Foundations in Teamwork

**3**

**Chapter 1**

**1. Introduction**

settings [7, 10, 11].

discuss them in this section.

Introductory Chapter: Teams in

to Have' to 'the Way to Go'

*Stanislaw P. Stawicki and Michael S. Firstenberg*

plays an essential role in achieving programmatic success [5].

**2. How do teams make healthcare better?**

*Nicholas Taylor, Israel Zighelboim, Farhad Sholevar,* 

Healthcare - A Voyage from 'Nice

Modern healthcare is characterized by the growing embrace of multidisciplinary, team-based approaches. This transformation is happening for a good reason. Because the degree of complexity across our health systems may exceed the effective operational capacity of a single provider, increasing reliance on healthcare teams, processes, and workflows is becoming a necessity [1, 2]. Despite the near universal deployment of health information technology, the overall growth in systemic complexity continues to outpace our attempts to address it [3, 4]. The ability to adapt and evolve also

The current team-based approach to healthcare originated in the 1990's in an attempt to enhance the performance, quality and safety of care delivery [6–8]. Through a series of incremental changes and reforms, significant improvements have been made over time, but the healthcare industry is still far from the safety, quality, and performance records achieved by our counterparts in financial and air transportation sectors [9]. Currently, a significant portion of the overall effort in this area revolves around reinforcing team-based approaches, including the incorporation of continuous quality and performance improvement initiatives into existing, multidisciplinary paradigms across a broad range of care delivery

Although there is something to be said about the expression, "the whole is greater than the sum of the parts," our current understanding of full benefits of a 'healthcare team' continues to be relatively limited [12–15]. The very presence of a 'team' does not inherently equate to enhanced levels of quality or safety. Yet there clearly is an evolving science dedicated to learning more and refining our approach to healthcare team effectiveness [16–18]. As a result, a number of key characteristics associated with optimal team performance have been proposed [15, 19, 20]. We will

Although this may be an 'obvious' statement, healthcare teams should be able to maintain high levels of functioning at all times [19]. More granular considerations in this area include constant focus on coordination, emphasis on responsibility, and the full commitment to open and honest communication (even if the latter exposes

#### **Chapter 1**

## Introductory Chapter: Teams in Healthcare - A Voyage from 'Nice to Have' to 'the Way to Go'

*Nicholas Taylor, Israel Zighelboim, Farhad Sholevar, Stanislaw P. Stawicki and Michael S. Firstenberg*

#### **1. Introduction**

Modern healthcare is characterized by the growing embrace of multidisciplinary, team-based approaches. This transformation is happening for a good reason. Because the degree of complexity across our health systems may exceed the effective operational capacity of a single provider, increasing reliance on healthcare teams, processes, and workflows is becoming a necessity [1, 2]. Despite the near universal deployment of health information technology, the overall growth in systemic complexity continues to outpace our attempts to address it [3, 4]. The ability to adapt and evolve also plays an essential role in achieving programmatic success [5].

The current team-based approach to healthcare originated in the 1990's in an attempt to enhance the performance, quality and safety of care delivery [6–8]. Through a series of incremental changes and reforms, significant improvements have been made over time, but the healthcare industry is still far from the safety, quality, and performance records achieved by our counterparts in financial and air transportation sectors [9]. Currently, a significant portion of the overall effort in this area revolves around reinforcing team-based approaches, including the incorporation of continuous quality and performance improvement initiatives into existing, multidisciplinary paradigms across a broad range of care delivery settings [7, 10, 11].

#### **2. How do teams make healthcare better?**

Although there is something to be said about the expression, "the whole is greater than the sum of the parts," our current understanding of full benefits of a 'healthcare team' continues to be relatively limited [12–15]. The very presence of a 'team' does not inherently equate to enhanced levels of quality or safety. Yet there clearly is an evolving science dedicated to learning more and refining our approach to healthcare team effectiveness [16–18]. As a result, a number of key characteristics associated with optimal team performance have been proposed [15, 19, 20]. We will discuss them in this section.

Although this may be an 'obvious' statement, healthcare teams should be able to maintain high levels of functioning at all times [19]. More granular considerations in this area include constant focus on coordination, emphasis on responsibility, and the full commitment to open and honest communication (even if the latter exposes

one's lack of specific/granular patient care knowledge) [19]. Some variability and customization of the overall team approach should be permitted, even encouraged, based on the setting, situation, or available resources. This provides the necessary flexibility to accomplish a much broader range (and types) of tasks. Beyond these fundamental values, effective healthcare teams must be highly skilled in their 'teamwork ability' – inclusive of dedicated education about interdisciplinary, nonhierarchical, consensus-based approaches [21]. In the perfect world, application of the above principles results in seamless delivery of care, with minimal or no biases, without silos, using data-driven, patient centered approaches [22, 23].

Unfortunately, a major assumption in the concept of adopting "team-based healthcare" is that "individuals" inherently desire to be part of a "team." However, as it is well known, such desire is not universal. While it may sound like a rhetorical question when asking, "Why healthcare providers would not want to be engaged in such an evolution?" – It is important to explore some of the potential motivating factors that contribute to the development of "team-based" care. Unfortunately, some of these factors involve certain key harsh realities that strongly influence healthcare providers. Everyone inherently claims that they "want what is best for the patient" – but such a concept is difficult to comprehensively and universally define, especially in the setting in which "individuals" might not want to be part of a "team" for several reasons:


While there are many reasons why team-based care models either work or do not work – the fundamental key or barrier to success is engagement, support, enthusiasm, and expertise, or lack thereof, by leaders, champions, and those who believe that team-based care is fundamentally better in terms of patient outcomes. This should be contrasted against individuals who may be "siloed" in their inflexible, individualistic, and potentially self-gain motivated models.

**5**

*Introductory Chapter: Teams in Healthcare - A Voyage from 'Nice to Have' to 'the Way to Go'*

The team-based setting is the optimal environment for the implementation of evidence-based practice. Inherent to the team approach in healthcare is the presence of ample cross-checks, safety protocols, and the ability to verify clinical plans via a consensus mechanism [24, 25]. This, in turn, helps facilitate the application of evidence-base practice, which appears to be both safer and relatively free of personal biases and/or opinions [8, 26]. Moreover, 'team healthcare' environment also provides an excellent substrate for determining that a given protocol or clinical pathway does not work, thus prompting constructive changes that tend to be evidence-based and systematic in nature [10]. Again, success is based upon the participation of champions, leadership support at all levels, and the recognition that such paradigm shifts within a 'culture of behaviors' are better for all aspects of the patient care, including both experiences and outcomes – and not just for individuals who continue to advocate that "the old ways," which might have worked to some degree in the past, are still acceptable across modern healthcare delivery platforms [7, 14, 24, 27].

In addition to the patient-specific benefits of team-based healthcare, growing amount of data point toward tangible provider benefits of team-based approaches, including reduction in burnout [28]. It has been suggested that implementation of certain structural changes, such as fostering communication between team members and cultivating a sense of teamwork and job control are very effective in reducing provider burnout [29]. An important factor in this general approach is the ability of teammates to motivate each other and to encourage accountability for key behaviors, such as regular physical exercise and gym attendance [30]. As with many other areas that depend on highly functioning teams (e.g., airline crews or professional sports), the ability of a team to function effectively and efficiently is the overarching priority, even when a particular team member is temporarily underperforming or sidelined. More complex performance issues, including disruptive team member behaviors, can also be addressed in a professional and collaborative

manner with the common team goals maintained as a priority [31, 32].

Healthcare teams contribute tremendously to structural institutional and systemic changes. In aggregate, such changes tend to occur more gradually and are typically due to consensus-building mechanisms inherent to team approaches. The resulting action plans, in general, tend to be both constructive and more readily

Healthcare settings require fluid, coordinated and effective work of various highly integrated teams across the continuum of care. Due to the complex nature and integrated character of the industry, effective "teaming" in healthcare must expand across organizational boundaries [33]. The effective delivery of health services typically requires the integration of special skills, equipment and care that must often be provided around the clock at variable locations. Additionally, the historic hierarchy encountered in hospitals generates status differences which may potentially contribute to misunderstanding, hesitation to communicate any disagreement, as well as difficulty in pointing out errors and opportunities [15, 34–36]. Finally, patients with complex or chronic diseases interact with multiple levels of a cumbersome

**5. Teams as agents of positive institutional change**

embraced by key stakeholders.

*DOI: http://dx.doi.org/10.5772/intechopen.95487*

**4. Team approaches help reduce burnout**

**3. Healthcare teams and evidence-based practice**

*Introductory Chapter: Teams in Healthcare - A Voyage from 'Nice to Have' to 'the Way to Go' DOI: http://dx.doi.org/10.5772/intechopen.95487*

#### **3. Healthcare teams and evidence-based practice**

*Teamwork in Healthcare*

a "team" for several reasons:

one's lack of specific/granular patient care knowledge) [19]. Some variability and customization of the overall team approach should be permitted, even encouraged, based on the setting, situation, or available resources. This provides the necessary flexibility to accomplish a much broader range (and types) of tasks. Beyond these fundamental values, effective healthcare teams must be highly skilled in their 'teamwork ability' – inclusive of dedicated education about interdisciplinary, nonhierarchical, consensus-based approaches [21]. In the perfect world, application of the above principles results in seamless delivery of care, with minimal or no biases,

Unfortunately, a major assumption in the concept of adopting "team-based healthcare" is that "individuals" inherently desire to be part of a "team." However, as it is well known, such desire is not universal. While it may sound like a rhetorical question when asking, "Why healthcare providers would not want to be engaged in such an evolution?" – It is important to explore some of the potential motivating factors that contribute to the development of "team-based" care. Unfortunately, some of these factors involve certain key harsh realities that strongly influence healthcare providers. Everyone inherently claims that they "want what is best for the patient" – but such a concept is difficult to comprehensively and universally define, especially in the setting in which "individuals" might not want to be part of

1.They do not feel that their participation in a team (i.e., morning multidisciplinary rounds) is helpful to the overall care of the patient.

2.They may not want to participate because they do not find the structure and function of the team as being compatible with their daily work-flow, or inherently useful in the context of their multiple competing obligations.

3.Financial (or professional) motives and/or agendas may not be compatible with the team-based culture. This is a consideration that is particularly applicable in various "pay-for-service" healthcare models in which team-based care might not inherently be in everyone's best financial interest. Such situations are becoming more common when team activities – such as patient-care conferences (e.g., "tumor boards" or "heart team meetings") – might be best for discussing patient care plans, but do not generate any immediate financial opportunities for participants while diminishing the latter due to built-in time constraints.

4.Team-based care models are not structured in a way to optimize the common goals – especially in a manner that is respectful of the expertise and time

5.Leadership (or senior administrators) may not inherently support the concept and applications of team-based approaches. Potential administrative roadblocks can be subtle, such as limiting resources available for the required support staff or not supporting (publicly or privately) the team goals or individual

While there are many reasons why team-based care models either work or do not work – the fundamental key or barrier to success is engagement, support, enthusiasm, and expertise, or lack thereof, by leaders, champions, and those who believe that team-based care is fundamentally better in terms of patient outcomes. This should be contrasted against individuals who may be "siloed" in their inflexible,

commitments of all participating stakeholders.

individualistic, and potentially self-gain motivated models.

without silos, using data-driven, patient centered approaches [22, 23].

**4**

champions.

The team-based setting is the optimal environment for the implementation of evidence-based practice. Inherent to the team approach in healthcare is the presence of ample cross-checks, safety protocols, and the ability to verify clinical plans via a consensus mechanism [24, 25]. This, in turn, helps facilitate the application of evidence-base practice, which appears to be both safer and relatively free of personal biases and/or opinions [8, 26]. Moreover, 'team healthcare' environment also provides an excellent substrate for determining that a given protocol or clinical pathway does not work, thus prompting constructive changes that tend to be evidence-based and systematic in nature [10]. Again, success is based upon the participation of champions, leadership support at all levels, and the recognition that such paradigm shifts within a 'culture of behaviors' are better for all aspects of the patient care, including both experiences and outcomes – and not just for individuals who continue to advocate that "the old ways," which might have worked to some degree in the past, are still acceptable across modern healthcare delivery platforms [7, 14, 24, 27].

#### **4. Team approaches help reduce burnout**

In addition to the patient-specific benefits of team-based healthcare, growing amount of data point toward tangible provider benefits of team-based approaches, including reduction in burnout [28]. It has been suggested that implementation of certain structural changes, such as fostering communication between team members and cultivating a sense of teamwork and job control are very effective in reducing provider burnout [29]. An important factor in this general approach is the ability of teammates to motivate each other and to encourage accountability for key behaviors, such as regular physical exercise and gym attendance [30]. As with many other areas that depend on highly functioning teams (e.g., airline crews or professional sports), the ability of a team to function effectively and efficiently is the overarching priority, even when a particular team member is temporarily underperforming or sidelined. More complex performance issues, including disruptive team member behaviors, can also be addressed in a professional and collaborative manner with the common team goals maintained as a priority [31, 32].

#### **5. Teams as agents of positive institutional change**

Healthcare teams contribute tremendously to structural institutional and systemic changes. In aggregate, such changes tend to occur more gradually and are typically due to consensus-building mechanisms inherent to team approaches. The resulting action plans, in general, tend to be both constructive and more readily embraced by key stakeholders.

Healthcare settings require fluid, coordinated and effective work of various highly integrated teams across the continuum of care. Due to the complex nature and integrated character of the industry, effective "teaming" in healthcare must expand across organizational boundaries [33]. The effective delivery of health services typically requires the integration of special skills, equipment and care that must often be provided around the clock at variable locations. Additionally, the historic hierarchy encountered in hospitals generates status differences which may potentially contribute to misunderstanding, hesitation to communicate any disagreement, as well as difficulty in pointing out errors and opportunities [15, 34–36]. Finally, patients with complex or chronic diseases interact with multiple levels of a cumbersome

health care delivery system (inpatient and outpatient settings, laboratories, imaging centers, etc). This, in turn, creates multiple opportunities for team-based paradigms to facilitate more unified, patient-centered approaches [37].

Deployment of effective "teaming" represents a valuable tool to exert positive institutional change. In doing so it is critical to reframe goals and objectives. Tasks in health care should be framed in a way that allows each team member to focus on the ultimate goal beyond the current intervention – the individual patient outcome. Such approach encourages team members to go beyond their limited area of expertise in order to seek and promote other beneficial interventions or services. In addition, effective teaming requires the use of safety as the quintessential bar to measure team effectiveness. In doing so, the team approach becomes the instrument to break through hierarchical barriers. At the end of the day, every member of the team will agree that providing safe care is a must. It is imperative to create structures and methodologies that foster open communication and trust. Tools such as the SBAR method (situation, background, assessment and recommendation) or the Team STEPPS approach are relatively easy to deploy and track [7, 15, 18, 38, 39].

A comprehensive transformation toward more widespread reliance on team approaches across our healthcare systems will help promote dependability, establish and/or strengthen mutual trust, foster open communication, and enhance collaboration among both individuals and teams. The result commonly translates into improved quality and safety, cost-effectiveness and importantly improved team members' satisfaction. All of the above are key elements for the success of any health care organization.

#### **6. Teams versus committees versus task-forces**

Understanding the differences between teams, task forces, and committees can help further solidify the importance of a collaborative environment with focused goals [40, 41]. While there are a variety of definitions of each, in the context of healthcare, there are certain key differences between the 3 groups [42–44]. Below we summarize the definitions that, in the Editors' opinion, are most applicable to this current book.

#### **6.1 Teams**


#### **6.2 Committees**

1.Typically consist of individuals who are selected to perform a specific function on behalf of a larger group;

**7**

*Introductory Chapter: Teams in Healthcare - A Voyage from 'Nice to Have' to 'the Way to Go'*

3.Finally, committees may not have a fixed endpoint or goal, and may be structured to delegate specific tasks to smaller groups (e.g., subcommittees) [46, 47].

1.These are typically small groups with densely concentrated content expertise,

3.Although there may be limited objective resources to achieve a highly specific goal, task forces are often asked to make recommendations to a Committee

It is important to remember that, as in many other functional organizational areas, there are overlaps in structure and function among these different groups. At the same time, each type of team/group participation is needed – for different purposes as noted above – within an organization to ensure stability and objective preemptive or responsive problem solving. Consequently, careful planning and

One of the greatest pitfalls of teams and team-based approaches is the everpresent danger of 'groupthink' [50]. Groupthink can be defined as the presence of social conformity within a group tasked with making a collective decision [51]. When analyzed retrospectively, group decisions based on 'groupthink' are often influenced by the 'single loudest voice' or authority within the group, with the apparent absence of critical thinking and/or the 'fortitude to question' exhibited by individual members of that group [50, 52]. At a much deeper level, 'groupthink' is a symptom of poor leadership, where the leader (whether positionally assigned or not) may not challenge or empower his or her team sufficiently enough to effectively question the course of the discussion around the prevailing group sentiment [53–56]. Hence, it is imperative that team leadership recognizes the potential for such disruptive forces and – as a sign of strength and wisdom – actively monitors for (and attenuates) the impact of factors and/or individuals capable of "inducing groupthink." Conceptually, mitigating against "groupthink" sounds easy, but in practice it can be extremely difficult – if not impossible – when the loudest voice is often the one with the greatest perceived influence [57, 58]. Such issues are unfortunately not uncommon in healthcare when significant financial and non-financial agendas might be directly linked to individuals or groups who may then be compelled to act in a manner that might be in their best interest, but not in the interest of the larger group or team. Such situations can be extremely difficult to manage or control – and ultimately require a significant disruptive event (like an institutional

As the reader embarks on exploring this book, it must be emphasized that certain activities and/or circumstances do not lend themselves to team approaches.

2.Task forces are usually organized on an "as needed basis" – potentially in

usually brought together to focus on a specific goal;

before any final changes are executed [48, 49].

balancing of goals, roles, and priorities is required.

**7. Pitfalls of teams and team-based approaches**

financial crisis or exodus of talent) or systemic change.

**8. Limitations to team approaches**

*DOI: http://dx.doi.org/10.5772/intechopen.95487*

**6.3 Task forces**

response to an event;

2.Committee is technically a structured organizational system – often with agenda, bylaws, and strong leadership;

*Introductory Chapter: Teams in Healthcare - A Voyage from 'Nice to Have' to 'the Way to Go' DOI: http://dx.doi.org/10.5772/intechopen.95487*

3.Finally, committees may not have a fixed endpoint or goal, and may be structured to delegate specific tasks to smaller groups (e.g., subcommittees) [46, 47].

#### **6.3 Task forces**

*Teamwork in Healthcare*

health care organization.

this current book.

task);

group;

**6.2 Committees**

on behalf of a larger group;

agenda, bylaws, and strong leadership;

**6.1 Teams**

health care delivery system (inpatient and outpatient settings, laboratories, imaging centers, etc). This, in turn, creates multiple opportunities for team-based paradigms

Deployment of effective "teaming" represents a valuable tool to exert positive institutional change. In doing so it is critical to reframe goals and objectives. Tasks in health care should be framed in a way that allows each team member to focus on the ultimate goal beyond the current intervention – the individual patient outcome. Such approach encourages team members to go beyond their limited area of expertise in order to seek and promote other beneficial interventions or services. In addition, effective teaming requires the use of safety as the quintessential bar to measure team effectiveness. In doing so, the team approach becomes the instrument to break through hierarchical barriers. At the end of the day, every member of the team will agree that providing safe care is a must. It is imperative to create structures and methodologies that foster open communication and trust. Tools such as the SBAR method (situation, background, assessment and recommendation) or the Team STEPPS approach are relatively easy to deploy and track [7, 15, 18, 38, 39]. A comprehensive transformation toward more widespread reliance on team approaches across our healthcare systems will help promote dependability, establish and/or strengthen mutual trust, foster open communication, and enhance collaboration among both individuals and teams. The result commonly translates into improved quality and safety, cost-effectiveness and importantly improved team members' satisfaction. All of the above are key elements for the success of any

Understanding the differences between teams, task forces, and committees can help further solidify the importance of a collaborative environment with focused goals [40, 41]. While there are a variety of definitions of each, in the context of healthcare, there are certain key differences between the 3 groups [42–44]. Below we summarize the definitions that, in the Editors' opinion, are most applicable to

1.Typically comprise of individuals linked together for a common purpose;

2.There is a shared leadership model (e.g., collaboration to achieve a specific

3.Members often have complementary skills and are encouraged to function as a

4.Team members share a common goal or purpose, with mutual accountability [45].

1.Typically consist of individuals who are selected to perform a specific function

2.Committee is technically a structured organizational system – often with

to facilitate more unified, patient-centered approaches [37].

**6. Teams versus committees versus task-forces**

**6**


It is important to remember that, as in many other functional organizational areas, there are overlaps in structure and function among these different groups. At the same time, each type of team/group participation is needed – for different purposes as noted above – within an organization to ensure stability and objective preemptive or responsive problem solving. Consequently, careful planning and balancing of goals, roles, and priorities is required.

#### **7. Pitfalls of teams and team-based approaches**

One of the greatest pitfalls of teams and team-based approaches is the everpresent danger of 'groupthink' [50]. Groupthink can be defined as the presence of social conformity within a group tasked with making a collective decision [51]. When analyzed retrospectively, group decisions based on 'groupthink' are often influenced by the 'single loudest voice' or authority within the group, with the apparent absence of critical thinking and/or the 'fortitude to question' exhibited by individual members of that group [50, 52]. At a much deeper level, 'groupthink' is a symptom of poor leadership, where the leader (whether positionally assigned or not) may not challenge or empower his or her team sufficiently enough to effectively question the course of the discussion around the prevailing group sentiment [53–56]. Hence, it is imperative that team leadership recognizes the potential for such disruptive forces and – as a sign of strength and wisdom – actively monitors for (and attenuates) the impact of factors and/or individuals capable of "inducing groupthink." Conceptually, mitigating against "groupthink" sounds easy, but in practice it can be extremely difficult – if not impossible – when the loudest voice is often the one with the greatest perceived influence [57, 58]. Such issues are unfortunately not uncommon in healthcare when significant financial and non-financial agendas might be directly linked to individuals or groups who may then be compelled to act in a manner that might be in their best interest, but not in the interest of the larger group or team. Such situations can be extremely difficult to manage or control – and ultimately require a significant disruptive event (like an institutional financial crisis or exodus of talent) or systemic change.

#### **8. Limitations to team approaches**

As the reader embarks on exploring this book, it must be emphasized that certain activities and/or circumstances do not lend themselves to team approaches.

#### *Teamwork in Healthcare*

Although this will not be the focus of this edited collection, we want the reader to be aware of those important limitations to team-based approaches. For example, there exists a balance between team-based and non-team-based management in the area of execution capability [59].

It is also important to know when and how to limit team sizes, especially when specific types of tasks or mission-critical endeavors demand such limited approach. In medicine, teams are ubiquitous. There are highly diversified health-care teams inclusive of medical/nursing students, residents, nurses, physicians, case managers, physical/occupational therapists and consulting physicians/teams. Not infrequently, the more complex the patient, the larger the care team tends to grow. At many institutions, there are annual celebrations of Trauma Systems, highlighting the health-care journey of trauma/critical care patients. During such celebrations, the entire health-care team caring for critically injured patients is gradually, person-byperson, brought on stage, with upwards of 100 people responsible for the successful door-to-door care involving each individual trauma survivor [60].

