HEALTHCARE MANAGEMENT STRATEGIES FOR ACHIEVING SUSTAINABLE DEPARTMENTAL PRODUCTIVITY IMPROVEMENTS

HEALTHCARE MANAGEMENT STRATEGIES FOR ACHIEVING SUSTAINABLE

DEPARTMENTAL PRODUCTIVITY IMPROVEMENTS

Section 1: Foundation of the Study

Healthcare organizations’ sustainability is linked to operational and fiscal

management. Leadership skills affect the outcomes of operational productivity and fiscal

performance. According to Conbere and Heorhiadi (2018), leadership actions potentiate

success of organizational operations. Talib et al. (2019) noted that poor productivity

performance and strategic goal progression suffered due to poor leader management

performance. The purpose of this qualitative multiple case study was to explore the

strategies that healthcare organizations’ leaders use to effectively identify, deploy, and

monitor departments’ goals for improving their overall organizations’ performance.

Background of the Problem

The nature of health care leadership is unique because of the intrinsic and

extrinsic factors driving operational and fiscal management (Conbere & Heorhiadi,

2018). The unique influences require competency of strategic planning, goal setting, and

execution that are often found to be insufficient in some health care leaders (Chiarini &

Vagnoni, 2017; Gleason & Bohn, 2017). According to Conbere and Heorhiadi (2018),

the barriers to effective leadership in the health care sector include structural

organization, the process of promotion, limitations of management training, professional

training of physicians, insufficient training in interpersonal interaction, and independence

of physicians. These barriers limit the ability of leaders in health care to be effective in

the management operational and fiscal factors.

When leadership is not effective, the productivity of health care institutions also

suffers (Chelagat et al., 2019; Govender et al., 2018). Preventable negative outcomes

occur each year because of poor leadership and the mismanagement of resources in health

care (Chelagat et al., 2019). Moreover, poor leadership has also been found to be

associated with sluggish organizational performance related to fiscal and operational

factors (Govender et al., 2018). The use of strategic tactics to manage resources

potentiates successful outcomes.

Strategic management is necessary to ensure that the productivity of the health

care system is not compromised (Conbere & Heorhiadi, 2018; Vince & Pedler, 2018).

Leadership development strategies are sometimes unfit with the intended goals of the

health care system (Vince & Pedler, 2018). Moreover, the lack of management training

among health care leaders has been reflected in the lack of strategic management in

health care (Conbere & Heorhiadi, 2018).

Problem Statement

Leadership in the health care setting has been found to be insufficient at the

departmental level because of poor strategic management, limiting the productivity

level of many organizations (Chiarini & Vagnoni, 2017; Talib et al., 2019). From 2007

to 2016, the productivity rate in health care institutions was at a moderate annual

increase rate of 0.7% in 2007 to 2016, which is a decline from the 1.7% annual increase

rate in 1993 to 2001 (U.S. Bureau of Labor Statistics, Office of Productivity and

Technology, 2019). The general business problem was that some healthcare

organizations’ leaders are unable to strategically manage productivity at the

departmental level. The specific business problem was that some healthcare

organizations’ leaders lack the strategies to effectively deploy and monitor departments’

productivity goals to improve their overall organizations’ performance.

Purpose Statement

The purpose of this qualitative multiple case study was to explore the strategies

that healthcare organizations’ leaders use to effectively identify, deploy, and monitor

departments’ goals for improving their overall organizations’ performance. The targeted

population included 20 departmental leaders who have developed, deployed, and

monitored progress against the derivative departments’ goals. The geographic location

was the Western region of the United States within acute healthcare organizations that

have successfully demonstrated success in improving their organizations’ departments’

productivity through achieving the organization’s leaders’ related goals for departments’

productivity improvements. Using or adapting this study’ findings could be the catalyst

for positive social change by encouraging better strategic leadership practices that enable

the public to access a more efficient and effective healthcare system for benefiting

communities’ citizens and families.

Nature of the Study

I selected a qualitative methodology for this study. Qualitative methods are

constructivist-based because data emerge from the deep reflections and experiences of the

participants (Edmonds & Kennedy, 2016). Qualitative research is the appropriate method

when the goal of the researcher is to frame a problem using exploratory methods to

inductively understand a phenomenon without being influenced or constrained by the

existing literature or conceptualizations (Lampard & Pole, 2015). Given the exploratory

nature of this study, the constructivist framework of understanding a phenomenon, and

the use of flexible data collection tool, the qualitative method was the appropriate

approach.

Quantitative research was not appropriate for this study because this method is a

post-positivist approach to scientific inquiry wherein variables are measured to determine

their characteristics or relationships (Babones, 2016). The quantitative method was not

appropriate for this study because using this approach would not have resulted in the

depth and complexity data necessary to fully capture the experiences of the participants.

According to Bryman (2017) the mixed method could also be used to answer more

complex research questions. The mixed method approach contains both qualitative and

quantitative elements and was not appropriate for this study.

I selected a multiple case study design involving 20 leaders in acute healthcare

organizations who have successfully improved their organizations’ performance by

identifying, deploying, monitoring, and achieving departmental goals. A case study is the

multiperspective and intensive exploration of a phenomenon without modifying the

natural environment of the people involved in the said phenomenon (Yin, 2017). Case

study was the appropriate design for this study because the design is suited to the use of

triangulation as a result of using data from different research sites and results in in-depth

exploration and characterizations of the phenomenon in its natural context.

Other qualitative designs such as phenomenology, ethnography, and narrative

research were not appropriate for the current study because of their limitations in scope

and misalignment with the research goals. Phenomenological research involves

exploring the personal meanings of the lived experience of individuals about a

phenomenon (Yüksel & Yıldırım, 2015). Phenomenological research design was not

appropriate because I did not explore personal deep emotional and psychological

processes. Ethnographic research involves a systematic inquiry of a problem rooted from

the practices and customs of ethnic or culturally unique group (Hammersley & Atkinson,

2007). Ethnographic research design was not appropriate because I did not explore a

specific culturally unique group that would necessitate immersive methods of inquiry.

Narrative research is the use of participants’ personal stories in illuminating the meaning

of a socially constructed phenomenon (Wang & Geale, 2015). Narrative research was not

appropriate for the study because the methodological emphasis of only using personal

stories would was not adequate in capturing the complexity of the current research

problem.

Research Question

What strategies do healthcare organizations’ leaders use to effectively identify,

deploy, and monitor departments’ productivity goals to improve their overall

organizations’ performance?

Interview Questions

1. What strategies have you used to develop, deploy, and manage your

organizations’ departments’ productivity performance goals to improve your

organization’s overall performance?

2. What specific strategies have you discovered to be particularly effective in

influencing departments’ productivity performance in your organization?

3. Based upon your experience, how did these strategies influence your

organizations’ departments’ productivity performance?

4. What were the key barriers to implementing your strategies for improving

your organizations’ departments’ productivity performance?

5. How did you address the key barriers to developing, deploying, and

implementing the goals for improving your departments’ productivity

performance?

6. What strategies have you used to monitor the performance of your

organizations’ departments’ productivity performance against their deployed

goals?

7. What key barriers have you encountered in monitoring the productivity

performance of your organizations’ departments against their deployed goals?

8. How did you address the key barriers to monitoring the productivity of your

organizations’ departments against their deployed goals?

9. What other relevant issues or insights that we have not yet discussed would

you like to share with regard to the strategies you used to identify, deploy,

monitor productivity goals for departments to improve the overall

performance of your organization?

Conceptual Framework

The conceptual framework of this study was based on the theories of

transformational leadership by Bass and Avolio (1994) and the policy development theory

of Akao (1991). Akao’s policy development theory (Hoshin Kanri) was expanded by

Joseph Juran with a focus on the managers’ role within the policy development process

(Barnabè & Giorgino, 2017; Kollberg et al., 2006; Sohn et al., 2017). The

transformational leadership theory was used as the basis for the leadership research

necessary to facilitate improvement in the organization. The policy deployment theory

was used as the framework for the processes needed to plan and drive improvements in

overall organizations’ productivity.

The theory of transformational leadership can be used to enhance organizational

productivity through the ability of leaders to inspire confidence among employees and

communicate shared vision with the organization through charisma (Yammarino &

Dubinsky, 1994). The transformational leadership theory underscores the importance of

building a positive relationship with employees for leaders to exert positive influence that

affects the entire organization (Breevaart & Bakker, 2018). Avolio’s theory was relevant

to this current study in that I explored the context regarding the effective strategies for the

deployment and monitoring of organizations’ goals for improving and sustaining

departmental productivity.

The four key elements of transformational leadership are idealized influence,

inspirational motivation, intellectual stimulation, and individualized consideration (Bass

& Avolio, 1994). Idealized influence refers to the charisma of leaders. Inspirational

motivation involves the ability of leaders to inspire their employees to act in ways that are

favorable to the organization. Intellectual stimulation refers to the ability of leaders to

challenge their employees to be creative and innovative. Individualized consideration

refers to the ability of leaders to communicate concern with every employee in an

organization.

Complementing the theory of transformational leadership, I also used policy

deployment theory as a component of the conceptual framework of this study. The main

principle of the policy deployment theory is based on the assumption that continuous

improvements are influenced by strategic objectives and having daily control of the

operations of the business (Duarte, 1993). The factors of strategic objectives and daily

control are the foundation of organization’s overall performance. According to Kollberg

et al. (2006), the policy deployment theory (Hoshin Kanri) contains four key processes

that need to be fulfilled to ensure the development of strategic objectives and that leaders

have daily control of the organization. First, policies need to be created to facilitate

change. Second, a plan needs to be developed based on the feedback from customers and

other managers. Third, policies need to be deployed based on a schedule that will allow

the assessment of goals and objectives. Fourth, the process is reviewed annually in order

to continue improving the overall organizational performance. These four processes are

central in improving the overall organization’s performance (Duarte, 1993). I used the

composite conceptual framework of transformational leadership and policy development

to identify and understand the strategies the leaders used to effectively identify, deploy,

and monitor departments’ goals for improving their overall organizations’ performance.

Operational Definitions

Clinical healthcare leader: Clinical healthcare leaders are leaders and change

agents at the point of clinical healthcare delivery in the progression of patient care (Noles

et al., 2019).

Management strategies: Management strategies coincide with strategy

development and actions related to leader planning and implementation of actions

towards decision making and change progression (Knight et al., 2020).

Organizational productivity: Organizational productivity is the use of labor,

capital, time, energy, and materials effectively to achieve a competitive business

advantage related to output versus input (Torabi & El-Den, 2017).

Organizational sustainability: Organizational sustainability includes a collective

of effective leadership and organizational insight with strategic development and

implementation necessary to sustain an organization by enhancing innovative ideas and

actions with outcomes of fiscal and community responsibility (Bilan et al., 2020).

Assumptions, Limitations, and Delimitations

Assumptions

Assumptions are thoughts and ideals considered to be true but are not verified

(Armstrong, & Kepler, 2018). I assumed that the participants would be honest and

forthright during the data collection. I mitigated the risk of having dishonest or deceitful

answers by reminding the participants about the confidentiality procedures that I used to

protect their identities and other important personal information. I also assumed that the

selection of 20 leaders in three acute healthcare organizations in the Western United

States would be sufficient in finding themes to answer the research questions.

Limitations

According to Theofanidis and Fountouki (2018), limitations refer to potential

weaknesses within the study. One potential limitation of this study was the small sample

size, which could have affected the transferability of the findings if incorrect conclusions

were deduced. Another limitation that is associated with the selection of a qualitative

design was the inability to make causal conclusions about the effect of leadership on the

organizational productivity in health care organizations (Yin, 2017). However, the use of

multiple sources and in-depth data collection tools facilitated a more nuanced description

and understanding of the strategies that departmental healthcare leaders use to manage

departmental level labor productivity.

Delimitations

According to Theofanidis and Fountouki (2018), delimitations refer to the bounds

or scope of the study. The study was bounded by conceptual framework of the theory of

transformational leadership by Bass and Avolio (1993). I based my assessment of

effective leadership in health care setting on the principles of transformational leadership.

Another delimitation of the study was that the study was confined by the philosophical

principle of qualitative research, which means that data was constructivist-oriented based

on the deep reflections and experiences of the participants. The constructivist framework

simplifies the thought of new propositions and reasoning (Edmonds & Kennedy, 2016).

Finally, the study was delimited to the participation of 20 leaders in acute healthcare

organizations that had successfully demonstrated employee labor productivity

performance in the Western United States.

Significance of the Study

The significance of the study is that the results may impact strategic practices in

health care organizations, increasing departments’ productivity. Effective leadership in

the health care setting is critical to strengthen quality and integrate care (Sfantou et al.,

2017). The results of this research study may be used to encourage leaders of other health

care organizations to align their organizations’ strategy to further support communities.

The contribution of this study to effective business practice is the possible

enhancement of the ability of other health care leaders to engage in strategic practices for

improving the productivity of their departments. The potential contribution of this study

to positive social change is the encouragement of better strategic leadership practices that

enable the public to have access to more efficient and productive health care systems for

improved quality of patients’ care.

