals do not always understand each other and cooperation does not always work (Schoop, 1999). Perceptions of teamwork have been shown to vary substantially by caregiver type, with physicians often rating aspects of teamwork higher than nurses (Makary et al., 2006). Team members often must juggle individual and team goals, interacting with multiple team goals (Keyton & Beck, 2010). Physicians, nurses, pharmacists, technicians, and other health professionals must coordinate their activities to deliver safe and efficient patient care (Baker et al., 2006). According to their different interests and tasks, healthcare professionals may notice different things about the same patient and, therefore, may prioritize and react to different aspects of patient care (Reddy et al., 2001). Healthcare workers perform interdependent tasks (e.g., a surgeon cannot operate until a patient is anesthetized) while functioning in specific roles (e.g., staff surgeon, surgical resident, anesthesiologist) and sharing the common goal of safe care.
In many ways, multidisciplinary healthcare teams represent the antithesis to the well-trained athletic team because of the tendency to be formed ad-hoc. The ad-hoc nature of many types of healthcare teams makes it difficult to practicenecessary skills such as communication, organization, group problem solving. For instance, hospital trauma teams (e.g. code-blue) serve to prevent death in suddenly critically ill patients (Sarcevic, Marsic, Lesk, & Burd, 2008). Such a critical function should be the target of frequent and effective training programs, as is the case with daily practice sessions of athletic teams. However, different professions are brought together quickly assembled (e.g. trauma resuscitation) or for a very short period of time (e.g. OR team). Once the crisis or procedure is over, they may never work together (in whole or in part) again. In many cases, several members of the team (or even the whole team) may have never worked together before. In sport, the connection between ineffective behaviours of a newly formed team and the outcome are clearer; delays in action, miscommunication, and errors can result in a fumbled play or a lost game. In healthcare, the lack of team familiarity could increase the likelihood of a fatal patient outcome. The consequences of poor performance in teams working in high-risk domains can be life altering, meaning teams must function at the highest level (Boies & Howell, 2011; Wilson, Salas, Priest, & Andrews, 2007).The characteristics of effective teams must be able to account for organizations that are complex, tightly coupled, hierarchical; time compressed and relies upon coordinated actions. The proponents of teamwork necessary for effective teams are discussed next.
Teamwork is not a fixed state; it is a multidimensional, dynamic construct that refers to a set of interrelated cognitions, behaviors, and attitudes that occur as team members perform a task that results in a coordinated and synchronized collective action (Salas, 2005; Salas et al., 2008). Teamwork, particularly in safety-critical, HRO domains, is not a natural product of working together (Undre, Sevdalis, Healey, Darzi, & Vincent, 2007). Teamwork must be ‘learned and practiced’ (Wallin, Meurling, Hedman, Hedegard, & Fellander-Tsai, 2007). At the same time that teamwork is deemed necessary, it is also an often underappreciated and misunderstood skill (Paris et al., 2000; West & Field, 1995). Training interventions designed to improve team processes in healthcare are often based on common sense notions, positive team climate, or tradition rather than on empirical validation of their effects on performance (Salas et al., 2008). In these cases, teamwork is often used interchangeably with the notion of ‘effective team coordination’-but the connection between team processes and performance is blurred. To differentiate these two descriptor, this section deals with a number of encompassing models of teamwork behaviours, followed by their coordinating mechanisms.
2.2.1 Models of Teamwork Behaviour
Teamwork is an essential component of safety in most work environments, especially in healthcare (Salas et al., 2012). Theories of teamwork provide an understanding of what is important to team effectiveness and hence what is important to measure, however multiple models exist. In the absence of an “ideal” model tailored for a specific domain (and has received empirical validation) general models provide an overview of the critical features of teamwork (Rosen et al., 2008). A number of desirable team characteristics (i.e., team knowledge, skills and attitudes) are identified in the literature as required for effective teams, regardless of membership consistency (Baker et al., 2006; Salas, 2005; Salas, Sims, & Klein, 2004). A frequently cited theoretical model, the “Big Five”, describes teamwork (see Table 1) in terms of five core dimensions (i.e., team leadership, mutual performance monitoring, backup behaviour, adaptability, and team orientation) and three coordinating mechanisms (i.e., mutual trust, shared mental models, and closed- loop communication; Salas, 2005). Adopting this model of teamwork as the basis for team performance measurement system would mean that measures would be developed to capture each of the five core teamwork dimensions and the three coordinating mechanisms.
The expectations associated with the role of team leadership are daunting because failure of the team’s leader to guide and structure team experience is posited by a wealth of research to have such an important impact on performance (e.g., Hunziker et al., 2010). Through a synthesis of the related research, the Big five identifies three overarching leader functions: 1) The team leader has a role in the creation, maintenance and accurate of the team’s shared mental model, that is, a shared understanding of the team objectives, the team constraints, the roles of each team member, and the resources available to them. 2) The team leader facilitates team effectiveness by monitoring the internal and external environment of the team to facilitate team adaptability and the ensure teams are not caught off guard when the changes occur. 3) The team leader establishes behavioural and performance expectations, while tracking the abilities and skill deficiencies of each member. Salas (2005) argues that team leaders ultimately facilitate team effectiveness not only by synchronizing and combining the individual contributions of each of the team members but also by insuring individuals on the team understand their interdependence and the benefits of working together.