Clearly, utilizing teams to leverage different areas of clinical expertise is necessary. Although these large teams are good at solving problems, the larger the team, the more likely communication failures can occur, increasing the aggregate risk of medical errors [61–63]. Smaller teams, on the other hand, have been shown to be more disruptive and innovative and will be more likely to identify new problems for the larger team to solve [64]. Across all aspects of patient care, limiting team size can reduce some of the less savory aspects of a team approach like conformity bias and social loafing [65]. Ultimately, it is important to select the most optimal team for the job [66] and limit team size when high-impact communication and innovation are critical. Larger teams can then be layered over the smaller teams to use the "wisdom of crowds" and improve decision making [65]. Regardless of team size, it is important to continue to study the different team-based approaches to determine whether we are succeeding in improving patient/system outcomes.

#### **9. Conclusions**

Modern healthcare is firmly set on its quest toward better, safer, more efficient, high quality patient care delivery. A critical part of this decades-long transition is the gradual realization that teamwork, based on multidisciplinary, data-driven, evidence-based, patient-centric approaches, is now 'the way to go' and much more than a 'good to have' luxury. This book is dedicated to the exploration of concepts critical to our better understanding of the dynamically evolving area of team-based healthcare.

**9**

**Author details**

Nicholas Taylor1

and Michael S. Firstenberg4

Bethlehem, Pennsylvania, USA

Bethlehem, Pennsylvania, USA

Bethlehem, Pennsylvania, USA

, Israel Zighelboim1

\*

4 William Novick Cardiac Alliance, Memphis, TN, USA

\*Address all correspondence to: msfirst@gmail.com

provided the original work is properly cited.

2 Department of Psychiatry, St. Luke's University Health Network,

, Farhad Sholevar2

1 Department of Obstetrics and Gynecology, St. Luke's University Health Network,

3 Department of Research and Innovation, St. Luke's University Health Network,

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

, Stanislaw P. Stawicki3

*Introductory Chapter: Teams in Healthcare - A Voyage from 'Nice to Have' to 'the Way to Go'*

*DOI: http://dx.doi.org/10.5772/intechopen.95487*

*Introductory Chapter: Teams in Healthcare - A Voyage from 'Nice to Have' to 'the Way to Go' DOI: http://dx.doi.org/10.5772/intechopen.95487*

#### **Author details**

*Teamwork in Healthcare*

**9. Conclusions**

healthcare.

of execution capability [59].

Although this will not be the focus of this edited collection, we want the reader to be aware of those important limitations to team-based approaches. For example, there exists a balance between team-based and non-team-based management in the area

It is also important to know when and how to limit team sizes, especially when specific types of tasks or mission-critical endeavors demand such limited approach. In medicine, teams are ubiquitous. There are highly diversified health-care teams inclusive of medical/nursing students, residents, nurses, physicians, case managers, physical/occupational therapists and consulting physicians/teams. Not infrequently, the more complex the patient, the larger the care team tends to grow. At many institutions, there are annual celebrations of Trauma Systems, highlighting the health-care journey of trauma/critical care patients. During such celebrations, the entire health-care team caring for critically injured patients is gradually, person-byperson, brought on stage, with upwards of 100 people responsible for the successful

Clearly, utilizing teams to leverage different areas of clinical expertise is necessary. Although these large teams are good at solving problems, the larger the team, the more likely communication failures can occur, increasing the aggregate risk of medical errors [61–63]. Smaller teams, on the other hand, have been shown to be more disruptive and innovative and will be more likely to identify new problems for the larger team to solve [64]. Across all aspects of patient care, limiting team size can reduce some of the less savory aspects of a team approach like conformity bias and social loafing [65]. Ultimately, it is important to select the most optimal team for the job [66] and limit team size when high-impact communication and innovation are critical. Larger teams can then be layered over the smaller teams to use the "wisdom of crowds" and improve decision making [65]. Regardless of team size, it is important to continue to study the different team-based approaches to determine

Modern healthcare is firmly set on its quest toward better, safer, more efficient, high quality patient care delivery. A critical part of this decades-long transition is the gradual realization that teamwork, based on multidisciplinary, data-driven, evidence-based, patient-centric approaches, is now 'the way to go' and much more than a 'good to have' luxury. This book is dedicated to the exploration of concepts critical to our better understanding of the dynamically evolving area of team-based

door-to-door care involving each individual trauma survivor [60].

whether we are succeeding in improving patient/system outcomes.

**8**

Nicholas Taylor1 , Israel Zighelboim1 , Farhad Sholevar2 , Stanislaw P. Stawicki3 and Michael S. Firstenberg4 \*

1 Department of Obstetrics and Gynecology, St. Luke's University Health Network, Bethlehem, Pennsylvania, USA

2 Department of Psychiatry, St. Luke's University Health Network, Bethlehem, Pennsylvania, USA

3 Department of Research and Innovation, St. Luke's University Health Network, Bethlehem, Pennsylvania, USA

4 William Novick Cardiac Alliance, Memphis, TN, USA

\*Address all correspondence to: msfirst@gmail.com

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### **References**

[1] Baker, D.P., R. Day, and E. Salas, *Teamwork as an essential component of high-reliability organizations.* Health services research, 2006. **41**(4p2): p. 1576-1598.

[2] Rosen, M.A., et al., *Teamwork in healthcare: Key discoveries enabling safer, high-quality care.* American Psychologist, 2018. **73**(4): p. 433.

[3] Tidd, J. and J.R. Bessant, *Managing innovation: integrating technological, market and organizational change*. 2018: John Wiley & Sons.

[4] Kellermann, A.L. and S.S. Jones, *What it will take to achieve the as-yetunfulfilled promises of health information technology.* Health affairs, 2013. **32**(1): p. 63-68.

[5] Pype, P., et al., *Healthcare teams as complex adaptive systems: understanding team behaviour through team members' perception of interpersonal interaction.* BMC health services research, 2018. **18**(1): p. 570.

[6] Stawicki, S., *Fundamentals of patient safety in medicine and surgery*. 2015: Wolters kluwer india Pvt Ltd.

[7] Tolentino, J.C., et al., *Introductory chapter: Developing patient safety champions.* Vignettes in Patient Safety, 2018. **2**: p. 1-23.

[8] Stawicki, S.P. and M.S. Firstenberg, *Introductory chapter: The decades long quest continues toward better, safer healthcare systems.* Vignettes in Patient Safety, 2017. **1**: p. 1.

[9] Portner, M., et al., *Learning from others: Examples from air transportation and industrial realms.* Stawicki S et al. Fundamentals of Patient Safety in Medicine and Surgery. New Delhi: Wolters Kluwer Health (India) Pvt Ltd, 2014.

[10] Saeed, M., et al., *Fact versus conjecture: Exploring levels of evidence in the context of patient safety and care quality*, in *Vignettes in Patient Safety-Volume 3*. 2018, IntechOpen.

[11] Gracias, V.H., et al., *Critical care nurse practitioners improve compliance with clinical practice guidelines in "semiclosed" surgical intensive care unit.* Journal of Nursing Care Quality, 2008. **23**(4): p. 338-344.

[12] Makadok, R., *Doing the right thing and knowing the right thing to do: Why the whole is greater than the sum of the parts.* Strategic Management Journal, 2003. **24**(10): p. 1043-1055.

[13] Salas, E. and M.A. Rosen, *Building high reliability teams: progress and some reflections on teamwork training.* BMJ quality & safety, 2013. **22**(5): p. 369-373.

[14] Green, A., S.P. Stawicki, and M.S. Firstenberg, *Introductory Chapter: Medical Error and Associated Harm-The The Critical Role of Team Communication and Coordination*, in *Vignettes in Patient Safety-Volume 3*. 2018, IntechOpen.

[15] Brathwaite, S., et al., *Teaching Characteristics and Fostering for Patient Leadership Safety.* Fundamentals of Patient Safety in Medicine and Surgery, 2015: p. 153.

[16] Thomas, E.J., *Improving teamwork in healthcare: current approaches and the path forward.* BMJ quality & safety, 2011. **20**(8): p. 647-650.

[17] Wilson, K.A., et al., *Promoting health care safety through training high reliability teams.* BMJ quality & safety, 2005. **14**(4): p. 303-309.

[18] Papadimos, T.J. and S.P. Stawicki, *Chapter 32: Role of Prospective Approaches in Patient Safety and Patient Safety Research.* Fundamentals

**11**

p. 32.

*Introductory Chapter: Teams in Healthcare - A Voyage from 'Nice to Have' to 'the Way to Go'*

2018.

[28] Smith, C.D., et al., *Implementing optimal team-based care to reduce clinician burnout.* NAM Perspectives,

[29] Linzer, M., et al., *A cluster randomized trial of interventions to improve work conditions and clinician burnout in primary care: results from the Healthy Work Place (HWP) study.* Journal of general internal medicine,

2015. **30**(8): p. 1105-1111.

[30] Weight, C.J., et al. *Physical activity, quality of life, and burnout among physician trainees: the effect of a teambased, incentivized exercise program*. in *Mayo Clinic Proceedings*. 2013. Elsevier.

[31] Ensor, K., M. Swaroop, and L. Tatebe, *Dealing with disruptive team members: Correcting" bad behaviors" while avoiding disruptions*, in *Fundamentals of Leadership for Healthcare Professionals*. 2018, Nova Science Publishers, Inc. p. 183-199.

[32] Tatebe, L. and M. Swaroop, *Disruptive physicians: How behavior can undermine patient safety.* Vignettes in

Patient Safety, 2018. **2**: p. 273.

2015. **93**(12): p. 2-5.

[33] Edmondson, A.C., *The kinds of teams health care needs.* Harv Bus Rev,

[34] Edmondson, A.C., *The fearless organization: Creating psychological safety in the workplace for learning, innovation, and growth*. 2018: John Wiley & Sons.

[35] Papadimos, T.J., et al., *The importance of emotional intelligence to leadership in an academic health center.* International Journal of Academic

Medicine, 2016. **2**(1): p. 57.

Science, 2020. **10**(1): p. 4.

[36] Marcks, V., K. Hayes, and S.P. Stawicki, *Operating room trauma simulation: The St. Luke's University Health Network experience.* International Journal of Critical Illness and Injury

*DOI: http://dx.doi.org/10.5772/intechopen.95487*

[19] Mitchell, P., et al., *Core principles & values of effective team-based health care.*

*Understanding.* Fundamentals of Patient Safety in Medicine and Surgery, 2015:

of Patient Safety in Medicine and

[20] Latchana, N., J.R. Peck, and S.P. Stawicki, *How and Why Things Go Wrong: The Art of Unbiased* 

[21] Boon, H., et al., *From parallel practice to integrative health care: a conceptual framework.* BMC health services research, 2004. **4**(1): p. 15.

[22] Erickson, S.M., et al., *Envisioning a Better US Health Care System for All: Health Care Delivery and Payment System Reforms.* Annals of internal medicine, 2020. **172**(2\_Supplement): p. S33-S49.

[23] Ochoa, J.G.D. and F. Weil, *From personalization to patient centered systems toxicology and pharmacology.* Computational Toxicology, 2019. **11**:

[24] Stawicki, S. and A. Gerlach, *Polypharmacy and medication errors: Stop, listen, look, and analyze.* Opus,

[25] Smith, E., et al., *Surgical safety checklist: Productive, nondisruptive, and the" right thing to do".* Journal of postgraduate medicine, 2015. **61**(3):

[26] Stevens, K., *The impact of evidencebased practice in nursing and the next big ideas.* The Online Journal of Issues in

[27] Bach, J.A., et al., *The right team at the right time–Multidisciplinary approach to multi-trauma patient with orthopedic injuries.* International journal of critical illness and injury science, 2017. **7**(1):

Surgery, 2015: p. 309.

NAM Perspectives, 2012.

p. 77.

p. 14-22.

p. 214.

2009. **12**: p. 6-10.

Nursing, 2013. **18**(2).

*Introductory Chapter: Teams in Healthcare - A Voyage from 'Nice to Have' to 'the Way to Go' DOI: http://dx.doi.org/10.5772/intechopen.95487*

of Patient Safety in Medicine and Surgery, 2015: p. 309.

[19] Mitchell, P., et al., *Core principles & values of effective team-based health care.* NAM Perspectives, 2012.

[20] Latchana, N., J.R. Peck, and S.P. Stawicki, *How and Why Things Go Wrong: The Art of Unbiased Understanding.* Fundamentals of Patient Safety in Medicine and Surgery, 2015: p. 77.

[21] Boon, H., et al., *From parallel practice to integrative health care: a conceptual framework.* BMC health services research, 2004. **4**(1): p. 15.

[22] Erickson, S.M., et al., *Envisioning a Better US Health Care System for All: Health Care Delivery and Payment System Reforms.* Annals of internal medicine, 2020. **172**(2\_Supplement): p. S33-S49.

[23] Ochoa, J.G.D. and F. Weil, *From personalization to patient centered systems toxicology and pharmacology.* Computational Toxicology, 2019. **11**: p. 14-22.

[24] Stawicki, S. and A. Gerlach, *Polypharmacy and medication errors: Stop, listen, look, and analyze.* Opus, 2009. **12**: p. 6-10.

[25] Smith, E., et al., *Surgical safety checklist: Productive, nondisruptive, and the" right thing to do".* Journal of postgraduate medicine, 2015. **61**(3): p. 214.

[26] Stevens, K., *The impact of evidencebased practice in nursing and the next big ideas.* The Online Journal of Issues in Nursing, 2013. **18**(2).

[27] Bach, J.A., et al., *The right team at the right time–Multidisciplinary approach to multi-trauma patient with orthopedic injuries.* International journal of critical illness and injury science, 2017. **7**(1): p. 32.

[28] Smith, C.D., et al., *Implementing optimal team-based care to reduce clinician burnout.* NAM Perspectives, 2018.

[29] Linzer, M., et al., *A cluster randomized trial of interventions to improve work conditions and clinician burnout in primary care: results from the Healthy Work Place (HWP) study.* Journal of general internal medicine, 2015. **30**(8): p. 1105-1111.

[30] Weight, C.J., et al. *Physical activity, quality of life, and burnout among physician trainees: the effect of a teambased, incentivized exercise program*. in *Mayo Clinic Proceedings*. 2013. Elsevier.

[31] Ensor, K., M. Swaroop, and L. Tatebe, *Dealing with disruptive team members: Correcting" bad behaviors" while avoiding disruptions*, in *Fundamentals of Leadership for Healthcare Professionals*. 2018, Nova Science Publishers, Inc. p. 183-199.

[32] Tatebe, L. and M. Swaroop, *Disruptive physicians: How behavior can undermine patient safety.* Vignettes in Patient Safety, 2018. **2**: p. 273.

[33] Edmondson, A.C., *The kinds of teams health care needs.* Harv Bus Rev, 2015. **93**(12): p. 2-5.

[34] Edmondson, A.C., *The fearless organization: Creating psychological safety in the workplace for learning, innovation, and growth*. 2018: John Wiley & Sons.

[35] Papadimos, T.J., et al., *The importance of emotional intelligence to leadership in an academic health center.* International Journal of Academic Medicine, 2016. **2**(1): p. 57.

[36] Marcks, V., K. Hayes, and S.P. Stawicki, *Operating room trauma simulation: The St. Luke's University Health Network experience.* International Journal of Critical Illness and Injury Science, 2020. **10**(1): p. 4.

**10**

2014.

*Teamwork in Healthcare*

p. 1576-1598.

**References**

John Wiley & Sons.

p. 63-68.

**18**(1): p. 570.

2018. **2**: p. 1-23.

Safety, 2017. **1**: p. 1.

[1] Baker, D.P., R. Day, and E. Salas, *Teamwork as an essential component of high-reliability organizations.* Health services research, 2006. **41**(4p2):

[10] Saeed, M., et al., *Fact versus conjecture: Exploring levels of evidence in the context of patient safety and care quality*, in *Vignettes in Patient Safety-*

*Volume 3*. 2018, IntechOpen.

**23**(4): p. 338-344.

**24**(10): p. 1043-1055.

[11] Gracias, V.H., et al., *Critical care nurse practitioners improve compliance with clinical practice guidelines in "semiclosed" surgical intensive care unit.* Journal of Nursing Care Quality, 2008.

[12] Makadok, R., *Doing the right thing and knowing the right thing to do: Why the whole is greater than the sum of the parts.* Strategic Management Journal, 2003.

[13] Salas, E. and M.A. Rosen, *Building high reliability teams: progress and some reflections on teamwork training.* BMJ quality & safety, 2013. **22**(5): p. 369-373.

[14] Green, A., S.P. Stawicki, and M.S. Firstenberg, *Introductory Chapter: Medical Error and Associated Harm-The The Critical Role of Team Communication and Coordination*, in *Vignettes in Patient Safety-Volume 3*. 2018, IntechOpen.

[15] Brathwaite, S., et al., *Teaching Characteristics and Fostering for Patient Leadership Safety.* Fundamentals of Patient Safety in Medicine and Surgery,

[16] Thomas, E.J., *Improving teamwork in healthcare: current approaches and the path forward.* BMJ quality & safety,

[17] Wilson, K.A., et al., *Promoting health care safety through training high reliability teams.* BMJ quality & safety,

[18] Papadimos, T.J. and S.P. Stawicki,

*Chapter 32: Role of Prospective Approaches in Patient Safety and Patient Safety Research.* Fundamentals

2015: p. 153.

2011. **20**(8): p. 647-650.

2005. **14**(4): p. 303-309.

[2] Rosen, M.A., et al., *Teamwork in healthcare: Key discoveries enabling safer, high-quality care.* American Psychologist, 2018. **73**(4): p. 433.

[3] Tidd, J. and J.R. Bessant, *Managing innovation: integrating technological, market and organizational change*. 2018:

[4] Kellermann, A.L. and S.S. Jones, *What it will take to achieve the as-yetunfulfilled promises of health information technology.* Health affairs, 2013. **32**(1):

[5] Pype, P., et al., *Healthcare teams as complex adaptive systems: understanding team behaviour through team members' perception of interpersonal interaction.* BMC health services research, 2018.

[6] Stawicki, S., *Fundamentals of patient safety in medicine and surgery*. 2015: Wolters kluwer india Pvt Ltd.

[7] Tolentino, J.C., et al., *Introductory chapter: Developing patient safety champions.* Vignettes in Patient Safety,

[8] Stawicki, S.P. and M.S. Firstenberg, *Introductory chapter: The decades long quest continues toward better, safer healthcare systems.* Vignettes in Patient

[9] Portner, M., et al., *Learning from others: Examples from air transportation and industrial realms.* Stawicki S et al. Fundamentals of Patient Safety in Medicine and Surgery. New Delhi: Wolters Kluwer Health (India) Pvt Ltd, [37] Gale, J., S.P. Stawicki, and M. Swaroop, *Patient-Centered Transformation: Case Clinical Examples.* Fundamentals of Patient Safety in Medicine and Surgery, 2015: p. 142.

[38] Papadimos, T.J., E. Gafford, and S.P. Stawicki, *Empowering Engagement, Patients and Families: Candor, and Disclosure.* Fundamentals of Patient Safety in Medicine and Surgery, 2015: p. 205.

[39] Gillenwater, T. and L.M. King, *Lessons Healthcare Learned Systems from Best-Performing.* Fundamentals of Patient Safety in Medicine and Surgery, 2015: p. 275.

[40] Heerwagen, J.H., et al., *Collaborative knowledge work environments.* Building research & information, 2004. **32**(6): p. 510-528.

[41] Mintzberg, H., et al., *Some surprising things about collaboration-knowing how people connect makes it work better.* Organizational dynamics, 1996. **25**(1): p. 60-72.

[42] Grigsby, R., *Committee, task force, team: what's the difference? Why does it matter?* Academic Physician & Scientist, 2008: p. 4-5.

[43] Roach, D. *What Is The Difference Between a Committee and a Team*. 2020 [November 30, 2020]; Available from: https://likeateam.com/what-is-thedifference-between-a-committee-anda-team/.

[44] Kaur, P. and J.C. Stoltzfuz, *All about governing stuctures and committees: Collective decision making in healthcare*, in *Fundamentals of Leadership for Healthcare Professionals*, S.P. Stawicki and M.S. Firstenberg, Editors. 2018, Nova Science Publishers: Hauppauge, NY. p. 241-260.

[45] Katzenbach, J.R. and D.K. Smith, *The wisdom of teams: Creating the* 

*high-performance organization*. 2015: Harvard Business Review Press.

[46] Sah, R.K. and J.E. Stiglitz, *Committees, hierarchies and polyarchies.* The Economic Journal, 1988. **98**(391): p. 451-470.

[47] Gerling, K., et al., *Information acquisition and decision making in committees: A survey.* European Journal of Political Economy, 2005. **21**(3): p. 563-597.

[48] Gibson, L.H. and P. Komlos-Hrobsky, *The Task Force as a Teaching Mechanism.* Clearinghouse Rev., 1984. **18**: p. 203.

[49] Falk, S., *Making citizen task forces work.* Public Management, 1993. **75**: p. 15-15.

[50] Neck, C.P. and C.C. Manz, *From groupthink to teamthink: Toward the creation of constructive thought patterns in self-managing work teams.* Human relations, 1994. **47**(8): p. 929-952.

[51] Janis, I.L., *Groupthink.* Psychology today, 1971. **5**(6): p. 43-46.

[52] Brookfield, S.D., *Teaching for critical thinking: Tools and techniques to help students question their assumptions*. 2011: John Wiley & Sons.

[53] Bowditch, J.L., A.F. Buono, and M.M. Stewart, *A primer on organizational behavior*. 2007: John Wiley & Sons.

[54] Foley, M., *Political leadership: Themes, contexts, and critiques*. 2013: Oxford University Press.

[55] Stawicki, S.P., N. Martins, and M.S. Firstenberg, *Leadership: What it is and what it isn't*, in *Fundamentals of Leadership for Healthcare Professionals - Volume 1*, S.P. Stawicki and M.S. Firstenberg, Editors. 2018, Nova Science Publishers: Hauppauge, NY.

**13**

*Introductory Chapter: Teams in Healthcare - A Voyage from 'Nice to Have' to 'the Way to Go'*

*interventions in health care?* Medical education, 2016. **50**(4): p. 400-408.

[66] Mao, A.T. and A.W. Woolley, *Teamwork in health care: maximizing collective intelligence via inclusive collaboration and open communication.* AMA journal of ethics, 2016. **18**(9):

p. 933-940.

*DOI: http://dx.doi.org/10.5772/intechopen.95487*

[56] Tang, D., et al., *Headership in healthcare: From theory to practical application*, in *Fundamentals of Leadership for Healthcare Professionals*, S.P. Stawicki, M.S. Firstenberg, and T.J. Papadimos, Editors. 2019, Nova Science

Publishers: Hauppauge, NY.

*practice*. 2010: Routledge.

Publishers.

html.

[57] Hargie, O., *Skilled interpersonal communication: Research, theory and* 

[58] Hackman, J.R., *Collaborative intelligence: Using teams to solve hard problems*. 2011: Berrett-Koehler

[59] Petrovich, M.V., *The Improvement Hierarchy.* Retrieved July, 2006. **5**.

[60] Lehman, P. *St. Luke's honors patients, first responders at 'Night of Heroes' event*. 2019 [December 12, 2020]; Available from: https://www.mcall. com/news/breaking/mc-nws-st-lukeshospital-night-of-heroes-20190922 wg3xgru5i5h6zaqimfohimud4m-story.

[61] Stawicki, S.P., et al., *Retained surgical items: a problem yet to be solved.* Journal of the American College of Surgeons, 2013. **216**(1): p. 15-22.

[62] Moffatt-Bruce, S.D., et al., *Risk factors for retained surgical items: a metaanalysis and proposed risk stratification system.* journal of surgical research,

[63] Stawicki, S.P., et al., *Natural history of retained surgical items supports the need for team training, early recognition, and prompt retrieval.* The American Journal of Surgery, 2014. **208**(1): p. 65-72.

