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Facilitating hospital patient flow: an exploratory analysis on reducing patient bed turnaround time

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Facilitating Hospital Patient Flow: An Exploratory Analysis on Reducing Patient Bed
Turnaround Time
by
Choong-Yop Julius Hahn

Rossier School of Education
University of Southern California
A dissertation submitted to the faculty
in partial fulfillment of the requirements for the degree of
Doctor of Education

August 2021

© Copyright by Choong-Yop Julius Hahn 2021
All Rights Reserved

The Committee for Choong-Yop Julius Hahn certifies the approval of this Dissertation

Maria Ott
Don Murphy
Jennifer Phillips, Committee Chair

Rossier School of Education
University of Southern California
2021

iv

Abstract
Hospitals are striving to achieve efficient bed turnaround to increase revenues, improve patient
satisfaction, and most importantly, improve patient outcomes. The bed turnaround process is
deceptively simple on the surface but is a complex process requiring collaboration among
multiple stakeholders. The purpose of this study was to explore the knowledge, motivation, and
organizational influences affecting bed turnaround at a hospital located in the western U.S. The
hospital attempted to improve its bed turnaround time without much success. The study
conducted document analysis of 26 patient flow related documents created between March 2019
– October 2020. In addition, the study conducted surveys and interviews with patient care staff
represented by a sample of nurses and environmental services workers. The results and findings
indicated that the patient care staff possessed the procedural knowledge to perform their job
functions, and the hospital provided a cultural model of accountability. However, the patient care
staff did not perceive that the hospital valued their perspectives or included them in the decision-
making process. Also, the patient care staff did not perceive that the hospital communicated bed
turnaround goals or provided enough resources to support efficient bed turnaround. Further
research is needed to evaluate the effectiveness of the new bed assignment workflow and the
impact of relentless communication sharing bed turnaround performance and goals with staff
members.
Keywords: bed turnaround, patient flow

v

Dedication
To my parents, Yoo Han and Ok Bae Han, for endless support and prayers. My parents grew up
in the post-Korean war and did not receive much education. My parents immigrated to the U.S.
in their 40’s to provide a better education for their children. All their children graduated from
college, and now one with a doctoral degree.
To my wife, Jiwon Song, I thank her for being my biggest cheerleader throughout this process.
She has constantly been encouraging me during the journey. I could not have achieved this
without her love and support.
To my late grandmother, Ok Rae Han, I attribute my work ethic to reaching the lifetime goal.
When I was in elementary school, my grandmother built stairs with a small garden hoe on the
hillside so my brother and I could go to school without going around the neighborhood. Her
memory continues to inspire me.

vi

Acknowledgements
I thank and acknowledge my dissertation committee: Dr. Jennifer Phillips (Chair), Dr.
Maria Ott, and Dr. Don Murphy. Dr. Phillips has shown her dedication to students like me by
providing thoughtful feedback on a timely basis.
I appreciate the USC Rossier School of Education faculty who taught me the invaluable
lessons that I will carry on with me. I have also learned from my classmates from the USC
Organizational Change and Leadership Cohort Thirteen. There are too many classmates to
mention from the Saturday classmates and later on the Thursday classmates. I especially want to
recognize Carlos Cruz, Catherine Rice, Ezequiel Ramirez, Jacqueline Dupont, James
Concannon, Jared Sinclair, Lena Cazeaux, Pamela Paspa, Tonya Skeen, Tracy Pearson, and
William Sherrod for their support and friendship.
I am grateful to my supervisors, Mayilvahanan Dharmarajan and Alen Oganesyan, for
their encouragement when I considered the program and support for the flexible schedule as I
desperately needed time to time.

vii

Table of Contents
Abstract .......................................................................................................................................... iv
Dedication ....................................................................................................................................... v
Acknowledgements ........................................................................................................................ vi
List of Tables .................................................................................................................................. x
List of Figures ................................................................................................................................ xi
Chapter One: Overview of the Study .............................................................................................. 1
Background of the Problem ................................................................................................ 1
Importance of Addressing the Problem .............................................................................. 3
Organizational Context and Mission .................................................................................. 5
Organizational Goal ............................................................................................................ 6
Description of Stakeholder Groups ..................................................................................... 7
Stakeholder Group for the Study ........................................................................................ 8
Purpose of the Study and Questions ................................................................................... 9
Overview of the Conceptual and Methodological Framework ......................................... 10
Definition of Terms........................................................................................................... 11
Organization of the Project ............................................................................................... 12
Chapter Two: Review of the Literature ........................................................................................ 13
How Bed Turnaround Became Important ......................................................................... 13
Potential Causes for Inefficient Bed Turnaround ............................................................. 18
Implications of Inefficient Bed Turnaround ..................................................................... 25
Previously Attempted Process Improvement Approaches at Hospitals ............................ 33

viii

Clark and Estes's (2008) Knowledge, Motivation, and Organizational Influences
Framework ............................................................................................................ 34
Stakeholder Knowledge, Motivation, and Organizational Influences .............................. 35
Conceptual Framework ..................................................................................................... 50
Summary ........................................................................................................................... 53
Chapter Three: Methodology ........................................................................................................ 55
Study Questions ................................................................................................................ 55
Overview of the Methodology .......................................................................................... 55
Data Collection, Instrumentation, and Analysis Plan ....................................................... 57
Ethics and Role of the Researcher .................................................................................... 67
Chapter Four: Results and Findings .............................................................................................. 70
Document and Artifact Analysis ....................................................................................... 72
Participating Stakeholders ................................................................................................ 74
Research Question 1: What Are the Patient Care Staff Knowledge and Motivation
Influences Related to Performing the Bed Turnaround Process? ......................... 81
Research Question 2: How Does the Organizational Culture and Context Impact Patient
Care Staff Capacity to Perform the Bed Turnaround Process? ............................. 89
Summary of Knowledge, Motivation, and Organizational Influences’ Data ................. 105
Chapter Five: Discussion and Recommendations………………………………………………109
Discussion of Results and Findings ................................................................................ 109
Recommendations for Practice ....................................................................................... 113
Limitations and Delimitations......................................................................................... 120
Recommendations for Future Research .......................................................................... 122

ix

Implications for Equity ................................................................................................... 124
Conclusion ...................................................................................................................... 125
References ................................................................................................................................... 126
Appendix A: Survey Protocol ..................................................................................................... 149
Appendix B: Spanish Language Survey ..................................................................................... 154
Appendix C: Interview Protocol ................................................................................................. 157
Appendix D: Document Analysis Protocol................................................................................. 163
Appendix E: Information Sheet for Exempt Research ............................................................... 164

x

List of Tables
Table 1: Organizational Mission, Goal, and Stakeholder Goal ....................................................... 9
Table 2: Knowledge Influence ...................................................................................................... 37
Table 3: Motivation Influences ..................................................... Error! Bookmark not defined.
Table 4: Organizational Influences ............................................................................................... 50
Table 5: Data Sources ................................................................................................................... 56
Table 6: Knowledge, Motivation, and Organizational Influences and Survey Questions ............ 71
Table 7: Document and Artifacts .................................................................................................. 73
Table 8: Demographics of Survey Participants (N = 40) .............................................................. 75
Table 9: Demographics of Survey Participants – Nurses Only (N = 27) ...................................... 76
Table 10: Demographics of University Hospital Nurses (N = 882) .............................................. 77
Table 11: Participants by Survey Data Collection Method (N = 40) ............................................ 78
Table 12: Lean Workgroup Tasks to Improve Bed Turnaround .................................................. 100
Table 13: Asset or Need by Document Analysis, Surveys, and Interviews ................................ 105
Table 14: Knowledge, Motivation, and Organizational Assets or Needs as Determined by the
Data ............................................................................................................................................. 106

xi

List of Figures
Figure 1: Bed Turnarround Process .............................................................................................. 22
Figure 2: University Hospital’s Bed Turnaround Process............................................................. 45
Figure 3: A Conceptual framework for the Integration of Knowledge, Motivation, and
Organizational Influences That Affect Bed Turnaround process .................................................. 52

Figure 4: Research Methods Design ............................................................................................ 57
Figure 5: Gender Results for Survey Participants (N = 39) ......................................................... 79
Figure 6: Race Results for Survey Participants (N = 40) ............................................................. 80
Figure 7: Years of Experience Results for Survey Participants (N = 40) ...................................... 81
Figure 8: Procedural Knowledge Average Score Distribution (N = 39) ...................................... 84
Figure 9: Motivation Value Average Score Distribution (N = 39) ............................................... 86
Figure 10: Motivation Self-Efficacy Average Score Distribution (N = 40) ................................. 88
Figure 11: Cultural Model – Value Staff Perspectives and Inclusion in Decision Making Process
Average Score Distribution (N = 40) ........................................................................................... 91

Figure 12: Cultural Model – Accountability Score Average Distribution (N = 39) ..................... 94
Figure 13: Cultural Setting – Communication Average Score Distribution (N = 40) .................. 98
Figure 14: Cultural setting – Goals and Align Incentives Average Score Distribution (N = 40) 104

1

Chapter One: Overview of the Study
Hospitals globally are facing bed management problems due to increasing patient volume
but a limited number of beds (Al-Qahtani et al., 2017; Bouneb et al., 2018; Cardoso et al., 2011).
According to the American Hospital Association, 2016 had 100,000 more hospital admissions
but 3,000 fewer beds in comparison to 2015 (He et al., 2019). Due to the limited hospital beds,
patients are experiencing delays in care, and some patients are denied care, which often results in
death (Bouneb et al., 2018). The COVID-19 pandemic exacerbates the bed shortage worldwide
(Ferstad et al., 2020; Ma & Vervoort, 2020; Maves et al., 2020; Quaedackers et al., 2020;
Wynants et al., 2020). Despite the needs, hospitals struggle with an inefficient patient flow
process, specifically, the slow turnaround of beds. Empirical research has supported evidence
that a slow bed turnaround has devastating consequences for patients (Lovett et al., 2016;
Groenland et al., 2019). This study evaluates the knowledge, motivation, and organizational
influences affecting the bed turnaround time and will present pragmatic recommendations to
shorten the time based on a modified Clark and Estes gap analysis model (Clark & Estes, 2008).
Background of the Problem
According to Ozcan (2008), health care performance can be measured based on the
entity's efficiency and effectiveness. The patient flow process is a movement of patients within
the hospital (NEJM Catalyt, 2018), which is vital for hospital efficiency, and many hospitals aim
to improve their bed turnaround time (Lovett et al., 2016; Walker et al., 2016). Hospitals use the
term patient throughput, which is defined as putting the right patient, in the right bed, at the right
time. Patient flow and patient throughput are considered interchangeable and the term patient
flow will be used throughout the paper. Bed capacity management is a complex process
involving many departments, often with different reporting lines, which creates communication
2

problems (Lovett et al., 2016; Winasti et al., 2018). Pellicone and Martocci (2006) found
communication problems between departments as well as within each department as the primary
cause for slow turnaround. Additionally, Tortorella et al. (2013) emphasized the downstream and
cumulative impact of communication problems. Furthermore, Thomas and MacDonald (2016)
found that interdepartmental communication issues are more likely to occur when information
has been communicated with other teams.
Lack of knowledge of using bed management software contributes to a significant
contribution to the slow turnaround problem. Several studies show that patient care staff are not
proficient in bed management software (Pellicone & Martocci, 2006; Tortorella et al., 2013; He
et al., 2019). Additionally, evidence suggests that patient care staff entered information
incorrectly in the bed management software due to a lack of knowledge (Brown & Kros, 2010;
Tortorella et al., 2013). Furthermore, the information in the bed management software was either
insufficient or entered in late, demonstrating the knowledge problem (Tortorella et al., 2013;
Walker et al., 2016)
In addition to communication and knowledge challenges, several studies cited
organizational factors as contributing to the slow bed turnaround problem. Nowak et al. (2012)
found that simply more patients needed care than what was anticipated by the hospitals
suggested a failure of planning. Research demonstrates that hospital processes are inefficient
(Nowak et al., 2012; Walker et al., 2016), manually based (Wyman, 2009), too complex (Winasti
et al., 2018), and vary between departments (Allder et al., 2010). The lack of resources, in
particular, the lack of sophisticated software with real-time bed information (Walker et al.,
2016), forcing hospitals to conduct manual bed allocation processes (He et al., 2019). The delay
in care due to unavailable beds has profound implications. Taken together, the existing published
3

research either focuses on the overall patient flow problems within a hospital or specifically, on
the patient flow from the Emergency Room perspectives, including boarding or optimization
problems.
Importance of Addressing the Problem
The results from the existing literature shed light on several implications of a slow
turnaround of bed turnaround: financial consequences, patient outcomes, and patient and staff
satisfaction. During the 2016 House and Means Committee Hearing, Michael Gallup, CEO of
TeleTracking, testified that if hospitals reduce ED boarding time by two hours, hospitals can see
9.7 million more patients and gain $12 billion (Exploring the use of technology and innovation to
create efficiencies, higher quality, and better access for beneficiaries in health care, 2016).
Wyman (2009) found $5 million of additional revenues from one hospital after implementing an
efficient bed capacity management process. Walker et al. (2016) and Brown and Kros (2010)
also discussed additional revenues for hospitals by improving bed turnaround time.
In addition to the financial implications, a slow turnaround causes a delay of care with
clinical implications. ED boarding time is the wait time for patients to be admitted with inpatient
status (Lovett et al., 2016; Walker et al., 2016). Several researchers found a correlation between
ED boarding time and mortality rate (Al-Qahtani et al., 2017; Liu et al., 2011; Stretch et al.,
2018). Specifically, Lovett et al. (2016) found that ED boarding time greater than two hours had
clinical impacts on patients, and Groenland et al. (2019) found that a boarding time greater than
2.4 hours is associated with increased mortality after admission. Refusal to accept patients due to
unavailable beds correlated with a mortality rate (Bouneb et al., 2018; Robert et al., 2012).
The following court case provides an example of the ultimate implication of bed
unavailability (Toston v. St. Francis Medical Center, 2012). A patient arrived at the ER at
4

Hospital A at 3:25 am on Sunday, November 24, 2002, with left flank pain. Due to the rapidly
deteriorating conditions, Hospital A could not treat the patient and sought to transfer the patient
to another hospital with the capability of treating complex care. Hospital A arranged a transfer
with Hospital B before 8:00 pm on Sunday. However, Hospital B rejected the transfer request
shortly after 8:00 pm because no Intensive Care Unit (ICU) bed was available. Upon
understanding the transfer was canceled, the treating physician frantically contacted two other
hospitals in the area, but neither hospitals had available bed space. The patient eventually arrived
in Hospital B ER at 11:35 am the next day and went to a surgical operation. The patient died at
5:10 pm, Monday, November 25, 2002. This case did not happen in a third-world country but in
the United States.
The implications of slow bed turnaround time are detrimental to patient outcomes. Some
patients left hospitals without being seen due to unavailable beds (Lovett et al., 2016). Walker et
al. (2016) found a correlation between care delay and poor quality of care: overutilization of
medication, prolonged stay, wrong treatment, and patient safety issues. Moreover, the ICU
admission delay affected patient outcomes (Kim et al., 2016; Mathews et al., 2018; Sagy et al.,
2018). Poor patient outcomes and long wait times often result in low patient satisfaction
(Pellicone & Martocci, 2006; Brown & Kros, 2010; Jayasinha, 2016; Nowak et al., 2012; Qin et
al., 2017). Wyman (2009) found that nursing staff dissatisfaction, due to less time spent with
patients, suggesting a correlation of employee satisfaction with interest, motivation, and value
problems.