A Review of the Professional and Academic Literature

Evaluation of healthcare productivity leadership and deployment of strategies

requires the comprehension of actions, knowledge, and activities by healthcare

departmental leaders. Leadership in the health care setting has been found to be

insufficient at the departmental level because of poor strategic leadership, limiting the

productivity level of many organizations (Chiarini & Vagnoni, 2017; Gleason & Bohn,

2017). The purpose of this qualitative multiple-case study was to explore the strategies

that health care departmental leaders use to lead employee labor productivity

performance. Some healthcare leaders are unable to strategically lead the departmental

level workforce, which results in loss of employee labor productivity performance. The

targeted specific population includes 20 departmental level leaders who are responsible

for workforce operations and employee labor.

The literature review is composed of five major headings. First, I focus on the

conceptual framework of transformational leadership. The second heading targeted the

professionalization of healthcare leadership. Linnander et al. (2017), proposed that

improved education and templated practices improve organizational performance. Within

the third research heading I evaluated healthcare leadership components and theory

supported by design thinking related to transformational leadership by Bass and Avolio

(1994) and the policy deployment theory of Akao (1991). Within the fourth heading I

outlined healthcare productivity and components to support improved performance. In the

final heading I discussed the use of data in leadership and how data can be implemented

to manage innovation activities, strategic development, and organizational outcomes.

The literature review is primarily focused on peer reviewed research and articles

that are within the anticipated 2018 to 2022, five-year approval of my study by Walden’s

chief academic officer. The literature review contains 178 total references with 68% of

the sources having a publication date of 2018 or later 168 peer reviewed articles that is

94% of the total research sources. I conducted a review of the recent literature using

electronic journal search engines. The following search engines were used to produce

relevant studies: Google Scholar, EBSCOHost, and JSTOR. The following search terms

were used individually and collectively to produce relevant studies: healthcare,

leadership, healthcare, productivity, technology, policy development, healthcare

productivity, employee leadership, evidence based leadership, cost efficiency,

organizational innovation, productivity metrics, organizational design, healthcare data

management, lean leadership, management in healthcare, organizational outcomes,

transformational leadership, policy deployment theory, and leadership strategy.

Conceptual Framework

The conceptual framework of this study was primarily based on the

transformational leadership theory by Bass and Avolio (1994). Bass and Avolio’s

leadership theory purports to enhance organizational productivity through the ability of

leaders to inspire confidence among the staff and communicate the shared vision with the

organization through charisma (Yammarino & Dubinsky, 1994). The transformational

leadership theory underscores the importance of building a positive relationship with

employees in order for leaders to exert a positive influence that affects the entire

organization (Breevaart & Bakker, 2018). I used transformational leadership theory to

explore labor leadership strategies of healthcare leaders use for sustainable departmental

productivity.

The four key elements of transformational leadership are idealized influence,

inspirational motivation, intellectual stimulation, and individualized consideration (Bass

& Avolio, 1994). Idealized influence refers to the charisma of leaders. Inspirational

motivation involves the ability of leaders to inspire their employees to act in ways that are

favorable to the organization. Intellectual stimulation refers to the ability of leaders to

challenge their employees to be creative and innovative. Individualized consideration

refers to the ability of leaders to communicate concern with every employee in an

organization. Transformational leadership also aligns with Hoshin Kanri’s concept of

policy development for ensuring that the goals of a company drive progress and action at

every level within that company (Akao, 1991).

Transformational leadership (Bass & Avolio, 1994) has been established as the

optimal leadership style in most organizational settings regardless of field.

Transformational leadership has been associated with positive employee outcomes,

including productivity and engagement (Breevaart & Bakker, 2018). Given the empirical

evidence supporting the effectiveness of transformational leadership, this leadership

theory was utilized in this study. The transformational leadership theory was used as a

framework for understanding the strategies that may be used by leaders to enhance

organizational productivity in health care departments. The transformational leadership

theory was applicable and applies to the current study because it provides a framework

for how leadership should be applied to influence optimal organizational outcomes

according to Bass and Avolio (1994). Bass and Avolio (1994), provided insight regarding

how policy development was used to evaluate the relevance of tactic implementation and

the progression of strategies within organizations.

Professionalization of Healthcare Leadership

The professionalization of healthcare leadership has occurred at different rates

internationally (Linnander et al., 2017). Drawing on a thematic review of the literature,

Linnander et al. (2017) determined the process by which nations professionalize their

healthcare leadership. The literature review uncovered five common themes across

healthcare leadership literature. The themes included: (a) a national context for healthcare

leadership demand, (b) a national framework which elevates leadership practices, (c)

standards for healthcare leadership and monitoring, (d) educational paths designed to

funnel individuals into healthcare leadership, and (e) professional associates at a lower

level to maintain the field. Based on the findings from their study, Linnander et al. (2017)

developed a long-run strategy at a national level for professionalizing healthcare

leadership practices. Though long-run professionalization of the healthcare field has

benefits for patients such as improved employee retention and better outcomes

(Linnander et al., 2016), there can be unintended consequences such as an overpowering

of community choice in favor of templated leadership practices (Blasi et al., 2018). As

indicated herein, there are both positive and negative aspects of the professionalization of

healthcare leadership practices. Due to the variable nature of healthcare systems and

politics, countries have unique and complex problems related to managing the healthcare

industry (Stefko et al., 2016). According to Stefko et al. (2016), countries often have

different health, economic, and social conditions that influence healthcare policy.

However, a commonality across all national health systems is a focus on cost reduction

and efficiency. This emphasizes the importance of healthcare leadership and prompts

deep exploration into leadership strategies for increasing efficiency. In a quantitative

study using Malmquist indices, Stefko et al. (2016) explored the use of day surgery

facilities in regions of Slovakia. Traditionally, healthcare leaders required most surgical

patients to remain in the hospital for multiple days. Stefko et al. (2016) explored the

feasibility of releasing patients who do not require continued follow-up care on the same

day. The results of the study indicate that day surgery is a viable option for healthcare

institutions, but leaders must ensure that their facilities have sufficient conditions related

to the following factors: healthcare system motivation, experienced staff, qualified

surgeons, anesthesiologist resources and qualifications, patient motivations, and patient

social backgrounds.

As indicated, opponents might suggest that logistical challenges exist with respect

to healthcare system amotivation, lack of experienced staff, unqualified surgeons,

limitations in resources, patient amotivation, and social factors (Stefko et al., 2016). The

variable nature of healthcare systems also leads to the requisite for context-specific

decisions regarding the implementation of health leadership strategies, as opposed to the

adoption of a universal approach that is demonstrated to be effective in the literature

(Roemeling et al., 2017).

Managing employee and institutional knowledge is a key function of healthcare

leadership (Karamitri et al., 2017). Hospitals and other medical care facilities have an

extreme amount of data and a need for interagency cooperation and data sharing. Using a

literature review format, Karamitri et al. (2017) explored strategies for managing

institutional knowledge in hospitals. Karamitri et al. (2017) found that literature on

knowledge leadership in hospitals and health agencies had key themes and elements. The

sample included 604 total articles and 20 which were eligible for analysis by the

researchers. The key themes were: perceptions of the need for knowledge leadership,

synthesis, dissemination, collaboration, and leadership’s role in knowledge leadership. In

addition to the key themes, Karamitri et al. (2017) found that barriers existed to

implementing better knowledge leadership through healthcare leadership structures. The

barriers included employee time restrictions and limited skill in knowledge leadership

amongst employees. To address the barriers, Karamitri et al. (2017) recommended that

hospital leadership be encouraged to take knowledge leadership seriously and serve as an

intermediary of knowledge for employees.

Further developing the understanding of how lean leadership applied to healthcare

institutions, Habidin (2017) conducted a quantitative assessment of lean leadership

strategies in healthcare to develop a framework. Habidin (2017) used confirmatory factor

analysis to analyze the data collected from 238 healthcare leaderships in the Malaysian

healthcare industry. After analyzing the data and results, Habidin (2017) confirmed that a

lean leadership construct would successfully improve healthcare competitiveness when

applied to most healthcare institutions. An analysis of the constructs revealed that eight of

the common constructs used in a lean healthcare leadership system framework were

sufficiently impactful to qualify for inclusion based on the study framework. The eight

constructs that were relevant to the healthcare institutions in Malaysia were: leadership,

employee involvement, organizational culture, customer focus, technological innovation,

process innovation, and healthcare performance (Habidin, 2017). According to Habidin

(2017), implementing lean frameworks that are used to focus on improving the

abovementioned relevant metrics would improve healthcare competitiveness. While the

literature abundantly supports lean leadership, opponents may suggest that such

leadership cannot be implemented without the presence of each of these eight factors and

that continuous monitoring may prove to be challenging in some healthcare contexts

(Narayanamurthy & Gurumurthy, 2018).

Healthcare Leadership Components

Preferred leadership characteristics within the healthcare profession is driven by

business research and proven organizational outcomes. Griffith (2018) discussed how

various leader components are driven by organizational partnerships and evidence-based

leadership thinking to potentiate successful outcomes. Healthcare strategies and human

centered need have some responsibility of leader expectation and organizational

competency (Gallagher-Ford, & Connor 2020).

This section includes a discussion of healthcare leadership components. First,

design thinking is discussed. Then, other subcomponents that comprise healthcare

leadership are considered, such as lean healthcare leadership, and evidence-based

leadership.

Design Thinking

Design-thinking is a commonly used business methodology which focuses on

setting up systems to meet the needs of customers (Roberts et al., 2016). According to

Roberts et al. (2016), healthcare systems could similarly benefit from design-thinking to

meet their needs by incorporating this methodology into their leadership practices.

Current healthcare practices effectively diagnose and treat illnesses, but the rise of

longterm illnesses caused by human behavior, such as diabetes, is complicated to lead

under the current system because it requires incorporating human behavioral change.

Fisher et al. (2016) determined that employing a design-thinking framework in the

healthcare system will require healthcare institutions to a) develop a capacity for greater

stakeholder engagement, b) engage more diverse stakeholders, c) rapidly test small

hypothesis and solutions. By incorporating design thinking, Fisher et al. (2016) argues

that healthcare systems will be better equipped to deal with and lead social change.

As the healthcare industry progresses and modernizes, researchers have

considered design-thinking frameworks that have been adapted to specific segments of

healthcare leadership research (Carroll & Richardson, 2016). Carroll and Richardson

(2016) highlight that a pivotal point of design thinking is to establish individual’s and

organization’s specific needs and pinpoint areas which need improvement. An example of

an adapted design thinking framework is Carroll and Richardson’s (2016) connected

health model for healthcare leadership. The Connected Health model for leadership is

intended to help healthcare leaders make businesses decisions in the healthcare sector

utilizing newly available technological resources. Carroll and Richardson (2016) argue

that progressive technology utilization is critical in the healthcare sector because

healthcare technology has the ability improve outcomes and patient leadership. The

principles of the Connected Health model focus on a) supporting software developers to

identify healthcare wants and requirements and b) extend and deepen existing software

utilization in healthcare.

Utilizing a case study methodology, Carroll and Richardson (2016) examined the

impact of the Connected Health model on an e-pharmacy. In keeping with the design

thinking methodology, Carroll and Richardson (2016) first focused on identifying areas

where the e-pharmacy needed to improve, specifically in relation to data and data

leadership. For the e-pharmacy, the ordering transmission system caused inefficiencies in

the ordering and delivering process. Carroll and Richardson (2016) found that improving

the ordering transmission system and inventory leadership systems resulted in cost

efficiencies and improved patient experiences. Additionally, Carroll and Richardson

(2016) found inefficiencies in the employee logging and workflow, which were corrected

through more rigorous data leadership protocols.

Productively applying design thinking to a healthcare framework requires a

certain degree of training and critical thinking (Ferreira et al., 2020). As emphasized by

Fisher et al. (2016), incorporating design thinking into the healthcare system requires

both stakeholder engagement and ability from healthcare leaders. Ferreira et al. (2020)

argued that students seeking to enter the healthcare industry should receive cross-cultural

design thinking training as part of their undergraduate or graduate level coursework.

Based on research, Ferreira et al. (2020) asserted that artificial intelligence use in the

healthcare industry has the potential to positively impact protocols of combatting breast

cancer but developing artificially intelligent technology that works in a cross-cultural

context requires developers and healthcare leaders to use a design thinking framework. To

evaluate their theory on the usefulness of cross-cultural design thinking at an

undergraduate level, Ferreira et al. (2020) provided a course to students. To assess design

thinking ability in a cross-cultural context, Ferreira et al. (2020) collected data using a

questionnaire. Ferreira et al. (2020) found that the students reported substantial growth in

the area of design thinking, specifically in a cross-cultural context. However, the

opposition to design thinking would be over templated leadership practices and an

overdependence on artificial intelligence to inform healthcare decision-making (Pope-

Ruark,

2019).

Lean Healthcare Leadership

Lean leadership strategies focus on reducing waste and increasing productivity

(Patri & Suresh, 2018). The healthcare industry, which focuses on cost reduction and

often experiences shortages of staff time, could benefit from lean leadership techniques

(Efe & Efe, 2016). In a 2016 study, Efe and Efe (2016) sought to determine if lean

leadership strategies could benefit a hospital emergency department in terms of

productivity, organizational efficacy, and patient care. Efe and Efe (2016) utilized an

approach which assessed patient value in individual organization and leadership

decisions. Efe and Efe (2016) assessed patient value in markers such as equipment

availability, quality of care instructions, approachability, and other factors influenced by

the hospital emergency department environment or staff. The researchers found that the

decision-making trail and evaluation laboratory (DMTEL) method successfully assessed

the value of certain lean leadership principles. The availability of equipment value was

the most impactful on patient experience, stating that patients highly value the ability to

use equipment when necessary. This marker influenced patient experience by reducing

wait times and improving overall efficiency (Efe & Efe, 2016). Efe and Efe (2016)

suggests that implementing a lean leadership strategy in healthcare emergency rooms

could influence patient experience, and that leaders should focus resources on ensuring an

adequate level of equipment availability.