[64] Wu, L., D. Wang, and J.A. Evans, *Large teams develop and small teams disrupt science and technology.* Nature,

[65] Kaba, A., et al., *Are we at risk of groupthink in our approach to teamwork* 

2019. **566**(7744): p. 378-382.

2014. **190**(2): p. 429-436.

*Introductory Chapter: Teams in Healthcare - A Voyage from 'Nice to Have' to 'the Way to Go' DOI: http://dx.doi.org/10.5772/intechopen.95487*

[56] Tang, D., et al., *Headership in healthcare: From theory to practical application*, in *Fundamentals of Leadership for Healthcare Professionals*, S.P. Stawicki, M.S. Firstenberg, and T.J. Papadimos, Editors. 2019, Nova Science Publishers: Hauppauge, NY.

*Teamwork in Healthcare*

p. 205.

2015: p. 275.

p. 60-72.

2008: p. 4-5.

a-team/.

NY. p. 241-260.

[37] Gale, J., S.P. Stawicki, and M. Swaroop, *Patient-Centered* 

*Transformation: Case Clinical Examples.* Fundamentals of Patient Safety in Medicine and Surgery, 2015: p. 142.

*high-performance organization*. 2015: Harvard Business Review Press.

*Committees, hierarchies and polyarchies.* The Economic Journal, 1988. **98**(391):

[47] Gerling, K., et al., *Information acquisition and decision making in committees: A survey.* European Journal of Political Economy, 2005. **21**(3):

[48] Gibson, L.H. and P. Komlos-Hrobsky, *The Task Force as a Teaching Mechanism.* Clearinghouse Rev., 1984.

[49] Falk, S., *Making citizen task forces work.* Public Management, 1993. **75**:

[50] Neck, C.P. and C.C. Manz, *From groupthink to teamthink: Toward the creation of constructive thought patterns in self-managing work teams.* Human relations, 1994. **47**(8): p. 929-952.

[51] Janis, I.L., *Groupthink.* Psychology

[52] Brookfield, S.D., *Teaching for critical thinking: Tools and techniques to help students question their assumptions*. 2011:

today, 1971. **5**(6): p. 43-46.

[53] Bowditch, J.L., A.F. Buono, and M.M. Stewart, *A primer on organizational behavior*. 2007: John

[54] Foley, M., *Political leadership: Themes, contexts, and critiques*. 2013:

[55] Stawicki, S.P., N. Martins, and M.S. Firstenberg, *Leadership: What it is and what it isn't*, in *Fundamentals of Leadership for Healthcare Professionals - Volume 1*, S.P. Stawicki and M.S.

Firstenberg, Editors. 2018, Nova Science

Oxford University Press.

Publishers: Hauppauge, NY.

John Wiley & Sons.

Wiley & Sons.

[46] Sah, R.K. and J.E. Stiglitz,

p. 451-470.

p. 563-597.

**18**: p. 203.

p. 15-15.

[38] Papadimos, T.J., E. Gafford, and S.P. Stawicki, *Empowering Engagement, Patients and Families: Candor, and Disclosure.* Fundamentals of Patient Safety in Medicine and Surgery, 2015:

[39] Gillenwater, T. and L.M. King, *Lessons Healthcare Learned Systems from Best-Performing.* Fundamentals of Patient Safety in Medicine and Surgery,

[41] Mintzberg, H., et al., *Some surprising things about collaboration-knowing how people connect makes it work better.* Organizational dynamics, 1996. **25**(1):

[42] Grigsby, R., *Committee, task force, team: what's the difference? Why does it matter?* Academic Physician & Scientist,

[43] Roach, D. *What Is The Difference Between a Committee and a Team*. 2020 [November 30, 2020]; Available from: https://likeateam.com/what-is-thedifference-between-a-committee-and-

[44] Kaur, P. and J.C. Stoltzfuz, *All about governing stuctures and committees: Collective decision making in healthcare*, in *Fundamentals of Leadership for Healthcare Professionals*, S.P. Stawicki and M.S. Firstenberg, Editors. 2018, Nova Science Publishers: Hauppauge,

[45] Katzenbach, J.R. and D.K. Smith, *The wisdom of teams: Creating the* 

[40] Heerwagen, J.H., et al., *Collaborative knowledge work environments.* Building research & information, 2004. **32**(6): p. 510-528.

**12**

[57] Hargie, O., *Skilled interpersonal communication: Research, theory and practice*. 2010: Routledge.

[58] Hackman, J.R., *Collaborative intelligence: Using teams to solve hard problems*. 2011: Berrett-Koehler Publishers.

[59] Petrovich, M.V., *The Improvement Hierarchy.* Retrieved July, 2006. **5**.

[60] Lehman, P. *St. Luke's honors patients, first responders at 'Night of Heroes' event*. 2019 [December 12, 2020]; Available from: https://www.mcall. com/news/breaking/mc-nws-st-lukeshospital-night-of-heroes-20190922 wg3xgru5i5h6zaqimfohimud4m-story. html.

[61] Stawicki, S.P., et al., *Retained surgical items: a problem yet to be solved.* Journal of the American College of Surgeons, 2013. **216**(1): p. 15-22.

[62] Moffatt-Bruce, S.D., et al., *Risk factors for retained surgical items: a metaanalysis and proposed risk stratification system.* journal of surgical research, 2014. **190**(2): p. 429-436.

[63] Stawicki, S.P., et al., *Natural history of retained surgical items supports the need for team training, early recognition, and prompt retrieval.* The American Journal of Surgery, 2014. **208**(1): p. 65-72.

[64] Wu, L., D. Wang, and J.A. Evans, *Large teams develop and small teams disrupt science and technology.* Nature, 2019. **566**(7744): p. 378-382.

[65] Kaba, A., et al., *Are we at risk of groupthink in our approach to teamwork*  *interventions in health care?* Medical education, 2016. **50**(4): p. 400-408.

[66] Mao, A.T. and A.W. Woolley, *Teamwork in health care: maximizing collective intelligence via inclusive collaboration and open communication.* AMA journal of ethics, 2016. **18**(9): p. 933-940.

#### **Chapter 2**

## The Impact of the Multidisciplinary Team on the Management of Prosthetic Joint Infection in Trauma and Orthopaedic Surgery

*Nemandra A. Sandiford and Konrad Wronka*

### **Abstract**

Periprosthetic Joint Infection (PJI) is a devastating complication of the Total Joint Arthroplasty (TJA). It presents a great challenge for the clinician to diagnose and manage it appropriately, with significant morbidity for the patients and cost for health care providers. The purpose of this study is to review and examine the role of multi-disciplinary team (MDT) approach in diagnosis and management of prosthetic joint infection (PJI) and how this approach can influence outcomes. All published literature examining the role of multidisciplinary care in the management of PJI and the influence of this approach to the management and outcomes of patients with this diagnosis were included. Studies published in languages other than English were excluded. There is a paucity of data on the influence of multidisciplinary care on outcomes of the management of PJI. Evidence suggests that the MDT has important role in ensuring all factors in the management of this complex group are considered and best possible care is delivered. Multicentre randomised clinical trials are required to assess the influence of MDT'S on outcome as well as important questions around the structuring of these teams.

**Keywords:** prosthetic joint infection

#### **1. Introduction and background**

Prosthetic joint infection (PJI) affects about 1–3% of patients undergoing total joint arthroplasty [1]. In some units the infection rate is reported to be as high as 5% [2]. It is one of the most devastating complications and poses significant challenges for the patient, health care providers and the treating institution. The financial cost of treating a single case of PJI can be as high as £100,000 [2]. Costs for patients are even higher, with long hospital stay, multiple operations, associated pain and suffering, reduced life quality as well as risks associated with surgical morbidity and mortality. Diagnosis and management of PJI remains controversial and complex. There is no universal definition of the PJI. The definition

of PJI proposed by the International Consensus Meeting on Periprosthetic Joint Infection is the most universally accepted one [3]. Other definitions also exist. George et al. [4] acknowledged 7 definitions produced by various consensus meetings. This illustrates that PJI remains a debatable and controversial topic and diagnosis is not straight forward. There is no one single test that can adequately diagnose PJI. Up to 10% of cases undergoing revision for aseptic loosening are later found to have prosthetic joint infection [5]. PJI can present in variety of ways and at varying phases from the time primary arthroplasty implantation. Tsukayama et al. proposed a classification system that divided PJI into four categories [6].

It can be challenging for an individual surgeon to make an accurate diagnosis when faced with a patient with a painful arthroplasty. One way of addressing this has been to manage this complex group of patients with a multidisciplinary team. Failure to make a timely and accurate diagnosis can significantly compromise therapeutic options and have a negative impact on the result of surgical treatment [7]. Furthermore, if PJI is not recognised, it may lead to systemic symptoms such as bacteraemia and septicaemia.

PJI can be challenging to treat, and patients may need a number of major surgical procedures, coupled with antimicrobial treatment for several weeks to eradicate the infection [8]. Treatment of PJI of the knee may be associated with a long period of disability with possible immobilisation of the knee. This may lead to a poor functional outcome. Recurrence of the infection is high and reported between 8% and 70% [9] and complications associated with surgery are common. Furthermore, PJI is associated with significant mortality. Berend et al. [10] reported that 11% patients treated for PJI with a 2 stage regime died between the first and second stages of surgical treatment. Zmistowski et al. [3] found that the 5-year survivorship of patients with PJI is worse than for some common cancers including breast cancer or testicular cancer. For this reason PJI must be managed expeditiously, providing patients with all available expertise to achieve the optimum outcome. Added to this is the psychological burden associated with the issues described and its impact on post operative function [11]. This combination of the knowledge that there has been a complication or suboptimal outcome, multiple surgical procedures, prolonged hospital stay, prolonged disability and associated medical comorbidities as well as social isolation and pain illustrates multiple issues which can be associated with patients presenting with PJI and the multiple facets which require management in a synchronised manner. These factors have been acknowledged in other aspects of orthopaedic surgery and it is acknowledged that optimal outcomes result from a multidisciplinary approach to management [12, 13].

#### **2. Treatment options for PJI**

*Debridement, Antibiotics and Implant Retention (DAIR):* When infection is diagnosed early, open debridement and exchange of modular prosthetic components followed by prolonged antibiotic therapy may lead to satisfactory results. Retention of the implant leads to superior functional results in cases where the infection is eradicated [14].

*Single stage revision:* Exchange off all components of the arthroplasty (both fixed and modular) is coupled with radical debridement of the joint and antibiotic treatment. Removal of all implants and reconstruction with new definitive prostheses. Single stage revision is usually performed in selected patients. The ideal patient is

#### *The Impact of the Multidisciplinary Team on the Management of Prosthetic Joint Infection… DOI: http://dx.doi.org/10.5772/intechopen.94124*

a well host, with a healthy soft tissue envelope, absence of a draining sinus and a known sensitive microbe are commonly accepted prerequisites [15].

*Two stage revision:* Removal of all implants during one surgical procedure is performed. The joint is excised with or without placement of a temporary spacer. Antibiotics are delivered locally (with cement or other delivery modes) and systemically. Following a prolonged period of antibiotic treatment (6 weeks or more), when infection is deemed to be eradicated, re-implantation (the second stage) is performed. During the second stage procedure further debridement takes place. The spacer is removed and the joint is reconstructed. The success rate is greater than of single stage revision procedure [10, 14]. The significant downside of this approach relates to the time between the 2 stages of the revision. During this time the patients' mobility is poor, joint function is very limited, and the patient is often required to stay in health care facility. The risk of complications (renal failure, *Clostridium difficile* diarrhoea) and mortality are significant. The patient also undergoes two separate major surgical procedures.

*Excision arthroplasty:* This involves removal of all the implants and excision of the joint followed by a course of antibiotics. The function of the joint is severely compromised, and the patient suffers significant disability. This salvage mode of treatment is reserved for the most complex infections in compromised hosts, with severe bone loss, presence of poly-microbial infection and an unhealthy soft tissue envelope exist [16].

*Amputation:* When the infection is not manageable or becomes a threat to the patient's life this might be the only option.

*Prolonged suppressive antibiotic therapy (PSAT):* In the presence of draining sinus and well-functioning joint, or when the host suffers from serious comorbidities that could preclude surgical intervention, antibiotic suppression may lead to satisfactory results. The infection cannot be eradicated, but it does not manifest itself systematically and symptoms related to the affected joint may be manageable for the patient. The senior author has previously reported found that infection control could be achieved in selected cases of PJI using this approach [17]. The patients' comorbidities and fitness for major surgery as well as psychological condition of the individual are also of incredible importance [18].

The physical, but also psychological needs of patients should be addressed. PJI may be emotionally difficult to cope with and lead to sequalae such as depression and anxiety [19]. Many patients struggle with the impact that the treatment of PJI has on their personal and family lives'. Patients' depression may require treatment and support during the treatment as well as during the recovery phases [20].

#### **3. The role of the MDT**

Ideally personnel should be present in the same location in order to provide a seamless, clinically and cost-efficient service to patients with PJI. They should be involved in all stages of the management pathways including, diagnosis, treatment (both surgical and non-surgical) and long term follow up. The multidisciplinary approach has made a significant difference in care of oncology patients. Time to diagnosis and clinical outcomes have all been shown to improve when the MDT functions well well [21, 22]. There is no published evidence to the authors' knowledge on the management of PJI with this approach however the principles of diagnosis and factors influencing management and outcomes of patients with PJI

are similar. It seems intuitive therefore that a similar approach to treatment might produce similar outcomes.

#### **4. What comprises a multidisciplinary team?**

Most published studies examining the benefits of MDT's have focused on clinical results [22]. There is a relative paucity of data on the components of the MDT. An important principle of care delivery in this setting is consideration of the wholistic needs of the patient and including appropriate specialists to address these issues. In the context of PJI the following team members are required:

*Orthopaedic Surgeon-* The surgeon coordinates and orchestrates the care of the patient. They need to establish the diagnosis, identify the individuals required to care for the patient and coordinate meetings. They are required to have the necessary skillset and to carry out the surgical treatment required. A minimum requirement would be fellowship training in revision arthroplasty surgery.

*Microbiologist-* A microbiologist is vital to the multidisciplinary team. With their expertise and specialist knowledge of microbial metabolism specific diagnostic requirements, mechanism of antibiotic function and interactions and the requirements for monitoring of these issues, their importance is non controversial. The role of musculoskeletal microbiology is rapidly evolving with developments in diagnosis such as 16 s polymerase chain reaction (16 s PCR) testing. This speciality has made significant contributions to the practical management of patients with PJI such as the OVIVA (Oral versus Intravenous Antibiotics for Bone and Joint Infection) trial [23]. A dedicated microbiology clinic also provides another medium for follow up and support of this complex group of patients.

*Musculoskeletal Radiologist-* Radiologists are central in the decision making process. This stage often requires judgement based on a variety of imaging modalities. An experienced radiologist is invaluable in advising on the optimal imaging modality and interpreting subtle signs on imaging. In the experience of the authors this is one of the most useful and educational parts of the MDT meeting.

*Nutritionist-* Nutritionists contribute significantly to pre and post operative optimisation of the patient. Malnutrition and vitamin D deficiency have been shown to positively correlate with PJI [24]. Low serum albumin level and low lymphocyte count are at increased risk of infection, wound dehiscence and medical complications [25–27]. Cross et al. [26] postulated that normalisation of the serum albumin level and tight glucose control may lead to better outcomes in orthopaedic surgery. Management of these factors has an important role in reducing the risk of reinfection following revision surgery.

*Physiotherapist-* The ultimate aim of revision surgery is restoration of a pain free, mobile with restoration of function and activity. Pre and post operative physiotherapy is vital to achieving these aims. Physical therapy has been shown to improve soft-tissue tension, joint range of motion, and muscle strength and can reduce pain and stiffness [28, 29].

*Clinical Nursing Specialist (CNS)-* The role of a dedicated nurse specialist care cannot be understated. Walker [30] acknowledged the vital role which nurses play in the management of patients undergoing joint replacement surgery. The multifaceted role of a CNS has also been described by Pertino et al. [31]. The nurse specialist has several key clinical and organisational roles including being the point of contact for referrals, organising investigations, coordinating care between multiple specialities when these are involved and being a point of contact for patients.

*The Impact of the Multidisciplinary Team on the Management of Prosthetic Joint Infection… DOI: http://dx.doi.org/10.5772/intechopen.94124*

#### **5. Examples of multidisciplinary teams**

At the authors' institution, there is an established referral network for complex cases including those presenting with PJI. Clinicians from the region can refer any patient who needs complex arthroplasty assessment and treatment, including those with PJI to a centralised hub. There is a standardised referral proforma and MDT coordinator who promptly responds to all referrals. There is a weekly MDT attended by complex arthroplasty surgeons, a CNS and radiologists with an interest in musculoskeletal medicine. Cases are discussed and either advice is provided or a decision on transfer of the patient to the Hub Hospital is organised. In complex cases when surgery is required, surgical planning is performed and details such as surgical approach, instruments and required implants are all discussed. Each week between 10 to 20 cases are discussed. Advice of plastic surgeons, vascular surgeons and microbiologists is available on request. There is also a monthly MDT meeting attended by the same team of complex arthroplasty surgeons as well as microbiologists with an interest in bone and joint infection and outpatient antibiotic treatment (OPAT) team. All cases undergoing treatment for infection are discussed, plans for surgical and non-surgical treatment are established and progress of treated patients is discussed. This ensures that most appropriate treatment plan is made for each individual.

The East Midlands Specialist Orthopaedic Network (EMSON) (Nottingham, UK) was established and its success has been reported [32]. All referrals are received by email by the MDT coordinator. The meetings are conducted using secure videolink, with complex arthroplasty surgeons from Nottingham University Hospital and microbiologists attending while consultants from neighbouring hospitals dial in to discuss challenging cases. During first 6 months 166 cases were discussed, 43% of which the initial plan was amended as the result of the discussion. In several cases, there was a significant alteration to the treatment plan. Referring surgeons are also encouraged to come to tertiary centre with the potential for joint consultant operating. This improves the experience of all clinicians involved.

#### **6. Why should we adopt a Multidisciplinary approach**

The potential benefits of care delivered via a MDT approach can be experienced on a variety of levels:

*The Unit level-* The centre which provides this level of care will likely benefit from an increased volume of patients and referrals. This will the increase the experience gained by clinicians in dealing with his condition.

*The clinician level-* Individual clinicians will have improved exposure to a larger number of cases. This has the potential to improve technical proficiency. Clinicians will also be motivated to receive further training and broaden their knowledge in field of PJI. This will also improve their knowledge and level of expertise by participating in MDT discussions.

*The patient level-* Patients are more likely to receive coordinated, individual care by specialists with greatest level of expertise in the field of PJI.

#### **7. Challenges to the establishment of a multidisciplinary team**

The treatment of PJI is labour and resource intensive. Patients often stay on the ward for extended periods and face a higher risk of surgical and medical

#### *Teamwork in Healthcare*

complications that non-infected cases. It is likely that the number of referrals and number of treated patients will increase over time which increases this burden [32].

Vaneghan et al. have shown that the cost of surgical treatment is significantly higher than septic revisions [33]. There is a potential risk of rapid depletion of financial resources. Renumeration strategies need to be established prior to starting this type of service [18, 33].

The logistics and practical aspects of establishing a MDT requires careful attention to detail. An understanding of what is required on a practical level is important. Meetings of large numbers of specialists takes these services from other departments. To the authors' knowledge there has been no definition of the optimal constituents a MDT or of the minimal number of specialists required or whether the teams involved in diagnostic and therapeutic parts of the patient journey should be different.

Job planning for all the members of the team should be coordinated to allow all members to meet or dial in to discuss cases. Surgeons, radiologists, microbiologists and other health care professionals involved in PJI management need to find time during their busy weekly schedule for MDT to work. Furthermore, when transfer of the patient is necessary to the specialist centre, logistical arrangements need to be in place to avoid delays.

Another unexplored aspect of delivering care in this way is the issue of responsibility and autonomy. The MDT moves away from the heirarchical system in which decisions are made by one senior individual towards one where there is shared decision making. This raises the subject of accountability. When a decision is taken by a group, who is responsible and who, if anyone, is accountable when things go wrong? For the same reason there can be a perceived risk to the autonomy of the referring surgeon. These issues have not been addressed.

In conclusion the management of PJI is complex and multifactorial. Multidisciplinary management has resulted in improved clinical results in similar settings setting such as tumour surgery however establishment of multidisciplinary care presents significant challenges to the treating institution.

#### **Author details**

Nemandra A. Sandiford1 \* and Konrad Wronka2

1 Trauma and Orthopaedic Surgery, Southland Teaching Hospital, Invercargill, New Zealand

2 Trauma and Orthopaedics, Prince Philip Hospital, Llanelli, GBR

\*Address all correspondence to: nemsandiford@gmail.com

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*The Impact of the Multidisciplinary Team on the Management of Prosthetic Joint Infection… DOI: http://dx.doi.org/10.5772/intechopen.94124*

#### **References**

[1] https: //www.hqip.org.uk/ wp-content/uploads/2018/11/NJR-15th-Annual-Report-2018.pdf (last accessed 5-05-2020)

[2] Blom A, Brown J, Taylor AH et al. Infection after total knee arthroplasty. J Bone Joint Surg Br. 2004;86(5):688-91.

[3] Briggs T. W. (2015). A national review of adult elective orthopaedic services in England. Getting it right first time. Retrieved from http://www. gettingitrightfirsttime.com/downloads/ GIRFT-National-Report.pdf [last accessed 23 June 2016].

[4] Parvizi J,Gehrke T, Chen A. Proceedings of the International Consensus on Periprosthetic Joint Infection. Bone Joint J 2013;95-B:1450-2.

[5] Tansey R, Mirza Y, Sukeik M et al. Definition of Periprosthetic Hip and Knee Joint Infections and the Economic Burden. Open Orthop J. 2016;10:662-668.

[6] Tsukayama DT, Goldberg VM, Kyle R. Diagnosis and management of infection after total knee arthroplasty. J Bone Joint Surg Am. 2003;85-A Suppl 1:S75-80.

[7] Saleh A, Guirguis A, Klika A et al. Unexpected Positive Intraoperative Cultures in Aseptic Revision Arthroplasty The Journal of Arthroplasty . 2014; 29:2181-2186.

[8] Tande AJ, Patel R. Prosthetic joint infection. Clin Microbiol Rev. 2014;27(2):302-45.

[9] M. Marín, J. M. Garcia-Lechuz, P. Alonso et al. Role of Universal 16S rRNA Gene PCR and Sequencing in Diagnosis of Prosthetic Joint Infection. Journal of Clinical Microbiology 2012; 50 (3) 583-589.

[10] Yi PH, Cross MB, Moric M et al. The 2013 Frank Stinchfield Award: Diagnosis of infection in the early postoperative period after total hip arthroplasty. Clin Orthop Relat Res. 2014;472(2):424-429.

[11] Deirmengian C, Kardos K, Kilmartin P et al. Diagnosing periprosthetic joint infection: has the era of the biomarker arrived?. Clin Orthop Relat Res. 2014;472(11):3254-3262.

[12] Moore AJ, Whitehouse MR, Gooberman-Hill R et al. A UK national survey of care pathways and support offered to patients receiving revision surgery for prosthetic joint infection in the highest volume NHS orthopaedic centres. Musculoskeletal Care. 2017;15(4):379-385.

[13] Kunutsor SK, Whitehouse MR, Lenguerrand E et al. Re-Infection Outcomes Following One- And Two-Stage Surgical Revision of Infected Knee Prosthesis: A Systematic Review and Meta-Analysis. PLoS One. 2016;11:11(3).