Mutual performance monitoring is defined in the big five model as the team’s ability to keep track of fellow member’s work while carrying out their own. The model proposes that the information gathered through mutual performance monitoring that affects team performance by identifying errors or lapses. This information, expressed through feedback and backup behavior, boosts the team from the sum of individual performance to the synergy of teamwork and ultimately to team effectiveness. Similar to leadership, the prerequisites for effective mutual performance monitoring are shared mental models of the task and team responsibilities. This mental model provides teammates with a common understanding of what other team members are supposed to be doing at any given moment, and what they should be doing next (Peterson, Mitchell, Thompson, & Burr, 2000).
As a compliment to mutual performance monitoring, the big five model proposes that backup behaviour affects team performance directly by ensuring that all aspects of the team tasks are completed. If a team member’s workload threshold is surpassed, the team can engage in backup behaviors by shifting work responsibilities to other underutilized team members as it becomes necessary (this is a form of team adaptation, described next). Should the tasks of the overloaded team member not be facilitated or taken over, it is expected that team performance will drastically degrade. Depending on the type of task, the compensatory behaviour may manifest differently (e.g., physically taking of the task, ensuring that the error is corrected, or providing support). Again, back up behaviour would require the existence of some type of shared mental model and mutual performance monitoring as they would form the foundation for team members’ decisions of when and how to provide the necessary backup.
Adaptability, as described by the big five model, is the team’s capacity to recognize deviations from the expected action and readjust their actions accordingly (Salas, 2005). Adaptive behaviour helps teams respond to unexpected demands and assign meaning to that change, and to carry out a new plan of action. From the Big Five’s perspective, adaptability includes not only the ability to changes, but that quality and effectiveness of that change to deal with the change in the environment. Like the connection among the aforementioned Big Five characteristics, effective adaptability is bound by shared mental models, effective engagement in mutual performance monitoring and backup behaviour.
The final Big Five dimension of team orientation is posited more as an attitude, versus a behavioural characteristic, as the previous four. Team orientation is viewed as both a preference for working with others but also a tendency to enhance individual performance through the coordination, evaluation and utilization of tasks inputs from other members while performing group tasks. Team orientation differs from team cohesion (e.g., Undre, Sevdalis, Healey, Darzi, & Vincent, 2006b), which is an attraction to work with a particular team rather than a general preference to work in team settings. This would have particular value to assessing healthcare teams which often work in ad-hoc conditions and evolving team membership.
Characteristics of Effective Teams; adapted from Salas (2005).
Team Knowledge, Skills, and Attitudes
Characteristics of Effective teams
Big 5 Core Dimensions
Have a clear and common purpose;
Team roles are clear but not overly rigid, involve the right people in decisions,
Conduct effective meetings,
Establish and revise team goals and plans;
Team members believe the leaders and care about them;
Distribute and assign work thoughtfully.
*Mutual Performance Monitoring
Effectively “span” boundaries with stakeholders outside the team;
Understand each other’s roles and how they fit together;
Examine and adjust the team’s physical workspace;
Periodically diagnose team effectiveness, including its results.
Compensate for each other;
Manage conflict (confront each other effectively); regularly provide feedback, both individually and team level (debrief);
“Deal” with poor performers;
Anticipate each other’s needs;
Recognize and adjust team strategy under stress
Select team members who value teamwork;
Believe in the team’s ability to succeed.
Communicate “often enough”
*Shared Mental Models
Coordinated without the need to communicate overtly
Trust other team members “intentions”
Note: * denotes measures that could be used within the scope of this thesis.
Recognizing the frequent overlap of team performance models, Rousseau, Aube and Savoie (2006) conducted a comprehensive literature review on the behaviors most likely to influence teamwork effectiveness. The authors defined a hierarchy of team behaviors (see Figure 1) organized around four functions based on action regulation theory (Frese & Zapf, 1994) whereby individuals steer their own activities in correspondence with some goal. During the cycle of action in regulation theory, people monitor their environments, gathering information in order to plan a course of action. While executing the plan, one can actively influence the environment, and the results are fed back regarding one’s actions (Frese & Zapf, 1994).
In Rousseau and colleague’s (2006) hierarchical model of team behaviours, goals, information collection, planning, execution, and feedback correspond to (a) preparation of work accomplishment, (b) task-related collaborative behaviors, (c) work assessment behaviors, and (d) team adjustment behaviors. Preparation for accomplishment refers to the analysis of the work to be done and planning the tasks. This includes the identification of the main tasks and analysis of the environment and available resources. Similar to the benefits of having good team leaderships as defined in the Big 5, the team is then able to define a common goal and develop a plan, meaning a series of actions to achieve this goal. Collaborative task-related behaviour refers to the implementation of the action plan established during the preparation stage. Similar to the Big 5’s communication and adaptability, this function includes behavior coordination, cooperation and information exchange of information. During collaboration, team members can share all of the necessary information and work together to perform the task. Work assessment behaviours refer to monitoring the overall performance of the team, and the environment in which the work is performed. Similar to mutual performance monitoring, this ensures that the team is going in the right direction and nothing is compromising team performance. Team adjustment behaviours refer to the way in which the team responds to changes or observed deviations from the initial plans. Like the Big 5, these include back up behaviours, intra-team coaching, collaborative problem solving and innovation provide support to adjustment behaviours. Lafond, Jobidon, Aube and Tremblay (2011) empirically tested aspects of the Big Five and Rousseau’s hierarchical model in a functional simulation of command and control in a complex and dynamic firefighting task. In their study of 24 three-person teams, the two most important predictors