Though research demonstrates substantial benefits associated with utilizing a lean

leadership framework in the healthcare context (Efe & Efe, 2016; Po et al., 2019), there is

a gap in research between lean leadership concepts in healthcare and the execution of lea

leadership practices in a clinical setting (Van Rossum et al., 2016). In order to address

the gap in research, Van Rossum et al. (2016) sought to develop a tool kit for healthcare

leaders looking to implement lean leadership practices in their healthcare facility. To

achieve the research objectives, Van Rossum et al. (2016) performed a cross-sectional

study at a Dutch medical center associated with a university. Van Rossum et al. (2016)

hypothesized that transformational leadership would be required to ensure a top-down

commitment to lean leadership. Meanwhile, more distributed team leadership was

expected to be associated with bottom-up organizational commitment.

To analyze the data, Van Rossum et al. (2016) conducted correlation and

regression analyses. The results of the analysis showed a positive correlation between the

utilization of transformational leadership and the development of team leadership styles.

This dual approach facilitated both top down and bottom-up organizational change within

the healthcare facility. Both leadership styles were positively correlated with lean

leadership implementation in the healthcare setting. Additionally, Van Rossum et al.

(2016) found that the flexibility of the workforce was strongly positively correlated with

successful implementation of lean healthcare leadership.

A flexible workforce is associated with organizational agility and was connected

to lean leadership by Van Rossum et al’s. (2016) research findings. Expanding on

understanding of organizational flexibility and healthcare leadership, Mishra et al. (2019)

argued that challenges exist for healthcare organizations seeking to balance agility and

leanness. Mishra et al. (2019) further argue that the rise of chronic diseases like

cardiovascular disease and diabetes increases the strain on healthcare systems and require

a combination of agility and leanness to successful lead in a cost-effective manner. To

assess the balance between agility and leanness, Mishra et al. (2019) utilized a discussion

group to gather data on the multi-Dimensional scaling method. The method is used to

visualize competing interests, like healthcare agility and leanness. The case study utilized

a case study approach and gathered data using focus groups. Mishra et al. (2019)’s

findings focused on the supply chain leadership and found that agility in healthcare can

be achieved through better product bundling and product assortment. Furthermore,

Mishra et al. (2019) found that standardizing the process for dispersing critical

medications to patients could improve the overall efficiency of healthcare organizations

and patient outcomes.

Though there are potential cost efficiencies associated with healthcare leadership,

there are other factors which should be considered when assessing hospital efficiency

(Hallam & Contreras, 2018; Mishra et al. 2019). Competing interests, such as quality of

care and patient satisfaction should also be assessed when determining the benefits of

lean healthcare leadership strategies (Poksinka et al., 2017). According to Poksinka et al.

(2017), there was a gap in research regarding the impact lean healthcare leadership

strategies had on patient satisfaction with their healthcare services. To address the gap in

research, Poksinka et al. (2017) utilized a case study methodology with both qualitative

and quantitative approaches. Poksinka et al. (2017) conducted a total of four case studies,

two of which were qualitative and two were quantitative. The sample included 23

primary care centers which utilized a lean leadership strategy and 23 centers which did

not use a lean leadership strategy as a control group. The results of the study indicated

that, in general, lean leadership strategies are targeted at cost-efficiency functioning and

largely did not consider the patient experience. The quantitative case studies

demonstrated no correlation between lean leadership strategy and patient satisfaction.

Additionally, Poksinka et al. (2017) found that there was no change in patient satisfaction

overtime.

While Poksinka et al. (2017) results do not show positive benefits associated with

a lean leadership strategy from the perspective of patient experiences, they also did not

show a negative correlation between lean leadership and patient experience. As stated by

Poksinka et al. (2017), lean leadership strategies are primarily focused on achieving

costefficiencies. If the strategies are successful at achieving cost efficiency without

sacrificing patient experience, then it could be argued that the lean strategies are positive

overall. Furthermore, Poksinka et al. (2017) study did not focus on how lean leadership

strategies impacted patient costs. Further avenues of research should explore if the

costefficiencies associated with lean leadership strategies are transferred to patients, and

if the cost saving impacts patient experience.

Evidence Based Leadership

Though many other countries have adopted evidence-based leadership

approaches in healthcare, the United States has been slow to adopt the widespread

practice (Gou et al., 2019). Evidence-based healthcare leadership is defined as leader

decision making about employees, teams, and organizations based on the judicious

application of four sources of data. According to Gou et al. (2019), the ideal four sources

of information include scientific research, organizational data, professional expertise, and

stakeholder feedback. The concept of evidence-based leadership in healthcare is derived

from evidence-based medicine, which makes medical decisions based on specific sources

of information. In a quantitative study using analysis of moment structures, Gou et al.

(2019) determined that administrators who intended to use evidence-based healthcare

leadership practices significantly predicated their attitudes towards decision making and

their perceived level of behavior control. Educating healthcare leaders on evidence-based

leadership strategies positively mediated their attitudes towards the strategy and their

intention to use it.

Other researchers ( Agnihothri & Agnihothri, 2018; Janati et al., 2018)

acknowledged the same gap in academic and professional understanding of

evidencebased healthcare leadership within the United States that was acknowledged by

Gou et al. in 2020 .Elaborating on the details provided by Gou et al. (2019), Janati et al.

(2018) state that evidence based healthcare leadership is a relatively new practice within

the United States and requires a paradigm shift within healthcare leadership systems. The

researchers state that a strength of evidence-based healthcare leadership is it bridges the

gap between theory and practice and improves organizational and leader performance. To

facilitate greater adoption of evidence-based healthcare leadership, the researchers

quantitatively assessed the attitudes and perceived barriers to adopting EBMgt at a

specific Iranian hospital. To conduct the study, the researchers performed semistructured

interviews with 45 participants including leaders, policymakers, and researcher leaders.

The data results indicated that most participant that evidence-based leadership was a

positive practice and would result in better organizational functioning. Some barriers to

implementation included a lack of skills, a lack of available data sources, and a lack of

training. Recommendations for practice included holding more trainings on

evidencebased leadership practices and developing data frameworks to facilitate hospital

or facility level adoption.

As previously mentioned, a lack of skill and understanding regarding data

collection for evidence-based leadership is a challenge for healthcare leaders (Janati et

al., 2018; Aloni et al., 2018). Part of the challenge for healthcare leaders stems from a

lack of understanding about the link between data sources, analysis, and subsequent

leader decision making (Roshanghalb et al., 2018). To clarify the connection between

data sources, analysis, and leadership decision making, Roshanghalb et al. (2018)

conducted a systematic review of literature on evidence-based leadership in a healthcare

setting. Utilizing a rigorous methodology, Roshanghalb et al. (2018) selected only articles

for empirical journals with a robust and time-tested method. After applying exclusion

criteria, Roshanghalb et al. (2018) included 30 studies in their review. The studies were

conducted between 2009 and 2014. Seventy percent of the studies were quantitative

studies assessing the effectiveness of and implementation strategies for evidence-based

leadership in a healthcare setting. The study results indicate that the main kinds of

decisions made through evidence-based leadership are performance assessment, staff

performance assessments, change leadership, organizational knowledge, and strategy

planning.

In terms of adoption of evidence-based healthcare leadership frameworks, certain

factors influence whether healthcare leaders will adopt the strategies (Janati et al., 2017).

To assess the factors of adoption, Janati et al. (2017) considered the facilitators, barriers,

sources of evidence, and process of the healthcare organization. Using both purposeful

and snowball sampling, Janati et al. (2017) conducted a Delphi study using

semistructured interviews with participants. The results of the study indicated that

numerous factors were related to utilization of evidence-based leadership strategies such

as leader characteristics, environmental factors, team barriers, scientific research barriers,

and training considerations. The study confirmed 46 factors which were related to

evidence-based leadership in healthcare, suggesting the complicated and interconnected

nature of leadership decision making. Overcoming the barriers to implementing

evidence-based leadership requires addressing many of the 46 factors identified, and

therefore interventions aiming to establish evidence-based leadership practices in a

healthcare setting likely must utilize a multiple-pronged approach (Guo et al., 2017;

Janati et al., 2017).

Healthcare Productivity

Healthcare costs in the United States are rapidly expanding, further extenuating

the need for viable healthcare productivity strategies. However, there is a gap in literature

on metrics and sub-classifications to define productivity metrics in a healthcare context

(Kamarainen et al., 2016). Undertaking a pilot study of healthcare productivity metrics,

Kamarainen et al. (2016) assessed the value of varying healthcare metrics in a healthcare

setting. One of Kamarainen et al’s (2016) key findings what that healthcare metrics need

to have varying viewpoints which include unit, organization, and system level viewpoint

assessments. The assessment metrics proposed by Kamarainen et al. include assessments

based on patient outcomes, assessments on patient need satisfaction, and metrics based on

financial benchmarks combined with value outputs.

Measuring productivity in the healthcare sector is notoriously difficult

(Boussemart et al., 2020; Sheiner & Malinovskaya, 2016). Healthcare productivity must

be considered from the perspective of decreased cost and increased care, but other factors

such as patient satisfaction and long-term patient outcomes must be considered and

measured. Sheiner and Malinovskaya (2016) noted that there was a gap in literature

surrounding the productivity impacts of recent United States healthcare initiatives, such

as the affordable care act. Understanding first if costs have come down, and second if

care has increased requires an overall assessment of the healthcare system productivity,

including consideration of the above-mentioned additional inputs. Using a literature

review format, Sheiner and Malinovskaya (2016) describes the different methodologies

for assessing healthcare productivity including diseased based approaches where

researchers assess healthcare productivity using data on specific marker diseases, or

patient care quality indexes. A common approach to assessing healthcare productivity

includes a cost analysis of indicator procedures and treatments. Sheiner and

Malinovskaya (2016) conclude by stating that there is value to utilizing a combined

assessment approach and found that the affordable healthcare act was likely to result in

long-run healthcare productivity improvement utilizing a number of different health

productivity assessment frameworks.

Difficulties measuring productivity in the healthcare sector extend to a lack of

reliability associated with mearing productivity in healthcare utilizing a contribution to

gross domestic product (GDP) framework (Blomqvist & Busby, 2017). According to

Blomqvist and Busby (2017) healthcare productivity measurement through an assessment

of contribution to GDP results in the mistaken impression that the healthcare industry has

not improved in productivity over recent decades. Utilizing a literature review format,

Blomqvist and Busby (2017) assesses strategies for measuring the productivity of the

healthcare system. The researchers assert that contributions from the healthcare system

are better assesses utilizing an input in, inputs out framework which implies that the

aging population and greater number of individuals served through the healthcare system

is a measurement of productivity increases. Despite the significant contributions from the

healthcare sector, Blomqvist and Busby (2017) found that there are inefficiencies in the

system Studies included in the literature review suggest that Canada, the focus of the

study, could increase healthcare productivity by focusing on adopting cost-effective

technologies. Blomqvist and Busby (2017) further assert that research and development

can serve an important role in healthcare productivity, but only if the country has the

infrastructure to cost-effectively support research and development. This finding aligns

with the research question. However, opposing viewpoints may be that research and

development do not serve an important role in healthcare productivity.

Metrics

Though there are numerous success metrics associated with the healthcare

industry, such as patient outcomes and patient experience metrics, productivity is an

essential metric to understanding the effectiveness of a healthcare system (Boussemart et

al., 2020). Analysis of productivity often occurs at a firm level or a country level, but

Boussemart et al. (2020) sought to measure productivity at an industry level, specifically

the Chinese healthcare industry. The purpose of the industry level analysis was to

determine the drivers of healthcare productivity so that they can be attributed to specific

inputs and expanded upon at a national level. In a quantitative study of healthcare

productivity, the researchers utilized a Luenberger productivity indicator to assess the

relevancy of specific variables to healthcare productivity. The results of the study indicate

that China’s productivity growth in the healthcare sector were primarily driven by

technological innovation. These results provide useful insights to other countries

attempting to increase productivity in the healthcare space. Additionally, the results are

consistent with the findings of Efe and Efe (2016), who found that equipment availability

was an important indicator of patient experience. Both studies suggest that investing in

equipment and technology could drive healthcare productivity.

Healthcare systems with similar components can have different objectives and

different resulting productivity levels (Atella et al., 2019). Comparing differing national

healthcare policies and objectives in relation to their resulting productivity can provide

useful insights on the drivers of productivity from a policy lens. Atella et al (2019)

conducted a comparative analysis between the English and Italian healthcare systems

with the purpose of understanding their impact on productivity. Atella et al (2019)

measured productivity growth of the two systems using a rate of change of outputs over a

rate of change of inputs. Outputs include patients treated, among other metrics, and input

are typically financial and related to human resources. The comparative analysis revealed

that the English healthcare system increased at a rate of 10 percent between 2004 and

2011, while the Italian healthcare system progressed at a rate of 5 percent over the same

period. In attributing the faster rate of increase in the English system, Atella et al (2019)

stated that, rather than focusing specifically on reducing cost, the English system focused

on increasing activities, reducing wait times, and improving quality of care. These results

suggest that improving healthcare productivity might be optimizable when focusing on

quality and efficiency of care over cost reduction.