[14] Berend KR, Lombardi AV Jr, Morris MJ et al. Two-stage treatment of hip periprosthetic joint infection is associated with a high rate of infection control but high mortality. Clin Orthop Relat Res. 2013;471(2):510-8.

[15] Zmistowski B, Karam JA, Durinka JB et al. Periprosthetic joint infection increases the risk of oneyear mortality. J Bone Joint Surg Am. 2013;95(24):2177-84.

[16] Boettner F, Cross MB, Nam D et al. Functional and emotional results differ after aseptic vs septic revision hip arthroplasty. HSS J 2011;7(3):235-238.

[17] Vaishya R, Agarwal AK, Rawat SK et al. Is Single-stage Revision Safe Following Infected Total Knee Arthroplasty? A Critical Review. Cureus. 2017;9(8):e1629.

[18] Shanmugasundaram S, Ricciardi BF, Briggs TW et al. Evaluation and Management of Periprosthetic Joint Infection-an International, Multicenter Study. HSS J. 2014;10(1):36-44.

[19] https://icmphilly.com/questions/ does-arthroscopic-surgery-have-anyrole-in-the-treatment-of-acute-orchronic-periprosthetic-joint-infectionpji-of-the-knee-or-the-hip/ (Last accessed 22/05/2020)

[20] Hamblen DL. Diagnosis of infection and the role of permanent excision arthroplasty. Orthop Clin North Am. 1993;24(4):743-9.

[21] Prendki V, Zeller V, Passeron D et al. Outcome of patients over 80 years of age on prolonged suppressive antibiotic therapy for at least 6 months for prosthetic joint infection. Int J Infect Dis. 2014;29:184-9.

[22] Parvizi J, Zmistowski B, Adeli B. Periprosthetic joint infection: treatment options. Orthopedics. 2010;33(9):659.

[23] Sandiford NA, Hutt JR, Kendoff DO et al. Prolonged suppressive antibiotic therapy is successful in the management of prosthetic joint infection. Eur J Orthop Surg Traumatol. 2020;30(2):313-321.

[24] Moore A J, Blom AW, Whitehouse MR et al. Deep prosthetic joint infection: a qualitative study of the impact on patients and their experiences of revision surgery. BMJ Open; 5(12): e009495.

[25] Birchall M, Bailey D, King P. South West Cancer Intelligence Service Head and Neck Tumour Panel: Effect of process standards on survival of patients with head and neck cancer in the south and west of England. Br J Cancer 2004;91(8):1477-1481.

[26] Croke JM, El-Sayed S: Multidisciplinary management of cancer patients: Chasing a shadow or real value? An overview of the literature. Curr Oncol 2012;19(4): e232-e238.

[27] Scarborough M, Li HK, Rombach I, et al. Oral versus intravenous antibiotics for bone and joint infections: the OVIVA non-inferiority RCT. *Health Technol Assess*. 2019;23(38):1-92.

[28] Namba RS, Inacio MC, Paxton EW. Risk factors associated with deep surgical site infections after primary total knee arthroplasty: an analysis of 56,216 knees. J. Bone Joint Surg. Am. 95:775-782. 10.2106/JBJS.L.00211.

[29] Bohl DD, Shen MR, Kayupov E et al. Hypoalbuminemia Independently Predicts Surgical Site Infection, Pneumonia, Length of Stay, and Readmission After Total Joint Arthroplasty. J Arthroplasty. 2016 Jan;31(1):15-21.

[30] Cross MB, Yi PH, Thomas CF et al. Evaluation of malnutrition in orthopaedic surgery. J Am Acad Orthop Surg. 2014 Mar;22(3):193-9.

[31] Snow R, Granata J, Ruhil AV et al. Associations between preoperative physical therapy and post-acute care utilization patterns and cost in total joint replacement. J Bone Joint Surg Am. 2014 Oct 1;96(19):e165.

[32] Walker J. Care of patients undergoing joint replacement. Nurs Older People.2012 Feb;24(1):14-20.

[33] Bloch B, Raglan M, Manktelow, A et al. The East Midlands Specialist Orthopaedic Network: the future of revision arthroplasty?. The Bulletin of the Royal College of Surgeons of England. 99. 66-70. 10.1308/ rcsbull.2017.66.

#### **Chapter 3**

## Teamwork in Healthcare Management

*Mercè Mach, António C.M. Abrantes and Ceferí Soler*

#### **Abstract**

Groups are pervasive in healthcare institutions and take on a variety of shapes. This paper uses a typology that allows us to understand the distinctive characteristics of team operations, based on interdependence and interactive dimensions. It looks at factors that influence team effectiveness in organizational settings. We review different frameworks that shed light in explaining the conditions that lead to group effectiveness. From the classical input-process-output (IPO) model to the input-mediator-output-input (IMOI) model of team effectiveness; the taxonomy of team process and emergent estates, as well as the teams understood as complex adaptive systems and also studied from the multiteam system perspective. We also report the need for more robust research designs to contribute to the field's further advancement. There is consensus among scholars demanding further conceptual frameworks, as well as powerful research designs that capture processoriented theory and research on team effectiveness. Some future directions and recommendations are suggested.

**Keywords:** teamwork, interaction, interdependence, effectiveness

#### **1. Introduction**

In recent decades, organizations have increasingly turned to using teams and made them a part of day-to-day routines [1, 2], and all for a variety of reasons, such as the ability to respond to emergencies, engage in continuous quality improvement efforts, and manage work projects through multidisciplinary teams. In the particular case of healthcare organizations, teamwork is essential to provide effective care, and the lack of teamwork has been identified in the literature as a key vulnerability in terms of service quality [3, 4]. In this chapter we propose revisiting the conditions that promote effective teamwork. We will first examine team work typology, using interaction and interdependence as the key dimensions characterizing and describing teams. We will then focus on teamwork effectiveness and review a few of the more influential frameworks that have driven research dedicated to teams. Finally, we will conclude with some directions for future teamwork research. But, first, we should briefly discuss what a team and teamwork are.

Kozlowski and Ilgen [5] provide a rather thorough definition of teams, describing them as "two or more individuals who socially interact (face-to-face or, increasingly, virtually); possess one or more common goals; are brought together to perform organizationally relevant tasks; exhibit interdependencies with respect to workflow, goals, and outcomes; have different roles and responsibilities; and are together embedded in an encompassing organizational system, with boundaries and linkages to the broader system context and task environment" (p. 79) [5]. Although exhaustive, this approach defines teams in a somewhat mechanistic way in terms of their design, with an external focus. This view has been countered with a different perspective which sees teams as more dynamic and as self-constructed entities. This led Humprey and Amy [6] to define teams as "assemblies of interdependent relations and activities organizing shifting sets or subsets of participants embedded in and relevant to wider resource and institutional environments" (p. 450) [6].

On the other hand, teamwork is a process that emerges from the interactions established among team members [7] and it can be defined as "a set of interrelated thoughts, actions, and feelings of each team member that are needed to function as a team and that combine to facilitate coordinated, adaptive performance and task objectives resulting in value-added outcomes" (p. 562) [8]. Teamwork reflects the minute-by-minute behaviours and interactions that take place between team members work when executing a task [9]. As proposed by Salas et al. [9], teamwork is guided by a number of fundamental principles: it is characterized by a set of behaviours, cognitions and attitudes that should be flexible and adaptive; team members should monitor each other and feel safe to provide feedback and comfortable when receiving it; team members should also be willing and capable of providing support to other team members in their operations and activities; teamwork involves clear, precise, and concise communication; team members must be able to coordinate interdependently to take collective action; teamwork requires leadership that provides direction, planning, distribution, and activity coordination; and, finally, teamwork is subject to external influences as well as to the requirements of the task itself.

#### **2. Typology of formal groups**

As in all organizations, groups are pervasive in healthcare institutions and take on a variety of shapes, ranging from different units or working groups that are permanent in nature to "ad hoc" groups (committees, meetings, etc.) which are eminently temporary. In order to manage this variety of groups, establishing a typology will allow us to understand the distinctive characteristics of their operations. In addition to varying relative to the purposes they serve, formal groups (permanent or temporary) also diverge according to the basic characteristics of how they operate. The way they function is determined by two basic dimensions: team interaction and interdependence. Team interaction relates to how team members "behav[e] together, in some recognized relation to one another" (p. 12) [10], for the purpose of performing a task. Team interdependence is the extent to which team members cooperate, depend on each other, and work interactively to complete team tasks [11]. Although related, the two concepts are independent in the sense that, although teams with high degrees of interdependence also have high degrees of interaction, the same does not always happen in the opposite sense. That is, teams with a high degree of interaction do not necessarily have a high degree of interdependence, since team members may interact but not depend on each other.

#### **2.1 Team interaction**

Team interaction is central to teamwork and represents complex, temporal phenomena with multilevel manifestations [12]. It is complex because it involves a web of behavioural connections between team members; it is temporal because the very execution of team tasks has a temporal dimension unfolding over time at a specific

#### *Teamwork in Healthcare Management DOI: http://dx.doi.org/10.5772/intechopen.96826*

rhythm and pace; and it manifests at several levels because it is nested in individual and collective behaviours. Team interaction is thus subject to influences from elements related to individuals, from elements within the team itself, and from relational factors. Individual factors can include, for example, team members' attitudes towards work and the team. Collaborative attitudes will promote better interactions than competitive ones. Regarding team factors, for example, Lehmann-Willenbrock and Allen [13] observed that humour considered at the team level has a positive influence on the incidence of interactions within the team. From a relational point of view, differences in status and power within the team also influence the level of interaction, with that interaction increasing the smaller the differences in status and power. The team's interaction level also has significant and positive outcomes for teams. One such consequence is the development of similar team mental models, which can be defined as a common understanding among team members about key elements in the relevant team environment [14]. The similarity of team mental models has positive effects on several dimensions such as team performance [15] and adaptive capacity [16].

#### **2.2 Team interdependence**

Although team interdependence can be considered a single general factor, it can also be seen in three distinct dimensions: task, goal, and outcome interdependence [17]. *Task interdependence* concerns the degree of task-induced interactions between members; *goal interdependence* refers to the relationships between members arising from the type of goal (whether individual or team, for example) that drives members' performance and efforts; *outcome interdependence* refers to interdependent feedback and rewards as they relate to individual or collective performance. These types of interdependence have different consequences on team performance. For example, in a meta-analysis, Courtright et al. [18] concluded that task and outcome interdependence affect performance via different mechanisms. Task interdependence is primarily associated with team performance through its effects on team functioning in relation to the task, such as through actions or transition processes or through team-efficacy. Contrarily, outcome interdependence is mainly associated with team performance through its effects on team functioning in relation to relational aspects, such as interpersonal processes or cohesion. However, although distinct, these three types of interdependence are highly related. As Gully et al. [17] argue, when team members are performing a highly interdependent task, they tend to have interdependent goals and outcomes.

In particular, task interdependence has been widely studied [19, 20] for its implications on the way teams operate and perform. For example, to determine how to assign outcomes to individual group members, the types of tasks the team performs have to be taken into account. Thompson's [21] group task model (**Figure 1**) can help to assess the extent to which the work performed by one member affects what other group members do, as well as identifying the most effective way to distribute outcomes and/or rewards. In essence, this model reveals the form that task interdependence can take.

In the *pooled interdependence* type of task, members only depend on each other because they belong to the same organization or department. Each member of the group makes a separate and independent contribution to overall team performance. They may compete for resources but, generally, they operate relatively independently [21]. There is little interaction among members and there are few potentially dysfunctional consequences. This pooled interdependence generates additive outputs. Classic examples include a group of sales representative in a pharmaceutical company or a group of physicians in a healthcare centre.

#### **Figure 1.**

*Types of task interdependence (based on Thompson model of group task [21]).*

Group tasks based on *sequential interdependence* require specific behaviours to be performed by the group's members in a predetermined order. The level of each member's performance, consequently, affects the performance of other members down the line. In this type of task, members' outputs are required for the following members to perform their duties. Problems arise if the first members do not perform their jobs effectively, potentially leading to the following members having to adopt defensive strategies. When group members' activities are sequentially interdependent, the performance level of the least capable or poorest-performing member of the group determines overall group performance [21]. Examples of sequential interdependence include any kind of assembly-line work, where the finished product is the result of all the group members' sequential inputs.

In tasks with *reciprocal interdependence*, the activities of all the work group's members are fully dependent on one another, so that each member's performance influences the performance of every other group member. Work groups performing tasks characterized by this reciprocal interdependence tend to experience considerable coordination problems due to unpredictable group relations and interactions. There is no set ordering of the group's activities when its tasks are organized reciprocally, unlike when tasks are organized in a sequential manner [21]. An example of reciprocal interdependence could be the protocols established for organ transplant surgery.

*Intensive interdependence* is one step ahead of reciprocal interdependence. This type of interdependence implies a fully connected communication network. Each group member's activities precede and are required for all the other group members' activities. Groups with this type of interdependence have the greatest potential for conflict, and they require the greatest number of effective communication mechanisms [21]. Examples of work groups whose tasks are intensively interdependent include operating room teams in hospitals, top management teams, emergency room teams, and R&D teams.

With increasing interdependence –pooled interdependence, sequential, reciprocal, and intensive–, the potential for conflict and dysfunctional behaviours can increase [22]. However, research provides strong evidence that the relationship between team efficacy (team perceptions regarding its ability to perform

a specific task) and performance is stronger when that interdependence is high compared to when it is low [17].

#### **2.3 Types of groups**

Based on the two team interaction and interdependence dimensions, we can distinguish four types of organisational groups (**Figure 2**): Staff/Crew, Remotecontrolled group, Coordinated group, and Team. In the *Staff/Crew* type of group there is proximity or social contact between the people who make up the group, although their tasks are not interdependent. This group's results are generally additive, that is, they correspond to the sum of the individual members' results. Contrarily, there is no interaction or interdependence among the members in a *Remote-controlled group*. The group is merely 'nominal' and exists for the purposes of the organization, but it does not act as a group in terms of the work conducted by its members. In the *Coordinated group* there is no direct contact between its members, although they may depend on each other to carry out their work. And the *Team* group is characterised by a high degree of interaction and interdependence among its members.

#### **2.4 Nature of team tasks**

There are numerous dimensions by which tasks can be classified. Above we saw a classification based on interdependence, but we can look at tasks from another perspective, for example, according to the team members' contributions. From this standpoint, tasks can be additive, conjunctive or disjunctive [23]. A task is *additive* when the group's success depends on the sum of the individual group members' performance. Additive tasks are divisible, and the group's performance is a function of the average competence of the individuals within it. For additive tasks, the group's potential performance increases with the size of the group. A typical example of an additive task is a relay race, in which the final result represents the sum of each member's performance. In general, more people putting in more effort will result in a better outcome. For example, a hospital's emergency room triage team performs an additive type of task when we consider the number of triaged patients as a measure of its performance. This number represents the sum of each triage team member's performance.

*Conjunctive tasks* are those requiring all group members to contribute to complete the product or output. The group task cannot be completed successfully

**Figure 2.** *Types of groups based on team interaction and interdependence dimensions.*

until all the members have finalized their portion of the job. This means that the speed and quality of the group's performance are determined by the least skilled or inferior member, such as in an assembly-line which is limited by its weakest link. Both potential and actual performance of conjunctive tasks decreases as group size increases. An example of a conjunctive task could be a taskforce to develop a new protocol to resolve bottlenecks in a hospital emergency room, in which each group member has specific knowledge without which the task cannot be completed [24].

A *disjunctive task* is one in which the group's performance depends on the performance by the best member of the group, typically a task involving decisionmaking or problem-solving. One example is a research team looking for a single error in a complicated computer program. Disjunctive tasks require group members to define a single solution or make a decision or recommendation that will be adopted on behalf of the entire group. This means that the group's performance tends to be determined by the most skilled or logical-minded member. The potential performance of groups performing disjunctive tasks increases with group size. In the healthcare industry, an example of a disjunctive task is a weekly clinical case meeting in a hospital (or its online version). Disjunctive tasks predominate in the coordinated groups and teams seen above, although conjunctive tasks are also frequently performed by these types of groups [23].

In this section we have looked at some typologies of formal groups and discussed the interdependence of the teams' tasks and their members' interaction. In the next section we will review some of the most influential frameworks driving research on work teams.

#### **3. Approaches to team dynamics**

The last three decades have seen a significant increase in the number of articles published on teams or groups. A literature review of articles published in the *Journal of Applied Psychology* over the last century found that studies referring to groups or teams have more than quadrupled since the 1990s [25]. Numerous studies have been carried out to shed light on which specific set of characteristics and processes possibly lead to effective team outcomes [6]. Today, we know a lot about teams and their dynamics: we know what influences them, how to develop them, how to lead them and make them more cohesive; we also know that to be effective they have to be adaptable and flexible [26]. Teams are complex dynamic systems that develop over time as their members evolve and adapt to the different situational demands they continually face [5]. Therefore, they are strongly influenced by a wide range of factors that make teams different in a variety of ways, from their skills and level of virtuality to their culture and personality [26]. Let's look at some factors that influence team effectiveness.

#### **3.1 Fundamental frameworks**

Scholars have developed different frameworks to attempt to explain the conditions that lead to group effectiveness. The classic input-process-output (IPO) model of team effectiveness [27, 28] guided developments in team research for several decades. Within the IPO model, the inputs are the antecedents, that is, the conditions that exist prior to the group activity (e.g., organizational context, task characteristics, and team composition). The processes are the interactions among group members that mediate the relationship between the team's inputs and outputs (e.g., communication and coordination processes). Lastly, the outputs are the results, the consequences of group activity (e.g., productivity/performance,

#### *Teamwork in Healthcare Management DOI: http://dx.doi.org/10.5772/intechopen.96826*

member satisfaction, and innovation). For example, the early IPO model proposed by McGrath [28] suggests that individual, group, and environmental-level factors are antecedents to group interaction processes with effects on performance outcomes such as quality, speed, number of errors, and other types of outcomes, such as member satisfaction or group cohesion.

The IPO model has been highly influential in research on teams and how members can combine their efforts and knowledge to complete a specific task. However, more recently, the model has been questioned as it has some limitations when considering the dynamic nature of teams [29, 30]. One criticism raised is that, despite involving team interactions, many researchers studying processes only assess these as static retrospective perceptions, ignoring how they emerge, their dynamics and evolution over time [29]. Furthermore, the IPO model does not take into account that all mediational factors are not necessarily processes but can also be emergent states [31] as we explore below. In addition, teamwork influences create a feedback loop in which reversal causal sequences are also possible, given that the results of a team's actions can also be an input for the following action, something not reflected in IPO models [31, 32]. To avoid some of these limitations, Ilgen, et al. [33] proposed the input-mediator-output-input (IMOI) model. In the latter, inputs are added at the end of the model to denote the system's cyclical nature, and processes are replaced by mediators to reflect a wider range of variables, namely processes and emergent states.

#### **3.2 Team processes and emergent states**

As seen above, not all team mediation mechanisms are processes; some are emergent states [31] . The difference between the two is fundamental, since processes imply interactions while emergent states do not. *Team processes* reflect the different types of activities and interactions that occur within a team and contribute to its end goals. They can be defined as "members' interdependent acts that convert inputs to outcomes through cognitive, verbal, and behavioral activities directed toward organizing task work to achieve collective goals" (p. 357) [31]. On the other hand, *emergent states* are an epiphenomenon (by-product) that results from the interaction between team members. Marks et al. [31] define them as "properties of the team that are typically dynamic in nature and vary as a function of team context, inputs, processes, and outcomes" (p. 357) [31]. Thus, when implementing processes, team members operate interdependently using the various resources at their disposal to achieve the team's objectives. For example, these resources may be their own competencies or the equipment they have available. As for emergent states, they are a product of the team's experiences and reflect its cognitive, motivational, and affective states. Although they are a product of interactions and, therefore, of processes, emergent states are also inputs to subsequent processes and outcomes.

This sequential notion in which a process or emergent state is both an output and an input of subsequent processes and emergent states leads us to the recurring phase model of team processes proposed by Marks et al. [31]. In their model, team performance episodes unfold over time, signalling specific periods in which action and transition phases occur. *Action phases* are periods of time in which teams are actively involved in executing a task, trying to achieve the proposed objectives. The teams' actions depend on their nature. For example, surgical teams perform operations; emergency medical teams treat acute patients without prior appointment; firefighting teams put out fires; and research teams collect and analyse data. *Transition phases* occur between the different action phases. In these transition phases, teams focus on evaluating the previous action phase and planning the next one. These are periods of reflection where actual and projected performance levels are compared

and potential performance gaps are addressed. In each of these phases there is an IPO model, that is, a set of processes that have antecedents and that result in outputs for the next phase. For example, a given action phase's performance quality is the input for the next transition phase. Antecedents such as member diversity, task interdependence, and team size affect team processes that, in turn, have a strong impact on team effectiveness and performance.

Marks et al. [31] developed a taxonomy of team processes that considers practices that typically occur in transition phases, those that occur in action phases, and interpersonal processes that occur in both. In transition phases, team members conduct three types of processes: mission analysis, goal specification, and strategy formulation. *Mission analysis* processes refer to teams interpreting and evaluating their mission and identifying their main tasks, the operational context, and available resources; g*oal specification* processes imply team members identifying and prioritising their goals and subgoals; and, lastly, *strategy formulation* processes include developing alternative courses of action to accomplish the mission, as well as defining contingency plans in case there is any change in the context. Typical processes in the action phase include progress, system and team monitoring as well as backup behaviours and coordination. *Progress monitoring* consists of overseeing the task and checking its progress; *system monitoring* implies tracking internal systems such as equipment or personnel and tracking external systems, for example, changes in the environment; *team monitoring and backup behaviours* refer to actions to help other team members perform their tasks (ranging from simple verbal feedback to replacing a colleague in performing a task); and, finally, *coordination* refers to orchestrating the sequence and synchronisation of interdependent actions. Coordination can be explicit, which implies that team members communicate with each other overtly, but it can also be implicit, consisting of the team's ability to act collectively, with members anticipating the needs of the task and other members and adjusting their behaviour accordingly, without the need to communicate overtly [34]. There are, however, other types of processes which may occur either in action or transition phases and which refer to processes that regulate interpersonal activities, that is, interpersonal processes. These comprise conflict management, motivation, and confidence building, as well as affect management. *Conflict management* can be both preventive, establishing the conditions to prevent, control, or guide conflict before it occurs, and reactive, which is a way of resolving conflicts when they do occur; *motivation and confidence building* consist of creating and maintaining a collective feeling of confidence, motivation, and cohesion, that is, creating emergent states that are positive for the mission; and *affect management* refers to regulating members' emotions when working.

Recently, Mathieu, Luciano et al. [35] have developed a team process survey tool that allows researchers to examine team processes more systematically (transition, action, and interpersonal processes). In its more extensive version, this tool includes 50 items, while its intermediate version has 30 and the reduced version only 10, one for each process. As recommended by authors [35], the use of the reduced 10-item version may be tempting, but it is not the most appropriate in all situations. The longer versions offer a more complete representation of the various dimensions. For example, Marks et al.'s taxonomy [31] includes several sub-processes that are not revealed in the 10-item version. When the aim is to get an in-depth view of the team's processes, the 30- and 50-item versions are more advisable. When only a quick look at how the team currently functions is desired or when this measure is included in a more extensive questionnaire along with other scales, using the 10-item version may be advantageous.