5

Organizational Context and Mission
The University Hospital (pseudonym: University) is an academic medical center located
in the Western United States. As a part of the Well-Known University (pseudonym: Well-
Known), University Hospital operates semi-independently due to the different nature of services.
Hospitals are hierarchical organizations with complex dynamics, and University Hospital has an
even more complicated hierarchical structure, mainly due to their affiliation with a medical
school and faculty physicians. The mission of University Hospital is to provide excellent care.
Similar to other academic medical centers, University Hospital is a tertiary medical center
known for performing complex surgeries for critically ill patients that community hospitals
cannot treat. University Hospital is one of the few tertiary hospitals in the region, so community
hospitals refer patients with dire and often desperate situations (Director, Transfer Center,
University Hospital, 2019). Because University Hospital is a teaching hospital that trains future
physicians and a part of a prestigious university system, the public's presumed expectation is
high-performance, and patients are willing to pay expensive fees to get treated. On par with the
American Hospital Association findings in 2016, the demand for University Hospital has
steadily grown due to the need for more hospitalizations across America (He et al., 2019).
In the past, University Hospital attempted to reduce its bed turnaround time but failed to
succeed (University Hospital, Lean Event, June 13, 2019). University Hospital sponsored multi-
departmental performance improvement initiatives over the years using popular tools such as
plan, do, study, and act (PDSA) and Lean. In addition to the internal process improvement
efforts, University Hospital hired a consulting firm to improve the problem. The latest attempt
was in 2019 when the average turnaround time was 5.1 hours (University Hospital Bed
Management Report, September 2019). According to the consulting firm, the best practice for
6

bed turnaround in the industry was 1.5 hours (University Hospital), demonstrating a considerable
gap between the industry's best practice and current University Hospital performance.
Organizational Goal
University Hospital desired to set SMART goals based on evidence using data
(University Hospital, October 1, 2020) and proposed goal setting as a recommendation to
achieve an effective bed turnaround (University Hospital, July 16, July 31, August 13, and
August 26, 2020). University Hospital proposed to establish SMART goals by January 2021
(University Hospital, October 1, 2020). However, it is unknown if University Hospital has
established SMART goals as no SMART goals were communicated to either the interviewees or
to me as the researcher. SMART goals are a widely used method for setting goals, which stands
for S specific, M measurable, A achievable, R relevant, and T time stamped. Perhaps the lack of
a clear goal explains why previous attempts failed to solve the problem. Benchmarking is
defined as a process for an organization to improve weak areas by comparing performance
against either a set of standards or like/aspirational organizations (Dowd, 2005). Bed turnaround
time does not have a national benchmark as governments, the largest payers of health care in the
U.S., do not collect the information. Also, the hospital accreditation agency does not consider the
measure in its evaluation. Further, professional associations and consumer advocacy groups do
not demand this information be recorded. Due to the operational nature of bed turnaround time,
hospitals do not make their bed turnaround time public. This study provides a specific and
measurable goal to shorten the bed turnaround time in hopes of serving patients with a better
outcome. If this goal is achieved, University Hospital could reduce its bed turnaround time to
minimize the delay of care to improve outcomes for the patients, especially those who are in life-
or-death situations.
7

Description of Stakeholder Groups
The majority of nurses are female, with a significant percentage of nurses belonging to
racial/ethnic minority groups. The majority of environmental services personnel (EVS) are
minority females. Further, EVS team members earn the lowest wage along with other hospitality
workers, who are mostly minority females. On the other side of the wage spectrum are
administrators who are mostly Whites. Most C-suite cabinet members are Whites, and White
males hold the top three positions. The Well-Known University’s Medical School employs all
physicians who practice out of the University Hospitals.
Within the hospital workforce, several stakeholder groups are involved in the bed
turnaround process, including administrators, EVS, nurses, the transfer center, transporters, and
IT. Administrators are responsible for setting a clear goal to reduce the turnaround time.
Administrators also maintain optimal resources and adequate training to perform a high caliber
bed turnaround. Nurses are accountable for scrubbing bed linens for potential needles,
medications, and the patient's personal belongings. After the bed is safe to clean, nurses notify
EVS to clean the bed by updating the bed boarding management software application system
(BBS, pseudonym). EVS is accountable for the thorough cleaning of the bed for patient safety.
EVS performs cleaning the bed and notifying nurses that the bed is ready to accept new patients.
Nurses then accept new patients to the cleaned bed. The transfer center coordinates patient
transfers from other hospitals to University Hospital. Transporters move patients within the
hospital. IT provides technology solutions for hospital operations.
External to the hospital workforce, researchers studying the patient flow will benefit from
this study as the bed turnaround is crucial for patient flow. Physicians will benefit from this
study as well as they can treat more patients at the hospital. Society at large will benefit from this
8

study as more people can go back to the workforce. Ultimately patients are the stakeholder group
who receives the most benefit from faster bed turnaround. However, this study focuses on
internal stakeholders who have moral obligations to improve their organizations (Nozick, 1981)
– EVS personnel and nurses.
Stakeholder Group for the Study
For the purpose of this study, EVS and nurses are combined and selected as the focused
stakeholder group. For the sake of clarity, this group will be referred to as patient care staff.
Combined, these two groups are primarily responsible for performing the actual bed turnaround
functions. Due to significant interdependencies in the workflow, the patient care staff must work
together to turn beds efficiently. Table 1 describes the organizational mission, organizational
goal, and stakeholder goal.

9

Table 1
Organizational Mission, Goal, and Stakeholder Goal
Organization mission
The mission of the University Hospital is to be the leader in health care providing excellent care
to patients.
Organizational performance goal
By June 2023, University Hospital will improve its bed turnaround time from the base year (July
2020 – June 2021).
Stakeholder group goal
By June 2023, patient care staff will reduce the bed turnaround time by 20% from 5.1 hours to 4.1
hours.

Purpose of the Study and Questions
The purpose of this project was to explore the patient care staff’s knowledge, motivation,
and organizational influences to efficiently performing bed turnaround time to improve patient
care. While a complete performance evaluation would focus on all stakeholders, for practical
purposes, the stakeholder to be focused on in this analysis was the patient care staff, comprised
of EVS and nurses. The focus of the present study is on patient care staff knowledge, motivation,
and organizational influences related to patient care staff competency. The study questions
(S.Q.s) that guided the evaluation study are as follows:
10

1. What are the patient care staff knowledge and motivation influences related to
performing the bed turnaround process?
2. How does the organizational culture and context impact patient care staff capacity to
perform the bed turnaround process?
3. What are the recommendations for organizational practice in the areas of knowledge,
motivation, and organizational influences?
Overview of the Conceptual and Methodological Framework
The Clark and Estes Gap Analytic Framework was used to address the problem of the
slow turnaround of beds at University Hospital. The gap analytic framework allows
measurement of the gap between goals and current practice via examining the knowledge,
motivation, and organizational influences and the formulation of recommendations (Clark &
Estes, 2008). With the emphasis on three performance influencers of knowledge, motivation,
and organization changes, the Clark and Estes gap analytic framework is known as the KMO
theory. In other words, using the KMO theory as a lens, organizations can achieve process
improvements by identifying goals, analyzing gaps, influencing knowledge, motivation, and
organization changes (Clark & Estes, 2008). This study's methodological framework was
mainly a quantitative research method including a survey with document analysis. The original
study design included a plan to incorporate qualitative interviews; however, due to a lack of
interview volunteers, the data from the two interviews that were completed is incorporated as
anecdotal evidence only.

11

Definition of Terms
• Ambulance Diversion: The inability to transfer patients requiring hospital admission
within a reasonable time frame out of ED because of lack of available inpatient beds
(Lovett et al., 2016).
• Bed Occupancy Rate: the overall bed utilization of the beds in service. Measured by the
number of admissions divided by the number of available beds (Zhu, 2011).
• Bed Turnaround Time: The time between when a patient is discharged, and the time the
bed is available for the next patient (Tortorella et al., 2013).
• BBS (pseudonym): A computer software application system that tracks, communicates,
and reports patient flow activities, specifically related to bed boarding (University
Hospital, 2019).
• ED: Instead of ER (Emergency Room), hospitals use the term ED to indicate emergency
department.
• ED Boarding Time: The wait time for beds to be assigned for the Emergency Department
patients so can they leave ED (Lovett et al., 2016; Walker et al., 2016).
• LWBS: Patients leave without being seen (Lovett et al., 2016).
• Patient Flow: A movement of patients within the hospitals (NEJM Catalyst, 2018).
• Patient Length of Stay (LOS): includes inpatient units LOS, ED LOS, and percentage of
patients who leave the room by noon (He et al., 2019).
• Patient Throughput: putting the right patient, in the right bed, at the right time (American
Hospital Association, 2012 cited in Walker et al., 2016; The Right Care, Right Setting,
and Right Time of Hospital Flow, 2017, Institute for Healthcare Improvement).
12

Organization of the Project
The study is organized in five chapters. Chapter 1 provided the readers with the key
concepts and terminology commonly found in a discussion about the University Hospital's bed
turnaround problem. The organization's mission, goals, and stakeholders and the project's
framework were also introduced in this chapter. Chapter 2 provides a review of the current
literature surrounding the scope of the study. Topics of possible causes and implications will be
addressed. Chapter 2 also presents the University Hospital patient care staff's knowledge,
motivation, and organizational influences that will be explored via the study. Chapter 3 details
the methodology for the choice of participants, data collection, and analysis. In Chapter 4, the
data are assessed and analyzed. Chapter 5 provides strategies and recommendations for
improvements in practice and future research.
13

Chapter Two: Review of the Literature
Chapter 2 focuses on five topic areas related to bed turnaround that emerged from the
literature review process. These topic areas include how bed turnaround became important,
potential causes for inefficient bed turnaround, implications of inefficient bed turnaround,
previously attempted process improvement approached at hospitals, and KMO influences related
to the bed turnaround problem. In addition, the literature review includes a special section that
supports a correlation between the bed shortage and the COVID-19 crisis. Although the literature
presented here applied to a variety of contexts, this review focuses primarily on the literature's
application to the problem of inefficient bed turnaround of general hospital beds in the U.S.
How Bed Turnaround Became Important
This section provides historical content of how the bed turnaround process became
important for hospitals from the existing literature. According to Ozcan (2008), health care
performance can be measured based on the entity's efficiency and effectiveness. The patient flow
process is critical for hospital efficiency, and many hospitals aim to improve their bed
turnaround time (Blanchard & Rudin, 2016; Kobis & Kennedy, 2006; Lovett et al., 2016; Matos
& Rodrigues, 2011; Walker et al., 2016; Winasti et al., 2017). This section presents the following
topics: principles of supply and demand, reimbursement paradigm shift, and ends with a special
consideration related to the COVID-19 pandemic.
Supply and Demand
Globally, bed capacity management becomes hugely challenging with the growing need
for hospital beds due to overall population growth and longer life expectancy but limited bed
availability (Al-Qahtani et al., 2017; Bouneb et al., 2018; Cardoso et al., 2011; Louriz et al.,
2012). Khanna et al. (2016) also stated that overcrowding in hospitals is a global issue. In the
14

U.S., 47 hospitals were closed in 2019, 23 closed in 2018, 16 closed in 2017, 21 closed in 2016,
and 28 closed in 2015 (Daly, 2019). According to Kobis and Kennedy (2006), 100,000 more
patients were admitted to U.S. hospitals, but close to 500 hospitals closed from 1990 to 2005
demonstrating the alarming supply and demand problem. Between 2002 to 2003 alone, 8,000
beds were purged due to 32 community hospital closures (Akcali et al., 2006). On the demand
side, the 2010 Affordable Care Act (commonly known as ACA or Obamacare) introduced an
additional 32 million patients in the U.S. (McCaughey et al., 2015). The supply and demand
problem was demonstrated by He et al. (2019) research that, in comparison to 2015, 2016 had
100,000 more patients admitted to hospitals but 3,000 fewer beds available in the U.S.
Supply Story: Too Expensive to Build New Beds
Building hospitals was expensive with the building cost per bed ranging from $437,500
to $1,750,000 with an average just below a million dollars at $937,500 (Fixr, n.d.). Another web
resource concurred with the high cost of building hospital beds with $1,500,000 per bed for a
large hospital construction (Susrut, 2019). Due to the expensive cost, hospitals were encouraged
to optimize bed management rather than building more beds (Kobis & Kennedy, 2006). Kobis
and Kennedy (2006) proposed several ways to improve bed capacity and determined that
building more beds is the most expensive way to increase bed capacity. In addition to the pricy
building costs, the authors warned other downsides of building more beds: the time required to
build more beds, hiring costly professional staff for the new beds, and the regulatory approval
process for building hospital beds with potential for denial (Kobis & Kennedy, 2006). Because of
the enormous cost of building more beds, optimizing the existing beds was the best way to
increase bed capacity (Akcali et al., 2006; Anonymous, 2006; Kobis & Kennedy, 2006; Winasti
et al., 2017). In addition, some scholars found that hospitals did not have enough bed capacity as
15

they did not anticipate more patient volume (Akcali et al., 2006; Mathews & Long, 2015; Nowak
et al., 2012). The supply side of hospital beds was shrinking due to hospital closures, yet the
demand side was increasing, especially with the 2010 Affordable Care Act (McCaughey et al.,
2015).
Demand Story: Undying Needs for Hospitalization
Gallup shared the sobering statistics that American patients were waiting 4.3 million days
per year for inpatient beds (Exploring the use of technology and innovation, 2016). According to
a 2005 American Hospital Association (AHA) report, key hospital volume measures were
increased from 2002 to 2003 as follows: inpatient admission by 0.9%, outpatient visits by 1.2%,
emergency room visits by 1.2%, adjusted average daily census by 1.0%, and average inpatient
occupancy rate by 1.9% (Akcali et al., 2006). Findings by the AHA in 2016 showed that the
demand for hospital beds steadily grew due to the need for more hospitalization across America
(He et al., 2019). With the increasing patient volume and fewer beds, Walker et al. (2016) saw an
increase in hospital bed occupancy rates. A higher occupancy rate was directly correlated to a
shortage of beds and strain hospitals (Zhu, 2011; Bagshaw et al., 2018). Due to the expected
patient volume increase, Pellicone and Martocci (2006) determined more beds should be made
available. Blanchard and Rudin (2016) found that from 2012 to 2014, the total admissions at all
four Health First hospitals increased by 27% and adult transfers within the system increased by
more than 300%. The following section briefly reviews why the demand for hospitals is rising.
With an aging population and increasing expectations, hospitalization continued to
increase (Boyd & Evans, 2016). Pellicone and Martocci (2006) also referred to an aging
population as a factor for increasing patient volume. In addition to the aging population,
Schneider et al. (2017) found that many adult Americans had three or more chronic diseases.
16

More than two-thirds of Americans are overweight or obese; many Americans suffer from
weight-related diseases such as Type II Diabetes (Hojjat & Amhed, 2018). Papanicolas et al.
(2018) estimated that 70.1% of Americans were overweight or obese. In addition to the
demographic changes, the Affordable Care Act opened the gate for substantial potential demand
for hospital care (McCaughey et al., 2015). Due to the decreasing supplies and increasing
demands, the importance of efficient patient flow emerges. In addition to supply and demand,
government reimbursement changes forced hospitals to practice efficient patient flow.
Reimbursement Paradigm Shift
Hospitals face continuous reimbursement decline and are forced to optimize their patient
flow (Kobis & Kennedy, 2006). In addition to the existing financial pressure, the ACA enacted
in 2010 required hospitals to improve quality and operations, focusing on value-based
reimbursement that resulted in fundamental changes in their operations (Blanchard & Rudin,
2016; Lovett et al., 2016; Walker et al., 2016). For example, the ACA mandated the Hospital
Readmissions Reduction Program (HRRP) started October 1, 2012 to reduce avoidable
readmissions via improving care coordination (Centers for Medicare and Medicaid Services,
n.d.). Due to the financial constraints, efficient patient flow emerged as an important part of
hospital operations (Lovett et al., 2016; Matos & Rodrigues, 2011; Winasti et al., 2018). As the
healthcare reimbursement paradigm shifts from fee-for-service to value-based care, managing
patient flow become imperative for hospitals (Blanchard & Rudin, 2016; Lovett et al., 2016;
Walker et al., 2016).