Cost-Efficiency

There are numerous methods for assessing cost efficiency in healthcare (Atella et

al, 2019; Asghar et al., 2019). Atella et al. (2019) utilized an “inputs in, inputs out”

framework for assessing cost productivity in healthcare, while Asghar et al. (2019) tested

the effectiveness of the cost Malmquist index. The cost Malmquist index assessed

technical, scale, and allocative efficiency change in healthcare systems. Asghar et al.

(2019) utilized Malmquist index data from the 55 countries included in the index and

found that cost productivity in healthcare was most impacted by technological changes.

The idea that cost productivity is impacted largely by technological progress was echoed

by Boussemart et al. (2020) who came to similar conclusions when assessing China’s

healthcare system productivity improvements. Asghar et al. (2019) found that other

factors influenced cost productivity in healthcare, including allocative efficiency and

price change, and scale efficiency. Among the other assessed factors, scale efficiency was

substantially impactful on healthcare cost productivity. In Asghar et al. (2019) study,

scale efficiency refers to the cost efficiencies associated with larger, more integrated

healthcare systems that have the ability to distribute costs among a large number of

customers and facilities. Examples of scale efficiencies can be seen in countries with

national health systems, like the United Kingdom’s National Health System (Boussemart

et al., 2020).

The Malmquist index is commonly utilized in assessing healthcare systems. As

previously mentioned, Boussemart et al. (2020) and Stefko et al. (2016) both utilized the

index to study healthcare productivity. Kim et al. (2016) conducted a similar study

utilizing a modified Malmquist index approach. Kim et al. (2016) assessed the

productivity changes in 30 Organization for Economic Co-operation and Development

(OECD) countries. The assessment period was 2002 through 2012. The assessment

determined that there have been healthcare productivity improvements in most of the 30

countries assessed. Kim et al. (2016) attributed the healthcare productivity improvements

to a combination of efficiency and technical improvements. These improvements relate to

hospital functioning protocols and better implementation of healthcare technologies. For

countries which have not demonstrated significant improvement between 2000 and 2012,

Kim et al. (2016) recommended that the country leadership consider what practices are

best achievable given the country’s economic conditions. For example, Kim et al. (2016)

found that less healthcare productivity increases occurred in countries with income

inequality.

In the United States, there is a disparity between spending levels and productivity

levels. Unlike other industries, where spending correlates with increased quality and

speed of production, higher funding levels in the healthcare industry are not necessarily

associated with improved patient outcomes or decreased treatment times (Chandra et al.,

2016). Quantitatively using hospital data, Chandra et al. (2016) developed a model for

determining hospital productivity using a number of indicators as independent variables.

Data was gathered using Medicare Part A claims for the years 1993 through 2007. The

results of the study indicate that, hospital productivity is difficult to model, and the data

often results in ideocratic results. For example, highly ensured patients are not

particularly price sensitive, and therefore there is sometimes little connection between

revenue input and quality of care outputs. Furthermore, there is limited data available to

customers regarding organizational quality.

Employee Leadership

Nurses and other non-medical doctor staff play an influential role in the

productivity of a healthcare organization (Coetzee, 2019; El Haddad et al., 2017;

Juanamasta & Yuwono, 2018; Xue & Tuttle, 2017). Costs associated with medical doctors

are high, and healthcare facilities increasingly use nurses and other staff people to

perform routine health maintenance of patients (Emmons, 2019; Munro et al., 2019).

Using a cross sectional analysis, Xue and Tuttle (2017) assessed the productivity of

nurses in a healthcare setting by examining the number of patients they saw a week and

assessing the overall organizational productivity that resulted from their work. According

to the results, nurses saw an average of 80 patients a week and 64 percent of the included

nurses had patients which they saw exclusively. The overall productivity of nurses was

mediated by the level of autonomy granted to the nurses to perform routine healthcare

maintenance and the extent to which nurses were responsible for managing the facility

billing practices (Xue & Tuttle, 2017). These results suggest that nursing staff play a vital

role in healthcare productivity, and that healthcare productivity might be improved by

granting nurses an appropriate level of autonomy and reviewing the institutional billing

practice with the aim of maximizing nurses’ ability to see patients.

One vital component of healthcare productivity is the lead leadership of social

and cultural differences between patients, nurses, doctors, and administrators. Altakroni et

al. (2019) stated that a lack of cultural competency among patients and medical staff can

result in inefficiencies and reduced patient care standards. To determine how cultural

differences impacted patient care, Altakroni et al. (2019) studied the socio-demographic

determinants of their productivity. Altakroni et al. (2019) utilized a quantitative

methodology with a cross-sectional survey of 256 participating nurses. The study aimed

specifically on collected data regarding employee life factors which might influence their

productivity at work. Interestingly, Altakroni et al. (2019) found that many life

circumstances anecdotally associated with lower productivity did not result in any

decrease in employee productivity. For example, Altakroni et al (2019) found that nurses

with children under the age of five were actually more productive than nurses who did

not, on average. Unmarried nurses were found to be more productive then married nurses.

The concept of organizational excellence is often tied to employee performance

and organizational innovation levels. Frameworks which focus on assessing the

connection between organizational excellence, as defined by innovation and employee

performance, can be used to make a connection between organizational excellence and

organizational productivity (Mohamed et al., 2018). In a quantitative study utilizing

structural equation modeling, Mohamed et al. (2018) considered data from 256

employees of the Abu Dhabi health authority. The results of the study indicated that

organizational excellence had a positive impact on the productivity of the organization.

Secondly, employee performance was a significant predictor of organizational

productivity. These results suggest that employees play a key role in organizational

productivity, and that organizations seeking to improve productivity may wish to consider

opportunities to improve and train employees. These results align with the results of

Atella et al. (2019), which found that productivity increases were tied to organizational

improvement rather than an emphasis on cost savings.

Preserving the physical, mental, and emotional well-being of nurses is a critical

problem for many healthcare systems in the United States (Goodwin & Richards, 2017).

Nursing staff are often expected to work long hours under physically and emotionally

demanding conditions and facilities often struggle to maintain sufficient staffing levels

(Goodwin & Richards, 2017). Due to the challenges associated with nursing as a

profession, Goodwin and Richards (2017) argue that hospital leadership staff must

actively promote self-care strategies among its nursing staff. For the purpose of exploring

self-care best practices, Goodwin and Richards (2017) conducted a review of recent

literature. The review of recent literature suggested that best practices for maintain the

well-being of nursing staff includes encouraging the same attention to individual health

that is provided to patient health, including adherence to yearly examines and nutritional

assessments. Additionally, Goodwin and Richards (2017) recommend that nursing staff

receive training and support to develop skills around mindfulness and self-soothing

behaviors to alleviate physical and emotional distress.

Due to the increasingly globalized nature of healthcare, there is a need for

healthcare leadership to exhibit and value intercultural competency (Moore et al., 2017).

Intercultural competency is highly relevant the healthcare facilities because they have a

diverse population of staff and patients and need to provide a baseline level of care and

comfort to everybody (Moore et al., 2017). Utilizing a systematic review format, Moore

et al. (2017) examined research on strategies for training healthcare leadership teams on

intercultural competence. The practices focused not on teaching intercultural competence

directly but on encouraging students to be interested in intercultural competency and

continuously improve their own skills. The study results found that healthcare leadership

needed to be dedicated and intentional with training intercultural competency. A course

approach worked in a healthcare setting if the course included opportunities for students

to develop competencies but was not the only effective method of increasingly

organizational intercultural competency. A top-down focus on intercultural competency

also was effective (Moore et al., 2017).

The need for better integration of intercultural competency into healthcare

leadership and practice was also established by Abad-Jorge et al. (2018) and others (Bein,

2017). Utilizing a literature review framework, Abad-Jorge et al. (2018) assessed

literature on strategies for increasing and incorporating intercultural competence. The

literature addressed a greater need for intercultural competency in education programs,

which was as similar finding to Ferreira et al. (2020). In assessing educational programs

which integrated intercultural competence into practice, Abad-Jorge et al. (2018) found

that student feedback played a critical role in tailoring the program to meet the needs of

the students and enhanced the educational experience and course effectiveness. Overall,

Abad-Jorge et al. (2018) found that the literature supported integrating cultural

competency into the educational framework, both as a separate course and through

general practices of intercultural competency in the classroom. Implementing such

programs required concerted efforts from the educational institution and support from

healthcare organizations served by the educational institutions (Calloway-Thomas et al.,

2017).

Innovation

No matter the healthcare system employed, nations are under increasing pressure

to meet productivity standards due to rising costs of healthcare, dynamic patient needs,

and limited healthcare budgets (Marjanovic et al., 2017). Some researchers have posited

that innovation can successfully drive productivity gains in the healthcare sector

(Marjanovic et al., 2017). Innovation in this context is defined as products, technologies,

or services which are new to a healthcare system, or can be applied in a new way, which

are aimed at improving affordability and care. In a national British organizational

assessment, Marjanovic et al. (2017) considered how different systems can work together

and innovate to produce higher quality results in the National Health Service. Based on

the results of the organizational assessment, Marjanovic et al. (2017) determined the

following best practices related to driving innovation in a healthcare context. The

practices include using interdependences of organizations as an assess, developing macro-

scale relationships, using structural and behavioral intervention, coordinating innovation

with other agencies, and adopting a portfolio healthcare approach. Substantially

expanding upon how innovation can be encouraged and nurtured in a healthcare setting,

Marjanovic et al. (2018) conducted a systematic analysis of literature on innovation after

completing an organizational assessment of innovation in a healthcare context one year

previously (2017). The systematic analysis of literature considered a number of recent

studies related to healthcare innovation. Marjanovic et al. (2018) coalesced the study

results into one cohesive set of findings on how to nurture innovation in a healthcare

context. The study findings indicated that innovation could be nurtured by considering

the complete package of institutional options related to innovation and selecting cohesive

interventions which work in conjunction and complementary to existing or newly

implemented interventions. Furthermore, Marjanovic et al. (2018) found that innovations

needed to be considered in an organizational context and not all innovation interventions

would be received optimally or positively in all contexts. Numerous studies related to

healthcare productivity linked innovation, technology, and healthcare efficiency

(Marjanovic et al., 2018; Kim et al., 2016). Okaunde and Osmani (2018) came to a

similar conclusion, finding a connection between technology and healthcare productivity.

However, Okaunde and Osmani (2018) emphasize that the technology utilization is not

restricted to advancing medical technology or new testing devices. Additionally,

healthcare technology includes both medical devices and technologies commonly used in

other industries to increase productivity, like information and communication

technologies. Using a literature review format, Okaunde and Osmani (2018) explore the

definition of healthcare productivity and the inputs to healthcare productivity, like drug

devices, medical devices, communication technology, data leadership, and other

platforms for managing patient health. Okaunde and Osmani (2018) further asserts that

the definition of healthcare productivity varies between nations, as countries have

different funding mechanisms for their healthcare systems which come with different

monetary inputs from customers or nations. While some productivity factors,

such as innovation, can be implemented within a clinical hospital setting, other

productivity factors call for a varying of treatment locations (Castor et al., 2020). Though

previous researchers discussed the economies of scale associated with nationalized

healthcare delivered through centralized hospitals, Castor et al. (2020) argued that

healthcare can be productively delivered in other settings, such in people’s residences, if

the circumstances are properly lead. Utilizing an observational follow-up study of

hospital care and home care for 32 children, Castor et al. (2020) determined that home

care resulted in cost and productivity savings for the healthcare. The productivity impact

of home care compared to hospital care is particularly substantial if elements such as

parent absenteeism from work is considered (Castor et al, 2020). Castor et al. (2020)

collected data utilizing a survey approach and conducted a comparative analysis of home

care and hospital care.

Big Data in Leadership

The purpose of this qualitative multiple case study is to explore the strategies that

health care departmental leaders use to lead employee labor productivity performance.

Big data offers one way in which to understand productivity performance (Baldominos et

al., 2018). Productivity performance can be measured in several ways by healthcare

leaders and leaders (Baldominos et al., 2018). These include factors like employee

efficiency, patient outcomes, wait times, and activity levels (Baldominos et al., 2018).

Big data serves an important function in healthcare leadership, and strategies for

visualizing bit data are crucial for organizational success (Senthikumar et al., 2018).

Senthikumar et al. (2018) argues that majority of data produced by healthcare

organizations are unstructured, and therefore require careful processing strategies. Using

a systematic review framework, Senthikumar et al. (2018) considered the visualization

tools which could beneficially be used by healthcare leaders to visualize unstructured

healthcare data. Senthikumar et al. (2018) found that 76 studies met the inclusion criteria.

The results of the study suggest that the big data challenges relating to healthcare are data

security and privacy issues, as well as visualization. Senthikumar et al. (2018)

recommendations for practice include utilizing the big data visualization tools available

on the market such as Nodebox and Float. In terms of data leadership, Senthikumar et al.

(2018) note that there are substantial regulations around data security and privacy, and

healthcare leaders must have an in-depth understanding of data security protocols.

Strategic use of technology by healthcare leadership teams can improve outcomes

and experiences for patients (Minniti et al., 2016). As previously mentioned, instituting

highly professionalized healthcare leadership can have to unintended consequence of

suppressing patient voice (Linnander et al., 2017). To ensure that patients continue to

have a voice in their health decision-making, Minniti et al. (2016) found that utilizing

technology to collect patient reported data can improve outcomes. Minniti et al. (2016)

argued that web-based technology platforms allow patients to communicate their needs

following procedures and seek continuous improvement in care processes. Minniti et al.