With regard to emergent states, an article by Grossman, Friedman and Kalra [36] summarises the emergent states emphasized the most in the literature,

#### *Teamwork in Healthcare Management DOI: http://dx.doi.org/10.5772/intechopen.96826*

dividing them into affective and cognitive mechanisms. In *affective mechanisms* we find cohesion, confidence, and trust; *cognitive mechanisms* consist of team mental models and transactive memory systems. *Team cohesion* is one of the most studied emergent states in team literature and across a wide range of disciplines, from sports psychology [37, 38], to military psychology [39]. It is "a dynamic process which is reflected in the tendency for a group to stick together and remain united in the pursuit of its goals and objectives" (p. 124) [40]. In the particular case of teams operating in highly stressful or very task-oriented environments, such as healthcare, research has shown that team cohesion is crucial for team performance [41]. *Team confidence* includes team efficacy and team potency. These two constructs are similar but distinct. While team efficacy refers to the shared belief that the team can perform a certain task, team potency refers to the belief about the team's ability to be successful in different tasks and contexts. Both dimensions have a positive effect on team performance, especially team efficacy, particularly when tasks are highly interdependent [17]. *Team trust* refers to the team members' shared willingness to be vulnerable to other members' actions [42, 43]. Without trust, team members are unlikely to be able to work effectively with each other. These three mechanisms, though independent, have some interactions. For example, Mach et al. [38] observed that team trust has an effect on performance through team cohesion. In other words, the greater the team trust, the more cohesive teams are, which contributes positively to their performance.

As far as cognitive mechanisms are concerned, *team mental models* play a major role. These are shared representations of key elements concerning the task environment, whether related to the task, to the team itself or even to temporal aspects [14, 44]. As seen above, team mental models have a positive effect on several team outcomes, from performance to adaptation. Another cognitive mechanism is the *transactive memory system*, which refers to a shared system that combines each member's memory system with a shared understanding of what each member knows and for what kind of knowledge they are responsible, that is, who knows what [45, 46]. In addition, this emergent state contributes to teams' successful performance [45], as it allows lightening each team member's cognitive load and also expands the pool of expertise and knowledge available. Emergent processes and states interplay with mutual precedence relations as well as with interaction relations. For example, in dynamic contexts when performing non-routine tasks, transactive memory systems moderate the relationship between implicit coordination and adaptive behaviours [47]. This means that, when teams are fully aware of who knows what within the team, the positive effect of implicit coordination processes on performance is more pronounced.

#### **3.3 Teams as complex adaptive systems (CAS)**

Since Arrow, McGrath and Berdahl [48] characterised teams as complex adaptive systems (CAS), multiple theoretical frameworks have emerged to capture and explain this idea. However, relatively few empirical studies have been able to examine how long it takes teams to become effective and how these effects develop over time [49–51]. CAS are open systems that are characterised by the level of uncertainty regarding their evolution over time given the interaction of their components [52]. Ramos-Villagrasa et al. [51] carried out a systematic review through the nonlinear dynamical system theory lens, supporting the view of teams as complex adaptive systems. Teams are complex because they are integrated within organisations that exhibit complex behaviour; they are adaptive because they dynamically cope with environmental changes; and they are systems because their functioning depends on the team's history and, therefore, on inputs, but also on the anticipated future, that is, on outputs. The continuous adaptive process that occurs within these teams allows them to adapt to contextual discontinuities and to make decisions according to both the team's antecedents and projected results [48]. The use of this new conceptual approach can help researchers to study teams in a non-linear and more dynamic way [51], as well as to address temporal problems [53, 54] by taking measures at different stages of the team's evolution.

In the case of healthcare teams, they cannot always function as CAS [55]. For example, in clinical situations where problems are identified and described in detail and solutions standardised in specific procedures, teams operate in a planned way, and guidelines are clear and executed in a simple way. However, when there is uncertainty about how to best handle a given situation, operating as a CAS may be the most appropriate option as it promotes the development of new ideas and approaches. This is based on 7 principles: (1) team members can operate autonomously guided by ground rules; (2) team members interact in non-linear ways, i.e., they are interdependent and affect other team members in different ways; (3) the team is sensitive to initial conditions; (4) interactions between team members can produce unpredictable behaviours; (5) these interactions can generate new behaviours; (6) the team is an open system interacting with the environment; and (7) team members function as attractors modelling team behaviour [55].

#### **3.4 Multiteam systems**

In the same complex adaptive system stream, teams can be studied from the multiteam system (MTS) perspective [56]. An MTS corresponds to "two or more teams that interface directly and interdependently in response to environmental contingencies toward the accomplishment of collective goals" (p. 289) [56]. These systems constitute "networks of interdependent teams that coordinate at some level to achieve proximal and distal goals" (p. 479) [57]. In a system of this nature, the processes established between the various teams, the cross-team processes, are even more important for the system's success than within-team processes [58]. In the case of the healthcare industry, the use of a multiteam system logic is very beneficial, but much remains to be studied. For example, one area where team research is needed is how best to form networks that integrate patients and their families over time [59]. Patients and their support structures are responsible for coordinating care tasks and helping interpret the information collected, extending beyond the boundaries of healthcare providers. Consequently, managing this extended multi-team system holistically will certainly have very positive results on patient care.

A literature review conducted by Shuffler and Carter [60] identified 7 important lessons for successful teamwork in an MTS: (1) MTS functioning is suited to contexts that are ambiguous, multifaceted, dynamic, and where there is a need for a sense of urgency; (2) MTS structures provide the specialisation, flexibility, and integration needed to deal with complex problems; (3) the teamwork phenomenon changes when moving from a teamwork logic within a team to a teamwork logic within an MTS, for example, cross-team processes take on sovereign relevance; (4) an MTS implies added barriers to collaboration that should be specifically addressed; (5) the incorporation of linking elements can benefit the system's performance; (6) the structure of the MTS and the design of its functioning should be carefully thought out; and (7) leadership plays a crucial role in an MTS and should be integrated and managed across the system [60].

#### **3.5 Facets of team effectiveness**

Another relevant framework used to study team effectiveness was suggested by Mathieu et al. [25] illustrating the simultaneous and interrelated relationships

#### *Teamwork in Healthcare Management DOI: http://dx.doi.org/10.5772/intechopen.96826*

among factors associated with team and individual outcomes. Based on a revision of team research published in the *Journal of Applied Psychology* (JAP) during the last century, Mathieu et al. [25] propose a summary construct domain framework with three main facets (**Figure 3**): (a) team task and structure; (b) member characteristics and team composition; and (c) team process and emergent states or mediating mechanisms. This framework captures the many overlapping facets of team effectiveness, providing an in-depth and integrative review of all the constructs that scholars have used thus far to help to advance the teamwork field.

Many of these constructs have been studied among healthcare teams. For example, O'Donovan et al. [62] recently developed a psychological safety measurement instrument designed specifically for healthcare teams. In this instrument, the authors combine the strengths of observation measures with survey measures, allowing for their application to longitudinal studies. Another tool has also been developed to measure the collective intelligence of primary healthcare teams [63]. Collective intelligence can prevent repeating past mistakes and help teams to be more efficient. Jean et al. [63] argue that intelligent teams produce high quality clinical services, so it is essential to better understand the concept and be able to measure it accurately.

Johnson [4] found that intra-team communication demonstrates recurring problems that make it difficult for healthcare teams to coordinate, proposing that teams should work within a common framework represented by formal, informal, market, and professional relationships, or a unique mix based on a mutual orientation towards patient outcomes. The formal approach is based on explicit knowledge and a shared system of codes that, for example, can be translated into written guidelines for hospitals. In addition, the formal approach considers that: personal relationships are also a source of informal information that can overcome the barriers created by formal panels; market logic relates to the creation of information and knowledge-exchange relationships that tend to be maintained through the


**Figure 3.** *Facets of the team effectiveness domain based of one century of JAP publications (Source: [61]. Note. MTS = Multiteam Systems. TMS = Transactive Memory System. Some of the constructs overlap dimensions, showing all possible relations between the squares' main facets. These small squares can be seen where coloured squares intersect.)*

investment that has been put into the relationship; and professional relationships relate to communication within the domain of professions by creating networks of contacts between professionals based on mutual help. Information-sharing and supportive behaviours have also been observed to have a positive impact on innovation in healthcare teams [64].

A study conducted by Jaca, et al. [65] revealed that the role of the external leader in healthcare teams is quite relevant, and his/her main function is to serve as a team performance coordinator. There is also a clear definition of roles, which facilitates decision-making and conflict management. Furthermore, internal communication and participation levels tend to be high. However, team recognition and training need to be improved, as these are the weakest points in healthcare teams. Several studies have also drawn attention to the importance of teamwork in healthcare and, in particular, the importance of interventions to promote teamwork [3, 66]. One of these types of interventions is "TeamSTEPPS" (Team Strategies and Tools to Enhance Performance and Patient Safety), developed by the Agency for Healthcare Research and Quality (AHRQ ) in the USA. TeamSTEPPS is based on communication, leadership, mutual support, and situation monitoring. Another useful model is CRM (Crew Resource Management), which has a significant impact on knowledge and behaviour in acute care settings, such as healthcare [3].

#### **4. Future research avenues**

Despite the remarkable advance in team work research, scholars agree on the need for more robust research designs to contribute to the field's further advancement. In addition to the meta-analysis contributions summarizing past empirical findings [17, 18, 67–69], there is consensus among scholars demanding further conceptual frameworks, as well as powerful research designs that capture processoriented theory and research on team effectiveness [29, 70].

Humphrey and Aime [6] call for a multilevel, multi-theoretical, and multiperiod framework to cope with the contextual dynamics and enhance the understanding of team dynamics. Likewise, Mathieu et al. [30] state that future advances on workgroup effectiveness will be linked with the ability to capture dynamic team properties (conceptually and methodologically); the complexity of team task environments; and the embeddedness in multilevel environments. In the special issue dedicated to *Creating High Performance Teamwork in Organizations*, O'Neil and Salas [2] glimpsed four themes to achieve a team's full potential: working across boundaries; building effective team processes and states; managing team development issues; and leveraging human capital –a combination of knowledge, skills, competences, and other members' and leaders' characteristics. Abrantes et al. [70] highlight 3 types of challenges for research on teams: a theoretical challenge related to team dynamics and the need to identify internal and external drivers that explain these dynamics; a temporal challenge that relates to the process of emergence of team variables and how and when these variables can be assumed to be truly existing phenomena; and a methodological challenge linked to the creation of tools that enable measuring dynamic processes in a non-invasive way. Furthermore, according to Ployhart et al. [71], the agenda for future research on high performance work teams will focus on the conception of teams as adaptive and self-adjusting social entities, embedded in multi-team systems, and as social networks within and outside the team. Therefore, beyond the need to embrace the organizational nature of teams and phenomena at various levels [6], there is also consensus on the need to grasp the dynamic nature of team processes, as they have been assessed primarily as static constructs [26, 29, 70, 72].

#### *Teamwork in Healthcare Management DOI: http://dx.doi.org/10.5772/intechopen.96826*

As seen, team scholars agree regarding the need for innovative research designs and new techniques to capture team dynamics over time. In this sense, Delice et al. [73] summarize and review existing empirical studies that use novel measurements to study team dynamics over extended periods. Some of these innovative research designs are based on techniques such as role-playing simulations, videotape and software coding, videogames, video-coding, team decision tasks, and whatsApp ICT (information and communication technology). Delice et al. [73] also propose longitudinal laboratory experiments and time-series analyses. Other alternatives include scenario-based studies, critical incident techniques, concept-mapping, cross-border e-business website analyses, and simulations (simulation tasks and longitudinal organizational, computer game-based, and dynamic decision-making simulations), as well as experiential learning approaches and performance assessments, among others. There is, therefore, a plethora of alternatives that should be used to further our understanding of teams that are dynamic and part of adaptive systems [73].

### **5. Concluding thoughts**

In summary, some of the key ideas for future research attempt to overcome the limitations of traditional self-reported assessments, which suffer from problems such as low response rates, response bias, or intrusiveness [29, 74, 75]. Some research strategies that can help to overcome these effects:


*Teamwork in Healthcare*

#### **Author details**

Mercè Mach1 \*, António C.M. Abrantes2 and Ceferí Soler3

1 Faculty of Economics and Business, University of Barcelona, Barcelona, Spain


\*Address all correspondence to: merce.mach@ub.edu

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Teamwork in Healthcare Management DOI: http://dx.doi.org/10.5772/intechopen.96826*

#### **References**

[1] Cross, R., Rebele, R., & Grant, A. (2016). Collaborative overload. *Harvard Business Review, 94*,74-79.

[2] O'Neill, T. A., & Salas, E. (2018). Creating high performance teamwork in organizations. *Human Resource Management Review*, *28*(4), 325-331.

[3] Buljac-Samardzic, M., Doekhie, K. D., & van Wijngaarden, J. D. (2020). Interventions to improve team effectiveness within health care: a systematic review of the past decade. *Human Resources for Health, 18*(1), 1-42.

[4] Johnson, J. D. (2019). Framing communication in health care action teams. *International Journal of Healthcare Management, 12*(1), 68-74.

[5] Kozlowski, S. W., & Ilgen, D. R. (2006). Enhancing the effectiveness of work groups and teams. *Psychological Science in the Public Interest*, 7(3), 77-124.

[6] Humphrey, S. E., & Aime, F. (2014). Team microdynamics: Toward an organizing approach to teamwork. *Academy of Management Annals, 8*(1), 443-503.

[7] Carley, K. (1991). A theory of group stability. *American Sociological Review*, *56*, 331-354.

[8] Salas, E., Sims, D. E., & Burke, C. S. (2005). Is there a "big five" in teamwork?. *Small Group Research, 36*(5), 555-599.

[9] Salas, E., Burke, C. S., & Cannon-Bowers, J. A. (2000). Teamwork: emerging principles. *International Journal of Management Reviews, 2*(4), 339-356.

[10] McGrath, J. E. (1984). *Groups: Interaction and performance* (Vol. 14). Englewood Cliffs, NJ: Prentice-Hall.

[11] Stewart, G. L., & Barrick, M. R. (2000). Team structure and performance: Assessing the mediating role of intrateam process and the moderating role of task type. *Academy of Management Journal*, *43*(2), 135-148.

[12] Lehmann-Willenbrock, N., & Allen, J. A. (2018). Modeling temporal interaction dynamics in organizational settings. *Journal of business and psychology*, *33*(3), 325-344.

[13] Lehmann-Willenbrock, N., & Allen, J. A. (2014). How fun are your meetings? Investigating the relationship between humor patterns in team interactions and team performance. *Journal of Applied Psychology, 99*(6), 1278-1287.

[14] Mohammed, S., Ferzandi, L., & Hamilton, K. (2010). Metaphor no more: A 15-year review of the team mental model construct. *Journal of Management, 36*(4), 876-910.

[15] Santos, C. M., Uitdewilligen, S., & Passos, A. M. (2015). A temporal common ground for learning: The moderating effect of shared mental models on the relation between team learning behaviours and performance improvement. *European Journal of Work and Organizational Psychology, 24*(5), 710-725.

[16] Maynard, M. T., Kennedy, D. M., & Sommer, S. A. (2015). Team adaptation: A fifteen-year synthesis (1998 – 2013) and framework for how this literature needs to "adapt" going forward. *European Journal of Work and Organizational Psychology, 24*(5), 652-677.

[17] Gully, S. M., Incalcaterra, K. A., Joshi, A., & Beaubien, J. M. (2002). A meta-analysis of team-efficacy, potency, and performance: Interdependence and level of analysis as moderators of

observed relationships. *Journal of Applied Psychology, 87*(5), 819-832.

[18] Courtright, S. H., Thurgood, G. R., Stewart, G. L., & Pierotti, A. J. (2015). Structural interdependence in teams: An integrative framework and metaanalysis. *Journal of Applied Psychology, 100*(6), 1825-1846.

[19] Bachrach, D. G., Powell, B. C., Collins, B. J., & Richey, R. G. (2006). Effects of task interdependence on the relationship between helping behavior and group performance. *Journal of Applied Psychology, 91*(6), 1396-1405.

[20] Kiggundu, M. N. (1981). Task interdependence and the theory of job design. *Academy of Management Review, 6*(3), 499-508.

[21] Thompson, J. D. (1967). *Organizations in Action*. New York: McGraw Hill.

[22] *D'Silva, J. L., Ortega, A., & Sulaiman, A. H. (2016). Influence of personal and task interdependence on task conflict and team effectiveness. Modern Applied Science, 10(4), 95-100.*

[23] Steiner, I. D. (1972). *Group process and productivity* (pp. 393-422). New York: Academic press.

[24] Mohammed, S. N., Mathieu, J. E., & Bartlett, A. L. (2002). Technical administrative task performance, leadership task performance, and contextual performance: Considering the influence of team- and task-related composition variables. *Journal of Organizational Behavior, 23*, 795-814

[25] Mathieu, J. E., Hollenbeck, J. R., van Knippenberg, D., & Ilgen, D. R. (2017). A century of work teams in the Journal of Applied Psychology. *Journal of Applied Psychology*, *102*(3), 452-467.

[26] Salas, E., Reyes, D. L., & McDaniel, S. H. (2018). The science of teamwork:

Progress, reflections, and the road ahead. *American Psychologist*, *73*(4), 593-600.

[27] Hackman, J. R., & Morris, C. G. (1975). Group tasks, group interaction process, and group performance effectiveness: A review and proposed integration. *Advances in Experimental Social Psychology*, *8*, 45-99.

[28] McGrath, J. E. (1964). *A social psychological: A brief introduction*. New York. NY: Holt Rinehart and Winston.

[29] Kozlowski, S. W., & Chao, G. T. (2018). Unpacking team process dynamics and emergent phenomena: Challenges, conceptual advances, and innovative methods. *American Psychologist,* 73, 576-592.

[30] Mathieu, J. E., Wolfson, M. A., & Park, S. (2018). The evolution of work team research since Hawthorne. *American Psychologist, 73*(4), 308-321.

[31] Marks, M. A., Mathieu, J. E., & Zaccaro, S. J. (2001). A temporally based framework and taxonomy of team processes. *Academy of Management Review, 26*(3), 356-376.

[32] *Forsyth, D. R. (2010). Group dynamics.* Belmont, CA: Wadsworth.

[33] Ilgen, D. R., Hollenbeck, J. R., Johnson, M., & Jundt, D. (2005). Teams in organizations: From input-processoutput models to IMOI models**.** *Annual Review of Psychologhy*, *56*, 517-543.

[34] Rico, R., Sánchez-Manzanares, M., Gil, F., & Gibson, C. (2008). Team implicit coordination processes: A team knowledge–based approach. *Academy of Management Review, 33*(1), 163-184.

[35] Mathieu, J. E., Luciano, M. M., D'Innocenzo, L., Klock, E. A., & LePine, J. A. (2020). The development and construct validity of a team processes survey measure.

*Teamwork in Healthcare Management DOI: http://dx.doi.org/10.5772/intechopen.96826*

*Organizational Research Methods*, *23*(3), 399-431.

[36] Grossman, R., Friedman, S., & Kalra, S. (2017). Teamwork processes and emergent states. In E. Salas, R. Rico, & J. Passmore (Eds.), *The wiley blackwell handbook of the psychology of team working and collaborative processes* (1st ed., pp. 245-269). John Wiley & Sons.

[37] Abrantes, A. C. M., Mach, M., & Ferreira, A. I. (2020). Tenure matters for team cohesion and performance: the moderating role of trust in the coach. *European Sport Management Quarterly*, 1-22.

[38] Mach, M., Dolan, S., & Tzafrir, S. (2010). The differential effect of team members' trust on team performance: The mediation role of team cohesion. *Journal of Occupational and Organizational Psychology, 83*(3), 771-794.

[39] Ahronson, A., & Cameron, J. E. (2007). The nature and consequences of group cohesion in a military sample. *Military Psychology, 19*(1), 9-25.

[40] Carron, A. V. (1982). Cohesiveness in sport groups: Interpretations and considerations. *Journal of Sport Psychology, 4*(2), 123-138.

[41] Charbonneau, D., & Wood, V. M. (2018). Antecedents and outcomes of unit cohesion and affective commitment to the Army. *Military Psychology, 30*(1), 43-53.

[42] Fulmer, C. A., & Gelfand, M. J. (2012). At what level (and in whom) we trust: Trust across multiple organizational levels. *Journal of Management, 38*(4), 1167-1230.

[43] Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. *Academy of Management Review, 20*(3), 709-734.

[44] Mohammed, S., Hamilton, K., Tesler, R., Mancuso, V., & McNeese, M. (2015). Time for temporal team mental models: Expanding beyond "what" and "how" to incorporate "when". *European Journal of Work and Organizational Psychology, 24*(5), 693-709.

[45] Austin, J. R. (2003). Transactive memory in organizational groups: The effects of content, consensus, specialization, and accuracy on group performance. *Journal of Applied Psychology, 88*(5), 866-878.

[46] Wegner, D. M. (1995). A computer network model of human transactive memory. *Social Cognition, 13*(3), 319-339.

[47] Marques-Quinteiro, P., Curral, L., Passos, A. M., & Lewis, K. (2013). And now what do we do? The role of transactive memory systems and task coordination in action teams. *Group Dynamics: Theory, Research, and Practice, 17*(3), 194-206.

[48] Arrow, H., McGrath, J. E., & Berdahl, J. L. (2000). *Small groups as complex systems: Formation, coordination, development, and adaptati*on. Thousand Oaks, CA: SAGE.

[49] Devaraj, S., & Jiang, K. (2019). It's about time – A longitudinal adaptation model of high-performance work teams. *Journal of Applied Psychology, 104*(3), 433-447.

[50] Kozlowski, S. W. J., & Bell, B. S. (2003). Work groups and teams in organizations. In W. C. Borman, D. R. Ilgen, & R. J. Klimoski (Eds.), *Handbook of psychology (Vol 12): Industrial and organizational psychology* (pp. 333-375). New York: Wiley

[51] Ramos-Villagrasa, P. J., Marques-Quinteiro, P., Navarro, J., & Rico, R. (2018). Teams as complex adaptive systems: Reviewing 17 years of research. *Small Group Research*, *49*(2), 135-176.

[52] Guastello, S. J., & Liebovitch, M. (2009). Introduction to nonlinear dynamics and complexity. In S. J. Guastello, M. Koopmans & D. Pincus (Eds.), *Chaos and complexity in psychology: The theory of nonlinear dynamical systems* (pp. 1-40). New York, NY: Cambridge University Press.

[53] McGrath, J. E., Arrow, H., & Berdahl, J. L. (2000). The study of groups: Past, present, and future. *Personality and Social Psychology Review, 4*, 95-105.

[54] Navarro, J., Roe, R. A., & Artiles, M. I. (2015). Taking time seriously: Changing practices and perspectives in work/organizational psychology. *Revista de Psicología del Trabajo y de las Organizaciones, 31*, 135-145.

[55] Pype, P., Mertens, F., Helewaut, F., & Krystallidou, D. (2018). Healthcare teams as complex adaptive systems: understanding team behaviour through team members' perception of interpersonal interaction. *BMC Health Services Research, 18*(1), 1-13.

[56] Mathieu, J. E., Marks, M. A., & Zaccaro, S. J. (2002). *Multiteam systems.* In N. Anderson, D. S. Ones, H. K. Sinangil, & C. Viswesvaran (Eds.), *Handbook of industrial, work and organizational psychology, Vol. 2. Organizational psychology* (p. 289-313). Sage Publications, Inc.

[57] Zaccaro, S. J., Dubrow, S., Torres, E. M., & Campbell, L. N. (2020). Multiteam systems: An integrated review and comparison of different forms. *Annual Review of Organizational Psychology and Organizational Behavior*, 7, 479-503.

[58] Marks, M. A., DeChurch, L. A., Mathieu, J. E., Panzer, F. J., & Alonso, A. (2005). Teamwork in Multiteam Systems. *Journal of Applied Psychology, 90*(5), 964-971.