17

Special Consideration: The COVID-19 Pandemic
The current COVID-19 pandemic exacerbated the bed shortage worldwide. By June 22,
2020, only a half year since the outbreak in early December 2019, the virus spread out to 188
countries (Wynants et al., 2020). COVID-19 is a newly emerging virus similar to Severe Acute
Respiratory Syndrome Corona Virus, SAR-SCoV, and Middle East Respiratory Syndrome
Corona Virus MERSCoV (Quaedackers et al., 2020). According to 2018 AHA statistics, the U.S.
had 5,256 hospitals with 96,596 ICU beds, including 68,558 for adults, 2,137 for kids, and
22,901 for newborns (Halpern & Tan, 2020). Using the greater New York City area as an
example, Maves et al. (2020) predicted up to 22,000 ICU bed census before the Summer of 2020.
Due to the contagious nature of COVID-19, a relatively small number of total COVID-19
patients could overwhelm hospitals and cause them to optimize capacity include beds (Maves et
al., 2020). In particular, low-income and middle-income countries had the potential to face a bed
shortage crisis with less than 2.5 ICU beds per 100,000 population (Ma & Vervoort, 2020).
Around the world, medical communities were considering a reduction of care due to resource
constraints and surging demand as a result of COVID-19 (Quaedackers et al., 2020). For
example, Dr. Christian Salaroli of Bergamo, Italy, had to decide which patients to admit from a
pool of new patients during the COVID-19 crisis (Saunders, 2020). The United States was not
immune to the bed shortage problem.
Weissman et al. (2020) used the COVID-19 Hospital Impact Model (http://penn-
chime.phl.io) to predict that the U.S. ICU beds would be full within weeks from March 23, 2020.
On March 16, 2020, Governor Larry Hogan of Maryland pleaded for 6,000 additional intensive
care unit beds for his state during televised news and internet media; the entire U.S. had less than
60,000 ICU beds (Hogan, 2020). On the same date, New York governor Andrew Cuomo
18

expressed his frustration with the limited number of ICU beds in his state (3,000), which
handcuffed the state’s response against the rapid outbreak (Capatides, 2020). Further, Governor
Cuomo suspected that with the limited beds, patients would be "on gurneys in hallways,"
implying delays in patient care are ultimately impacting the quality of care (Capatides, 2020).
Some patients died while waiting for a bed at Elmhurst Hospital Center, New York (Thompson,
2020).
Due to the increasing number of infections, medical communities were advancing models
and proposals to prepare health care systems. Ferstad et al. (2020) developed a model to predict a
potential shortage of ICU beds by estimating ICU beds' demands and availability. Maves et al.
(2020) prepared three proposals to manage hospital bed capacity from conventional capacity
with a 20% increase to contingency capacity with a 100% increase by modifying spaces. Maves
et al. (2020) advanced a proposal of triaging patients as hospitals cannot increase bed capacity
above 100% due to limited space, staff, and supplies. Adalia et al. (2020) presented a mixed
approach to boost ICU capability by converting existing spaces and transferring patients to non-
ICU hospital beds, including step-down units and post-anesthesia care units. The capacity of
hospital beds is challenging to increase, especially in the U.S. One way to decrease delays in care
is a faster turnaround of beds. The next section explores potential causes of inefficient bed
turnaround from existing literature.
Potential Causes for Inefficient Bed Turnaround
Scholars found a myriad of potential causes for inefficient bed turnaround. Several
challenges of managing bed turnaround included unpredictable patient length of stay, demand
fluctuations, and varying patient recovery time (He et al., 2019; Winasti et al., 2018). This
19

literature review focuses on four potential causes: limited communication, complicated
processes, antiquated technology, and lack of resources.
Limited Communication
Several studies have been focused on understanding the importance of communication in
inefficient bed turnaround. According to Schein (2017), organizational effectiveness required
stakeholders to feel free to communicate across different subcultures within organizations. Clark
and Estes (2008) stated that senior management must constantly involve in process improvement
by communicating their vision. Burke (2018) emphasized how to communicate, including how
often and how much is important as the message. According to the American Hospital
Association's 2012 survey of 75 hospitals, communication problems were a leading cause of
poor patient flow (Walker et al., 2016). Communication within hospitals is indispensable for
patient safety and effective delivery of care (Thomas & MacDonald, 2016; Walker et al., 2016).
Bed turnaround management was a complex process involving many departments, often
with different reporting lines, causing communication problems (Lovett et al., 2016; Winasti et
al., 2018). Communication problems between departments and within each department during
care transition were key causes for slow turnaround (Pellicone & Martocci, 2006; Thomas &
MacDonald, 2016; Tortorella et al., 2013; Rivers et al., 1998). In addition, Tortorella et al.
(2013) found the multiple forms of communication, the downstream, and the cumulative impact
of communication problems were all potential causes of the bed turnaround problem. North
Shore University Hospital in Manhasset, NY, found the delay of communication to nurses who
admit patients as a key contributor to slow turnaround (Pellicone & Martocci, 2006). Johns
Hopkins Hospital increased the occupancy rate from 85% to 92% and reduced patient delays by
20

improving communication to streamline bed turnaround by establishing a command center and
co-locating different departments (Kane et al., 2019).
Complicated Process
Clark and Estes (2008) referred to the work process as the coordination of people,
equipment, and materials to achieve goals. Further, Clark and Estes (2008) stated that
organizations would fail to achieve goals if the work process was inefficient even with
knowledge and motivation. Bed turnaround was a complex process involving many departments
(Walker et al., 2016), and some stakeholder departments belonged to different reporting lines
pushing additional complexity (Lovett et al., 2016; Winasti et al., 2018). Bed availability was
difficult to predict as it depended on many factors (Rivers et al., 1998).
The Bed Turnaround Process
Each hospital has its own bed turnaround process. Brown and Kros (2010) described nine
subprocesses of turning beds where Pellicone and Martocci (2006) explained the bed turnaround
process using six subprocesses. Tortorella et al. (2013) presented a four-step process used to
illustrate a typical bed turnaround process. First, hospitals needed to let nurses know which
patients to discharge from the beds. Nurses met daily to plan for clinically appropriate patient
discharges. If nurses did not have accurate patient discharge information, bed turnover would be
delayed. Nurses needed to input the discharge information in the BBS to alert transporters that
patients were ready to discharge. Second, transporters carried patients out of the beds. Unless
transporters knew which patient from which bed to transport, bed turnaround would be delayed.
After the bed was vacated, nurses input the bed availability in the BBS to alert the EVS workers
to clean the room. Unless the EVS staff knew which room to clean, the bed turnaround would be
delayed. Communicating between nurses with EVS staff could be challenging and slowed the
21

turnaround time as they did not have assigned computer access or carry tablets. Third, EVS
workers cleaned the room. After the cleaning, EVS workers needed to alert nurses that the room
was cleaned. Again, EVS workers did not have assigned computer access or carry tablets, so
communication was challenging. Fourth, nurses to notify the admission office that the room was
ready for a patient. See Figure 1 for a visual representation of this process.

22

Figure 1
Bed Turnaround Process

Note. Source of bed turnaround process concept is Tortorella et al. (2013). Figure created for the
purpose of this study by this author.

23

Poor Quality Process
Scholars found many problems with the bed turnaround process. Walker et al. (2016)
found that lack of hospital-wide policy yielded process variations among bed turnover, causing
stakeholders to not implement new processes. Other scholars echoed the process variation or lack
of standardization as an issue and advocated to remove inconsistency and variation to achieve an
optimal process (Allder et al., 2010; Brown & Kros, 2010; Nowak et al., 2012). The University
of Rochester Medical Center removed an outdated policy to streamline the bed turnaround
(Anonymous, 2006). Wyman (2009) advocated a centralized call center to improve the
coordination among bed turnaround stakeholders after seeing inefficient communication between
transporters and EVS workers, which could add up to an hour to the bed turnover. After
implementing the centralized call center to coordinate services between transporters and
housekeeping, the Methodist hospital saved more than $5 million annually by reducing the bed
turnaround time from 90 minutes to 60 minutes (Wyman, 2009). Tortorella et al. (2013)
highlighted an inefficient process due to some bed turnaround information was entered in the
BBS multiple times, but some were not entered at all.
Antiquated Technology
In modern healthcare, utilizing technology became essential for efficient operation,
especially for managing bed turnaround (Boyd & Evans, 2016). Coupled with a multitude of
processes and financial constraints, hospitals were looking for a computerized system to track
and manage beds (Matos & Rodrigues, 2011). However, many hospitals lacked sophisticated
software with real-time bed information (Walker et al., 2016), forcing hospitals to conduct
manual bed allocation processes (He et al., 2019). Until Stony Brook University Hospital had a
competent bed tracking computer system, EVS workers struggled with the bed turnaround
24

(Carillo, 2006). Wyman (2009) revealed technology as an essential ingredient to improve bed
turnaround process and specifically recommended hospitals to evaluate technology in building a
centralized call center and central transportation department.
Technology is a major contributor to U.S. healthcare costs, which is not sustainable
(Kumar et al., 2011); hospitals must be judicious about deploying the technology. The following
example portrayed a new technology implementation that went wrong for bed turnaround
improvement efforts. The University of Texas MD Anderson Cancer Center distributed tablets
for transporters and EVS workers to speed up the bed turnaround process. However, the EVS
workers abandoned tablets within three months because the devices were too heavy to carry
around (Tortorella et al., 2013). McCaughey et al. (2015), in a retrospective examination of 23
studies, found that technology was important for bed capacity management but could not find
specific technology or name of bed capacity management software in implementation cases.
Unsatisfactory computer systems were examples of inadequate resources.
The Scarceness of Resources
Hospitals are under financial pressure and unpredictable patient surges. Hospital
administrators struggle with resources that include equipment, staffing, and supplies (Winasti et
al., 2018), and lack of resources has been identified as a cause of patient flow problems (Walker
et al., 2016). Tortorella et al. (2013) advocated for mobile devices instead of computers, as
transporters and EVS workers were found to require simple equipment to improve the
communication process to reduce the turnaround time. However, as identified previously, tablets
may not be an effective solution. In addition to communication devices, hospitals lacked robotic
cleaning machines. The average cost of an ultraviolet (U.V.) disinfection robot was $76,000 in
2015 (Lee & Rice, 2015). However, even a large hospital with a significant number of infectious
25

patients, including COVID-19 patients, could not afford a single U.V. cleaning robot (EVS
director from a hospital, personal communication, June 12, 2020).
Walker et al. (2016) analyzed a survey of 75 hospitals for staffing resources and found
inadequate staffing caused by a delay of bed turnaround demonstrated by nursing shortage at
hospitals. According to the bed turnaround time measurement conducted by Batamark and Ndjee
(2017), the most extended interval for bed turnaround time was on Saturdays at 78.3 minutes,
which correlated to the fewer number of EVS staff on duty during the weekends. He et al. (2019)
found staff shortages as one of the reasons for the delay in patient discharge which negatively
impacted the planning of bed resources. Simply put, hospitals did not have more beds, which was
perhaps the most fundamental resource for bed turnaround (Akcali et al., 2006). In this section,
the examination took the place of the potential causes for delay of bed turnaround. The next
section explores the implications of inefficient bed turnaround.
Implications of Inefficient Bed Turnaround
Research revealed that delay in patient care due to unavailable beds had serious
implication resulted in adverse patient outcomes (Al-Qahtani et al., 2017; Cardoso et al., 2011;
Kim et al., 2016; Lovett et al., 2016; Mathews et al., 2018; Sagy et al., 2018; Walker et al.,
2016). Walker et al. (2016) noted the following as examples of poor quality of care resulting
from inefficient bed turnaround: overutilization of medication, prolonged stay, wrong treatment,
and patient safety issues. Scholars urged hospitals must be efficient in turning around beds to
reduce adverse patient outcomes (Al-Qahtani et al., 2017; Cardoso et al., 2011; Lovett et al.,
2016; Walker et al., 2016). This section explores potential implications grouped under five
subheadings starting with the more dire implication: the correlation between the delay of care
and mortality. The section continues with other important implications to include lengthened
26

hospital stays, ambulance diversion, patients leaving without being seen, patient satisfaction, and
financial implications.
Correlation with Mortality
The inefficient bed turnaround delayed in care results in poor patient outcomes, including
a higher mortality rate (Bekmezian & Chung, 2012; Cardoso et al., 2011; Clark & Normile,
2012; Robert et al., 2012; Yergens et al., 2015; Urizzi et al., 2017; Walker et al., 2016). Bagshaw
et al. (2018) conducted an extensive population analysis of roughly four million residents in
Alberta, Canada. They showed a connection between delayed admission due to limited bed
availability and high occupancy and mortality rate. Existing literature indicated a correlation
between ED boarding time and mortality rate (Al-Qahtani et al., 2017; Liu et al., 2011; Kane et
al., 2019; Singer et al., 2011; Stretch et al., 2018). Howell et al. (2010) found that critical care
patients result in higher mortality rates with delays in ICU admission than those admitted to ICU
in a timely manner. More specifically, Chalfin et al. (2007) observed a higher mortality rate for
the patients with delayed admission with waiting for beds longer than 6 hours: 17.4% for delayed
patients vs. 12.9% for non-delayed patients. The post-anesthesia care unit (PACU) delay to ICU
due to bed availability was associated with higher ICU mortality (Bing-Hua, 2014). In summary,
scholars found a strong correlation between the delay of care due to bed availability and
mortality. The following subsection visits the association with bed delays and longer hospital
stays for patients.
Longer Hospital Stays
Protracted hospital stays engendered extra costs, and negatively impact patient
satisfaction (He et al., 2019). There were a myriad of causes for protracted hospital stays,
including not limited to: medication and IV treatment variations (Ashjael et al., 2019),
27

uncontrolled psychological and social aspects of illness (Walker et al., 2019), patients having a
pre-existing health condition demonstrated through patients with epilepsy (Kariuki et al., 2015),
a peri-operative procedure issue (Robinson et al., 2015), a post-operative procedure issue
(Wolthuis et al., 2016), social factors (Lim et al., 2006), and inconsistent surgical team members
(Xiao et al., 2015).
One of the causes for a longer hospital stay, which this study explores, is prolonged wait
times for inpatient beds due to lack of beds (He et al., 2019). In particular, ED boarding delay
due to inpatient bed shortage was linked to a longer hospital stay (Kane et al., 2019). The bed
shortage was one of the most common causes of ED boarding delay (Esmaeili et al., 2018;
Gomez-Rosado et al., 2019). Singer et al. (2011) found that the mean hospital length of stay
(LOS) for patients admitted after 24 hours was 8.7 days compared to 5.6 days for the patients
admitted within two hours. Chalfin et al. (2007) studied the hospital stays for the patients with
delayed admission, defined as waited for beds longer than six hours: 7.0 days for delayed
patients vs. 6.0 days for non-delayed patients. Maintaining ICU bed availability was a way to
reduce additional hospital stays (Long & Mathews, 2018). However, it was worth noting that
Bing-Hua's research (2014) did not find a relationship between prolonged wait for inpatient beds
and increase hospital stay for patients. Another implication of bed shortage is delay and denial of
care.
Delay and Denial of Care
Due to the limited bed supply and growing care demand, hospitals may delay care, even
refusing to accept patients (Bouneb et al., 2018; Robert et al., 2012), which caused patients to
leave without getting care (Lovett et al., 2016) and some patients dying as a result (Bouneb et al.,
2018). Based on a six-month study of adult patients at Ibn Sina University Hospital in Morocco,
28