(2016) used an interactive patient reporting model called P-IHM (Patient-interactive

Healthcare Leadership). Utilizing an experimental design, Minniti et al. (2016) found that

the P-IHM system increased the customizability of individualized care and avoided

unnecessary medical costs.

As previously mentioned, big data has broad implications for healthcare

leadership through concerns related to data security and the ability of patients to

participate in their care (Minniti et al., 2016; Senthilkumar et al, 2018). Utilizing and

managing data is an important consideration of healthcare leadership, but big data can

also be useful in making healthcare leadership decisions. According to Lame and

Simmons (2018), big data enables healthcare leaders to run simulations to test the impact

of healthcare decision making without impacting patients in the real world. Utilizing

simulations could allow healthcare leaders to reduce, replace, or complement traditional

strategies which focus on exploration through trial and error. These strategies have real

world consequences which could impact patients. Using a literature review format, Lame

and Simmons (2018) explore how simulation can be used to investigate, understand, and

improve healthcare leadership. The results of the study indicate simulation can be

effective, quick, and low cost for leadership decision making exploration, but leaders

should be cautious of the limitations and assumptions embedded in each analysis

approach before implementing the policy solutions.

Though big data has successfully been utilized to enhance leadership strategies in

numerous fields such as policy and business, utilization of data science for the

leadership of the healthcare industry is still relatively unexplored by literature (Chiu &

Yu-Chuan, 2018; Groves et al., 2016). According to Chui and Yu-Chuan (2018), data

science can enhance the patient experience dramatically by improving outcomes and

optimizing care regimes. By implementing technological platforms in healthcare

facilities, leaders could improve outcomes and patient experience (Chui & Yu-Chuan,

2018). Chui and YuChuan (2018) demonstrated the strength of healthcare leadership

facilitated through data science in a study which examined an automated dose tracking

system for adaptive radiation therapy. According to Chui and Yu-Chaun (2018),

calculating the appropriate patient dose daily is a significant and time-consuming task

which is liable to create error. According to the study results. Automated dose tracking

systems resulted in higher facility efficiency and improved patient outcomes (Chui &

Yu-Chuan, 2018). With increased access to data leadership technologies and

solutions, healthcare leaders are able to utilize patient data in new ways to optimize

patient outcomes and improve healthcare productivities (Baldominos et al, 2017;

Natarajan et al., 2018). In addition to utilizing healthcare information to make decisions

about hospital leadership and patient care, healthcare leaders can utilize big data sets to

forecast the potential usefulness of solutions into the future and identify data markers

which might suggest incoming inefficiencies (Baldominos et al., 2017). Baldominos et

al. (2017) tested big data applications in a healthcare setting to determine their impacts

on hospital productivity and leadership decision making. Baldominos et al. (2017) found

that the data leadership system was able to provide intelligent recommendations to

healthcare leaders that had positive impacts on daily productivity. Hospital leaders

reported beneficial use of the system warning features for inefficiencies. These results

suggest that data applications can have real-world impacts for healthcare leaders.

Big datasets also open new avenues for comparing healthcare facilities for the

purpose of conducting a comparative assessment of individual facility productivity (Harle

et al., 2016). There is a substantial quantity of research and data dedicated to assessing

individual facility productivity. Until recently, that data was often kept within the facility

or individually presented within journals. Though important, the lack of cohesion

between healthcare assessments resulted in a disconnect between productivity research

and productivity improvement in healthcare (Harle et al., 2016; Malik., Abdallah &

Ala’raj, 2018). To address the gap, Harle et al. (2016) used data leadership and analysis

techniques to collect and collate the healthcare data into a single dataset. This work has

implications for healthcare practice which include assessing healthcare facilities based on

the productivity of similar facilities and considering the characteristics which may result

in higher or lower healthcare productivity.

In addition to providing crucial insights to healthcare professionals and leaders,

big data can improve hospital productivity by providing information to patients which

can help them lead their long-term health (Dimitrov, 2016). Using a systematic review

format Dimitrov (2016) reviewed wearable healthcare technology and its impact on

individuals and healthcare productivity. As previously mentioned, the rise of chronic

conditions in the United States (Buttorff et al., 2017) coupled with an aging population

(Marcus-Varwijk et al., 2018) makes the overall healthcare burden substantial. Dimitrov

(2016) found that there is substantial research to show that wearable devices can help

individuals lead their weight, physical activity, cardiac health, and blood pressure. By

helping individuals to lead these conditions, Dimitrov (2016) found that healthcare

facilities experienced productivity benefits.

Nations professionalized the healthcare field differently, depending on the

structure of the healthcare system (Linnander et al, 2017). According to Stefko et al.

(2016), countries often have different health, economic, and social conditions which

influence healthcare policy. However, a commonality across all national health systems is

a focus on cost reduction and efficiency. Managing employee and institutional knowledge

is a key function of healthcare leadership (Karamitri et al., 2017). Healthcare institutions

use a variety of techniques to lead their productivity, including lean leadership, agile

leadership, and design thinking (Roberts et al., 2016; Ferreira et al., 2020).

Carroll and Richardson (2016) highlight that a central point of design thinking is

to establish individual’s and organization’s specific needs and pinpoint areas which need

improvement. This framework benefits healthcare institutions be identifying areas of

weakness envisioning a structure to lessen the weaknesses. In some cases, lean leadership

can be useful for organizations seeking to improve productivity. Lean leadership is

associated with a cost reduction, but it does not necessarily improve patient experience

(Efe & Efe, 2016; Poksinka et al., 2017; Van Rossum et al., 2016). Managing the

productivity of healthcare organizations requires a balancing between cost productivities

and improved efficiency from a patient perspective. Balancing dual objectives can be

facilitated through evidence-based leadership, which considers multiple sources of data

before concluding about organizational direction (Gou et al., 2019).

In addition to the strategic usage of healthcare leadership strategies, healthcare

productivity is associated with specific characteristics such as: competent employees

(Coetzee, 2019; El Haddad et al., 2017; Juanamasta & Yuwono, 2018; Xue & Tuttle,

2017), careful assessments of productivity using viable metrics (Boussemart et al., 2020),

a balance between quality and cost efficiency (Atella et al, 2019; Asghar et al., 2019), and

innovation (Marjanovic et al., 2017). Innovation was found to be central to healthcare

productivity, as it created an environment where leaders were able to test new ideas and

strive for improvement (Marjanovic et al., 2017). Nurses and hospital staff played a large

role in productivity, so proper leadership of human resource was associated with

productivity. Finally, Atella et al (2019) found that the largest improvement in healthcare

productivity arose when leaders focused on improving quality and efficiency, rather than

reducing cost.

Transition

The previous section summarized recent literature related to lean strategies,

leadership, healthcare productivity, and data within healthcare organizations. Effective

healthcare productivity is affected by organizational factors inclusive of leadership

knowledge, motivation, tactic, policy development, data review, and strategy progression.

Healthcare organizations’ leaders identify tactics that could be strategically deployed and

used to monitor organizations’ productivity performance. In the literature review I

evaluated the transformational leadership theory and how its alignment with the policy

development theory facilitates development and adoption of tactics inclusive of

leadership roles, data review, and performance monitoring to achieve desired outcomes.

Section 2 includes a comprehensive review of the researcher role, research

population, and research method and design. The section with illustrate my role as the

researcher to meet ethical research requirements. Section 3 contains a presentation of

research component findings inclusive of interviews, organizational processes,

implications to professional practice, impact to social change, further recommendations,

and research conclusions.

Section 2: The Project

Section 2 will include a description of (a) the purpose statement, (b) role of the

researcher, (c) participants, (d) research method and design, as well as (e) population and

sampling. I addressed the aspects of ethical research, data collection instrumentation, data

collection techniques, as well as validity and reliability in conjunction with the previously

identified sections.

Purpose Statement

The purpose of this qualitative multiple case study was to explore the strategies

that healthcare organizations’ leaders use to effectively identify, deploy, and monitor

departments’ goals for improving their overall organizations’ performance. The targeted

population included 20 departmental leaders who had developed, deployed, and

monitored progress against the derivative departments’ goals. The geographic location

was the Western region of the United States within acute healthcare organizations that

have successfully demonstrated success in improving their organizations’ departments’

productivity through achieving the organization’s leaders’ related goals for departments’

productivity improvements. Using or adapting the study findings could be the catalyst for

positive social change by encouraging better strategic leadership practices that enable the

public to access a more efficient and effective healthcare system for benefiting

communities’ citizens and families.

Role of the Researcher

As the researcher, I conducted data collection, participant coordination, as well as

validation that was supported by research design and methodology. According to

Thurairajah (2019), the research must be comprehended by the researcher from the

personal extent of involvement within the research process to manage biases and

involvement. Responsibility of the sole researcher and data collector led me to serve as

interviewer, assessor, and principal data collector of participant responses and

organizational documentations. The primary expectations of the researcher were to

provide comprehension and align all aspects of the research question to the overall

research project. As discussed by Thurairajah (2019), methodology of research alignment,

bias limitation, and scrutinization was an expectation of the qualitative researcher. As the,

researcher I assumed sole responsibility for data analysis, research processes,

methodologies, management of limitations, presentation of results, and adherence to

ethics.

Within this study, I evaluated strategies related to effective identification,

deployment, and monitoring of productivity goals that potentially improve organizational

performance. The research topic was selected based on my experience within healthcare

operations and the expectation to effectively manage departmental productivity.

Lyubovnikova et al. (2018) discussed how shared experiences contributed to the

comprehension of organizational dynamics and team theory.

To adhere to principles of ethical research, I referenced the Belmont Report to

ensure that all participants are informed, that there was an appropriate assessment of risks

and benefits while adhering to the appropriate selection of participants. The Belmont

Report’s ethical principles of respect, beneficence, and justice guided the appropriate

research protocol for human participants in social research (Friesen et al. 2017). I used

the Belmont Report principles to establish the protocol for my research study. The

primary principles of the Belmont Report are autonomy, beneficence, and justice (Kamp

et al., 2019). The process was defined through obtaining consent of participants to

illustrate respect. I also conducted an assessment to evaluate any potential risks and

benefits to the study participants with a goal to provide a positive experience of current

and potential participants.

Adherence to qualitative research principles and guidelines was achieved by

removing personal biases and establishing expected research protocols. As discuss by

Thurairajah (2019), the removal of personal biases and establishment of standard research

processes promotes viable qualitative research. As part of the research protocol, I ensured

data collection processes were initiated to mitigate bias. Bracketing was enlisted to

suspend preconceptions during the interview process. According to Tufford and Newman

(2012), bracketing by the researcher reserves biases from previous experiences and

misconceptions. The research interview was conducted using a structured qualitative

interview process using sequenced open-ended questions. The interview pool contained

20 participants with experience specific to managing healthcare productivity relative to

the specific business problem. The data collection process began once I gained clearance

from the Institutional Review Board (IRB). The interview protocol was mapped to

include Zoom and telephone interviews of the 20 participants. The informed consent

process included processes related to pre and post interview actions (see Appendix A). A

clearly defined interview process and informed participants potentiates the return of

valuable information (Dodds et al., 2018). To ensure qualitative research ethics were

adhered to, I followed recommendations of the Belmont Report and guidelines discussed

by Roth and Unger (2018) related to protection of the human subjects aligned to

principles of: respect for persons, justice, and beneficence. The guidelines were managed

through the process of informed consent, selection of subjects, as well as assessment of

risks and benefits.

Participants

Ensuring research reliability and validity required selecting the appropriate

participants that aligned to the research question. Englander (2012) noted that ensuring

participant selection and research question alignment supports validity and is the primary

phase of the interview process. The participants of this study were healthcare organization

leaders from the Western United States. The participants of the case study were 20

healthcare leaders who effectively identified, deployed, and successfully monitored

productivity goals with improved organizational performance.

I researched healthcare organizations within the Western United States to find

insight into potential participants processes of productivity and organizational

performance management. I identified organizations that have departments dedicated to

reviewing productivity and performance outcomes with a formalized education plan for

healthcare leaders. The leaders for the selected departments were contacted by e-mail to

establish participant and Zoom interview potential.

The interviews were conducted through the Zoom platform using a single

participant process to gain insight into participant experiences, processes, and operational

methodology. I provided honest, direct, and clear lines of communication with each

participant to build trust and the willingness to engage in the research study. According to

Dodds et al. (2018) and Tufford and Newman (2012), lack of trust within the interview

process places limitations on data collection and valid information. I established an

effective researcher relationship by collaborating with the participants’ work schedules

and establishing alternatives to traditional face to face interviews. Research honesty and

ethical principles were implemented throughout the research process according to Friesen

et al. (2017).

Research Method and Design

Research Method

The qualitative research methodology was used to explore strategies healthcare

leaders use to identify and monitor productivity goals to improve organizational

performance. I established that the qualitative method was appropriate based on the

constructivist framework of understanding a phenomenon, and the use of flexible data

collection to encourage depth in the information collected from the participants.

According to Lampard and Pole (2015), the qualitative method provides answers through

exploratory methods to understand experiences and phenomenon. The justification for

use of the qualitative method is supported by the need to comprehend experiences of the

research participants (Edmonds & Kennedy, 2016). As suggested by Yin (2017), I

evaluated participant experiences and phenomenon within discussions, stories, and

research questions response details.