[59] Weaver, S. J. (2016). From teams of experts to mindful expert teams and multiteam systems. *Journal of Oncology Practice, 12*, 976-979.

[60] Shuffler, M. L., & Carter, D. R. (2018). Teamwork situated in multiteam systems: Key lessons learned and future opportunities. *American Psychologist, 73*(4), 390-406.

[61] Construct domain for team research. Reprinted from "A century of work teams in the Journal of Applied Psychology" by J. E. Mathieu, J. R. Hollenbeck, D. van Knippenberg, and D. R. Ilgen, 2017, Journal of Applied Psychology, p. 456. Copyright, 2017, by the American Psychological Association

[62] O'Donovan, R., Van Dun, D., & McAuliffe, E. (2020). Measuring psychological safety in healthcare teams: developing an observational measure to complement survey methods. *BMC Medical Research Methodology, 20*(1), 1-17.

[63] Jean, E., Perroux, M., Pepin, J., & Duhoux, A. (2020). How to measure the collective intelligence of primary healthcare teams?. *Learning Health Systems, 4*(3), e10213.

[64] Moser, K. S., Dawson, J. F., & West, M. A. (2019). Antecedents of team innovation in health care teams. *Creativity and Innovation Management, 28*(1), 72-81.

[65] Jaca, C., Viles, E., Tanco, M., Mateo, R., & Santos, J. (2013). Teamwork effectiveness factors in healthcare and manufacturing industries. *Team Performance Management: An International Journal, 9*(3/4), 222-236.

[66] West, M. A., & Lyubovnikova, J. (2013). Illusions of team working in health care. *Journal of Health Organization and Management, 27*(1), 134-142.

*Teamwork in Healthcare Management DOI: http://dx.doi.org/10.5772/intechopen.96826*

[67] LePine, J. A., Piccolo, R. F., Jackson, C. L., Mathieu, J. E., & Saul, J. R. (2008). A meta-analysis of teamwork processes: Tests of a multidimensional model and relationships with team effectiveness criteria. *Personnel Psychology, 61*(2), 273-307.

[68] Schmutz, J. B., Meier, L. L., & Manser, T. (2019). How effective is teamwork really? The relationship between teamwork and performance in healthcare teams: A systematic review and meta-analysis. *BMJ open, 9*(9), e028280.

[69] Stewart, G. (2000). Meta-analysis of work teams research published between 1977 and 1998. *Academy of Management Proceedings* (Vol. 2000, 1, pp. 1-1). Briarcliff Manor, NY 10510: Academy of Management.

[70] Abrantes, A. C. M., O'Neill, T. A., & Passos, A. M. (2020). A temporal perspective on teams. In *Handbook on the Temporal Dynamics of Organizational Behavior* (pp. 274-289). Edward Elgar Publishing.

[71] Ployhart, R.E., Nyberg, A.J., Reilly, G., & Maltarich, M.A. (2014). Human capital is dead; long live human capital resources! *Journal of Management, 40*(2), 371-398.

[72] Mathieu, J. E., Gallagher, P. T., Domingo, M. A., & Klock, E. A. (2019). Embracing complexity: Reviewing the past decade of team effectiveness research. *Annual Review of Organizational Psychology and Organizational Behavior*, *6*, 17-46.

[73] Delice, F., Rousseau, M., & Feitosa, J. (2019). Advancing teams research: What, when, and how to measure team dynamics over time. *Frontiers in Psychology, 10*, 1324.

[74] *Feitosa, J., Grossman, R., & Salazar, M. (2018). Debunking key assumptions* 

*about teams: The role of culture. American Psychologist, 73(4), 376-389.*

[75] Golden, S. J., Chang, C. H., and Kozlowski, S. W. (2018). Teams in isolated, confined, and extreme (ICE) environments: Review and integration. *Journal of Organizational Behavior, 39,* 701-715.

[76] Kozlowski, S. W., Chao, G. T., Grand, J. A., Braun, M. T., & Kuljanin, G. (2016). Capturing the multilevel dynamics of emergence: Computational modeling, simulation, and virtual experimentation. *Organizational Psychology Review*, *6*(1), 3-33.

#### **Chapter 4**

## Spiritual Environment Management Tool

*Maria Joelle*

#### **Abstract**

This chapter is about the spiritual environment management tool, which includes spirituality at work and spiritual practices. This management tool is divided into two steps: diagnostic of the worker's perceptions about spirituality at work (first step) and spiritual practices design (second step). By meaning, spirituality at work can help healthcare managers to build effective teamwork in medicine. Spirituality at work has a multidimensional and measurable nature and is aligned with the three principles of the World Health Organization, based on two arguments: the new approach should be preventive and should promote partnership. This fact allows the managers as well the human resource department to classify the organizational environment on the next spiritual issues in the first step: meaningful work; opportunities for inner life; the sense of community; alignment with the organization's value; emotional balance and inner peace. The reduction of medical errors to improve patient safety require the performance of multistep tasks of the great complexity of healthcare professionals, and this chapter pretends to show how the spiritual environment management tool can contribute with the "all working together" goal through a multi-disciplinary care team.

**Keywords:** teamwork, healthcare, spiritual environment, management tool, well-being

#### **1. Introduction**

The purpose of this chapter is to contribute with the concern of this book: the need for a multi-disciplinary care Team - all working together - to help coordinate and optimize the care of patients with complex medical problems.

Can the development of a spiritual environment to help with this concern? This chapter pretends to answer this question and to demonstrate how can spiritual environment as a management tool to help healthcare professionals.

It's very important to contextualize this concept to help us understand how its implementation can contribute to the effective creation of teamwork in healthcare.

Spirituality at work approach is aligned with the principles of different international organizations [1], and the academic evidence shows us, that this approach is useful as a management tool, guiding healthcare practitioners.

The world can be defined as an organization's society, and the scientific community has the responsibility to explain the power and the role of organizations for the achievement of the well-being of the society as a whole.

#### *Teamwork in Healthcare*

Spirituality at work has a multidimensional nature, and is aligned with the next three principles of the World Health Organization:


The '60s represent a decade in which emerges the consciousness of the negative impacts of the organizational practices on the workers' health and well-being. Attending to this fact, the World Health Organization, appealed to define new perspectives that inspire a positive organizational behavior based on two arguments: these new perspectives should be preventive; and the new human resources policies should promote authenticity, trust, and partnership.

The World Health Organization refers precisely both globalization and technological advance as the two big drivers in the labor world transformation, opening opportunities for a dangerous global competition, looking for financial results in detriment of fundamental human rights and well being.

The new developing technologies and the internet came to show that the line that separates professional and private life became almost invisible and worklife balance became questioned. The stress from this new reality during the XXI century influences negatively the workers' physical and mental health causing absenteeism, low motivational levels, satisfaction and creativity decrease, and organizational productivity and competitiveness reduction. In other words, we are facing conditions that are globally a concern to us all, that represent serious social and financial costs.

Currently, we are facing with weakened economy healthcare and a rapidly changing and increasingly high-tech environment, which requires healthcare workers more contact with screens than with patients [2]. The same authors claim that during these times of high burnout and low engagement levels, the healthcare workers feel the need to bring their whole selves to the work. Please, take note of the previous expression: "whole selves to the work"!

Work environments with a superior spiritual environment have higher individual and organizational outcomes, both within and outside of the healthcare industry [1–3]. So, we are facing the moment to establish the connection between the spiritual environment and the need for a multi-disciplinary care Team - all working together - to help coordinate and optimize the care of patients with complex medical problems.

Exist a connection between spirituality and organization, which is easily established after a deep analysis of the spirituality at work concept and its impacts on the improvement of attitudes and individual performance at work (**Figure 1**), as we will see next. To establish this connection and answering all inheriting concerns related to conceptualization and measurement, a dialog was established between spirituality and science, accepting the Maslow's idea [3]:

"I want to demonstrate that spiritual values have naturalistic meaning, that they are not the exclusive possession of organizational churches, that they are well within the jurisdiction of a suitable enlarged science, and that, therefore they are the general responsibility of all mankind".

This responsibility mentioned in the previous paragraph has been assumed in social sciences by several authors, where the search for the "naturalistic meaning"

#### *Spiritual Environment Management Tool DOI: http://dx.doi.org/10.5772/intechopen.94125*

**Figure 1.** *Spiritual environment. Source: Developed by the author.*

is guided by organizational excellence, looking for an organizational purpose or humanitarian aid [4, 5].

The presence of spirituality at work is related to several concerns that should be familiar to leaders, as values and integrity, as the contribution to society, as to take care and support, and as being true [6]. The same authors refer that spirituality at work is both organizational and individual concerns. That is, the leaders need to value the spirituality in their own lives to develop this approach as an organizational management tool, and they will play a role differentiator inside organizations where they belong.

So, spiritual leadership is about creating meaning and value for people, in work life, family life, or community life, as a person who inspires others, promoting higher levels of workforce engagement with their jobs and organizations [4].

The spiritual leadership is related to workplace spiritual intelligence attribute, and both can improve lower levels of job stress, higher levels of workforce engagement, that is greater motivation to improve performance [5].

If the hottest buzz [7] is about the triple bottom line (3 P), a commitment with people, planet, and profit, this buzz should be aligned with another triple bottom line (3 E): employees, environment, and economic.

We need, together, think and act about the importance of both 3 P and 3 E.

So, the humankind needs an evolution more healthy. If we need a more healthy planet/environment, if we need a solid profit/economy, we need healthy people/ employees too. And the work, in this complex world, can play a vital role, through a spirit-team-at-work.

So, can we survive without the wealth professionals? Can the world survive without them? We need them. The world needs you! And, we must not forget: you are employees and human beings first!

We can state that health professionals have the mission to supports the pains of humanity. Which is not easy! It is here that spirituality at work emerges as a management tool, as suggested by several authors [1, 2, 7] described in the next section.

#### **2. Teamwork and spiritual environment**

This section brings us to a series of doubts and questions which will be addressed in the next subsections:


#### **2.1 Spirituality at work concept**

In the year 2000 spirituality at work met a shift mark, with the research developed by the authors Ashmos and Duchon [8] setting the conceptual frontiers and measurement, therefore enabling the research of the spiritual impacts on workers´ attitudes and work-related outcomes. Their initial investigation was published in the Journal of Management Inquiry, based on previous theoretical developments, their conceptualization and measurement inspired most of the subsequent investigations. Spirituality at work has been explored as a multidimensional concept, mainly due to the work of the authors mentioned above. They have been considered the first authors to produce a serious approach to spirituality at work [9–13].

Spirituality at work is not about religion [14] conversion or about getting people to accept a specific belief system [1, 3, 15] and "has taken many forms" [14] (p.80). It is primarily identified with an open mind and involves connectedness [9] and with the connection between others and the workplace environment, and it is related to self-actualization [10].

Spirituality at work is the recognition that workers perform work with meaning and purpose, for them and society as a whole, including a strong sense of enjoyment at work. The workers can find an opportunity at work to express many aspects of one's being, not just the ability to perform physical or intellectual tasks, and they feel work as a source of spiritual growth and connection with coworkers.

In the organizational level spirituality at work is the link between personal values and the organization's mission and purpose, and the source of employee's emotional balance and inner peace. When organizations introduce spirituality at work, it means that they take care of both the mind and spirit of their employees, finding a more holistic picture of the human being [8]. Returning to Maslow's theory of needs, self-actualization and self-transcendence imply the valence of the individual mind and spirit involved in the work component.

If Maslow created the roots and produced the seminal work that showed the importance and gave rise to the spirituality concept on the organizational field, the authors Ashmos and Duchon developed the basic boundaries of the concept and its measurement, giving place to the most significant developments in this field.

The next figure (**Figure 1**) show us the fundamental words which be part of a spiritual environment. The spiritual environment includes five dimensions and spiritual practices. The same figure includes spirituality at work impacts: job resourcefulness (ability and imagination to solve; intelligence), organizational affective commitment (family feeling), individual productivity (effectiveness), and job performance (quality, relative capacity).

The previous figure (**Figure 1**) shows spirituality at work as a multidimensional concept defined by the next five dimensions:

#### *Spiritual Environment Management Tool DOI: http://dx.doi.org/10.5772/intechopen.94125*

• Meaningful work involves a deep sense of meaning and purpose in one's work, for workers and society as a whole including the sense of contribution to the community (items related to work that coincides with personal life values and is helpful for the community) and sense of enjoyment at work (items related to a sense of joy and pleasure at work).

The work can be a way to understand the meaning of life. Meaningful work happens when people experience a deep sense of meaning when they perform their work. People have an intrinsic drive and motivation to learn and find meaning in their work and to be a member of a group, where they feel valued for their contribution to the group's performance [11].

• Opportunities for inner life is about finding an opportunity at work to express many aspects of one's being.

Opportunities for inner life measures the degree to which organizations respect the spiritual values of the workers [12] and was identified by the authors Ashmos and Duchon [8] as spiritual identity: "an opportunity at work to express many aspects of one's being, not merely the ability to perform physical or intellectual tasks" (p.136).

Spirituality at work begins by acknowledging that people have an inner and outer life, and inner life exists when workers find their inner strengths and use them to perform their tasks at work.

• A sense of community or sense of connection between workers is a human goal at work because although money is important it is not the most important goal for most people. A sense of community is described as the feeling of connectedness that workers develop with other coworkers.

A sense of community represents another fundamental dimension to create effective teamwork because people want to feel connected to work and they want to feel connected at work [8, 9].

This concept captures the degree to which employees feel the existence of teamwork connects them as a family in the organization to which they belong, as well as the perception that the supervisors do their best to encourage the presence of effective work.

A sense of community is described as the feeling of connectedness that workers develop with other coworkers [13], and success can be described using terms such as being connected and balanced.

The sense of connection is a feeling far beyond oneself, with a genuine sense of community arising from the presence of affections [9].

Spirituality at work is related to the teamwork concept, once the sense of connection is better understood when we realize meaningful work, and colleagues take the place of family and social groups.

• Alignment with the organization's values is about the personal values and the organization's mission and purpose [16].

This dimension captures the gap between the workers' perceptions and attitudes and the values of their organizations.

Alignment with the organization's values measures aspects related to the leader's interests, particularly if there are concerns beyond financial issues. Issues as the perceptions about the organization's future, the way that workers inner life and peace are respected, and finally, the leadership's attitudes to society. These issues are fundamental to create effective teamwork.

• Emotional balance and inner peace capture the emotional balance and inner peace at an individual level when workers perform meaningful work, as explained above. This dimension reinforces the coherence of the overall spirituality at work concept and covers these aspects already anticipated by Maslow [3, 15, 17], and to enrich the traditional approaches of spirituality at work and reinforce the overall coherence of the concept, currently based on four dimensions: inner life, meaningful work, sense of community and values alignment. Emotional balance and inner peace are related to the importance of the happiness that can be felt through work, allowing to find a feeling of inner peace and emotional balance when something goes wrong [1]. With this dimension, we can create a clear connection between the individual and organizational levels, since employees with higher welfare and better life balance are stronger and more persevering [15].

#### **2.2 The link between spirituality at work and teamwork**

Through analysis of Maslow's Theory of Needs, a reason was found to establish a link between spirituality at work and organizations [18]. So to begin the understanding about this link, we need to remember the following question, built by Maslow on September 14, 1967, in San Francisco, where he delivered a public lecture titled "The farther reaches of human nature":

• "What are the moments which give you … the greatest satisfaction? What are the moments of reward which make your work and your life worthwhile?"

The answer to the previous question can be contextualized through the updated version of the Maslow's hierarchy, that includes the next six motivational levels [18]:


Maslow's studies created the foundations of the spirituality at work concept, identifying the dimensions to the self-actualization and self-transcendence [17]: unique self, peak experience and transcendence, spirituality and meaning, and esthetic-creative element.

Maslow gave an additional contribution to helping the launch in 1969 of the Journal of Transpersonal Psychology, and several other specialists found there a good opportunity to clarify the spirituality at work concept and the role of spirituality may have in the organizational context, in the management, and leadership fields [17].

This new approach contributes to a natural commitment to actions related to justice, trust, beauty, order, simplicity, meaning, and purpose [17]. It is important to note that self-actualizing people are committed with themselves and with the

#### *Spiritual Environment Management Tool DOI: http://dx.doi.org/10.5772/intechopen.94125*

well-being of their groups and community [17] when they are committed whit these intangible being-values [3].

Many academic studies give us insights about how a spiritual environment can help organizations increase their performance and improve the link between workers, between workers and the organization. And, both the self-actualization need and the self-transcendence need, are related to spirituality, and more specifically, related to spirituality inside organizations.

As Maslow explained a self-actualizing person can transcend to individual concerns when he/her being feels actualizing. And these individual concerns are related to intrinsic willingness to serve others, devotion to an ideal, or involvement with a cause as social justice. The self-transcendence person can become relatively egoless. So, to be self-actualizing is not enough for a full description of the human being.

This description of the human being leads us to the words of Chattopadhyay [4]: good people management is more important than all other factors since organizations need to create a work environment that helps them attract, keep and motivate the workers. Dr. (Prof) Debaprasad Chattopadhyay reinforces the idea stating that "the creation of challenge and meaningfulness for employees has become a priority" and "how individuals within organizations can maintain inner and outer balance is an important issue" (p.75).

A multi-disciplinary care team - all working together - to help coordinate and optimize the care of patients with complex medical problems, as mentioned before, meaning effective teamwork, that can reduce medical errors, improving patient safety, requires good people in the management. The presence of spirituality at work can help, since can represent the link between the concern of this book with the words of Dr. (Prof) Debaprasad Chattopadhyay.

How spirituality can help health professionals? Spirituality at work can help them by making organizations socially responsible [7] what includes: the impacts on the environment, the impacts on the community, and the possibility to create a better world. Once, spirituality at work look at people not only as human resources but as whole human beings, including their spiritual needs [19] helping workers "become a spiritual being on a human journey" [17] p.747. And, spiritual leadership is about identifying and affirming shared core values, vision, and purpose with meaning for everybody; meaningful work and community [4].

#### **3. Spiritual environment implementation**

The Institute of Medicine (IOM) identified six key measures to improve the overall quality of the healthcare system: safety, effectiveness, patient-centeredness, timeliness, efficiency, and equity [20]. The balanced pursuit of these six key measures is not easy with the current challenges facing by healthcare organizations, as well as other organizations belonging to other industries.

Rational common interests and rational individual interests conflict [20] frequently, and this issue does not promote effective teamwork.

The academic research shows us some evidence which justified the relationship between teamwork and patient safety [21]: investigations about the factors contributing to critical incidents and adverse events have shown that teamwork plays an important role in the causation and prevention of adverse events; some studies focusing on healthcare providers' perceptions of teamwork demonstrated that staff's perceptions of teamwork and attitudes toward safety-relevant team behavior were related to the quality and safety of patient care, and perceptions of teamwork and leadership style are associated with staff well-being, which may impact clinician' ability to provide safe patient care; observational studies on teamwork

#### *Teamwork in Healthcare*

behaviors related to high clinical performance have identified patterns of communication, coordination, and leadership that support effective teamwork.

The creation and implementation of a spiritual environment may be one strategic imperative of the new millennium, once "people with heart" are "good people management", and good people management is more important than other organizational factors [4]. A spiritual environment includes spiritual practices and spirituality at work [22], as we can see in **Figure 1**, and in this subsection, we will address the implementation.

In the introduction of this chapter, was asked to note the expression "whole selves to the work". This expression helps us to understand the importance of the spiritual environment for healthcare professionals, to bring their whole selves to their organizations where they belong.

The spiritual environment as a management tool can have a positive impact on the development of a care team, with the natural meaning of this concept as claimed by Maslow. Even as create an environment where workers may find meaning in their lives, resilience to overcome obstacles upon fulfilling a fundamental human need [23].

A solid healthcare system requires a healthy work environment, with the compassion feeling between workers as between workers and patients, so the workers need to perform their tasks where the expression "whole selves to the work" is a priority. And we cannot forget: they support all the pain of the humankind. They represent the hope for those who suffer from the most varied pathologies.

The author Pfeffer [24] (p.32) noted four dimensions that workers seek in the workplace:


True spiritual leadership enables workers to find these four dimensions, through a training program about a shared spiritual environment, which includes both spiritual practices and spirituality at work concept.

The first step should be diagnostic and measure the level of spirituality at work through the five dimensions previously described, which include 22 questions. This first step allows us to know the organization about the presence of spirituality at work. That is, it allows us to answer the question:

• Have the healthcare professionals the recognition that they have an inner life that nourishes and is nourished by meaningful work that takes place in a community context, with a sense of alignment between individual and organizational values with a sense of emotional balance and inner peace?

After the diagnostic, the next step (second step) should be the development of spiritual practices to implement a spiritual environment.

The implementation of spiritual programs in the workplace can have results at the individual level contributing to the multi-disciplinary care team. Corporate

#### *Spiritual Environment Management Tool DOI: http://dx.doi.org/10.5772/intechopen.94125*

programs and spiritual practices should be custom-designed and adapted to the individuality, values, and perspectives of the workers [25].

Academic studies identified a set of spiritual practices: fitness relaxation practice, meditation, reiki, health programs, hygiene and food education, yoga, pilates, dance, diversity support programs, and music [1].

All these spiritual practices benefit health and well-being and are reported in various investigations in the areas of psychology and health. These different practices may contribute to the development of a sense of community within the team, alignment with organizational values, meaningful work, opportunities for the inner life, and emotional balance and inner peace. And this impacts explained why the practices mentioned called by "spiritual practices" can contribute with the goal "all working together".

#### **4. Conclusion**

In the introductory chapter "Medical Error and Associated Harm - The Critical Role of Team Communication and Coordination" of the book "Vignettes in Patient Safety" the authors claim that "the focus on patient safety has its genesis in the combined desire and duty to "do the right thing" in conjunction with the realization that there is an unacceptably high prevalence of avoidable adverse events, we must all join forces and make the effort to meaningfully contribute at the personal, team, and institutional levels" [26]. We find here words and issues which by natural meaning are connected with the spiritual management tool, supporting the link between spirituality at work concept and effective teamwork.

Many researchers emphasize the importance of spirituality at work within organizations, and this growing interest among academics, managers, and the general public [27]. This approach can be seen as a new paradigm change inside the academic context and management thinking.

In recent years, research using diverse methodological approaches has led to significant progress in teamwork research in healthcare [21]. This chapter explained the spiritual environment management tool concept and how can contributes to the creation of a multi-disciplinary care team - all working together to help coordinate and optimize the care of patients with complex medical problems.

To achieve collective prosperity through work, the International Labor Organization considers fundamental values as freedom, human dignity, social justice, security, and non-discrimination. The spiritual environment is aligned with these values as well as principles of the World Health Organization.

Spiritual environment, can play a fundamental role in healthcare organizations since spirituality at work is definable and measurable, and the inclusion provides intrinsic and extrinsic reasons as organizational affective commitment, job resourcefulness [22], and organizational performance [9, 28].

Attending the spirituality at work concept, a spiritual environment is created when the companies respect cultural diversity and personal values of workers by implementing employee development programs, employee participation in the decision, and healthy employer-employee relations [14]. The implementation of spiritual programs can boost results at the individual level, such as self-efficacy, greater willingness to cooperate, grow, learn, and adapt to challenges [1, 25, 27].

The spiritual practices should respond to organizational and individual concerns as explained in Subsection 2.2. To create a spiritual environment a greater consensus is required to move the whole organization.