Louriz et al. (2012) observed that 40.6% of patients were denied admittance to the ICU because
beds were full and bed shortage was often a cause for delay in ICU admission. However, the
study did not investigate the drivers leading to bed shortages, so it cannot be presumed that bed
turnaround or other patient flow issues are the source of the shortage. Louriz et al. (2012)
extended the global problem of delay and denial of care to the rationing of beds.
The U.S. has a long history of delay and denial of care by limiting access to racial and
ethnic minorities that stemmed from persistent and institutionalized racism (Flores et al., 2016;
Rhee et al., 2019; Tung et al., 2019; Snowden, 2012). Even though both delay and denial of care
for racial and ethnic minorities are serious issues, these specific factors are excluded from this
discussion since resolving the inefficient patient flow process is expected to increase access to
the bed for all groups but cannot resolve other systemic practices that create inequities in access
to care for racially and marginalized groups. Returning to the implications of inefficient bed
turnaround, ambulance diversion, and leaving without being seen (LWBS) are two implications
of delay or denial of care.
Delay of Care: Ambulance Diversion
In the U.S., many ambulances are turned away by hospitals due to a lack of ED beds
available to accept new patients, which yields a poor quality of care, a decrease in patient
satisfaction, and financial loss for hospitals (Kolker, 2008; McCaughey et al., 2015). On
September 14, 2016, in testimony before the House Ways and Means Subcommittee on Health,
Gallup provided a statistic that hospitals turn away ambulances every minute due to lack of beds
(Exploring the use of technology and innovation, 2016). For example, Lovett et al. (2016)
watched Thomas Jefferson University Hospital turned away ambulances as high as 164 hours per
month before implementing a comprehensive patient flow process. With a faster bed turnaround
29

from the new approach, Thomas Jefferson University Hospital reduced the average ambulance
diversion per month from 86 hours to 7 hours by admitting more patients due to more available
beds (Lovett et al., 2016).
Government and researchers found ambulance diversion affects race and ethnic minority
populations disproportionally. According to the Center for Disease Control and Prevention
(CDC), about one-third of hospitals diverted ambulances in 2002 (Burt & McCaig, 2006).
Intercity hospitals reported a higher ambulance diversion (50.1%) than non-intercity hospitals
(9.2%). The study found the lack of inpatient beds and ED crowding as common causes for
ambulance diversion (Burt & McCaig, 2006). Shen and Hsia (2016) examined 29,939 Medicare
patients from 2001 to 2011 and found black patients with acute myocardial infarction (AMI),
also known as heart attacks, had worse outcomes than white patients due to ambulance diversion.
The higher rate of ambulance diversion for black patients was due to black patients generally
transported to the hospitals predominantly serving minorities which experienced more
ambulance diversion than the hospital serving predominantly serving non-minorities (Shen &
Hsia, 2016). Another study for AMI patient outcomes for the general population found that black
patients had higher mortality than white patients because black patients encountered increased
ambulance diversion across almost all months during 2001 – 2011 (Hsia et al., 2017).
Denial of Care: Leaving Without Being Seen
Leaving without being seen (LWBS) measures the number of patients leaving hospitals
due to the lack of available ED beds (Lovett et al., 2016). From a retrospective study of ED bed
capacity management, McCaughey et al. (2015) found LWBS was included as a performance
metric and an outcome metric. On September 14, 2016, before the House Ways and Means
Subcommittee on Health, Gallup reported that due to unavailable ED beds, 1.9 million people
30

waited, then left the ED without being seen (Exploring the use of technology and innovation,
2016). LWBS had serious consequences for hospitals as demonstrated by Thomas Jefferson
University, which achieved an additional $2.1 million by reducing their LWBS to 3.6% (Lovett
et al., 2016). A hospital closure impacted LWBS to go up 123% and 76% to two neighboring
hospitals in Rhode Island (Lawrence et al., 2019). Delay and denial of care affect patient
satisfaction.
Satisfaction Implications
Patient satisfaction is vital for hospitals and represents a key performance metric (Carillo,
2006). Poor patient outcomes and long wait times due to lack of bed availability also lead to poor
patient satisfaction (Pellicone & Martocci, 2006; Brown & Kros, 2010; He et al., 2019; Knarr &
MacArthur, 2012; Nowak et al., 2012; Rivers et al., 1998; Qin et al., 2017). Singer et al. (2011)
found that long ED boarding decreased patient satisfaction. The Olive View–UCLA Medical
Center saw an increase in patient satisfaction from 87% to 95% when the wait time fell from 113
to 90 minutes (Jayasinha, 2016). Satisfaction implications were not limited to patients. Wyman
(2009) learned that a long wait due to bed unavailability decreased nursing staff satisfaction due
to the lower amount of time spent with patients. In addition to the negative impact on patient
satisfaction, inefficient patient flow has enormous financial implications for hospitals.
Significant Financial Implications
From a financial perspective, efficient patient flow yields additional income for hospitals
without building additional beds (Morrissey, 2004) and not serving patients due to lack of beds
equates to lower revenues (Morrissey, 2004; Wyman, 2009). Walker et al. (2016) and Brown and
Kros (2010) also discussed additional payments for hospitals by improving turnaround time as
hospitals can treat more patients by increasing bed capacity without adding more beds.
31

Testifying before the House and Means Committee Hearing in 2016, Gallup stated that if
hospitals reduce ED boarding time by two hours, hospitals can see 9.7 million more patients and
gain $12 billion in revenue (Exploring the use of technology and innovation, 2016). Subsequent
studies demonstrated financial implications of inefficient bed turnaround.
The Methodist Hospital, located in Houston, predicted $5 million of additional revenues
by reducing turnaround time to care for other 9,000 patients per month (Wyman, 2009). A
community hospital in Pennsylvania estimated an additional $3,960,264 in revenues from
reducing ED boarding to under 120 minutes, allowing more patients to be treated in ED (Falvo et
al., 2007). Process changes resulted in $14,000 monthly savings for a small Army medical
facility (Rivers et al., 1998). Loss of transfer volume and referral could be sizable revenue
opportunities for hospitals (McCaughey et al., 2015; Pellicone & Martocci, 2006). According to
Mr. Burton, the keynote speaker and CEO of HealthCatalyst, at the 2017 Health Analytics
Summit, the profit margin for U.S. hospitals and other providers was a mere 2.5% implying that
every dollar counts for a hospital's bottom line.
The definition of bed turnaround is when a patient is discharged and when the bed is
available for the next patient (Tortorella et al., 2013). Thus, the financial pressure to turn beds
quickly is for hospitals to make more beds available, allowing the hospitals to admit more
patients without building more beds, leading to more efficiency and revenues. Admitting more
patients will reduce the ambulance diversion, LWBS, improve patient satisfaction by reducing
the waiting time for admission, and improve patient care by treating patients timely as delay and
denial care have a detrimental impact on patient outcomes. The literature review pointed to
inefficient bed turnaround as a cause for the implications, and some articles highlighted how
hospitals achieved better results by reducing bed turnaround time. Because doctors and patients
32

are not involved in the bed turnaround time as this time period is after a patient is discharged
until the bed is available for the next patient, potential financial influence for doctors to
discharge patients prematurely is not discussed in this paper. The following paragraph briefly
introduces the U.S. government's Medicare inpatient hospital reimbursement methodology,
which may pressure hospitals' discharge decisions before the value-based reimbursement,
especially prior to October 1, 2012, the ACA's effective date.
The Center for Medicare and Medicaid Services (CMS) is the largest health insurance
payer in the U.S., with close to 90 million Americans covered through one of its insurance
programs (CMS, n.d.). CMS pays hospitals with the inpatient prospective payment system
(IPPS) for patients admitted to inpatient settings (CMS, n.d.). An eligible hospital inpatient case
is paid based on the average resources required to treat patients in the diagnostic-related group
(DRG) the case belongs to. According to the IPPS, hospitals get a fixed fee per case under the
DRG except for outlier cases. In other words, hospitals would get the same payment regardless
of LOS if cases belong to the same DRG, except for outlier cases. Under the traditional IPPS
methodology, hospitals were incentivized to discharge patients faster than the average resources
represented by the average LOS assigned to the DRG. With the emergence of the value-based
reimbursement paradigm, CMS designed a new IPPS model to shift hospital payments from
volume to value by incorporating quality adjustments to the fee-for-service payments, including
the Hospital Readmissions Reduction Program, HRRP (CMS, n.d.). In summary, inefficient
patient flow has significant financial implications. The following section briefly reexamines
several process improvement approaches attempted to enhance hospital patient flow process.
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Previously Attempted Process Improvement Approaches at Hospitals
Scholars researched several process improvement approaches to ameliorate the inefficient
bed turnaround process. Some of the cost savings and revenue examples were based on process
improvement approaches. Common process improvement approaches examined were Lean Six
Sigma, simulation modeling, and statistical analysis.
Lean is defined as a quality improvement tool to eliminate wastes by improving
efficiency, where Six Sigma is defined as a tool to reduce variation (Jayasinha, 2016). Blanchard
and Rudin (2016) added that the key to the Lean concept was delivering values to customers.
Healthcare had $750 billion of annual wastes, and Lean methodologies could be applied to
remove an estimated $375 billion of inefficiencies (Exploring the use of technology and
innovation, 2016). North Shore University Hospital in Manhasset, NY, reduced the bed
turnaround time by 136 minutes using the Six Sigma methodologies (Pellicone & Martocci,
2006). Health First, a Florida based health system, achieved a leaner patient flow process,
including faster bed turnaround using Lean methodologies (Blanchard & Rudin, 2016). In the
case of the Olive View – UCLA Medical Center, non-value-added steps were removed to gain
efficiency by decreasing time for various patient flow-related activities (Jayasinha, 2016).
Batamack and Ndjee (2017) documented a quicker ICU turnaround using Lean Sig Sigma.
Other scholars used heuristic strategies to reduced bed turnaround time (Brown & Kros,
2010) or proposed models (He et al., 2018; Schmidt et al., 2013). In terms of modeling, Mateo
and Rodrigues (2011) encouraged hospitals to adopt mathematical models to make bed
management decisions but recommended using other information to support the decisions.
Devapriya et al. (2015) used a complex simulation model for hospital bed capacity to predict
patient wait time before admitting to hospital beds by entering inputs based on different
34

scenarios. A simulation model from a Singapore regional hospital determined a bed shortage
when hospital beds were occupied by more than 90% (Zhu, 2011). With the increasing need for
efficient patient flow, scholars developed various computer-based models to improve the process
(Kim et al., 2016; Mathews & Long, 2015; Qin et al., 2017).
Clark and Estes's (2008) Knowledge, Motivation, and Organizational Influences
Framework
The gap analytic framework measures the gap between goals and current practice and
formulates knowledge, motivation, and organization change solutions (Clark & Estes, 2008). In
other words, using the gap analysis framework as a lens, organizations can achieve process
improvements by identifying goals, analyzing gaps, influencing knowledge, motivation, and
organization changes (Clark & Estes, 2008). With the emphasis on three performance influencers
of knowledge, motivation, and organization changes, the Clark and Estes gap analytic framework
is known as the KMO theory. The KMO theory allows organizations to set a goal based on
strategies, including cost-benefit and impact analyses (Clark & Estes, 2008). After establishing a
goal, organizations can conduct a gap analysis by measuring the difference between the current
performance and the desired performance.
Using the gap analysis theory, organizations can evaluate individuals' knowledge
involving the process to the system, identify motivational issues related to the problem, and
propose solutions and determine organizational problems. After the KMO exercises,
organizations can evaluate progress and results to narrow the gap between the desired state and
the current state. The Clark and Estes Gap Analytic framework is a practical concept to solve
real-world problems. The following section reviews knowledge, motivation, and organizational
influences related to the bed turnaround problem.
35

Stakeholder Knowledge, Motivation, and Organizational Influences
Although there are several stakeholders in the patient flow process, this study refers to
patient care staff as key stakeholders as they are the main group turning patient beds. Patient care
staff in this study refers to nurses and EVS workers. Patient care staff’s goal is efficiently
performing the bed turnaround process.
Knowledge Influence: Implementing Best Practices for Bed Turnaround
Knowledge is defined as the accrual of information and framing the information
(Northouse, 2016). According to the gap analysis, knowledge is a cause of performance gaps
(Clark & Estes, 2008). De Dreu and Nijstad (2017) stated that people could become more
creative with some domain-relevant knowledge that fuels innovation. Dyer et al. (2011) posited
that experts who have an ability to amalgamate knowledge and untried ideas could be more
creative, which is a DNA for innovators. The association between knowledge and bed
turnaround performance was founded in the existing literature.
Knowledge of how to perform bed turnaround tasks is critical to improving the inefficient
turnaround time (Sternberg, 2017). Among other issues, procedural knowledge, including
process efficiency and standardization, was a cause of inefficient bed management (Baru et al.,
2015). North Shore Hospital found that staff was incorrectly using the bed tracking system
(Pellicone & Martocci 2006). Walker et al. (2016) demonstrated that staff members did not have
enough information to manage bed capacity management. Brown and Kros (2010) rationalized
that for a BBS to be effective, the staff must enter information timely and have enough
knowledge of downstream impact if they don’t enter accurate information in the computerized
system.
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A parallel analysis from the hotel industry is informative here for understanding the role
of process in bed turnaround, which has a similar need for an efficient process. According to Hsu
et al. (2011), the hotel housekeeping department ensured the room was cleaned, comfortable, and
safe, similar to the hospital EVS department’s responsibilities of making the room clean and safe
(Schierhorn, 2016). The hotel room turnaround time is an important metric for hotels (Hsu et al.,
2011), similar to the room turnaround time for hospitals (Carrillo, 2006; Lovett et al., 2016;
Schierhorn, 2016). Tra (2020) emphasized the working knowledge and skills for hotel
housekeepers to perform their job responsibilities efficiently. Similarly, EVS workers'
knowledge and skills are important to reduce bed turnaround time (Carrillo, 2006; Schierhorn,
2016).
Despite using well known problem-solving tools, including Lean and PDSA, University
Hospital had struggled with turning beds efficiently. University Hospital's previous approach to
solving the slow bed turnaround time problem had been based on rapid process improvement
models that had not marked improvement. A comprehensive review of every stakeholder
associated with the persistent problem may shed light on the causes and possibly find solutions,
including environmental service workers. This study focuses on the procedural knowledge of
nurses and EVS workers as a combined group of patient care staff. Table 2 presents the
knowledge influence that impacts bed turnaround time.