The qualitative methodology enabled the researcher to dissect meanings within

individual experiences. The researcher collected data from participant experiences to

evaluate similarities and meanings within descriptions (Edmonds & Kennedy, 2016).

Implementation of a qualitative method supports the collection of data through discussion

and experiences (Sherry, 2013). As discussed by Edmonds and Kennedy (2016) and

Sherry (2013), use of a qualitative methodology provides insight and reflection of

research participant experiences. The implementation of a qualitative research

methodology is more appropriate to explore strategies used to manage and improve

productivity performance in healthcare, use of a quantitative methodology would only be

appropriate if examining relationships. The quantitative research method is a

postpositivist approach to scientific inquiry wherein variables are measured to determine

their characteristics or relationships (Babones, 2016). According to Babones (2016), the

quantitative methodology is primarily used to evaluate relationships among variables and

test a defined hypothesis. The mixed method approach is used to study constructivist

framework, as opposed to a complex integrative framework. The mixed method process

was not used for this study, as defined by Bryman (2017) the mixed method is used to test

hypothesis of quantitative and qualitative data.

Research Design

I used a qualitative multiple-case study design. The selection of multiple sites and

individuals with the purpose of exploring processes, methods, and outcomes supported

the selection of a multiple-case study. The case study design facilitates exploration into

experiences of the participants using interviews and documents (Yin, 2017). According to

Yin (2017), the use of the case study design is appropriate when attempting to

comprehend phenomena of a select group. As discussed by Berends and Deken (2019),

using a qualitative multiple-case study design is beneficial in addressing the defined

research question and comprehension of organizational processes.

Use of the multiple-case study design was chosen after evaluation of the

ethnography and phenomenology design. However, since the study was not evaluating

patterns within a group, the ethnography design was not appropriate for this study. As

discussed in Goldstein et al. (2014), ethnography design is used to study adoption of like

actions or shared patterns within a group. The study was not focused on examining lived

experiences which determined that phenomenology was not an appropriate design for the

current research. According to Thomas (2021), phenomenology evaluates collected

knowledge related to experiences of phenomenon within a culture.

Ensuring data saturation within the research process enhanced validity of the data

and analysis. According to Lowe et al. (2018), data collection requires sufficient

collection or saturation to support research validity. Implementation of a data saturation

process ensured finalization and diligence of the research process. To potentiate data

saturation, I reviewed all interview data as a cross check. As recommended by Fusch and

Ness (2015), implementation of member checking during the interview process improved

accuracy and validity. I used Fusch and Ness (2015) to implement a process of reviewing

transcripts, read back of responses, validation of interpreted participant responses, and

continuous checking until no new data was obtained. Lowe et al. (2018) supports the

process of member checking to ensure the appropriate level of data saturation.

Population and Sampling

The population for the defined study consisted of individuals in the Western

region of the United States within healthcare organizations that had successfully

demonstrated success in improving their organizations’ departments’ productivity through

achieving the organization’s leaders’ related goals for departments’ productivity

improvements. Purposeful sampling was used to evaluate and recruit potential

participants with the desirable knowledge and organizational experience. The purposeful

sampling methodology supported the selection of a specified research sample through the

use of criteria to select participants (Bungay et al., 2016; Coyne, 1997). The use of

purposeful sampling was suitable to use in this qualitative research study because of the

effectiveness to target participants based on the research context and problem while

evaluating phenomenon. To achieve data saturation, I interviewed 20 hospital leaders

within the Western region of the United States who used strategies to effectively deploy

and monitor departments’ productivity goals to improve their overall organizations’

performance. I contacted healthcare leaders who have oversight of facility operations and

organizational outcomes. The rationale for participant selection depended on the ability to

manage productivity goals, and the leadership skills to strategically develop improvement

measures. The selected leaders ensured compliance to ethical and regulatory standards as

outlined by facility policies and standards.

The selection of research participant sample size was based on what was deemed

as an appropriate sample for research validity and saturation. Daggenvoorde et al. (2013)

suggested that a minimum of 15 participants is required to achieve an appropriate

research sample. Dworkin (2012) stated that a wide range of five to 50 participants as an

acceptable participant research sample within qualitative research. However, Fusch and

Ness (2015) argued that there is no relevance to the sample size within qualitative

research but that the process should focus on gathering reliable data. Reliable and rich

data collection is not achieved through the process of extending the participant size for

comparison reasons. The research process should encompass a process of sample size

selection that potentiates data saturation (Fusch, & Ness, 2015). I selected the process of

sample size selection based on Fusch and Ness’s (2015) recommendations by selecting

20 participants to achieve data saturation.

Ethical Research

Ethical research involves coordination and cooperation between the researcher

and participants while adhering to research guidelines. The interview process was not

initiated until appropriate participant consents were obtained. Participants did not receive

incentivization for participation in the study nor recognition for their organizations. The

consents focused on individual rights and protection during the interview process as well

as clarification of voluntary participation (see Appendix C). According to McGrath et al.

(2019), the qualitative research interviews require detailed interview processes to ensure

adherence to defined standards. I conducted the research using a defined interview

protocol (see Appendix A) after gaining approval from the Walden University

Institutional Review Board (IRB), approval number 08-25-21-0568675. The IRB process

was used as a guide to conducting data collection and included the IRB approval number

once the approval process was completed. Consent request was provided via e-mail with

follow-up phone calls to provide clarification and answer questions if needed.

During follow-up phone calls research participants were given the opportunity to

express concerns regarding research participation and the opportunity to withdraw.

Through the consenting process I informed the study participants that the process was

voluntary and of the ability to withdraw from the study at any time without repercussions.

Study participants could withdraw via e-mail, telephone, or verbal request during the

interview process. Participants that withdrew from the study had their privacy

maintained. According to Drake (2014), clarification of the research study while

providing the opportunity for participants to withdraw should be inclusive of establishing

a well-defined interview process and protecting participants rights.

Participant confidentiality and trust is crucial to obtaining reliable data.

Adherence to participant privacy was discussed during the consenting process (see

Appendix C). The participating organization and each participant were assigned a

research code to ensure confidentiality during research publication. All collected data was

saved and kept in a password protected file for a 5-year retention period. Protecting

participant identity and securing collected data builds trust between the researcher and

participant while protecting participant privacy (Wendler, 2020).

Data Collection Instruments

The process of data collection encompassed collecting data from peer-reviewed

literature, qualitative studies, and semistructured interviews. As the researcher my role as

the primary data collection tool was key to the qualitative process. According to Cypress

(2018), as the primary data collection tool the researcher is the most valuable tool in

qualitative research.

As the researcher and primary data collection tool, I used the semistructured

interview process to obtain information related to participant experiences and particular

phenomenon. As a preferred means of data collection in qualitative research, the

semistructured interview process assisted in primary data collection and evaluation of

phenomenon (Cypress, 2018). I collected participant experience data using a

semistructured instrument tool. I asked interview questions (see Appendix B) from the

participants and recorded responses related to strategies to effectively deploy and monitor

departments’ productivity goals to improve their overall organizations’ performance.

Upon completion of the interviews, I conducted member checking to ensure

validity and reliability of the data collection process. Qualitative research uses the process

of member checking to improve reliability and validity of researcher data through sharing

data and cross-checking interpretation (Cypress, 2018; Wendler, 2020). Each research

participant received a copy of interview interpretation and synthesis to validate

information. Clarification and validation of responses aided in the analysis of information

and recognition of themes.

Data Collection Technique

The qualitative case study explored strategies used to monitor and improve

organizations’ performance. The primary research question was: What strategies do

healthcare organizations’ leaders use to effectively identify, deploy, and monitor

departments’ productivity goals to improve their overall organizations’ performance? The

data collection strategy was primarily semistructured in-person interviews.

Semistructured interviews with open-ended questions provided insight into management

processes and organizational operations.

Once approval was gained through the IRB process, I conducted Zoom supported

face to face interviews scheduled for a 60-minute period. The participants were coded

during the interview process to ensure adherence to privacy. Audio was recorded to

maintain truth in data during the transcription process. The interviews contained

semistructured open-ended questions. I also maintained a positive relationship of trust to

promote participant engagement. Trust between participant and researcher was enhanced

through the appropriate capture of interview responses.

To ensure appropriate capture and accurate transcription of interview responses I

used a secure transcription application that could be imported to computer text easily.

According to Yin (2017), the use of recording devices and the process of transcription is

more dependable than manual note taking. The use of telephone interviews was restricted

due to the potential for variability when attempting to develop a connection. Telephone

interviews tend to provide less detailed responses due to the limited relationship

development between participant and researcher (Mealer, & Jones, 2014).

Data Organization Technique

After collection and transcription of data, organization and analysis was crucial to

the process of recognizing themes. Case study research requires organization to evaluate

data phenomenon (Yin, 2017). Establishing a database to collect and house data

facilitated an organized review process. I used the digital data transcription process to

manage data by dates, time, and coded participant. The use of an electronic data

management process eased the management of digital recordings and transcriptions. I

tracked coded participant audio files to transcribe data within an excel spreadsheet. The

files were password protected and saved for 5 years. As an archival backup I used a

password protected and encrypted cloud-based platform. According to Penuel et al.

(2011), selection of a digital data management platform potentiates security and ease of

tracking.

Data Analysis

Qualitative data analysis is a review of data that allows the researcher to evaluate

themes and occurrences that may provide relevance to the outlined research question.

According to Yüksel and Yıldırım (2015), data analysis is the progression towards

resolution of the defined research question. I conducted data analysis through the

collection and review of semistructured interviews. Data analysis is a systematic and

complex process that requires detailed review and management of information to identify

themes and meaning (Cypress, 2019).

Upon completion of the data collection process, I used a structured approach to

data organization and electronic input. According to Maher et al. (2018), the complexity

of data analysis is benefited by having a structured approach to analysis. I used the

thematic data analysis process outlined by Yin (2017) to initiate analysis of the collected

case study data. Yin outlined the analysis process as: (a) compile and organize, (b)

manage data in fragments, (c) input the collected data in sequenced groups, (d) interpret

meaning, and (e) establish findings. Use of the detailed process aided in the discovery of

themes and pattern related to strategies used to monitor and improve organizations’

performance.

The establishment of a defined collection, organization, and data review protocols

with proven electronic data analysis tools facilitated identification of themes through the

process of coding and categorization. According to Parameswaran et al. (2020),

qualitative research consists of collecting and reviewing rich descriptions to identify

patterns and themes. After collection of data, I conducted a preliminary review of

transcripts and used codes and categorization to identify themes. Open coding progresses

the identification of prominent patterns and themes within collected interview data (Wan,

2018; Williams, & Moser, 2019).

Progression of the data analysis process also included entering collected data

within the NVivo software. The NVivo software assisted in a detailed data review,

analysis, and recognition of themes that may have been missed within the open coding

process. Maher et al. (2018) proposed that the use of NVivo facilitates management of

copious quantities of data while providing credibility and accuracy during the analysis

process. Once I uploaded the data into the NVivo software, I used mind-mapping and

coding results to further organize data into relationships that supported or disputed the

research question.

Reliability and Validity

Reliability

Qualitative research should be inclusive of reliable and valid information that was

obtained ethically. According to Cypress (2017) and McGrath et al. (2019), qualitative

research requires that the researcher implement protocol to ensure trust within the

research process and validity of data. Hess et al. (2014) further emphasized qualitative

research reliability through the actions of the researcher check for data accuracy. By

aligning the research data collection process to the research question with the ability to

replicate results, I could further support research reliability. Moon (2019); Rose and

Johnson (2020) proposed that the ability to replicate research results supports reliability.

Hess et al. (2014) further supported the ability to replicate results as well as cohesive

research design and data collection to obtain valid research results.

A comprehensive research design and methodical data collection process ensured

the collection of relevant data and accurate recognition of themes. According to Moon

(2019), clear descriptions and protocols with use of member checking facilitates accuracy

and reliability of collected data. I used member checking to validate interpretations for

accurate results. In collaboration with member checking ensuring comprehensive

descriptions of research design, protocols, interviews, and participant feedback is

essential to promoting dependability and the ability to replicate. Lishner (2015) and

Campbell et al. (2013) defined the demonstration of research dependability as the ability

to present rich descriptions with the ease of replication. I implemented a research protocol

(Appendix A) aligned to my research process to ensure standardization and collection rich

interview data.

Dependability

Dependability in qualitative research is crucial to trustworthiness of research

results and the ability to replicate study findings. According to Bakhshi and

RodriguezNavas (2020); Yin (2017), dependability of research is related to the ability

within research protocol implementation to replicate and analyze like phenomenon.

Implementation of a defined interview protocol and research design potentiates

dependability (Yin, 2017). During the research process I used an interview protocol with

detailed collection of interview responses. Data triangulation was used to enhance

dependability of research results by evaluating several sources of information. According

to Jentoft and Olsen (2019), triangulation is used to test dependability and validity

through the convergence of various sources. To further enhance dependability and

validity I employed member checking with participants and interview transcripts. Fusch

and Ness (2015) noted that transcription review with research participants verified

accuracy and validity of data.

Validity

Qualitative research validity is crucial to the accuracy of design, processes, and

data. Validity of qualitative research encompasses the elements of creditability,

transferability, and confirmability. Cypress (2017) noted that researcher’s comprehension

of creditability, transferability, and confirmability is essential to research confidence.