*Teamwork in Healthcare*

#### **Author details**

Maria Joelle Polytechnic Institute of Coimbra, Coimbra, Portugal

\*Address all correspondence to: mariajoelle7@gmail.com

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Spiritual Environment Management Tool DOI: http://dx.doi.org/10.5772/intechopen.94125*

#### **References**

[1] Joelle M, Coelho A. The impact of a spiritual environment on performance mediated by job resourcefulness. *Int J Work Heal Manag*; 12. Epub ahead of print 2019. DOI: 10.1108/ IJWHM-05-2018-0058.

[2] Doram K, Chadwick W, Bokovoy J, et al. Got spirit? the spiritual climate scale, psychometric properties, benchmarking data, and future directions. BMC Health Serv Res. 2017;**17**:1-7

[3] Wulff DM, Maslow AH. Religions, Values, and Peak-Experiences. J Higher Educ. 1965;**36**:235

[4] Chattopadhyay, Debaprasad. Case Study Spiritual Motivation in Management : A Case Study on how Spirituality in Management can be used by a health-care provider \*\*\* " SGCC & RI - A big leap in the service to Mankind". 2016; 74-87.

[5] Roberts G. Leadership coping skills: Servant leader workplace spiritual intelligence. J Strateg Leadersh. 2013;**4**:52-69

[6] Smith JA, Rayment JJ. The Global SMP fitness framework: A guide for leaders exploring the relevance of spirituality in the workplace. Manag Decis. 2007;**45**:217-234

[7] McLaughlin C. Spirituality and ethics in business. Eur Bus Rev. 2005;**17**:236-243

[8] Ashmos DP, Duchon D. Spirituality at Work: A Conceptualization and Measure. J Manag Inq. 2000;**9**:134-145

[9] Daniel JL. Workplace spirituality and stress: Evidence from Mexico and the US. Manag Res Rev. 2015;**38**:29-43

[10] Deshpande AR. Workplace Spirituality, Organizational Learning Capabilities, and Mass Customization: An Integrated Framework. Int J Bus Manag. 2012;**7**:3-18

[11] Milliman J, Czaplewski AJ, Ferguson J. Workplace spirituality and employee work attitudes: An exploratory empirical assessment. J Organ Chang Manag. 2003;**16**:426-447

[12] Rego A, Pina E, Cunha M. Workplace spirituality and organizational commitment: An empirical study. J Organ Chang Manag. 2008;**21**:53-75

[13] Joelle M, Coelho A. Adding a new dimension to the spirituality at work concept: Scale development and the impacts on individual performance. Manag Decis. 2019;**58**:982-996

[14] Gupta M, Kumar V, Singh M. Creating Satisfied Employees Through Workplace Spirituality: A Study of the Private Insurance Sector in Punjab (India). J Bus Ethics. 2014;**122**:79-88

[15] Sanders JE, Hopkins WE, Geroy GD. From Transactional to Transcendental: Toward An Integrated Theory of Leadership. J Leadersh Organ Stud. 2003;**9**:21-31

[16] Milliman J, Ferguson J. In Search of the 'Spiritual' in Spiritual Leadership: A Case Study of Entrepreneur Steve Bigari. Bus Renaiss Q. 2008;**3**:19

[17] O'connor D, Yballe L. Maslow revisited: Constructing a road map of human nature. J Manag Educ. 2007;**31**:738-756

[18] Koltko-Rivera ME. Rediscovering the later version of Maslow's hierarchy of needs: Self-transcendence and opportunities for theory, research, and unification. Rev Gen Psychol. 2006;**10**:302-317

[19] e Cunha MP, Rego A, D'Oliveira T. Organizational Spiritualities. Bus Soc 2006; 45: 211-234.

[20] Berwick DM, Nolan TW, Whittington J. The triple aim: Care, health, and cost. Health Aff. 2008;**27**:759-769

[21] Manser T. Teamwork and patient safety in dynamic domains of healthcare: A review of the literature. Acta Anaesthesiol Scand. 2009;**53**:143-151

[22] Joelle M, Coelho AM. The impact of spirituality at work on workers' attitudes and individual performance. Int J Hum Resour Manag. 2019;**30**:1111-1135

[23] Yeoman R. Conceptualising Meaningful Work as a Fundamental Human Need. J Bus Ethics. 2014;**125**:235-251

[24] Pfeffer, J.: 2003, 'Business and the Spirit: Management Practices that Sustain Values', in R. A. Giacalone and C. L. Jurkiewicz (eds.), Handbook of Workplace Spirituality and Organizational Performance (M.E. Sharpe, Armonk, NY), pp. 29-45.

[25] Karakas F. Spirituality and performance in organizations: A literature review. J Bus Ethics. 2010;**94**:89-106

[26] Green A, Stawicki S. Firstenberg M. Introductory Chapter: Medical Error and Associated Harm - The Critical Role of Team Communication and Coordination. 2018. DOI: 10.5772/ intechopen.78014

[27] Sprik PJ. The Depersonalization of Medicine, and the Promises of Spiritual Care. Society. 2019;**56**:147-152

[28] Brophy M. Spirituality Incorporated: Including Convergent Spiritual Values in Business. J Bus Ethics. 2015;**132**:779-794

#### **Chapter 5**

## Learning Health-Care Worker Networks from Electronic Health Record Utilization

*You Chen*

#### **Abstract**

The health-care system is a highly collaborative environment where health-care workers collaborate to care for patients. Health-care organizations (HCOs) design and develop various types of staffing plans to promote collaboration among healthcare workers. The existing staffing plans describe the cooperation at a coarsegrained level, such as team scheduling. They seldom consider connections among health-care workers and investigate how health-care workers receive and disseminate information, which is essential evidence to inform actionable staffing interventions to improve care quality and patient safety. In this chapter, we introduce how to apply network analysis methods to electronic health record (EHR) utilization data to learn connections among health-care workers and build networks to describe teamwork in a fine-grained level. The chapter includes: (i) a brief description of the EHR utilization data, (ii) approaches to learn connections among health-care workers, (iii) building health-care worker networks, (iv) developing survey instruments to validate health-care worker networks, (v) introducing sociometric measurements to quantify network structures and positions of health-care workers in the networks, (vi) using statistical models to test associations between teamwork structures and patient outcomes, and (vii) listing examples to learn health-care worker networks in an HCO and a specific setting, including neonatal intensive care unit and trauma.

**Keywords:** network analysis, methodology, collaboration, care team, patient outcome, electronic health record, data-driven, data mining, bottom-up, health-care worker network, health-care organization, sociometric measurement, audit logs, statistical model, survey instrument, network structure

#### **1. Introduction**

The United States health-care system has been moving to patient-centered care by incorporating different levels of collaborations, including those occurring within a health-care organization (HCO) or between HCOs [1, 2]. A classic model [3] proposed to understand patient-centered care divides the health-care system into four nested levels: (1) the individual patient; (2) the care team made up of health-care workers (e.g., clinicians, pharmacists, social workers, and utilization managers) to care for patients; (3) the HCO (e.g., hospital, clinic, and nursing home) that supports the development and work of care teams by providing infrastructure and complementary resources; and (4) the political and economic

environment (e.g., regulatory, financial, payment regimes, and markets) that support hospital collaborations with other HCOs and payers on population health management. To promote patient-centered care, HCOs create infrastructures and develop staffing strategies to encourage collaboration among health-care workers to care for patients [4, 5]. Collaboration among health-care workers can improve care quality (e.g., reducing readmission rates) [6], patient safety (e.g., preventing medical errors) [7], and patient outcome (shortening length of stay) [8–10].

Staffing plans describe collaboration at a macro-level. For instance, an intensive care unit (ICU) may use an intensivist-centered care team (closed model) or an ad hoc group consisting of nurses, nurse practitioners, and physicians (open model) to care for critically ill patients [11]. The macro-level staffing strategies seldom specify how health-care workers connect and how they receive and disseminate information to care for patients. Thus, it is difficult for HCOs to monitor those topdown staffing strategies implemented in clinical practice. Without the micro-level knowledge of teamwork (e.g., health-care worker connection), it is challenging for HCOs to assess their staffing strategies to identify inefficient and ineffective parts for further collaboration optimization.

Measuring connections among health-care workers is very challenging due to complex clinical workflows and dynamic structures of teamwork [12, 13]. That is also one of the reasons why HCOs do not specify connections among health-care workers in their staffing plans. Recent studies show connections among health-care workers can be learned from their activities in electronic health record (EHR) systems [14–19]. EHR systems are a platform used by health-care workers to diagnose patients and exchange diagnostic results [20, 21]. In modern health-care environments, an increasing number of health-care workers utilize EHR systems as the primary tool to diagnose patients and exchange health information [22]. Therefore, the volume and scale of the EHR system utilization data have been increasing exponentially in recent years, which provide abundant resources for researchers to learn collaborations through the EHR system utilization [14–19].

In this chapter, we provide a network analysis of the EHR system utilization data to learn teamwork structures and specify connections among health-care workers. We believe the chapter can provide researchers a new way to model teamwork/ collaboration in health care. We anticipate the data, methods, and applications introduced in this chapter will be of interest to the teamwork in health-care readership, particularly those focused on network analysis, secondary data analysis, EHR utilization, and care teams.

#### **2. EHR system utilization data**

EHR systems provide a platform for care coordination across a diverse collection of health-care workers [22–25]. Coordination activities occurring in EHR systems play an increasingly important role in the establishment of high-efficient healthcare worker collaboration networks. Various studies, including our prior research, have leveraged health-care worker activities in EHR systems to infer patterns of collaboration [9, 10, 14–19]. The proportion of care activities performed via EHR systems has steadily increased with the adoption of EHR through meaningful use of incentives [22, 26].

Health-care worker activities occurring in EHR systems have been documented in the form of audit logs. When a provider accesses or moves between modules in the EHR interface, such as moving from Progress Notes to Order Entry, a record of these activities are documented, including the time the event occurred, the healthcare worker and patient IDs, and the computer location. Audit logs include all

*Learning Health-Care Worker Networks from Electronic Health Record Utilization DOI: http://dx.doi.org/10.5772/intechopen.93703*

health-care worker interactions to EHRs of patients, which provides an opportunity to study connections among health-care workers. The continuous data collection of the EHR audit logs provides robust, readily available data. Since health-care worker activity is documented in the EHR in near real time, it is free from recall bias and variation introduced when health-care workers are retrospectively surveyed to describe their activities in EHRs.

The activities performed by health-care workers stem from six primary sources [10], including conditions (e.g., assigning a diagnosis), procedures (e.g., intubation), medications (e.g., prescription), notes (e.g., progress note writing), orders (e.g., laboratory test ordering), and measurements (e.g., measuring respiratory rate).

**Figure 1** shows an example to illustrate health-care worker activities in EHR systems. Each event, such as *requesting a lab test*, includes a health-care worker, an EHR, and the time stamp. The four events depicted in the example demonstrate the hidden collaborations between health-care workers. For instance, the physician ordered a lab test and shared the order with the lab user; next, the lab user conducted the laboratory test and shared the test results of the patient with a healthcare worker in the physician office; finally, the nurse practitioner reviewed and analyzed the results.

#### **3. Transforming utilization data into matrices**

Events document interactions of health-care workers to EHRs of patients, but they do not capture the direct connections among health-care workers. As shown in **Figure 1**, the four health-care workers performed events to EHRs of a patient, and they are not directly connected. We leverage events to measure the hidden connections among health-care workers. A hidden connection between two health-care workers is defined based on their interactions with the EHRs of patients. We call a hidden connection as an indirect relationship, because the two health-care workers do not communicate directly, but care for the same patients via performing actions to their EHRs. For instance, a physician ordered a lab test and sent the order to a

#### **Figure 1.**

*An example to illustrate data elements in EHR audit logs. Four health-care workers performed their actions to EHRs of a patient at different time stamps on the same day.*

laboratory test user. The physician and the lab user have a hidden relationship that is built upon the lab test order. Hidden relations are essential knowledge to characterize processes of health information sharing and dissemination among health-care workers in EHR systems, which can potentially impact teamwork, and the following care quality and patient safety.

We use a bipartite graph of EHR users (health-care workers) and EHRs of subjects (patients) to represent events a user performed to EHRs of a subject. **Figure 2** shows an example of a bipartite graph, and a binary matrix to characterize interactions of six users to EHRs of seven subjects. In the example depicted in the figure, we use a binary matrix to represent if a health-care worker performed events to EHRs of a subject within a period (e.g., hour, day, week, or length of stay). Researchers can determine the period and whether using a binary value or the number of events to represent interactions of a health-care worker with EHRs of a patient according to their research purpose. To simplify our process, we use a binary matrix *A,* as shown in **Figure 2**. As mentioned above, if two health-care workers performed events to EHRs of the same patients within a period (e.g., a day), then there exists a hidden relationship between them. For instance, u1 and u3 both performed events to EHRs of s1 and s2. Thus, in the binary matrix, *A(1,1), A(1,3), A(2,1), A(2,3)* are all ones. To transforming health-care works' interactions to EHRs to connections among health-care workers, we use binary matrix multiplication. For instance, the relationship between u1 and u3 can be learned by multiplying matrix *A* and its transpose matrix *AT*. The results of matrix multiplication are shown in matrix *B* in **Figure 3**. *B(1,3)* or *B(3,1)* represents the number of subjects whose EHRs were managed by both u1 and u3. From **Figure 2**, we can see the number of subjects co-managed by both u1 and u3 is 2, which is equal to *B(1,3)* or *B(3,1)*. The larger the cell values in matrix *B*, the more strength of the relationship between health-care workers.

Matrix *A* represents the interactions of health-care workers to EHRs of subjects, and *B* describes the relationships between health-care workers. We show a simple way (matrix multiplication) to learn *B* from *A*. There are many alternative or advanced approaches that can be applied to matrix A (binary or nonbinary version) to measure hidden relationships between health-care workers. Examples of such methods include term-frequency, inverse documentary frequency (TF-IDF) [27], principal component analysis (PCA) [28], and similarity measurements

#### **Figure 2.**

*Events performed by health-care workers (ui) to EHRs of patients (sj) are represented by a bipartite graph (left) and corresponding binary matrix (right). In the right subfigure, if a health-care worker, ui, performed events to EHRs of a patient, sj , then the cell value A(i,j) in the matrix will be 1, otherwise 0.*

*Learning Health-Care Worker Networks from Electronic Health Record Utilization DOI: http://dx.doi.org/10.5772/intechopen.93703*

#### **Figure 3.**

*Using the product of binary matrix A and the transpose matrix AT to calculate the number of subjects whose EHRs are managed by a pair of users. Each cell value B(i,i) in the diagonal represents the number of subjects whose EHRs are managed by ui. Each cell value B(i,j) (i ≠ j) represents the number of subjects whose EHRs are co-managed by both ui and uj .*

(e.g., cosine, Kullback-Leibler (KL) divergence, edit distance, and Jaccard distance) [29]. For instance, if the size of the matrix is big (a large number of subjects or health-care workers), we can apply PCA to it to reduce the dimensionality first, and then measure relationships for pairs of health-care workers based on the principal components.

#### **4. Building networks**

#### **4.1 Relationship measurement**

There are two types of relationships: directed and undirected between healthcare workers. Directed relation emphasizes on the ordered relations, for instance, the connections from health-care worker A to B, and B to A that are different. To learn directed connection from the utilization data, we use time stamps of events to describe the ordered relationships. As shown in **Figure 1**, lab test ordering occurred ahead of lab test results uploading. Thus, the relationship between the physician who ordered the lab test and the lab user who uploaded the test results is directed. Upon the directed relations, we can create direct networks. We will use an example to illustrate the creation of directed networks of health-care workers concerning the management of each patient. Examples of undirected networks are used to describe structures of collaborations among health-care workers within a unit or across a HCO.

#### **4.2 Directed health-care worker networks**

As mentioned above, we define actions performed by a health-care worker in EHR systems as events. Events affiliated with EHRs of a patient constitute a sequence of information flow. We provide a simple scenario to understand the series of information flow as follows.

*"The night respiratory therapist documents an increased need for oxygen in a patient's EHRs"* → "*the daytime nurse documents the patient's vital signs and notes that the patient has tachypnea"* → "*on rounds the nurse practitioner and attending review the recorded vital signs focusing on the need for more oxygen and elevated respiratory rate"* → "*the physician prescribes a diuretic*.*"*

In this example, the nurse practitioner and attending's comprehension of the patient's condition grew with each update to the EHR. Health-care workers depend on their colleagues to provide information for clinical updates as they are essential to health-care workers' decision-making. As mentioned above, we call this virtual worker-worker interaction a hidden connection. A hidden connection does not mean a face-to-face interaction occurred, but rather, there existed the potential for the neighboring health-care workers to directly exchange information on the patient's condition via the EHR and arrive at the same conclusion, which in this scenario was to prescribe medication for pulmonary edema. We build networks that represent the hidden connections facilitating the dispersion of patient-related information. We call them patient-level health-care worker networks because they are composed of all health-care workers that treated a common patient.

To start, we create a simplified sequence dataset by condensing consecutive events by the same health-care worker into a single event. In this scenario, we can filter the self-loop relationships of health-care workers. In a network or a graph, a self-loop relationship is an edge that connects a vertex/node to itself. For example, health-care worker W1 made three EHR events consecutively to EHRs of a patient, and we condensed them into one event; one could interpret the simplified sequence as a workflow in EHR. Based on the sequences, we identified relationships between health-care workers whenever their events occurred consecutively (health-care worker W2 used the patient's EHR after health-care worker W1). We characterized each hidden connection with the frequency by which they occurred.

**Figure 4** shows an example of how we build a health-care worker network from a patient's sequence. As shown in **Figure 4**, the health-care worker W1 interacted with the EHR before health-care worker W2, so the arrowhead on the right points to health-care worker W2. The edge weight is the number of times the hidden interaction occurred. Note an edge exists if an interaction occurred at least once. While an observed interaction was not guaranteed to be an exchange of information, it did have the potential to be one.

#### **4.3 Undirected health-care worker networks: care for a group of patients**

The structure of teamwork learned from a single patient is hard to represent the pattern of collaboration concerning the management of a group of patients. In this section, we introduce the creation of undirected health-care workers for the management of a group of patients. We assume health-care workers participating in the care of the same patients (performed events to EHRs of the same patients) on the same day have a relationship. Based on such an assumption, we can create a binary matrix (as shown in **Figure 3**) to describe whether a health-care worker performed events to EHRs of a patient. The cell value 1 is for Yes, and 0 for No. Based on the binary matrix of health-care workers and EHRs of patients, we can use the matrix multiplication, as shown in **Figure 3**, to get the daily relationships between pairs of

#### *Learning Health-Care Worker Networks from Electronic Health Record Utilization DOI: http://dx.doi.org/10.5772/intechopen.93703*

health-care workers. Each non-diagonal cell value shows the number of patients; any two health-care workers both performed events to their EHRs on the same day. Two factors determine the strength of the relationship between two health-care workers. The first is the number of patients the two workers performed events to their EHRs on the same day, and the second is the number of days when the two workers performed events to EHRs of the same patients. We build a health-care worker network for a group of patients by using the relationships which are cumulatively added based on the number of days and patients.

We use a simple scenario to explain health-care worker networks built upon a group of patients. Assuming a medical intensive care unit (MICU) adopted a new scheduling strategy in a pandemic (e.g., COVID-19), and the health-care organization plans to investigate the changes in the structure of collaboration among health-care workers before and after the adoption of the new scheduling strategy. In this scenario, we use 8 months (4 months before and after adopting the new scheduling strategy) of EHR utilization data to learn the changes. To implement the study, we create two groups: critically ill patients admitted to the MICU before and after the new scheduling strategy adopted. To ensure the studied two groups share similar confounding factors (e.g., demographics and health conditions), we can use propensity score matching to create them. We use events performed by health-care workers to EHRs of the two groups of patients to measure relationships between health-care workers before and after the adoption of the scheduling strategy, respectively. Based on the relationships, we can build two health-care worker networks: before and after the adoption of the new scheduling strategy. The differences in the structures of the two networks can be measured using sociometric measurements, which are introduced in the following sections.

#### **4.4 Undirected health-care worker networks: care for patients within an HCO**

When learning a collaboration network at the level of a health-care organization, the number of patients and health-care workers investigated will be much bigger, and the relationships between health-care workers will become more complex. If we have a large number of patients, then it may complicate the measuring of the relationships between health-care workers. For instance, if we investigate 10,000 health-care workers and 1,000,000 patients, then the size of the health-care worker-patient matrix is 10 K by 1 M. There is a necessity to reduce the dimensionalities of the matrix to ensure it is appropriate for the following approaches to measure relationships between health-care workers. As mentioned above, PCA can be applied to the matrix to reduce dimensionalities. After the dimensionality reduction, we can use similarity measurements (e.g., cosine similarity or KL divergence) to calculate the relationships between health-care workers, which are used to build networks of health-care workers. If PCA is unable to represent the variance of the data in the matrix, an alternative way is to transform the matrix of health-care workers and patients into a higher level. Instead of building networks of healthcare workers, we can create networks of operational areas (e.g., medical intensive care unit, and burn center). Also, we can cluster patients into groups according to their phenotypes and transform the matrix of health-care workers by patients into operational areas by patient groups. Based on the new transformed matrix, we measure relationships between operational areas and build a collaboration network of operational areas.

**Figure 5** shows an example to illustrate the process of transforming interactions between health-care workers and EHRs of patients into interactions of health-care workers to EHRs of groups of patients. Patient groups can be learned by conducting phenotyping algorithms on patient health conditions and demographics. For instance, a typical topic modeling algorithm – Latent Dirichlet Allocation (LDA) can be used to learn topics to represent phenotypes of each patient. Based on the phenotypic topics, patients can be clustered into groups. As shown in **Figure 5**, the transformation from *Apatient × health condition* to *Bpatient × patient group* can be implemented by using LDA.

**Figure 6** shows an example to illustrate the process of transforming interactions between health-care workers and EHRs of patient groups into the interactions between operational areas and EHRs of patient groups, which are further leveraged to measure relationships between operational areas. The transformation from *Dhealth-care worker × patient group* and *Eoperational area × health-care worker* into *Foperational area × patient group* is implemented using matrix multiplication. *Eoperational area × health-care worker* represents the affiliations of healthcare workers to operational areas. Similarity measurements can be applied to *Foperational area × patient group* to learn relationships between pairs of operational areas *Roperational area × operational area*. Collaboration networks of operational areas can be built upon the *Roperational area × operational area*. To learn stable relationships between operational areas, we may need to create the matrix *Chealth-care* 




#### **Figure 5.**

*An example to illustrate the process of transforming the interactions between health-care workers and EHRs of patients (Chealth-care worker × patient) into interactions between health-care workers and EHRs of patient groups (Dhealth-care worker × patient group).*

*Learning Health-Care Worker Networks from Electronic Health Record Utilization DOI: http://dx.doi.org/10.5772/intechopen.93703*

#### **Figure 6.**

*An example to illustrate the process of transforming interactions between health-care workers and EHRs of patient group (Dhealth-care worker × patient group) into relationships between operational areas (Roperational area × operational area).*

*worker × patient* by setting a longer window size, such as 1 week/month rather 1 day we used in the previous examples. A study shows it requires at least 4 weeks to get stable relationships between operational areas by using interactions between health-care workers and EHRs of patients [30].

#### **5. Validating relationships among health-care workers learned from the EHR system utilization**

Concerns over the trustworthiness of the results of automated learning methods are not limited to the health-care worker network learned in this chapter. Instead, this is a problem that manifests when any knowledge is learned from the secondary analysis of EHR data. Researchers always need to review the knowledge learned from the data for their plausibility. As we mentioned above, the relationships between health-care workers learned from the utilization data are indirect. In other words, they are not explicitly documented by health-care organizations. To use networks built upon such relationships to describe or interpret structures of collaborations among health-care workers, we need to validate the relationships.