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Table 2
Knowledge Influence
Assumed knowledge influence Knowledge type
Patient care staff needs to effectively turn beds
Procedural
consistently with the best practice from

continuous learning

Motivational Influences
Clark and Estes (2008) defined motivation largely based on Pintrich and Schunk’s (1996)
definition that it was what drives a person to do things and solicit the amount of effort to
complete goals. Clark and Estes (2008) proposed four influences to increase motivation:
confidence, organizational belief, emotional climates, and values. In this section, discussion
takes place on value and self-efficacy influences related to the bed turnaround time.
Valuing Efficient Bed Management
Expected value theory (EVT) is important to evaluate to address the inefficient
turnaround of beds. According to EVT, expectancy and values are essential for predicting
performance and task choice (Wigfield et al., 2017). Researchers delineate subjective task values
into four categories: attainment value, utility value, intrinsic value, and cost (Wigfield et al.,
2017). Using EVT, Clark and Estes (2008) advised linking the person's values and the dividends
of accomplishing goals. Further, Clark and Estes (2008) proposed aligning the person's interests,
abilities, utility, and goals to increase interest value, skill value, and utility value, respectively.
According to Schunk and Rice (1987) experiments, students benefited more from their
reading comprehension measured by self-efficacy and skills when receiving multiple value
38

information. In Experiment 1, Schunk and Rice provided specific value information, general
value information, combined value information, and no reading strategy value information to 40
students (20 fourth-graders and 20 fifth graders) and found the combined value information
yielded the biggest gain in students self-efficacy and skills from pretest to posttest. In
Experiment 2, Schunk and Rick provided specific feedback, general feedback, and combined
feedback to 30 students (15 fourth-graders and 15 fifth graders) and found the combined
feedback yielded the biggest gain in student self-efficacy and skills from pretest to posttest.
Since values are subjective, individuals rank values differently. If individuals value a task
higher than other tasks, more likely, they will choose the task they value the most. In summary,
if individuals believe they can do the task, are interested in the task, and are motivated, they are
more likely to succeed in the task than the individuals who think they cannot do the task, are not
interested in the tasks, and are not motivated. Based on Schunk and Rice’s experiments,
encouraging individuals with multiple values would yield better outcomes than a single value
influence. Therefore, the association between valuing bed turnaround and performance was
grounded in the existing literature.
Blanchard and Rudin (2016) found that nurses who had to find and assign beds manually
were not always motivated to take new patients at Health First Hospitals. Further, researchers
found that patient transport services within the hospitals operated in a fragmented, decentralized
fashion: Transporters would be notified of potential jobs, but they had the option to select more
desirable jobs and refuse others. According to Carillo (2006), in a study on Stony Brook
University Hospital, employees' morale was at an all-time low, and positions were eliminated
with no plans to change cleaning frequencies or staffing partners. The competition for dollars
often focused on clinical patient care areas, leaving most of the significant cost reductions for
39

non-revenue-producing departments such as environmental services. After senior leaders began
to consistently recognize EVS workers by sharing thank you letters from clinical staff and
patients, EVS workers' morale improved and valued their job more (Carillo, 2006). Another EVS
study from Schierhorn (2016), Wentworth-Douglass Hospital showed that the hospital reduced
turnaround time and recognized by a national patient satisfaction survey for room cleanliness and
courtesy when the organization valued measurable outcomes. Wyman (2009) advocated for
promoting interest among staff members by educating the financial consequences of slow
turnaround and the role of environmental services (EVS) within the bed capacity process.
University Hospital may review the literature to address the bed turnaround problem.
Using EVT as a lens, this study evaluates subjective task value beliefs regarding reducing
bed turnaround time. Since University Hospital's bed turnaround time was behind the
consultants' industry benchmark, evaluating attainment value to understand how vital the bed
turnaround process is for stakeholders, especially against competing priorities, will assist in
achieving the stakeholder goal. The potential correlation between intrinsic value and
performance outcomes could be researched further to improve bed turnaround time. Based on
EVT, staff needs to value fast bed turnaround as a critical component of their work. If staff
members value quick bed turnaround, they are more inclined to see correlations between positive
patient outcomes and their work. The positive correlations will increase expectancy, resulting in
a faster bed turnaround.
On the other hand, negative emotions, including anxiety, leads to uneasiness,
nervousness, worries, and avoidance motivation resulting in a negative performance (Pekrun,
2006). According to the control-value theory, emotions related to achievement are influenced by
control over activities, outcomes, and values (Pekrun, 2006). Applying the control-value theory
40

to patient care staff, nurses could have a positive expectation or negative expectation based on
how much control they have over bed turnaround activities and outcomes and how much they
value the bed turnaround activities and outcome. Further, Pekrun (2006) found that certain
situations could trigger subconscious emotions and anxiety in students when entering their
classroom. Emotions such as anxiety could reduce interest and motivation and avoid activities
(Pekrun, 2006). According to Johnston et al. (2013) study, over 80% of nurses were stressed
when demand was high but had low control and put significant effort but received a low reward.
Employing the demand control and effort reward imbalance models to the bed turnaround
process, nurses have high demand but low control and high effort and low reward when they
accept new patients to the nursing beds. This event could induce subconscious emotions,
including anticipatory anxiety, which influences nurses to intentionally or unintentionally avoid
patient onboarding activities (a former charge nurse, personal communication, October 8, 2020).
The next section reviews correlation between self-efficacy in using bed turnaround software and
performance.
Self-Efficacy in Using Bed Turnaround Software
The main concept of social cognitive theory is self-efficacy, a belief that completing a
task likely yields better performance (Bandura, 1997; Bandura, 2012). According to Marsh et al.
(2017), self-efficacy is associated with future performance, emotion, and motivation. Because
people with self-efficacy believe in attaining a task, they are willing to establish challenging
goals and deliberately plan to achieve them (Zimmerman et al., 2019). However, self-efficacy
could construct as harmful if one feels high self-efficacy for an easy task, overestimating one’s
ability to perform (Ryan & Moller, 2017). The following paragraph evaluates the association
between self-efficacy and bed turnaround performance from the existing literature.
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Brown and Kros (2010) found that staff members who were not comfortable using their
bed boarding software (BBS) either did not enter data on time or entered data incorrectly.
Tortorella et al. (2013) also listed the staff's self-efficacy level in BBS usage as a cause of
inefficient bed turnaround. In addition to the self-efficacy in using computerized bed
management software, a 770-bed hospital improved its patient flow by focusing on competencies
of nursing leaders, which motivated front-line nurses (Knarr & MacArthur, 2012). In this
section, two motivational influences, value, and self-efficacy, were explored for their impact on
bed turnaround. Table 3 presents a summary of motivation influences that impact bed turnaround
time.

Table 3

Motivation Influences

Value
Patient care staff needs to value

efficient bed management as a critical

component of their work

Self-efficacy
Patient care staff needs to have self-efficacy

in using bed turnaround software

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Organizational Influences
In addition to knowledge and motivational influences, organizational influences affect
performance (Clark & Estes, 2008). Possible organizational influences include processes,
resources, and culture (Clark & Estes, 2008). Schein (2017) referred to organizational culture as
the shared values the organizational stakeholders appreciate. Gallimore and Goldberg (2001)
explained culture with two components, cultural models, which are shared beliefs, and cultural
settings, which are shared environments. Walker et al. (2016) found many hospitals do not
prioritize bed capacity management even though it is an essential component of patient quality
and safety, implying a lack of organizational interest. The following subsections explore existing
literature for professional accountability, communication, goal setting, and resources.
Professional Accountability Among Staff Members
Accountability is a cultural keyword that is more than responsibility, but the way society
interacts within legal, organizational, professional, and pollical settings. Among the four
accountability types based on the settings, professional accountability focuses on staff
accountability (Burke 2004; Firestone & Shipps, 2005; Romzek & Dubnick, 1987). Based on the
principle of professional accountability, directors delegate decision-making to providers (Burke,
2005; Firestone & Shipps, 2005; Romzek & Dubnick, 1987). Burke (2004) predicted that
professional accountability would increase with the knowledge recommendations as the staff will
consistently deliver best practices. According to Schein (2017), leaders rely more on workers in
their organizations as knowledge and skills were not centralized but spread out throughout the
organization. Thus, Schein declared that the traditional bureaucratic culture of the organization is
not productive. The association, then, between professional accountabilities and bed turnaround
performance was grounded in the existing literature.
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The problem of inefficient bed turnaround time can also be explained using the potential
conflicts between the professional and bureaucratic accountability types. Applying the concept of
professional accountability, nurses and EVS workers are providers who are delegated to turn
beds due to their expert knowledge. However, the hospital industry is largely hierarchical, and
the decision-making rights may not be clearly agreed upon or practiced. Hentschke and
Wohlstetter (2004) required the director and the providers to agree on decision-making rights.
Blanchard and Rudin (2016) contributed to Health First hospitals' bed turnaround success by
ensuring front-line managers listened to staff and created an environment of accountability.
Decision-making was shifted from nursing management to nurses in the staff meeting
after nurses voiced problems and proposed solutions to reduce the patient flow time as the staff
could make decisions by themselves instead of waiting for nursing management (Knarr &
MacArthur, 2012). Mayo Clinic achieved better patient flow results when hospital administrators
shifted their roles to facilitators, and front-line workers became decision-makers (Toussaint &
Berry, 2013). Wentworth Douglass Hospital enhanced patient satisfaction for bed cleanliness and
improved bed cleaning efficiency after involving EVS workers in the hospital’s cross-
disciplinary Lean Six Sigma program (Schierhorn, 2016). During the Lean Six Sigma events,
EVS workers helped analyze existing work processes, proposing and carrying out solutions
(Schierhorn, 2016). Elmore (2002) advocated reciprocal accountability with the rationale that if
organizations expect their employees to perform the jobs, they must provide training and time to
practice. In addition to the vertical dimension of professional accountability, a horizontal
dimension among team members was important, as depicted by the working relationship
between nurses and EVS workers at University Hospital.
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At University Hospital, the typical bed turnaround process follows certain steps. Nurses
have three main accountabilities to turn beds. Hospitals cannot turn beds when patients are still
in the rooms. Thus, for hospital beds to be available, nurses are responsible for discharging
patients in a timely matter. Discharging patients is a complex process and is the antecedent
process of the bed turnaround process. Among the many parties involved in the discharge
process, nurses are the key providers to ensure patients are ready to leave the hospital by
providing discharge instructions with patients and family members, providing follow up care
instructions, and reconciling medications. An inefficient discharge process also delays hospitals
in admitting patients. Due to the significance and enormity of the process, the discharge process
warrants its own research and is out of scope for this paper. University Hospital’s bed turnaround
process is graphically presented in Figure 2. First, nurses are accountable for scrubbing the bed
linens for potential leftovers, including syringes, tubes, and patients' personal belongings.
Second, nurses notify EVS that the beds are ready for cleaning by updating the BBS. Third, after
EVS cleans the beds, nurses are completing the bed turnaround process cycle by accepting new
patients, and the process begins again. Each step should be documented in the University
Hospital's BBS, which is a part of its electronic medical record system (EMR).

45

Figure 2
University Hospital’s Bed Turnaround Process

EVS performs cleaning the beds and notifying nurses that the beds are ready to accept
new patients. EVS is accountable for the thorough cleaning of beds for patient safety. The first
part of the bed cleaning process is for EVS to know which beds to clean. University Hospital has
multiple nursing floors, further divided into units. Typically, EVS staff is zoned in specific
nursing units, so they do not have to travel far to clean beds. Once EVS staff knows which bed to
clean, they must accept the bed assignment in the BBS, eliminating potential duplicate cleaning
efforts by other EVS staff. After accepting the assignment, they need to travel to the bed, clock
in the BBS to indicate arrival at the bed, which automatically updates the start time of cleaning.
After the bed is cleaned, the EVS staff clocks again in the BBS, indicate completion of cleaning,
which automatically notifies nurses that the bed is available to accept the next patient. The EVS
process starts over with another bed cleaning notification. Employing Burke's (2004) reciprocal
responsibility concept, responsibilities between nurses and EVS workers are essential for a faster
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turnaround of beds and illustrate the multi-dimensional nature of professional accountability. In
addition to professional accountability, communication is another crucial organizational
influence that affects bed turnaround performance.
Relentless Communication of Bed Turnaround Status
For effective communication, Clark and Estes (2008) encouraged organizations to
regularly share plans and communicate updates with stakeholders to increase trust, manifesting
as an increase in performance. Lewis (2019) instructed organizations to value employees' input
and clarify vision to create a successful perception using frequent communication. Lewis further
articulated the importance of horizontal communication between employees to get messages
across the organizations. Schein (2017) suggested that organizations execute complete and open
communication based on a culture of trust. Burke (2018) advocated the term "relentless
communication," in which leaders in the organizations share communication as a key priority
and commit to constant communication. The association between relentless communication and
bed turnaround performance is founded in the existing literature.
Carillo (2006) demonstrated the power of communication by the morale boost and
consequent bed cleaning improvement by EVS workers when the CEO started to publicly share
patient letters thanking the hospital housekeepers throughout the hospital. The Johns Hopkins
Hospital implemented a command center to coordinate communication among different
departments to increase its bed occupancy rate and reduce the bed assignment delays (Kane et
al., 2019). Other scholars valued instituting a centralized call command center to improve
communication as a critical factor in reducing bed turnaround time (Kobis & Kennedy, 2006;
Walker et al., 2016; Wyman, 2009). Establishing open communication between nursing
leadership and staff nurses was the key to improving patient flow (Knarr & MacArthur, 2012). In
47