According to Kim and Li (2013), creditability, transferability, and confirmability

potentiate trustworthiness in research findings.

Creditability

Credibility of qualitative research our through the process of accuracy and quality.

Moon (2019) proposed using member checking to enhance accuracy and data credibility.

Jentoft and Olsen (2019) supported the use triangulation to confirm source research to

further enhance credibility. I established research credibility and consistency by gathering

rich data from multiple sources and member checking during the interview process. Use

of triangulation and member checking mitigated researcher biases.

Transferability

Transferability within qualitative research is established once the researcher can

provide evidence that the research findings can be aligned to other situations, populations,

and times. Building a descriptive research process supports transferability of findings

(Korstjens & Moser, 2018;2017). I presented a comprehensive discussion of study

purpose, participants, and data collection. According to Graneheim and Lundman (2004),

a comprehensive discussion and rich description of research protocol and findings

facilitate association of research to other situations.

Confirmability

Qualitative research confirmability allows the ability of verification by other

researchers. Implementation of initial and subsequent member checking potentiates

confirmability. During the interview process I provided rich descriptions of participant

responses with member checking and descriptive data analysis. Fusch and Ness (2015)

suggested integration of triangulation to further enhance confirmability. The expected

integration of member checking, triangulation, and multiple source review potentiates

data saturation and confirmability of the research study (Fusch, & Ness, 2015; Yin,

2017).

Data Saturation

Collecting data through rich interview descriptions and replication facilitates

progression towards data saturation. According to Fusch and Ness (2015), the use of

member checking during the interview process also enhances the data saturation process.

I conducted member checking during the interview process and post transcription to

ensure accuracy while obtaining detailed descriptions until data became repetitive.

Respective information with no additional themes or patterns is essential to achieving

data saturation (Hennink et al., 2019; Saunders et al., 2018).

Transition and Summary

Section 2 outlined a comprehensive description of the qualitative research process

with insight into the research methods, design, ethics, as well as data collection, analysis,

and interpretation processes. In this section I also evaluated the data collection

instruments while reviewing reliability and validity. Section 3 will include study finding

and recommendation for future research.

Section 3: Application to Professional Practice and Implications for Change

Sections 1 and 2 provided an analysis into why the outcomes and findings from

this study are important to healthcare organization and departmental leaders as they

balance productive employee workforces. The previous sections also provide detailed

discussions related to research design, methodology, and implementation process. Section

3 focused on providing relevance to professional practice through the (a) introduction, (b)

presentation of findings, (c) application to professional practice, (d) implications for

social change, (e) recommendations for action, (f) recommendations for research, (g)

reflections, and (h) summary and study conclusions.

Introduction

The purpose of this qualitative multiple case study was to explore the strategies

that healthcare organizations’ leaders use to effectively identify, deploy, and monitor

departments’ goals for improving their overall organizations’ performance. The targeted

population included 20 departmental leaders that participated in detailed interviews

specific to the development, deployment, and monitoring of departmental productivity

goal improvement.

Upon completion of the data analysis, the study findings identified five practical

strategic themes for developing, deploying, and managing the organizations' departments'

productivity performance goals and improving overall performance. To ensure I achieved

data saturation during the research process recommendations by Fusch and Ness (2015)

were followed by initiating member checking through participant interview review and

verification to ensure that no additional themes emerged. The first strategy theme was

revealed as communication, which included organizational and interpersonal

communication between stakeholders at multi-operational levels of the organization.

Further analysis revealed the second strategy theme as information transparency which

stipulated that healthcare leaders should clearly communicate financial and nonfinancial

information to stakeholders. The third strategy theme was identified as employee

engagement which refers to the process of positively motivating employees cognitively,

emotionally, and behaviorally towards achieving organizational outcomes. Employees

who were highly engaged exhibited elevated productivity levels, had psychological

ownership, and were more committed to the organization and its goals. Data review,

analysis, and data-driven decision making was identified as the fourth strategy theme.

Research indicates that various data analytic tools can be utilized by health systems to

manage, model, and conduct predictions with the available large sets of health data. The

use of data analytics has benefits for patients, communities, and health systems, such as

saving costs, predicting disease outbreaks, and putting prevention interventions where

needed. The fifth strategy theme was performance management; the study findings

indicated the primary aspect of target setting. However, health systems could benefit from

performance management which includes identifying, measuring, and developing

individuals and teams' performance aligned to organizational goals. Table 1 below

indicates the distribution of strategy themes in the interview transcripts.

Table 1

Strategy Themes and their Frequency in the Data

Theme

Number of times code

appeared in data

Participant interviews

containing code

Communication

Data review, analysis and

decision making

Employee Engagement

Information Transparency

Performance Management

Presentation of the Findings

The current section provides an overview of various themes that emerged from

my study’s data in an effort to answer the research question: What strategies do

healthcare organizations’ leaders use to effectively identify, deploy, and monitor

departments’ productivity goals to improve their overall organizations’ performance? The

conduction of semistructured interviews with 20 healthcare leaders was the primary

source of data collection and analysis. The conceptual framework progressed from the

theories of transformational leadership by Bass and Avolio (1994) and the policy

development theory of Akao (1991). The research question and semistructured interview

data analysis identified the five core themes as (a) communication and information

sharing, (b) information transparency, (c) engaging employees, (d) data review, (e)

analysis and data-driven decision making, and (f) performance management healthcare

leaders use to identify, deploy, and monitor departments’ productivity goals and

performance.

RQ1: What Strategies do Healthcare Organizations’ Leaders Use to Effectively

Identify, Deploy, and Monitor Departments’ Productivity Goals to Improve Their

Overall Organizations’ Performance?

The research question explored strategies used by organizational leaders to deploy

effectively and monitor departmental productivity goals and improve overall

organizations’ performance. Five themes were found to address the research question, and

they are discussed in the section below. Quotes from the data illustrate each theme, and

findings of previous studies on the concepts are provided.

Theme 1: Communication

This theme discusses communication as a strategy that health care organization

leaders have utilized to identify, deploy, and monitor departments’ productivity goals to

improve the overall performance of their organization. Communication in this context

refers to the formal and informal systems through which meaning is transferred between

leaders and employees within the organization.

Six participants reported that they had used communication as a strategy to

monitor the productivity of departments within their health care organizations. For

instance, Participant 19 reported that multilevel communication had been effective in

providing positive results related to productivity performance: “Multilevel

communication within the department has been effective in providing positive results

related to productivity performance, it engages individuals at all operational levels.”

Information sharing was identified as an important strategy by 13 of the participants.

They argued that it was important to share information with leaders and employees within

the health system. Participant 13 argued that they used the strategy of information sharing

with leaders and employees, saying, "The strategies are transparency of data, daily

huddles with frontline leaders, and dissemination of information to

Frontline staff."

Participant 15 argued that it was the role of individuals in positions of leadership

to ensure effective communication on departmental and organizational goals. The

participant suggested a top-down approach whereby communication flows from leaders

to clinical staff within a health facility. In the following quote the leader’s role in

communication was identified: “…a leader has to ensure that the knowledge is

understood by clinical staff as we empower them with the ability to implement actions

that directly have impact to departmental and organizational goals.”

Downs et al. (1993) identified three dimensions of communication that were

relevant to this study. The first dimension is communication climate which refers to

organizational and personal level communication. It includes aspects such as the extent to

which communication influences workers to meet organizational goals and how that aids

them to identify with the organization. It also incorporates employees' attitudes towards

communicating within the organization. The second aspect is organizational integration,

which is about the extent to which individuals receive information about departmental

plans and job obligations. The third aspect is corporate information, which is concerned

with general information about the organization. It includes providing stakeholders with

information about the change, financial standing, and overall organizational goals and

policies.

Literature on communication and organization productivity indicates that

communication skills are important for leaders and employees within organizations that

desire to involve employees in performance evaluations. As work teams increase, the

importance of information sharing becomes pronounced as the core for team functioning.

One of the key aspects of communication in health care settings is communication among

healthcare providers to coordinate patient care, and failure in this setting could lead to

significant medical errors (Edwards et al., 2009), and a past report on patient flow

associated poor communication to sentinel events (Edward et al., 2009).

In summary, the study findings focus mainly on organizational communication as

opposed to personal communication. However, there was no mention of communication

between health providers and their patients, which is also an important aspect of

communication within health care settings (Chichirez & Purcărea, 2018). Hospitals and

other medical care facilities have large amounts of data and require cooperation and data

sharing.

Theme 2: Information Transparency

Information transparency discusses was identified as an effective strategy to

influence departmental productivity and is helpful in monitoring departmental

productivity against deployed goals. The current study adopts Bushman et al. (2004)

definition of information transparency which refers to the company's financial and

nonfinancial information accessibility for external users. Eight participants in the current

study reported that information transparency was an effective strategy. For instance,

Participant 6 indicated that information transparency was an important strategy to

monitor departmental productivity against deployed goals, stating, "To monitor

performance of departmental productivity I used the strategy of data transparency and

communicating information to key stakeholders.”

Participant 1 observed that information transparency was crucial to encourage

leaders display of accountability. Also, it enabled leaders to manage productivity in an

appropriate manner. Participant 1 identified the benefits of information transparency on

the health facility leadership: “I found that data sharing and information transparency was

key to engaging frontline leaders in appropriately manage productivity actions. allowing

leaders to take ownership builds accountability.” Participant 20 argued that information

transparency could be achieved through posting data for employees to ensure that

everyone was aware of the performance outputs. Participant 20 stated: “Data posting

provides an element of transparency so that everyone is aware of performance targets and

performance outcomes equally.”

Kundeliene and Leitoniene (2015) identified that transparency of financial reports

facilitated disclosure of the economic aspects of a business in a sense that users would

understand. On the other hand, nonfinancial information transparency was linked with an

organization’s social responsibility activities. Literature indicates that information

accessibility and transparency promote reliability, confidence in a company and lowers

isolation between the organization and stakeholders (Kundeliene & Leitoniene, 2015).

Information transparency could also lead to negative outcomes; for instance, users may

misunderstand the specified information resulting in baseless expectations from the

company. However, with information transparency analysis and evaluation, companies

can avoid the negative outcomes.

McWilliams (2013) argued that information alone is not likely to influence

consumer behavior in health care. Advocates of market-based transparency strategies

favor combining the information with financial or non-financial nudges. Nudges may

include tier-based, price-based, or value-based cost-sharing, insurance exchanges or

employers actively guiding consumers to the best plans, or default pathways supporting

high-value options. Packaging information into more effective signals is also a type of

nudge. Nudging is a form of agency instead of an extension of transparency. Rather, it is a

form of agency.

Kaplan (2018) argued that health care providers cannot achieve transparency with

their clients without first having internal openness at all levels of the health organization.

In addition, scholars have argued that there is a link between transparency and

productivity. When comparative productivity information about employees is

disseminated within a healthcare organization or made available to the broader

community, healthcare workers tend to be more diligent due to the scrutiny of their peers.

Theme 3: Engaging Employees

This theme refers to employee engagement as an effective strategy to influence

departments’ productivity performance. Cesário and Chambel (2017) defined employee

engagement as the process of positively motivating employees cognitively, emotionally,

and behaviourally toward achieving organizational outcomes. Research shows that

leaders who are actively working toward fully engaging their employee’s gain elevated

levels of productivity, organizational citizenship behavior, and general job performance

(Christian et al., 2011; Rich et al., 2010; Shuck, et al., 2011).

Nine participants reported that engaging employees was an effective strategy

influencing departments’ productivity performance in their organizations. For example,

Participant 6 noted that: “through the process of shifting cultural ownership and review I

engage clinical leaders to own the process of productivity performance.”

Employee engagement is one of the greatest challenges in the workplace (Osborne

& Hammoud, 2017). Bersin (2014) indicated that globally, only 13% of employees are

fully engaged at work. In addition, twice as many are so disengaged that this undesirable

behavior is spread to their fellow employees (Bersin, 2014). Employee engagement is an

important aspect in preserving the organization’s vitality, survival, and profitability

(Albrecht et al., 2015; Farndale & Murrer, 2015). Organizations with highly engaged

employees have greater profits, enhanced customer satisfaction, profits, and employee

productivity (Osborne & Hammoud, 2017; Ahmetoglu et al., 2015).

In summary, employee engagement results in a sense of involvement, and as a

result, employees acquire feelings of influence. Employee influence facilitates

organizational collaboration that progress towards empowerment. Feelings of power

generate psychological ownership, which leads to commitment to the organization and its

goals.

Theme 4: Data Review, Analysis and Data-Driven Decision Making

This theme refers to a strategy whereby data is reviewed, analyzed and the outputs

are utilized to inform decision making within the health facility. About eleven of the

participants indicated that they found data review and data use in decision making as an

effective tool to influence departmental productivity and overall organization

productivity. For instance, Participant 10 indicated that they used data for decision

making at their health facility: "The strategies of data review and comprehension of the

data source was used to determine daily, weekly, and monthly improvement actions.

Actions were based on clinical volume, patient acuity, and expected operational functions

within the department.” (Participant 10)

Participant 12 indicated that they analysed data to identify actions to improve

departmental productivity: “The strategy of data review and analysis as well as actionable

follow-up is key to managing departmental financial performance an operational resource

to improve departmental productivity.” (Participant 12) In addition, four participants

pointed to the importance of carrying out data reviews at departmental level. Participant

13 argued that the collaborative data review facilitated the monitoring of a department’s

performance. In the quote below the Participant identifies some of the advantages of data

review: “To assist with monitoring departmental performance against deployed goals the

team implemented collaborative reviews of departmental productivity missed targets.”