To do so, we design and deploy an online survey to assess the plausibility of relationships among health-care workers. **Figure 7** depicts an example of 626

**Figure 7.**

*Relationships between pairs of health-care workers ranked by their strength. Each of the two shaded areas represents relationships with high and low strengths, respectively. Each node in the graph represents a relationship.*

relationships ranked on a log scale and the strength of the relationships. This is clearly more relationships than a human can evaluate without fatigue, and so we need to sample a small number of them for respondents to assess. For instance, we can randomly select 20 relationships: 10 of high, and 10 of low strength. A survey can be designed to evaluate a specific hypothesis of the form: hospital employees can correctly distinguish between relationships of high and low strengths.

A survey contains a series of questions. The hospital employees who respond to the survey are presented with questions of the form: "*An internal medicine physician performed actions to the record of patient John Doe. How likely is it that an internal medicine nurse practitioner performed actions to the same patient's record?*". Respondents are not presented with the strength of the relationship between internal medicine physicians and nurse practitioners. The respondents are asked to choose one of five candidate answers: "Not at all likely," "Slightly likely," "Moderately likely," "Very likely," and "Completely likely." In order to conduct a survey analysis through statistical models, we can convert these answers into integer values in the range 1–5 (e.g., "Not at all likely" is mapped to 1).

A set of respondents will answer each question in the survey. We can conduct a pretest to obtain feedback from the experts to refine the surveys and estimate the required number of experts via a power analysis. REDCap, which is a secure web application for building and managing online surveys and databases [31], can be used to implement the online survey. Details of the plausibility validation of the relationships between health-care workers can be found in our previous works [30, 32]. If we can verify with statistical significance that the learned relationships are often in line with the expectations of hospital employees, then we can suggest that collaboration networks of health-care workers, as well as strategies built on such networks, may be reliable and scalable.

#### **6. Sociometric measurements**

Sociometric measurements include network- and node-level metrics. The network-level metrics such as size, graph density, reciprocity, triads, average path length, clustering, cohesion and density, core-periphery, centralization, diameter, and K-core are used to characterize the structure of a network; while the node-level metrics such as degree, closeness, betweenness, eigencentrality, and eccentricity are used to describe the characteristics of each node in the network. In this section, we explain those measurements in the health-care worker networks.

#### **6.1 Network-level metrics**

**Diameter.** The diameter is defined as the number of steps in the longest path in the network. There are two types of paths for any two nodes in the network. The first one is the shortest path, which is defined as the smallest number of steps between the two nodes, and the other one is the longest path, which has the largest number of steps between the two nodes. The network diameter is the number of steps between the two nodes, who have the largest number of steps in their longest path. Given two networks of health-care workers, if the diameter of the first one is larger than the second, then the information sharing and dissemination among health-care workers in the first network requires more steps.

**Density and cohesion**. Graph density is defined as the total number of edges within the network, divided by the number of edges that could exist. The cohesion of a network is described by the diameter and the average path length. The average path length is the average of the steps between all the nodes in the network. The low diameter or low average path length indicates a cohesive network with little clustering. Usually, when density increases, the average path length decreases because high-density network provides many paths along which to connect nodes. Studies show the relationship between density and average path length is nonlinear [33]. Density values above 0.5 indicate networks have many redundant paths between nodes, and it is hard to identify structures of networks [33]. If density values are very low, then there will be no network structures. To learn structures of healthcare worker collaboration networks, we may need to prune the networks by using density values (e.g., <0.5). For instance, we can filter edges whose weight strength is low to decrease the density values of networks.

**Core-periphery.** Core-periphery structures are networks in which there is a group of nodes that are densely connected to one another (the core) and a separate group of nodes loosely connected to the core and loosely connected to each other (the periphery). It is not uncommon to find core-periphery networks in the health-care domain. In the NICU, nurses, neonatologists, and anesthesiologists work in a core network [9]. In contrast, otolaryngology residents, endocrinology physicians, and hematology physicians collaborate in a periphery network [9].

**Centralization.** The typical calculation of centralization is as: ( ( ) ) ( ) <sup>=</sup> <sup>=</sup> ∑ − −+ <sup>2</sup> <sup>1</sup> max / 3 2 *i n i i <sup>i</sup> vv n n* , where *<sup>i</sup> v* is the centrality score (e.g., degree, betweenness, and closeness) of a node in the network, and n is the total number of nodes in the network [34]. In the centralized network, one or a few people hold a position of power and control in the network. An alternative way to calculate centralization is to measure the standard deviation of the node centrality scores. A large standard deviation indicates a lot of variation in the individual centrality scores, and hence a centralized structure. In contrast, a small standard deviation suggests little variation and hence a decentralized structure. In a network of health-care workers, if we can identify workers with high centrality scores in the centralized network, then we can further investigate how those workers share and disseminate information in the EHR systems. Do they act like broadcasters to reach many health-care workers quickly, or do they act as gatekeepers to slow down the information sharing and dissemination?

**Clustering coefficient**. A clustering coefficient is a measure of the degree to which nodes in a graph tend to cluster together. The metric can be defined at the network- and health-care worker levels. The network-level clustering coefficient gives an overall indication of the clustering in the network. The network-level clustering coefficient is measured as: # /# *of closed triplets of all triplets* , where a triplet is three nodes that are connected by either two (open triplet) or three (closed triplet) undirected edges. If a health-care worker network has a high clustering coefficient, then health-care workers are connected in dense pockets of interconnectivity. There are two types of network structures that connect clustered subgroups. The first is the bridge structure, in which the clustered subgroups are connected by bridges (intermediates), and the second one is the centralized structure, in which central health-care workers connect the subgroups.

**Reciprocity.** Reciprocity is used to characterize the symmetry in relationships between health-care workers. In network science, the reciprocity is measured in direct networks. A typical approach [35] to calculate the network reciprocity is: ( )( ) ( ) ≠ ≠ ∑ ∑ −− − <sup>2</sup> ,, , / , *i j ji i j i j i j a aa a a a* where *ai j* , is one if a link from i to j exists, and 0, otherwise. ( ) <sup>≠</sup> = ×− ∑ , / 1, *i j i j a a nn* where n is the number of health-care workers in the network. If a network has a higher value of reciprocity, then the greater likelihood of health-care workers to be mutually linked in information receiving and dissemination in the network.

**K-core.** The K-core is a subset of the network, in which each health-care worker within the K-core is connected to at least K other workers. A health-care worker in the K-core sub-network is considered as one of the cores in the whole network.

#### **6.2 Health-care worker-level metrics**

**Degree.** The degree of a health-care worker is the total number of edges connected. The weighted degree is the sum of the weights of connected edges. In the health-care worker network, the weight of an edge can be the strength of the relationship.

**Clustering coefficient.** The clustering coefficient of a health-care worker is the proportion of connections among their adjacent health-care workers divided by the number of connections that could possibly exist between them. One can think of the clustering coefficient as quantification of how close a health-care worker's neighbors are to be a clique of clinicians (e.g., a small group of clinicians, with shared interests in common patients). A health-care worker with a large clustering coefficient is the one who shares patients with health-care workers who also share patients with each other [9].

**Betweenness.** The betweenness is defined as the number of shortest paths between two health-care workers that pass through the specific health-care worker. Betweenness refers to whether a health-care worker lies on the path of others who are not directly connected. A health-care worker with a broad skillset could frequently be in a high-betweenness position. For instance, in **Figure 8**, clinicians 2, 3, and 4 have the largest number of shortest paths going through them. Betweenness reflects a health-care worker's access to diverse communication channels about evidence-based practice. A high betweenness worker cares for a wide spectrum of patients.

**Eigencentrality**. Eigencentrality is used to quantify the influence or leadership of a health-care worker on the collaboration and coordination among health-care workers in the network. A health-care worker with a high eigencentrality is connected to workers who have high eigencentrality. An example of health-care workers with high eigencentrality is shown in **Figure 9**. A high eigencentrality health-care worker acts as a leader in the sharing of patients in the network.

*Learning Health-Care Worker Networks from Electronic Health Record Utilization DOI: http://dx.doi.org/10.5772/intechopen.93703*

**Figure 8.** *Examples of health-care workers with the highest betweenness.*

**Figure 9.** *An example of a health-care worker with the highest eigencentrality.*

#### **7. Statistical models to test hypotheses related to network structures**

Most of the research studies in health care are hypothesis-driven. One of the goals of the network analysis in health care is to provide evidence on network structure to assist in the designing and development of teamwork-based hypotheses. Various hypotheses can be developed between sociometric measurements and clinical outcomes, including delayed ICU admission, ICU readmission, medication error, adverse event, length of hospital stay (LOS), mortality risk, and health-care cost.

#### **7.1 Relationships of sociometric measurements with clinical outcomes**

Structures of teamwork among health-care workers can be quantified by using both network- and node-level sociometric measurements. It has been recognized that structures of teamwork are associated with clinical outcomes. To inform actionable staffing interventions, we can develop hypotheses for each of the sociometric measurements and validate their relationships with clinical outcomes. For each inpatient stay (ranging from their admission to discharge), we can create a network to describe the structure of teamwork among health-care workers during the patient stay. Hypotheses can be designed based on the network. An example

of the hypotheses can be: the clustering coefficient of a network is associated with LOS. Statistical models can be leveraged to test the hypotheses. The distributions of most network measurements are not Gaussian distributed, so we can use rankbased approaches to measure associations between the measurements and clinical outcomes. For instance, we can use the Spearman rank-order correlation to measure the association between the clustering coefficient and LOS. If we want to investigate multiple sociometric measurements or add confounding factors (patient demographics, the severity of sickness), we can use advanced statistical models, such as a proportional-odds (PO) logistic regression model.

The PO model can be thought of as a set of logistic regression models, where each model describes the log-odds of LOS (continuous variable) being higher than some threshold j (rather than lower than or equal to), and where j = 1, 2, …, J represents all possible thresholds by which LOS can be dichotomized, and J is equal to the number of unique outcome values minus one. The set of models is collapsed into a single model, via the proportional odds assumption that coefficients for predictor variables are the same across the threshold values. Even when this assumption is not met, a coefficient from the proportional odds model can be thought of as a weighted average of coefficients across all the threshold-specific logistic regression models.

Some outcomes, such as ICU readmission, delayed ICU admission, or mortality risk are categorical variables. In that case, we can use the Mann Whitney U test or analysis of variance (ANOVA) to test the differences in the sociometric measurements between networks. The hypotheses can also be developed between nodelevel measurements (e.g., betweenness, eigencentrality, and degree) and clinical outcomes. For instance, critically ill patients who were cared for by more highbetweenness nurses were significantly less likely to die in their ICU stays.

#### **7.2 Changes in structures of health-care worker networks**

Analyzing changes in the collaboration network structures and measuring relationships of the changes with outcomes are very important research questions in the teamwork in health care. When a health-care organization adopts a new staffing intervention (e.g., creating a new team scheduling), they will need to assess and monitor the changes in the behavior of collaboration among health-care workers before and after the interventions and how such changes impact clinical outcomes. Getting feedback from the adoption of a new staffing intervention can provide evidence to identify weak and ineffective parts to do further optimization. For instance, ICUs adopt staffing interventions in the COVID-19 pandemic, and the responses may change the structure of collaboration. We can use network analysis approaches to analyze the changes in the network structures from pre-COVID-19 to intra-COVID-19 and measure the relationships of such changes with clinical outcomes such as ICU readmission or delayed ICU admission. Examples of hypotheses can be: neonatology physicians have higher betweenness after the staffing intervention or the health-care worker network after the staffing intervention has a larger diameter (the difficulty of sharing patients increases). Since sociometric measurements are not Gaussian distributed in many situations, we can apply a Mann-Whitney U test to measure the significance of the difference.

#### **8. Applications**

We introduce three applications to show how we use network analysis to identify care teams in a health-care organization, measure associations between collaborations and length of stay in the trauma setting, and assess health-care worker networks for the management of surgical neonates, respectively.

#### **8.1 Care team identification**

We applied network analysis to the EHR utilization records of over 10,000 hospital employees and 17,000 inpatients at a large academic medical center during a 4-month window [19]. The study aimed to learn collaboration structure across the entire health-care system, and thus it built networks of departments (higher level) rather than the networks of health-care workers. Each node in the network is a department. LDA models were used to cluster patients into groups. As shown in **Figures 5** and **6**, matrix multiplications were conducted to transform the matrix of health-care workers and patients into the matrix of departments and patient groups. Connections among 317 departments were inferred from the departmentpatient group matrix. We identified 34 collaborative groups of departments [19]. Each of the groups is a subnetwork and could be considered as a care team across various types of departments. The results suggested that, although the over 17,000 patients exhibited over 1400 different types of phenotypes, the health-care workers treating them tended to work in only 34 collaborative groups. When the 34 groups were presented to health-care experts via online surveys, 27 (79.4%) of 34 were confirmed as administratively plausible. Of those, 26 teams depicted strong collaborations, with a clustering coefficient >0.5.

#### **8.2 Length of stay and trauma team structures**

We started the network analysis of trauma team structures by creating a matrix of ~5000 health-care workers and EHRs of ~5500 patients based on the EHR system utilization data [10]. The difference is we applied a spectral co-clustering methodology to the matrix to infer groups of patients and clusters of health-care workers simultaneously. By using the co-clustering algorithm, we created three trauma patient groups, each of which has a corresponding network of health-care workers. For each network of health-care workers, we calculated sociometric measurements to quantify their structures. Length of stay was used as the outcome. The association between a sociometric measurement (e.g., clustering coefficient) and length of stay was measured by using statistical models incorporating various confounding factors (e.g., demographics and admission dates). We found a remarkably clear distinction in LOS: those patients experiencing the largest quantity of collaborations between health-care workers had the shortest LOS, while those subject to fewer collaborations (i.e., supported by less well-integrated care teams), spent much longer in hospital, indicating greater financial cost as well, of course, as pain, distress, and inconvenience to the patient [10].

#### **8.3 Length of stay and NICU team structures**

We extracted EHR data of 15 NICU gastrostomy patients from the day prior to the patient's surgery day until postoperative day 30. The study aims to validate the associations between health-care worker networks and post-surgical length of stay (PLOS) [36]. For each patient ICU stay, we built a directed network to show how information was received and disseminated among health-care workers in the NICU. For each patient's stay, we created a simplified sequence dataset by ordering health-care worker actions based on their time stamps starting from the day prior to the patient's surgery until postoperative day 30 or the patient's discharge date. Based on the sequences, we identified connections between health-care workers whenever

their actions occurred consecutively. We learned 15 patient-level health-care worker networks. We used the sociometric measurements, including in-degree, out-degree, and betweenness, to quantify the structures of each patient-level network.

We modeled patient PSLOS with each structure measurement controlling for patient age and weight using a proportional-odds logistic regression model. Study results show health-care workers, whose patients had lower PSLOS, tended to disperse patient-related information to more colleagues within their network than those, who treated higher PSLOS patients (P = 0.0294). Our results demonstrate in the NICU that improved dissemination of information may be linked to reduced PSLOS.

#### **9. Conclusions**

This chapter provides an introduction of a network analysis of secondary EHR system utilization data to learn health-care worker networks. We introduce five main components when applying network analysis to team structures and clinical outcomes: (i) matrix multiplication to build connection among health-care workers, (ii) survey instruments to validate the plausibility of the learned connections among health-care workers, (iii) sociometric measurements to characterize network structures, (iv) hypothesis development to connect network structures with clinical outcomes, and (v) statistical models to test the hypotheses. Finally, we use three examples to show the application of network analysis in health care. In short, EHR data provide an efficient, accessible, and resource-friendly way to study teamwork using network analysis tools.

#### **Acknowledgements**

The research studies introduced in this chapter were supported, in part, by the National Library of Medicine of the National Institutes of Health under Award Numbers K99LM011933, R00LM011933, and R01LM012854.

#### **Conflict of interest**

The authors declare no conflict of interest.

#### **Author details**

You Chen Vanderbilt University Medical Center, Nashville, Tennessee, United States

\*Address all correspondence to: you.chen@vanderbilt.edu

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Learning Health-Care Worker Networks from Electronic Health Record Utilization DOI: http://dx.doi.org/10.5772/intechopen.93703*

#### **References**

[1] Fix GM, VanDeusen Lukas C, Bolton RE, Hill JN, Mueller N, LaVela SL, et al. Patient-centred care is a way of doing things: How healthcare employees conceptualize patientcentred care. Health Expectations. 2018;**21**(1):300-307

[2] Gusmano MK, Maschke KJ, Solomon MZ. Patient-centered care, yes; patients as consumers, no. Health Affairs. 2019;**38**(3):368-373

[3] Ferlie EB, Shortell SM. Improving the quality of health care in the United Kingdom and the United States: A framework for change. Milbank Quarterly. 2001;**79**(2):281-315

[4] D'Lima DM, Murray EJ, Brett SJ. Perceptions of risk and safety in the ICU: A qualitative study of cognitive processes relating to staffing. Critical Care Medicine. 2018;**46**(1):60

[5] Upadhyay S, Weech-Maldonado R, Lemak CH, Stephenson AL, Smith DG. Hospital staffing patterns and safety culture perceptions: The mediating role of perceived teamwork and perceived handoffs. Health Care Management Review. 25 October 2019. DOI: 10.1097/ HMR.0000000000000264. [Epub ahead of print] PMID: 31702706

[6] Newman MW. Integrated and collaborative care: Quality improvement in action. Psychiatric Annals. 2017;**47**(7):374-377

[7] Ma C, Park SH, Shang J. Inter-and intra-disciplinary collaboration and patient safety outcomes in US acute care hospital units: A cross-sectional study. International Journal of Nursing Studies. 2018;**85**:1-6

[8] Reeves S, Pelone F, Harrison R, Goldman J, Zwarenstein M. Interprofessional collaboration to improve professional practice and healthcare outcomes. Cochrane Database of Systematic Reviews. 2017;**6**(6):CD000072

[9] Chen Y, Lehmann CU, Hatch LD, Schremp E, Malin BA, France DJ. Modeling care team structures in the neonatal intensive care unit through network analysis of EHR audit logs. Methods of Information in Medicine. 2019;**58**(4-05):109

[10] Chen Y, Patel MB, McNaughton CD, Malin BA. Interaction patterns of trauma providers are associated with length of stay. Journal of the American Medical Informatics Association. 2018;**25**(7):790-799

[11] Chowdhury D, Duggal AK. Intensive care unit models: Do you want them to be open or closed? A critical review. Neurology India. 2017;**65**(1):39

[12] Rosen MA, DiazGranados D, Dietz AS, Benishek LE, Thompson D, Pronovost PJ, et al. Teamwork in healthcare: Key discoveries enabling safer, high-quality care. The American Psychologist. 2018;**73**(4):433

[13] Jagannath S, Sarcevic A, Marsic I. An analysis of speech as a modality for activity recognition during complex medical teamwork. In: Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare; 21 May 2018. pp. 88-97

[14] Durojaiye AB, Levin S, Toerper M, Kharrazi H, Lehmann HP, Gurses AP. Evaluation of multidisciplinary collaboration in pediatric trauma care using EHR data. Journal of the American Medical Informatics Association. 2019;**26**(6):506-515

[15] Wu DT, Smart N, Ciemins EL, Lanham HJ, Lindberg C, Zheng K. Using EHR audit trail logs to analyze clinical workflow: A case study from community-based ambulatory clinics. In: AMIA Annual Symposium Proceedings. Vol. 2017. San Francisco, CA: American Medical Informatics Association; 2018. pp. 1820-1827

[16] Amroze A, Field TS, Fouayzi H, Sundaresan D, Burns L, Garber L, et al. Use of electronic health record access and audit logs to identify physician actions following noninterruptive alert opening: Descriptive study. JMIR Medical Informatics. 2019;**7**(1):e12650

[17] Wang JK, Ouyang D, Hom J, Chi J, Chen JH. Characterizing electronic health record usage patterns of inpatient medicine residents using event log data. PLoS One. 2019;**14**(2):e0205379

[18] Chi J, Bentley J, Kugler J, Chen JH. How are medical students using the electronic health record (EHR)?: An analysis of EHR use on an inpatient medicine rotation. PLoS One. 2019;**14**(8):e0221300

[19] Chen Y, Lorenzi NM, Sandberg WS, Wolgast K, Malin BA. Identifying collaborative care teams through electronic medical record utilization patterns. Journal of the American Medical Informatics Association. 2017;**24**(e1):e111-e120

[20] Durojaiye AB. A novel approach for the investigation of multidisciplinary collaboration using social network analysis on electronic health record data [doctoral dissertation]. Baltimore MD: Johns Hopkins University; 2018

[21] Van Liew JR. Balancing confidentiality and collaboration within multidisciplinary health care teams. Journal of Clinical Psychology in Medical Settings. 2012;**19**(4):411-417

[22] Henry J, Pylypchuk Y, Searcy T, Patel V. Adoption of electronic health record systems among US non-federal acute care hospitals: 2008-2015. ONC Data Brief. 2016;**35**:1-9

[23] Platform IT. Open or closed: A project proposal for investigating two different EHR platform approaches. Context Sensitive Health Informatics: Sustainability in Dynamic Ecosystems. 2019;**265**:207

[24] Ballaro JM, Washington ER. The impact of organizational culture and perceived organizational support on successful use of electronic healthcare record (EHR). Organization Development Journal. 2016;**34**(2):11-29

[25] Serbanati LD, Ricci FL. EHR-centric integration of health information systems. In: 2013 E-Health and Bioengineering Conference (EHB); 21 November 2013. IEEE. pp. 1-4

[26] Adler-Milstein J, Adelman JS, Tai-Seale M, Patel VL, Dymek C. EHR audit logs: A new goldmine for health services research? Journal of Biomedical Informatics. 2020;**101**:103343

[27] Chen K, Zhang Z, Long J, Zhang H. Turning from TF-IDF to TF-IGM for term weighting in text classification. Expert Systems with Applications. 2016;**66**:245-260

[28] Ku W, Storer RH, Georgakis C. Disturbance detection and isolation by dynamic principal component analysis. Chemometrics and Intelligent Laboratory Systems. 1995;**30**(1):179-196

[29] Xuecheng L. Entropy, distance measure and similarity measure of fuzzy sets and their relations. Fuzzy Sets and Systems. 1992;**52**(3):305-318

[30] Chen Y, Nyemba S, Malin B. Auditing medical records access via healthcare interaction networks. In: AMIA Annual Symposium Proceedings. Vol. 2012. American Medical Informatics Association; 2012. p. 93

*Learning Health-Care Worker Networks from Electronic Health Record Utilization DOI: http://dx.doi.org/10.5772/intechopen.93703*

[31] Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O'Neal L, et al. The REDCap consortium: Building an international community of software platform partners. Journal of Biomedical Informatics. 2019;**95**:103208

[32] Chen Y, Lorenzi N, Nyemba S, Schildcrout JS, Malin B. We work with them? Healthcare workers interpretation of organizational relations mined from electronic health records. International Journal of Medical Informatics. 2014;**83**(7):495-506

[33] Friedkin NE. The development of structure in random networks: An analysis of the effects of increasing network density on five measures of structure. Social Networks. 1981;**3**(1):41-52

[34] Freeman LC. Centrality in social networks conceptual clarification. Social Networks. 1978;**1**(3):215-239

[35] Garlaschelli D, Loffredo MI. Patterns of link reciprocity in directed networks. Physical Review Letters. 2004;**93**(26):268701

[36] Kim C, Lehmann C, Schildcrout J, Hatch D, France D, Chen Y. Provider networks in the neonatal intensive care unit associate with length of stay. In: IEEE 5th International Conference on Collaboration and Internet Computing. Los Angeles, CA: IEEE; 2019:127-134

### Section 2