summary, communication is an important organizational influence affecting bed turnaround
efficiency. The existing literature found that goal setting is another organizational influence
affecting performance.
Importance of Goal Setting
Clark and Estes (2008) explained a performance goal as an activity that one must
complete as specified by due dates and standards. Another definition of a goal is the purpose of
behavior in different time horizons (Elliot & Hulleman, 2017). According to Latham and Locke
(1991), some people perform tasks better than others because they have different goals. Further,
Latham and Locke (1991) stated that goal setting and self-efficacy affect performance. The gap
analysis measured the difference between the current state and goal (Clark & Estes, 2008).
According to Bolman and Deal (2017), goals project stakeholders' expectations. Schein (2017)
recommended that organizations agree on the mission first, which could help develop shared
goals. According to Bandura (2005), motivated people master tasks by setting challenging goals
and exert to achieve goals. It is worth noting that Bolman and Deal (2017) cautioned focusing
too much on the management side in the goal-setting theory; rather, they recommended that
employees' human needs be reflected in the goal-setting process.
Bensimon et al. (2003) urged organizations to have a target goal supported by continuous
monitoring. The first principle of Denning's seven principles of continuous innovation is setting
the goal (2005). According to Lewis (2019), goal setting requires specific outcomes the
organization expects to achieve. Further, Lewis stated that sensemaking and sensegiving
regarding goals were essential to complete the goals.
The researcher is aware that the organization wants to turn beds faster than before based
on leadership statements during administrative meetings but is not familiar with a particular
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timeline and specific outcome. University Hospital sets annual strategic goals based on
foundational pillars. However, the researcher had not observed a faster bed turnaround as a
strategic goal (University Hospital). Clark and Estes (2008) warned a potential persistence
problem of workers if they could not focus on the goal due to various issues stemming from
having multiple goals. Existing literature supported that setting clear goals were crucial for
improving patient flow measures.
Stony Brook University Hospital improved patient satisfaction and reduced wait time for
beds by establishing goals and objectives (Carillo, 2006). Setting clear goals was instrumental
for Health First hospitals in producing impressive stats in cutting ED bed assignments from 90
minutes to 45 minutes, complete ED orders from 30.2 minutes to 8.8 minutes, and EVS cleaning
time from 56.4 minutes to 49.6 minutes (Blanchard & Rudin, 2016). Knarr and MacArthur
(2012) found that incorporating nursing competencies in nurses' individual performance goals as
a critical component achieved a more rapid bed turnaround at a 770-bed hospital.
Assessing Resources to Achieve an Efficient Bed Turnaround
Lewis (2019) defined resources as ways of doing, beliefs, and possessions in an
organization to request new ideas or maintaining activities. Burke (2018) explained that
providing resources acts as fuel for change, specifically calling out an increased budget, more
staff, additional space, and incentives. Schein (2017) echoed that providing resources is
important for change, along with other dimensions of resources, mainly time and feedback.
Managing resources is important due to the scarceness of resources (Wheelan, 2019). Sternberg
(2017) listed resource allocation as one of seven metacognitive skills indicating the level of
competence required to allocate scarce resources properly. Wheelan (2019) elucidated how
different organizations allocate resources, including private organizations, to put resources to the
49

area with the greatest earning potential. Carrillo (2006) found a similar approach was observed in
private hospitals.
As stated in the scarceness of resources subheading, hospitals have limited resources,
making resource allocation a challenging process (Akcali et al., 2006; Chartier et al., 2016;
Howell et al., 2010; Kobis & Kennedy, 2006; Schmidt et al., 2013). He et al. (2019) pointed out
limited hospital beds as a fundamental resource issue for hospitals. Mathews and Long (2015)
identified that an inefficient allocation of resources caused delays in intensive care unit bed
admission. In the meta-analysis of 107 studies, Winasti et al. (2018) found that the patient flow
process was competing for resources in hospitals. Carrillo (2006) observed a negative association
between EVS worker motivation and reduced financial resources at Stony Brook University
Hospital, causing longer bed turnaround. Training emerged as a more relevant resource for bed
turnaround time.
The action plan to master the knowledge and perform best practices consistently starts
with training. The EMR vendor offers a computerized learning module overviewing the bed
turnaround process and offers a computerized learning module for each stakeholder of the bed
turnaround process (University Hospital, EMR Bed Boarding Application, 2019). Toussaint and
Berry (2013) advocated that hospitals invest front-line workers with the education, skills
training, and tools necessary to perform patient flow well. Carillo (2006) related to offering EVS
workers training to reduced employee turnover. Hopman et al. (2005) found the quality of
cleaning improved significantly after EVS workers received additional training. Table 4 presents
a summary of organizational influences that impact bed turnaround time.

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Table 3
Organizational Influences
Category Organizational influences
Cultural model influence 1
The hospital needs to value staff perspectives on policies,

processes, and procedures, and include staff in the

decision-making process
Cultural model influence 2
The hospital needs to have a culture of professional

accountability regarding bed turnaround
responsibilities
Cultural setting influence 1
The hospital needs to effectively communicate bed

turnaround performance data and gaps with staff
Cultural setting influence 2
The hospital needs to prioritize improving the bed turnaround

process by setting clear goals and aligning incentives

Conceptual Framework
According to Maxwell (2013), a conceptual framework is the primary concept or the
theory that researchers plan to study and explain what and why of the phenomena researchers are
exploring. A conceptual framework is the lens researchers use to draw assumptions, concepts,
definitions, expectations, models, terms, and theories that support and inform research (Maxwell,
2013; Merriam & Tisdell, 2016). Merriam and Tisdell (2016) did not distinguish between
theoretical framework vs. conceptual framework. Instead, Merriam and Tisdell (2016) asserted
that framework is an underlying concept that affects the entire research process from identifying
problems, generating research questions, conducting and analyzing data, and presenting the
findings. Maxwell (2013) further expanded a conceptual framework as a researcher’s own
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unique concept or theory specific to the study based on four key constructs: researcher’s
knowledge on the subject, findings from existing literature, exploratory research, and thought
experiments.
Existing literature provides theories and perspectives of previous researchers that could
be either used as an existing theory or grounded based on a review of collective theories.
Researchers design pilot studies to understand the concepts that could be used for their own
theory. Concept maps are graphical tools for organizing and representing research design (Novak
& Canas, 2008). This study seeks practical solutions for reducing the bed turnaround time at
University Hospital using the Clark and Estes Gap Analysis. Figure 3 depicts the conceptual
framework for the integration of knowledge, motivation, and organizational influences on
inefficient bed turnaround.

52

Figure 3
A Conceptual Framework for the Integration of Knowledge, Motivation, and Organizational
Influences That Affect Bed Turnaround Process

This study seeks practical solutions for reducing the slow bed turnaround at University
Hospital using the Clark and Estes Gap Analysis. Figure 3 depicts the interrelationship between
53

knowledge, motivation, and organizational influence factors. The patient is at the center of the
diagram indicating that patient’s outcome is the most important outcome for this study.
Knowledge, motivation, and organizational influence factors surround the patient’s bed as they
are required for efficient bed turnaround.
Chapter 2 described the interrelationship between knowledge, motivation, and
organizational influences, facilitating patient flow in hospitals, and focused on the bed
turnaround process. According to Clark and Estes (2008), improving knowledge, motivation, and
focus on organizational goals were must-haves to success. Conversely, the gap analysis examines
knowledge, motivation, and organizational influences as three main causes of performance gaps
under the assumption that high performing organizations have all three influence factors (Clark
& Estes, 2008). From the Stony Brook University Hospital research, patient care staff’s
increasing knowledge of doing their job more efficiently was associated with motivation
(Carillo, 2006). With improved competencies of their job tasks with improved motivation,
patient care staff could take on decision-making previously done only by management (Knarr &
MacArthur, 2012). After the front-line staff was equipped with knowledge and encouragement
from management to make decisions on their tasks, Mayo Clinic saw better patient flow
(Toussaint & Berry, 2013).
Summary
Chapter 2 included two parts to explore the inefficient bed turnaround process. The first
part reviewed existing literature to gain insight on how bed turnaround became important,
potential causes and implications of inefficient bed turnaround, as well as a brief review of a few
process improvement approaches to improve bed turnaround. The second part delved into Clark
and Estes’s (2008) gap analysis using the knowledge, motivation, and organizational influences
54

framework to examine the University Hospital’s inefficient bed turnaround. Lastly, a conceptual
framework was introduced with a concept map for a visual display of the framework. The review
also focused on well-known process improvement methodologies to improve bed turnaround,
including Lean, Six Sigma, a combination of Lean and Six Sigma, mathematical modeling, and
computer-assisted simulation analysis. Based on the literature review, many studies did not use
the knowledge, motivation, and organizational influences as their framework to examine the
problem of inefficient patient flow. Instead, some studies found the knowledge, motivation, and
organizational influences affected the patient flow performance from their research. Further, it
was difficult to find literature addressing all three influence factors within the context of this
study’s focus. Chapter 3 presents the research design, data collection methods, and analysis of
the bed turnaround process.

55

Chapter Three: Methodology
Chapter 3 presents this study’s research design and data collection methods and analysis.
The purpose of this study was to explore the patient care staff’s knowledge, motivation, and
organizational influences related to efficiently performing bed turnaround time to reduce delays
in patient care. This chapter begins with study questions and methodology. The next section
describes the data collection and analysis plan for the three instruments employed in this study:
survey, interview, and document analysis. The following section reviews ethics and roles of the
researcher. Chapter 3 concludes by exploring possible limitations and delimitations.
Study Questions
The research questions that guided the evaluation study are:
1. What are the patient care staff knowledge and motivation influences related to
performing the bed turnaround process?
2. How does the organizational culture and context impact patient care staff capacity to
perform the bed turnaround process?
3. What are the recommendations for organizational practice in the areas of knowledge,
motivation, and organizational influences?
Overview of the Methodology
The hospital bed turnaround problem is a deceptively simple yet complex problem with
many stakeholders and severe consequences, including death. The gravity of these consequences
call for a comprehensive design to facilitate better understanding of the underlying influences of
inefficient bed turnaround. Consequently, a quantitative design was used for the research
supported by qualitative methodology. In this way, the research design capitalized on integrating
the combined information of numerical quantitative data and in-depth, information-rich
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qualitative data. This strategy is described in Johnson and Christensen (2015), who expanded
mixed methods into intermethod and intramethod. Intermethod utilizes more than one data
collection method, such as mixing quantitative and qualitative methods. Intramethod uses one
method to collect both quantitative and qualitative data, such as mixing open-ended and closed-
ended questions in a survey. Table 5 depicts the research design employed intermethod in that it
included survey, a quantitative method, and interview and document analysis, qualitative
methods. Also, this research design employed intramethod in that the survey protocol mixed
open-ended and closed-ended questions. Due to only two interviews, the interviews produced
anecdotal data and did not hold the same weight as the survey or document analysis.

Table 4
Data Sources
Study questions Survey Interview Document analysis
What are the patient care staff knowledge
X X X
and motivation influences related to
performing the bed turnaround process?

How does the organizational culture and
X X X
context impact patient care staff capacity
to perform the bed turnaround process?

The third study question, “recommendations for organizational practice,” will be
addressed following data collection based on findings and results related to research questions
one and two.
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Data Collection, Instrumentation, and Analysis Plan
This study, which was quantitative in nature, used attempted to employ three data
collection methods: survey, interview, and document analysis. Figure 4 displays all three
methods originally designed to examine the research questions. Ultimately, only survey data and
document analysis contributed to this study’s conclusions.

Figure 4
Research Design

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The first data collection method was a survey, a method for collecting data by asking
questions (Robinson & Leonard, 2019). In contrast to interviews, surveys are administered to get
data from many participants (Merriam & Tisdell, 2016). One downside of the survey is a shallow
understanding of data (Maxwell, 2013). The second data collection method was an interview,
which is an instrument for collecting rich information (Crawford & Lynn, 2019; Creswell &
Creswell, 2018). Compared to a survey, an interview has fewer participants and is time-
consuming (Robinson & Leonard, 2019). The third data collection method was documentation
analysis, which is a process of collecting already available data that are written, visual, digital,
and physical material (Merriam & Tisdell, 2016)
The research design included a triangulation of survey, interview, and document analysis
to enhance the quality of findings. Triangulation is a process to evaluate multiple methods, data
sources, investigators, or theories to confirm the study findings (Creswell & Creswell, 2018;
Merriam & Tisdell, 2016). This research design started with surveying a select group of patient
care staff, further described under the survey section. After conducting the survey, the study
sought volunteers for interviews with the survey participants. While conducting the survey and
the interview, the study pursued documentation analysis.
Method One: Survey
According to Creswell and Creswell (2018), a survey is a data collection method to
gather information from a sample to research the population. A sample represents a subset of a
larger group called population (Johnson & Christensen, 2015). A non-random sampling was
applied to this research using Fink’s (2012) definition of non-random sampling with convenience
based on availability instead of random sampling where everyone in the population has an equal
chance of being selected. The survey design included 17 questions to collect data on how patient
59