(Participant 13)

On average, as of 2015 an average-sized hospital produced 665 terabytes of data

(Wills, 2014). Scholars have argued that despite the large amounts of data, there is not

adequate applicable information to accompany the data (Wills, 2014). Data analytics

offers a solution to managing large amounts of data. IBM defines data analytic as "the

systematic use of data and related business insights developed through applied analytical

disciplines to drive fact-based decision making for planning, management, measurement,

and learning." Data analytics offers the following solutions to health care organizations,

enhancing the quality of care, containing costs, and managing operational duties (Prewitt,

2012).

Dash et al. (2019) stipulates that there is a new field of science referred to as data

science which aids the health care system to manage the large volumes of data. They

define data science as a field that deals with various aspects of data, including data

management and analysis, to extract deeper insights for improving the functionality or

services of a system. Additionally, some tools allow users to visualize data post-analysis.

Therefore, data science enables users to understand how a complex system such as health

care functions.

The digitization of health records is a widely accepted system across many health

facilities. The digitized health records are often referred to as electronic health records

(EHR), and they allow health systems to collect data on clients' medical history, current

health situation, including medical imaging, and socio-behavioural, and environmental

data. There are other digitized health systems beyond EHR, such as electronic medical

record (EMR), which stores the standard medical and clinical data gathered from the

patients. Also, there are personal health record (PHR), medical practice management

software (MPM), and many other healthcare data components. The digitized health

records have the capacity to jointly enhance the quality, service efficiency, and costs of

healthcare, as well as reduce medical errors (Dash et al. 2019).

Although electronic health records are not without challenges, they facilitate

advanced analytics and aid clinical decision-making by making enormous amounts of

data available. Experts indicate two ways in which data analytics contribute to healthcare

decision-making. The first avenue is predictive modeling, which analyses current and

historical data to predict future outcomes. These have benefits at a patient-level where

treatment outcomes, risk of self-harm, and potential risk of chronic illness can be

anticipated. Predictive levels at the macro or population level allow the health system to

detect outbreaks and prevent specific future health outcomes. Also, at the health facility

level, predictive modeling can be used in administrative applications to lower costs and

improve efficiency.

Secondly, data analytics can result in a reduction in health care costs through

predictive and prescriptive analytics. Health leaders have access to models that can

reduce costs and patient risk. These models offer value to health care clients and provide

solutions to health care bottlenecks such as reducing appointment no-shows, managing

supply chain costs, preventing equipment breakdown, and decreasing fraud.

The participants in the current study did not expound on the process that they

followed in carrying out collaborative data reviews. However, as indicated in the previous

section engaging employees in organizational practices leads to ownership of strategies

and commitment to organizational goals. Evidence presented in the strategy on data

review, analysis and decision-making shows that health stakeholders have access to

various data tools that can facilitate decision-making and offer value to the health system

and clients.

Theme 5: Performance Management

Performance management discusses performance targets as a strategy to develop,

deploy, and manage the health organizations' departments' productivity performance

goals. Nine participants discussed the process and indicated that they had established

performance targets and had periodic progress reports on performance. For instance,

Participant 4 stated that they were using target setting to develop, deploy, and manage

their organizations' departments' productivity performance goals and ultimately improve

the overall performance: "I decided to use the process of target setting as well as staffing

to volume." (Participant 4). Similarly, Participant 14 indicated that monthly reports on

performance were an effective strategy to manage departmental and overall organization

productivity: "The strategies used are information sharing and performance reviews at

regular intervals to make improvements." (Participant 4) Also, Participant 15 observed

that performance targeting helped employees to understand the impact of data on

operations and implement improvements: “Performance targeting has been a particularly

effective strategy because of the inclusiveness related to data awareness, comprehension

of how the data impacts operation, and implementation of improvement activities.”

(Participant 15)

Performance indicators refer to measurable elements of practice performance for

which there is evidence or consensus that they can be used to assess the quality, and

hence change of quality, of care provided’) and performance frameworks (‘conceptual

frameworks that set out the rationale and design principles for an indicator set’) are

typically designed to routinely monitor aspects of healthcare performance such as

effectiveness, efficiency, safety and quality(Crampton et al. 2004; Arah et al., 2006).

Target setting is one of the components of performance management. According

to Aguinis (2013), performance management is a continuous process of identifying,

measuring, and developing the performance of individuals and teams and aligning

performance with the strategic goals of the organization. It is referred to as a continuous

process because it is ongoing and constitutes of establishing goals and objectives,

monitoring performance, and providing and receiving coaching and feedback. Aligning

performance with strategic goals requires that managers ensure alignment of employees’

activities and outputs with the organization’s goals and, ultimately, aide the organization

achieve a competitive (Aguinis, 2013).

An effective and logical healthcare performance measurement system can enhance

the quality of medical service, lower costs, augment service processes, and accomplish

optimal resource distribution (Soysa et al., 2018; Van der Wees et al., 2014). As evidence

of the achievement of organizational goals, a growing number of scholars concentrate on

the advancement of hospital management utilizing performance indicators (Christiansen

& Vrangbæk, 2018; Ali et al., 2018).

Measuring productivity within healthcare settings is problematic (Boussemart et

al., 2020; Sheiner & Malinovskaya, 2016). Healthcare productivity must be judged from

the standpoint of reduced cost and increased care, but patient satisfaction and long-term

patient outcomes must also be considered and measured. Using a literature review format,

Sheiner and Malinovskaya (2016) illustrated the diverse methodologies for assessing

healthcare productivity, including disease-based approaches or patient care quality

indexes. A common approach to evaluating healthcare productivity includes a cost

analysis of indicator procedures and treatments.

Sheiner and Malinovskaya (2016) conclude by stating that there is value to

utilizing a combined assessment approach and found that the affordable healthcare act

was likely to result in long-run healthcare productivity improvement using several

different health productivity assessment frameworks. In summary, performance

management (including target setting) is an essential strategy that directly links employee

performance and organizational goals and clarifies the employees' contribution to the

organization.

Connecting Findings to the Conceptual Framework

The current study utilizes a conceptual framework that was primarily based on the

transformational leadership theory according to Bass and Avolio (1994). The theory

posits that improved organizational productivity can be achieved through the leaders’

ability to inspire confidence among staff and share the organizations’ vision through

charisma. The transformational leadership theory underscores the importance of building

a positive relationship with employees in order for leaders to exert a positive influence

that affects the entire organization (Breevaart & Bakker, 2018). The direct application of

this theory to the current study is that transformational leadership provides a context to

the strategies that foster sustainable departmental productivity such as communication,

information transparency and employee engagement. For instance, individualized

consideration refers to the ability of leaders to communicate concern with every

employee in an organization whereas transformational leadership ensures that the goals of

a company drive progress and action at every level within that company as defined by

Akao (1991). Breevaart and Bakker (2018) identified engagement as a positive employee

outcome of transformational leadership. Therefore, the transformational leadership theory

applies to the current study because it provides a framework for application of leadership

to influence optimal organizational outcomes (Bass & Avolio, 1994).

Applications to Professional Practice

The United States currently spends 18% of its gross domestic product (GDP) on

healthcare, yet the system does not optimally deliver high-quality, affordable, and

convenient patient care. Poor productivity in the healthcare delivery industry contributes

to high spending. Focusing on productivity would enable the health system to deliver

more with fewer costs. Also, increased productivity would allow the health system to

continue advancing medicine to meet the increasing need for health services while

improving affordability. This study identifies strategies that healthcare organization

leaders can utilize to effectively identify, deploy, and monitor departments' goals to

improve their overall performance.

For instance, leaders could reinforce their communication with employees and

ensure they are aware of the organizational goals, departmental goals plans and job

obligations. Also, ensuring that other stakeholders outside the health system are regularly

updated on any changes, the health facility's financial standing, and their overall goals

and policies (Downs et al., 1993). It is also important for health systems to focus on

communication between health care providers concerning their patient's care towards

positive health outcomes (Edward, 2009). Communication is also crucial to share

information and foster teamwork within the various sub-teams in the health facilities.

The current study indicated the importance of financial and non-financial

information transparency. A health facility that practices this strategy gains confidence

and is perceived as reliable by its stakeholders (Kundeliene & Leitoniene, 2015). In

addition, literature proposes that health care leaders should combine information

transparency with financial and non-financial nudges as a form of stakeholders’ agency.

The study indicates that high levels of employee level engagement should be

viewed as a strategy for increasing organizational productivity. Literature indicates that

organizations that engage their employees benefit from preserving the organizations

vitality, survival, and profitability (Albrecht et al., 2015; Farndale & Murrer, 2015).

Pathways to these positive outcomes include employees' feelings of influence,

psychological ownership, and commitment to the organization and its goals.

Leaders could utilize data science to manage the large amounts of health data,

enhancing its quality, and managing operational duties (Prewitt 2012). They could utilize

data analytics to analyze, utilize modelling to visualize and predict future outcomes at

patient level or population level. The health facilities could also benefit from data

modelling to lower administrative costs and improve efficiency (Prewitt 2012).

Leaders should proceed beyond target setting and practice performance

management. Performance management is a continuous process of identifying,

measuring, and developing individuals and teams’ performance in line with an

organization’s strategic goals. Literature shows that there are health system benefits of an

effective healthcare performance measurement system including improved quality of

medical service, lower costs, augment service processes, and gain optimal resource

distribution (Soysa et al., 2018; Van der Wees et al., 2014). Leaders must strike a balance

between reduced costs and patient satisfaction. Performance management links employee

performance and organizational goals are clarifying their contribution to the organization.

Implications for Social Change

In this section, the implications of the study findings are expressed in terms of

tangible improvements to key stakeholders in the health system. The key stake holders

are inclusive of healthcare leaders, employees (clinical and non-clinical), patients, and

wider communities within the vicinity of the health facility.

The study findings indicate the benefits of effective communication to the health

system, ensuring that all stakeholders are aware and working towards common goals.

There is also a bottom-up approach whereby health leaders provide an opportunity to

employees to give feedback. Departmental leaders practicing positive communication

skills need to communicate departmental plans and job obligations to their work teams.

Also, health facility leaders are responsible for communicating with internal and external

stakeholders about financial standing, any changes made, and goals and policies that

guide the health system. In addition, another aspect of health providers' communication is

to promptly coordinate patient care.

Information transparency has benefits for the health system in general. The health

facility leadership gains stakeholder confidence and better reliability when they are

transparent about their financial and non-financial information. The information needs to

be clear to avoid misunderstandings and baseless expectations from stakeholders

(Kendeliene & Leitoniene, 2015).

High levels of employee engagement have benefits for both the organization and

employees. Employees who are engaged at their place of work have more psychological

ownership and better motivation in their job. On the other hand, a health facility that

engages its employees has greater profits because employees are more productive, and

their customers are satisfied. Therefore, healthcare leaders’ ways of improving employee

engagement such as ensuring job fit, giving their employee’s proper training, ensure

employees are tasked with meaningful work, use formal and informal check-in strategies,

and frequently discuss engagement with employees (Gleeson, 2017).

The health system, employees, and patients and wider community benefit from

use of data analytics to inform decision making. For instance, when leadership and

employees invest in digitized health records the quality of health data improves, health

services are more efficient and the healthcare costs are reduced (Dash et al., 2019). In

addition, data modelling allows the health system to predict disease outbreaks and put

prevention or response measures at the population level in place. Health system costs,

specifically administrative costs, can be reduced through predictive modelling. Health

leaders have the responsibility to partner with experts in health information systems and

statisticians to enjoy the full benefits of data science.

Managers have the responsibility of driving continuous performance management

in a health care system. The strategy has benefits for employees because it improves

motivation and self-esteem, and performance, clarifies job tasks and duties, provides

selfinsight and development opportunities, and clarifies supervisors’ expectations

(Aguinis, 2013). For managers, it allows them to understand employees’ activities and

goals, allow for fair and suitable administrative actions, allow for clarity in

communication of organizational goals. It also provides insights to managers on good and

poor performers, and aids in driving organizational change, and enhance employee

engagement (Aguinis, 2013).

In this section the implications of the study findings are expressed in terms of

tangible improvements to key stakeholders in the health system. Key stakeholders

inclusive of leaders, employees (clinical and non-clinical), patients, and wider

communities within the vicinity of the health facility are expected gain positive outcomes

from the knowledge gained through the identification of themes within research findings.

The study findings indicate the benefits of effective communication to the health

system as a whole ensuring that all stakeholders are aware and working towards common

goals. There is also a bottom-up approach whereby health leaders provide an opportunity

to employees to give feedback. Departmental leaders practicing positive communication

skills need to communicate departmental plans and job obligations to their work teams.

Also, health facility leaders have an obligation to communicate with internal and external

stakeholders about financial standing, any changes made, and goals and policies that

guide the health system. In addition, another aspect of health providers communication is

to promptly coordinate patient care.

Information transparency has benefits for the health system in general. The health

facility leadership gains stakeholder confidence and better reliability when they are

transparent about their financial and non-financial information. The information needs to

be clear to avoid misunderstandings and baseless expectations from stakeholders

(Hofmann & Strobel, 2020).