care staff’s knowledge, motivation, and organizational influences affect bed turnaround time.
Locke et al. (2010) wrote about the generalizability of findings from quantitative research. Thus,
the survey explored general understanding from the sample population triangulated with
qualitative methods, namely document analysis, to provide a deeper understanding.
Participating Stakeholders
University Hospital has 1,200 nurses working in different shifts. The participants were
selected from the weekday shift nurses and EVS workers who turn beds, collectively known as
patient care staff. The sample pool was purposely further limited to two nursing units with the
highest patient discharges as they will receive the most benefit from a more efficient bed
turnaround. Each nursing unit was composed of 50 nurses so the nurse sample was 100 nurses.
The daytime EVS workers numbered 17 employees. Thus, the total sample size was 117 patient
care staff members. Purposeful sampling was used to select a specific sample group to explore
the most understanding from the population (Creswell & Creswell, 2018; Merriam & Tisdell,
2016).
Due to the COVID-19 pandemic, only scheduled hospital employees could enter the
hospital, so I could not recruit patient care staff in-person or post notices in the hospital elevators
or cafeteria or breakroom, typically used for clinical study research recruiting at University
Hospital. I obtained email distribution lists of the two selected nursing units from a nursing
director at University Hospital. Thus, I did not have individual email addresses of any patient
care staff for administering the survey, only nursing unit A and nursing unit B department email
addresses. Potential ethics issues related to this approach were addressed in the ethics section.
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Instrumentation
Especially with the COVID-19 pandemic, an online survey is a useful data collection
instrument over an instrument that requires a physical visit to the setting – observation. Due to a
high degree of survey fatigue and high stress during the COVID-19 pandemic, patient care staff
did not have time to fill out a lengthy survey (Nurse leaders at various hospitals, personal
communications; June 12 and July 22, 2020). The survey included 17 questions with 15 closed-
ended questions and two open-ended questions (Appendix A). Out of 15 closed questions, 13
questions asked for an ordinal level of measurement and two questions asked for nominal
measurement. According to Robinson and Leonard (2019), an ordinal level of measurement is
responses that could be grouped in an orderly fashion and a nominal level of measurement is
responses grouped into categories without any particular order. Because patient care staff would
most likely complete the survey using their cell phones, the format of questions was mobile
friendly (Robinson & Leonard, 2019). Most of the questions employed a 7-point Likert scale
from strongly disagree to strongly agree.
I was unable to find any surveys appropriate to adopt. The survey questions were adapted
from Bennett et al. (2016) and incorporated personal conversations with other professionals. The
survey questions were designed to ask one question at a time to eliminate potential double-
barreled questions (Robinson & Leonard, 2019). Further, the survey questions were designed to
exclude any leading questions and were written without ambiguous words (Robinson & Leonard,
2019). The University of Southern California approved information sheet for exempt research in
Appendix E. The information sheet was provided to survey participants to disclose a brief
description of the purpose, participant involvement, interview compensation, confidentiality, and
contact information for any questions about this study. Upon approval of the survey by the
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dissertation committee, the questions were translated into Spanish and were reviewed by Spanish
speaking doctoral students. Appendix B was inserted for Spanish language survey.
Data Collection Procedures
Due to the limited time, the initial survey method was an online survey via Qualtrics.com
to collect response data. Online surveys are the most time-efficient survey method that works
best for dissertation research (Robinson & Leonard, 2019). However, I conducted two different
ways to collect survey data as requested by the hospital leadership. The hospital leadership
advised me that the nurses were technologically savvy to use computers and mobile phones to
participate in the survey online. I emailed a survey link and a QR code to two nursing unit
department email addresses on 1/25/2021. However, the hospital email server blocked my USC
school email. With the permission from the dissertation chair, I sent the survey link and the QR
code from my University Hospital’s email address on 1/26/2021. The survey remained available
for three weeks.
Additionally, the hospital leadership requested that I prepare paper survey in Spanish and
print them out and fold them in self-sealed envelopes for EVS workers. Further, the leadership
asked to prepare a sealed cardboard box with a slit opening for EVS workers to drop their
completed surveys in the sealed envelopes. Thus, I printed Spanish-translated surveys and
stuffed them in self-sealed envelopes. Per the instructions from hospital leadership I cut a slit
with an architect utility knife in a gift box and taped the opening and edges to seal the box. I
made an arrangement with the EVS director for the paper surveys as I could not have in-person
contact with EVS workers. I drove to the hospital and met the EVS director in front of the
hospital to drop off the surveys and the box. I picked up the sealed box on February 16, 2021
three weeks after the drop off date.
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The survey was conducted anonymously to increase the response rate (Pazzaglia et al.,
2016). I sent a weekly reminder to the nursing units encouraging the non-responders to complete
the survey. I checked Qualtrics every other day to tally the number of completed surveys. The
survey design called for the 20% threshold for statistically significant study.
Data Analysis
Qualtrics was used for data analysis as the survey tool has native data and analysis and
reports functions. The response rate of 34% was calculated by dividing 40, the number of
participants who complete the survey, by 117, the total number of survey participants. Not all
participants responded to every question resulting n = 39, or n = 40 depending on question. Due
to the small size of the survey, Qualtrics’ StatIQ, which provide more advanced analysis than the
Qualtrics’ Data and Analysis section, did not find any statistically significant relationship
between the influences and the demographic questions: gender, race, and experience. Thus,
descriptive data analyses were calculated, including means, standard deviations, and ranges of
scores. Lastly, the analysis described key findings from the responses to the survey questions,
which were designed to answer the research questions focusing on the knowledge, motivation,
and organizational influences impacting bed turnaround. Either figures or tables were used to
portray the findings more effectively than word descriptions alone (Locke et al., 2010).
Validity and Reliability
Scholars emphasize the importance of validity and reliability of survey data. Validity is
the confidence level related to the purpose of the study, and reliability is the confidence level
related to the consistency (Salkind, 2014). Salkind (2014) stated validity and reliability are
associated with confidence in a study, and Robinson and Leonard (2019) elucidated validity and
reliability are needed to replicate the study. Creswell and Creswell (2018) separated validity into
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two categories: internal validity is tied to the quality of study and external validity measures
transferability to another study.
According to McEwan and McEwan (2003), a good research design is the best way to
ensure the integrity of internal validity. The survey questions were designed with clear language
to remove ambiguity as ambiguous questions cause a lack of internal validity. The closed-ended
survey questions were formatted with the same 7-point Likert scale, except three demographic
questions, to achieve an internal consistency, which is the critical component of reliability.
Qualtrics reports were used to reveal if respondents answered the same questions consistently.
Method Two: Interview
The interview gathered a deep understanding of knowledge, motivation, and
organizational influences affecting bed turnaround by interacting with participants, and included
probing questions for clarification and further understanding. Interviews provided a detailed and
rich description of the research topic (Locke et al., 2010; Merriam & Tisdell, 2016). For this
research, interviews were conducted to understand the bed turnaround process from the patient
care staff’s perspectives, referred to as the emic or insider’s perspective by Merriam and Tisdell
(2016). Unlike the quantitative method, qualitative research designs, including interviews are
flexible and can change during the data collection (Creswell & Creswell, 2018; Locke et al.,
2010). The interview protocol in Appendix C guided the data gathering plan rather than designed
to execute a fixed plan.
Participating Stakeholders
All participants who received the survey link were invited to participate in an interview
by submitting their contact information through a Qualtrics link not connected to their survey
responses. The goal was to interview 10 to 12 participants from the survey participants,
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comprising 100 nurses and 17 EVS workers. Due to the significantly larger number of nurses
over EVS workers, the ideal ratio of patient care staff interviewees would be eight nurses and
four EVS workers. If too many volunteered for an interview, I would have considered the first
eight nurse volunteers and the first four EVS volunteers. If not enough survey participants
volunteered to be interviewed, I offered an incentive of no more than $25 for all who volunteered
to be interviewed, including those who have already interviewed. The IRB application for this
study included the incentive information.
Instrumentation
An open-ended, semi-structured interview protocol was employed for the study to allow
the flexibility of data gathering (Creswell & Creswell, 2018; Merriam & Tisdell, 2016). With the
semi-structured protocol, I planned to ask all participants the questions listed in Appendix C and
ask open-ended questions to understand insights not captured from the specific questions. The
protocol included ten specific questions with the flexibility to probe based on the interviewees’
responses.
Patton (2002) question options can categorize the protocol questions. Out of 10 questions,
one belonged to Patton’s experience category. Three belonged to Patton’s feeling category, and
six belonged to Patton’s opinion category. One question addressed knowledge influences by
exploring processes and tasks, and three questions addressed motivational influences by
exploring value, interest, and self-efficacy. Six questions addressed organizational influences by
exploring cultural mode and setting.
Data Collection Procedures
One of the interview participants stated that the management mentioned this study during
the morning huddle. However, due to the COVID-19 pandemic, patient care staff were extremely
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busy and stressed. At the end of the data collection date, 3/8/2021, only two 30-minute
interviews were conducted via Zoom. Audio and video were recorded with the explicit
permission of the interviewees. I would destroy the recordings three years after the study is
published. To protect the interviewee, the recording feature was turned off by default when
starting each meeting with the interviewees until permission to commence recording was
obtained from the interviewee. The interviewee was afforded the option to mask their name in
the Zoom session to protect their identity further. Both interviewees joined the Zoom interviews
with their phones without video on. Finally, both interviewees provided the approval to record
the session.
EVS workers are predominately Spanish language speakers. Since I do not speak
Spanish, two Spanish speaking doctoral students from the Rossier School of Education were
identified to conduct interviews with EVS workers. The Spanish speaking doctoral students are
in the same cohort of the Organizational Change and Leadership program as me. They completed
the Human Subject Research module from the CITI training before I applied to the USC IRB.
Unfortunately, due to the COVID-19 pandemic, EVS workers were not available for interviews.
No EVS workers agreed to participate in the interviews for this study.
Data Analysis
Data analysis was used to make sense of interview data (Creswell & Creswell, 2018). To
make sense of rich data, I planned to winnow the data by categorizing detailed data into a
manageable number of themes (Creswell & Creswell, 2018). I planned to use a qualitative
computer software program, Nvivo, to assist the analysis as computer software programs can
store, locate, and analyze interview data more efficiently than manual methods (Creswell &
Creswell, 2018). With only two interviews, I did not use the Otter transcription service to
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double-check the coding for emerging themes. Coding is a process of categorizing data into
themes (Creswell & Creswell, 2018). I did not use Nvivo after all. Instead, I conducted manual
coding of the two completed interviews using the Zoom automated transcription feature.
Credibility and Trustworthiness
Qualitative researchers must earn trust by carefully designing the method to assure the
conclusion is based on a sound study (Locke et al., 2010). To gain credibility and
trustworthiness, qualitative researchers write a detailed description of why and how the study
was conducted, and what the findings reveal (Locke et al., 2010). Further, quantitative
researchers must have a deep understanding of the data source to be credible (Locke et al., 2010).
I employed the method of triangulation (Merriam & Tisdell, 20160). The findings from
quantitative data were either validated or contradicted qualitative data to enhance credibility and
trustworthiness.
Method Three: Document Analysis
According to Bowen (2009), documentation analysis is used with other qualitative
research methods to triangulate the study. The qualitative documents for the bed turnaround topic
were University Hospital’s operation meeting documents related to the patient flow. I did not
have firsthand knowledge of the documents but expected PowerPoint Presentations and meeting
minutes for regularly held hospital operation meetings. I requested that University Hospital share
the documents available after the IRB approval. The operation meeting documents were
considered as secondary documents per Merriam and Tisdell (2016) as others already prepare
them, and I had no firsthand experience with preparing the documents. The operation meeting
documents provided advantages and disadvantages as they were considered secondary
documents as described by Bowen (2009) and Boslaugh (2010).
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Data Collection Procedures
I scheduled a meeting with a Vice President (VP) of University Hospital in late
September 2020 to request the historical bed turnaround documents dating to 2019. I assured the
VP that the documents would only be used for the research. The pseudonym, University
Hospital, was used throughout the study to protect the hospital’s confidentiality. While staff in
the VP’s department pulled historical data, I obtained IRB approval. After receiving IRB
approval, I contacted the department for the bed turnaround data. University Hospital meetings
typically use the Microsoft PowerPoint application for discussion. Thus, the operation
department emailed 26 documents via email without breaching the email file size limitation.
Data Analysis
I reviewed the operation meeting documents related to bed turnaround, organizing the
data in chronological order and removing any data unrelated to bed turnaround. The time period
of the operation meeting documents ranged from March 2019 to October 2002, which provided
the historical context of the bed turnaround problem at University Hospital. I analyzed the
documents for corroborating findings as well as contracting findings from surveys. The historical
data revealed trends regarding bed turnaround time.
Ethics and Role of the Researcher
Literature referred the researcher as a crucial instrument for the study as they gather and
interpret data (Creswell & Creswell, 2018; Locke et al., 2010; Merriam & Tisdell, 2016). The
researcher must be aware of their subjectivity and consciously regulate how their personal bias
and role affect the research (Locke et al., 2010; Merriam & Tisdell, 2016). Creswell and
Creswell (2018) recommended that the researcher reflects on how their subjectivity and personal
bias shape the study and encourage them to use memos. Further, researchers should acknowledge
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why their study is important for them (Locke et al., 2010). According to Locke et al. (2010),
researchers must be cautious with building a relationship with participants as it may cause
problems.
According to Creswell and Creswell, 2018, the researcher must protect participants,
including names and places. Rubin and Rubin (2012) dedicated a section for the ethical
responsibilities of researchers in Chapter Six of their book, appropriately named The Art of
Hearing Data. The first responsibility is respecting participants by sharing information without
hiding facts. The second responsibility is honoring promises that the researcher may even go to
jail to protect the participant identity they promise to protect. The third responsibility is not
pressuring potential participants to participate in the study or forcing participants to answer the
questions they do not wish to respond. The researcher must disclose to the volunteered
interviewees that they can end the interview at any time during the interview and are not
obligated to answer any question. The fourth and last responsibility is to do no harm by not
exploiting participants, including removing information against the participants.
I hold a management position at University Hospital but was a permanent remote worker
and did not work with patient care staff. I did not know a single patient care staff member.
Therefore, I was an insider of the organization but was an outsider of the bed turnaround process
and obtained email addresses of participants from management. Based on this arrangement, staff
may feel obligated to participate in the study. Also, I hold the title of director and was a member
of core leadership at University Hospital. My job position within the organization may pose an
unintended power dynamic. I declared my role as a doctoral student and clearly explained this
study was entirely voluntary. I let the participants know they did not have to answer any
questions and could stop the interview during the interview. To protect the confidentiality, data
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were not be shared with the management. Further, the data file is stored in my personal
computer, protected with a password. The computer was also protected with Sophos anti-virus
software before April 2021 and with SentinelOne anti-virus software starting April 2021 and
VPN Unlimited since January 2019.
I learned the inefficient bed turnaround problem at University Hospital from a 2019 Lean
event. I was a participant in the 2019 Lean event and three subsequent meetings in 2019.
However, I did not participate in any other meetings related to patient flow strategy. I was a
member of one of four workgroups mainly assigned with technical tasks including streamlining
the bed board role in the software, assessing the right level of access for staff members, and
creating a real-time dashboard. With over 20 years of leadership experience in healthcare, I
brought a particular bias to this study. My subjectivity and personal bias influenced how to
collect the data and interpret the data. In addition to student researcher and employee of the
organization, my own experience of receiving surgery at University Hospital played a role in the
study. In other words, reflecting a patient perspective added another dimension to my
positionality. This study underwent USC’s expedited IRB process, and I abided by IRB’s
guidance for any ethical issues related to the research design
(https://oprs.usc.edu/training/students/).

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Chapter Four: Results and Findings
Chapter 4 presents the results and findings of this study from the data collected using the
research method described in Chapter 3. The purpose of the study is to understand the influences
that affect University Hospital to improve bed turnaround time and in turn, reduce delays in
patient care. The data were analyzed using a surveys, documents, and anecdotally by interviews.
The results and findings of this study are based on the qualitative and quantitative data
synthesized from the KMO influences: procedural knowledge, value, self-efficacy, inclusion,
accountability, communication, and aligning incentives. Under each influence, the results and
findings are detailed in Chapter 4 in the document analysis, survey, and interview sub-sections in
the same order as the data collection order.
The 12 closed-ended survey questions incorporated 7-item Likert-scale questions,
ranging from 7 (strongly agree) to 1 (strongly disagree). The threshold to determine if an
influence is an asset or a need was M = 5.1 from the survey responses. This is calculated by
adding 1 SD (0.3) to the mean of all influences (M = 4.8). Any influences with a mean meeting
the threshold were considered assets, and those that below the mean were considered needs. This
study included two interview participants. Due to the limited number of interviews, the
researcher could not utilized the interview findings to either support or disagree with the findings
and results from document analysis and surveys. Thus, the interview data were examined
through the lenses of the KMO gap analysis related to the findings and results from document
analysis and surveys anecdotally only and were not used to formulate their own themes. At the
end of each influence, the quantitative data results and qualitative data findings were synthesized
for the asset or needs evaluation. Table 6 lists the assumed influences with associated survey
questions and their average mean scores.
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Table 5
Knowledge, Motivation, and Organizational Influences and Survey Questions
Assumed influence

SQ M
Procedural knowledge influence

Q1
5.3

Q2
Motivation - value

Q3
4.9
Motivation - self-efficacy

Q4
4.8
Cultural Model - value staff perspectives,

Q5
4.5
inclusion in decision-making

Q6
Cultural Model - accountability

Q7
5.1

Q8
Cultural Setting - communication

Q9
4.3

Q11
Cultural Setting - goals and align incentives

Q10
4.9

Q12

Note. SQ = survey questions, M = Mean

Chapter 4 starts with an overview of document and artifact analysis, followed by a brief
description of participating stakeholders for both surveys and interviews. This is followed by an
exploration of the first research question of knowledge and motivation influences related to the
stakeholders' performance of the bed turnaround process. Results and findings were based on the
qualitative and quantitative data synthesized from procedural knowledge, motivation value, and
motivation self-efficacy influences. The second research question explores organizational culture
and context that impact patient care staff capacity to perform the bed turnaround process.
Chapter 4 concludes with a summary of the interaction between knowledge, motivation, and
organizational influences. Chapter 5 continues with the third research question exploring
recommendations for organizational practice in knowledge, motivation, and organizational
influences.
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Document and Artifact Analysis
Document and artifacts include 26 PowerPoint presentations from events and meetings at
University Hospital between March 2019 and October 2020. Nineteen of the documents were
meeting minutes from the patient flow strategy meetings from March 2019 to October 2010. The
patient flow strategy group appeared to force University Hospital to improve bed turnaround
time throughout the documentation period. The group facilitated a June 13, 2019 Lean event and
continued to identify issues and proposed workflows and reports. A July 31, 2020 document
indicated that the group called themselves the patient flow change management for the first time.
Before July 31, 2020, previous documents did not have the term “change management,” and the
group worked through a project charter, problem statement, and goals. Starting July 31, 2020, the
scope was defined, and process metrics were proposed. Further, the charter listed team members
and formulated a motto: “Patients receive timely, well-coordinated care, how, where, and when
they want it.”
Two March 2019 patient flow strategy group documents were meeting minutes
discussing the bed turnaround problem leading to the 2019 Lean event. Two documents captured
highlights from the June 2019 Lean event and described the problem, a process reducing bed
turnaround steps from over 70 to 20, and action items by four sub-workgroups using photos,
graphs, and narration. Three documents were 30-day, 60-day, and 90-day report-outs from the
2019 Lean event. One document was meeting minutes from a broader clinical/operational
committee meeting from July 2019, including reviewing the June 2019 Lean event. Seventeen
documents were meeting minutes from the patient flow strategy team as they continued to work
on the bed turnaround problem after the 2019 Lean event. Lastly, one document visualized
various patient flow dashboards. I attended the June 2019 Lean event. This event covered
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strategies to reduce the waste of the current bed turnaround process and propose improvement
opportunities in my capacity as a university employee. Table 7 displays the documents by the
meetings and by years.

Table 6
Document and Artifacts

Documents 2019 2020 Total
Patient flow strategy meeting minutes 2 17 19
Lean event and report out 5

5
Clinical/operations committee meeting minutes 1

1
Dashboard visualization

1 1

8 18 26

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Participating Stakeholders
The sample patient care staff comprised 100 nurses and 17 EVS workers working at
University Hospital during the data collection period January 26, 2021, through March 8, 2021.
Patient care staff availability was limited due to the high demand work environment of the
hospital plus the high number of infected hospital staff due to the COVID-19 pandemic. As
reported from the University Health System’s daily COVID dashboard, as of April 13, 2021,
1,109 staff members tested positive with COVID-19 since the inception of the pandemic.
Survey Participants
The reported gender and racial identities of the sample of survey participants represent
the overall patient and care staff population. A large proportion of the staff population is female
and Latinx as described below. There are two sub-groups within the patient care staff in this
sample: 100 nurses belong to two units and 17 EVS workers cover the entire hospital. From the
117 individuals in the sample population, 40 participated with responding to some or all
questions resulting in n = 39, or n = 40 depending on the question. Table 8 displays the
demographic information of the survey participants.

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Table 7
Demographics of Survey Participants (N = 40)
Demographic Category n %
Gender

Male 7 17.5

Female 31 77.5

Prefer not to say 1 2.5
Race

White 5 12.5

Black or African American 1 2.5

Asian 9 22.5

Native Hawaiian or Pacific Islander 1 2.5

Hispanic or Latino or Latinx 22 55.0

Some other race 2 5.0
Experience

<1 year 2 5.0

1-3 years 4 10.0

4-5 years 7 17.5

6-10 years 5 12.5
>11 years 22 55.0

Note. One participant did not select any response choice from the Gender question.

The survey demographic does not resemble the national demographic for nurses. Males
represent 12.6%, and races are represented as White 75.3%, Black 13.4%, Asian 8.7%, and
Latinx 7.9% (U.S. Bureau of Labor Statistics, last modified date, January 22, 2021). The survey
demographic factors are also significantly different from the California nurse demographic
where males represent 13% of the nurse population. The most common demographic categories
are White 41.6%, Native Hawaiian or Pacific Islander 20.6%, Asian 11.2%, and Other 10.7%
(Spetz & Chu, 2020). Table 9 shows the demographic characteristics of the nurse participants.

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Table 8
Demographics of Survey Participants – Nurses Only (N = 27)
Demographic Category n %
Gender

Male 6 22.2

Female 20 74.1

Prefer not to say 1 3.7
Race

White 4 14.8

Black or African American 0 0

Asian 9 33.3

Native Hawaiian or Pacific Islander 1 3.7

Hispanic or Latino or Latinx 11 40.7

Some other race 2 7.4
Experience

<1 year 2 7.4

1-3 years 2 7.4

4-5 years 5 18.5

6-10 years 1 3.7
>11 years 17 63

Table 10 shows the university hospital nurse population (University Hospital Human
Resource, May 4, 2021). The survey gender characteristics represented the gender breakdown of
the hospital with 24.5% males vs. 75.5% females respectively. However, the race characteristics
of the survey were significantly different from the hospital where Asian was the most prominent
race with 53.5%. The years of experience category of the survey was largely represented by the
greater than 11 years at 56.1%. The general hospital population was more dispersed over the
years of experience categories. Overall, the demographic of the survey did not represent the
population of the hospital nurses.

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Table 9
Demographics of University Hospital Nurses (N = 882)
Demographic Category n %
Gender

Male 216 24.5

Female 666 75.5

Prefer not to say

Race

White 180 20.4

Black or African American 58 6.6

Asian Native Hawaiian or Pacific 472 53.5

Islander

Hispanic or Latino or Latinx 151 17.1

Some other race 21 2.4
Experience

<1 year 120 13.6

1-3 years 207 23.5

4-5 years 86 9.8

6-10 years 178 20.2
>11 years 291 33

Note. University Hospital does not track Pacific Islanders as a separate race/ethnicity category.
In California, Filipino nurses count 20.6% of nurses in 2018 (California Board of Registered
Nursing, 2018 Survey of Registered Nurses).

Twenty-seven out of 100 nurses participated in the survey, but not every participant
answered all questions, especially the open-ended questions. Thirteen of seventeen EVS workers
participated in the survey but not every participant answered all questions, especially the open-
ended questions. Per the hospital leadership’s instruction, the researcher applied three survey
methods to recruit participants: web link, QR code, and paper survey. Table 11 presents
participants by survey method.

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Table 10
Participants by Survey Data Collection Method (N = 40)
Method Surveys sent Participant n %
Nurses
Link 100 19 19
QR 100 8 8
Electronic (link & QR) 100 27 27
EVS
Paper 17 13 76
Total
All 117 40 34

Note. The online survey was sent to a distribution list that included 100 email recipients. The
email provided two options for completing the survey: a Qualtrics link and a OR code.

The gender identities of the survey were based on the responses to survey question 13,
including seven males (17.9%), 31 females (79.5%), and one prefer not to say (2.6%). Since the
survey sample was predominately comprised of participants who identified as female, there was
insufficient data to carry out a gender-based comparative statistical analysis. Figure 5 shows the
participants by gender.

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Figure 5
Gender Results for Survey Participants (N = 39)

The summary results about the racial identities of the participants were based on the
responses to the survey question 14, including 5 (12.5%) White, 1 (2.5%) Black or African
American, 9 (22.5%) Asian, 1 (2.5%) Native Hawaiian or Pacific Islander, 22 (55.0%) Hispanic
or Latino or Latinx, and 2 (5.0%) Some Other Race. Figure 6 visualizes participants by race.

17.9%
79.5%
2.6%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Male Female Prefer not to say
Participant
Gender
80

Figure 6
Race Results for Survey Participants (N = 40)

The experience results were based on the responses to the survey question 15, including
two (5.0%) less than one year, four (10.0%) 1-3 years, seven (17.5%) 4-5 years, five (12.5%) 6-
10 years, 22 (55.0%) greater than 11 years. Figure 7 represents participants by experience.

12.5%
2.5%
22.5%
2.5%
55.0%
5.0%
0%
10%
20%
30%
40%
50%
60%
White Black or African
American
Asian Native Hawaiian
or Pacific
Islander
Hispanic or
Latino or Latinx
Some other
race
Participant
Race
81

Figure 7
Years of Experience Results for Survey Participants (N = 40)

Interview Participants
The invitations for the survey and interview were communicated to the sample group on
January 26, 2021. No one volunteered for the interview during the three weeks when the survey
was open. I sent several reminder emails and spoke with the nursing and EVS directors regarding
the interviews. Despite these attempts, only two participants volunteered for 30-minute Zoom
interviews. Due to the low number of interviews, the interview participants’ demographic data is
not disclosed to protect their identity. Further, the interview data were only used anecdotally and
not used to confirm or diverge the survey results and document analysis findings.
Research Question 1: What Are the Patient Care Staff Knowledge and Motivation
Influences Related to Performing the Bed Turnaround Process?
Research Question 1 sought to explore knowledge and motivation influences that affect
patient care staff to performing the bed turnaround process. Comparing the survey scores to the
5.0%
10.0%
17.5%
12.5%
55.0%
0%
10%
20%
30%
40%
50%
60%
< 1 Year 1-3 Years 4-5 years 6-10 years >11 years
Participant
Years
Experience Results
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asset-need threshold (M = 5.1), the knowledge influence was an asset, but the motivation
influences were needs. The mean survey responses for the knowledge influences scored 5.3,
which exceeded the asset-need threshold. However, the motivation influences, value, and self-
efficacy scored means of 4.9 and 4.8, respectively, below the asset-need threshold.
The next sections provide findings and results for each of the knowledge and motivation
influences from document analysis, survey, and interviews from the study. Document analysis
examined the knowledge influence from 26 PowerPoint Presentations. Survey questions 1 and 2
explored the knowledge influence, survey question 3 examined the motivational value influence,
and survey question 4 explored queried self-efficacy. Interviews were used to provide additional
insight into the data.
Patient Care Staff Efficiently Turned Beds Consistently Using Best Practice from
Continuous Learning
The procedural knowledge influence sought to assess whether patient care staff
consistently follow the best practice presented by the patient flow strategy group during the 2019
Lean event yielding the 1.5-hour bed turnaround time. Further, the procedural knowledge
influence sought to assess whether patient care staff are continuously learning new knowledge.
Based on the findings and results, the procedural knowledge influence is a mixed result for the
stakeholder group. The following section describes the survey results and ends with the findings
from the interviews. Document analysis did not provide relevant findings.
Survey Results
According to the survey results, the majority of participants could execute bed turnaround
tasks independently (SQ1) and knew how to utilize resources to complete the tasks (SQ2) (M =
5.2, SD = 1.6, n = 39). The mean score of 5.2 exceeded the asset-need threshold of 5.1, indicating
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that procedural knowledge was an asset to the organization. Seventy percent of the participants
perceived that they could execute bed turnaround tasks independently (SQ1). According to the
survey, 77.5% of participants perceived that they know how to utilize resources available to
complete bed turnaround tasks (SQ2). The survey questions did not specifically ask if
participants followed the best practices from continuous learning, but the two questions together
explored if patient care staff was confident in their ability to turn beds.
However, 11 of 20 participants who responded to the open-ended questions voiced
process issues as barriers to achieving efficient bed turnaround and recommended improving the
process. Two participants shared that the room status was incorrectly updated in the computer
system before the room was stripped. Other responses included, “Rooms are not stripped, meds,
sheets, equipment, etc.,” “Room not completely stripped…”, “Cleaning staff will try to start
cleaning a room that has not been stripped.” Figure 8 shows the survey score distribution for the
knowledge influence.

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Figure 8
Procedural Knowledge Average Score Distribution (N = 39)

Interview Findings
Both interviewees recited their bed turnaround process without hesitation, implying they
know how to conduct their tasks. Both interviewees stated that they knew how to utilize
resources to complete bed turnaround tasks. Interviewee 1 described a step-by-step bed
turnaround process:
So we go through the room and we take off all the linens … in a linen bag, and then we’ll
take out the pump that IV pump and the IV tubing and stuff, and SCD machine, ….
Anything left over like cups or stuff for water containers…Anything in the bathroom
that’s like toothbrushes and stuff … throw in the trash.
Further, Interviewee 1 stated that, “It’s not really a difficult process.”
2.6% 2.6%
5.1%
0.0%
2.6% 2.6%
10.3%
7.7% 7.7%
5.1%
20.5%
33.3%
0%
5%
10%
15%
20%
25%
30%
35%
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5
Participatnt
Scores
Knowledge (SQ1 & SQ2)
85

Patient Care Staff Do Not Value Efficient Bed Management as a Critical Part of Their Job
Patient care staff has demanding work, and bed turnaround is only one part of the work.
Analysis of the assumed value influence sought to understand whether by patient care staff value
the bed turnaround. In particular, the value influence explores whether patient care staff consider
bed turnaround as a critical task when they have other responsibilities. Based on the findings and
results, the value influence is a need for the patient care staff. Based on the interview findings
and survey results, valuing bed management influence is a need for the stakeholder group.
Document analysis did not reveal enough information for the asset or need assessment.
Survey Results
According to the survey results, participants did not consider bed turnaround tasks as the
most critical tasks (M = 4.8, SD = 1.6, n = 39). The value influence was a need to University
Hospital as the 4.8 mean score was below the asset need threshold of 5.1. Bed turnaround tasks
were the most critical tasks (SQ3) to 62.5% of survey participants. In addition to the closed-
ended question results, three open-ended responses referred to competing priorities as barriers to
completing bed turnaround tasks efficiently. Survey results indicated the motivation value
influence as a need. Figure 9 shows the survey score distribution for the motivation value
influence.

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Figure 9
Motivation Value Average Score Distribution (N = 39)

Interview Findings
Interviewee 1 stated that the bed turnaround process was not important until a patient
arrives, and the hospital needs the bed, indicating “it’s not as important as like admitting a patient
or giving a message to patients.” Interviewee 2 said that if the nurse did not turn beds,
supervisors would speak with them to address the problem, starting “…if we don’t get that bed
ready, then they will talk to our managers and then the managers will come talk to us…” This is
indicative of a punitive consequence as motivation to complete the bed turnaround tasks.
Interviewee 1 identified other activities, such as admitting a patient or giving a massage to
patients, as examples of other more important tasks than the bed turnaround tasks. When
Interviewee 1 had to choose between either admitting a patient or turning a bed, they admitted a
patient.
5.1% 5.1%
7.7%
20.5% 20.5%
28.2%
12.8%
0%
5%
10%
15%
20%
25%
30%
1 2 3 4 5 6 7
Participant
Score
Motivation Value Survey Results
87

Patient Care Staff Had Low Self-Efficacy in Using Bed Turnaround Software
University Hospital has bed turnaround software as a part of their electronic medical
record system (EMR). The bed boarding management software application system (BBS,
pseudonym) provides computerized support for patient care staff to record, communicate, and
track nursing bed information. The assumed self-efficacy influence explored whether patient care
staff is efficacious in using the bed turnaround software. Based on the survey results, the self-
efficacy influence is a need for patient care staff. Document analysis was not conducted for the
self-efficacy influence.
Survey Results
According to the survey results, participants could not use the bed turnaround software
proficiently to complete bed turnaround tasks (M = 4.8, SD = 1.8, n = 40). The 4.8 mean score
was below the asset-need threshold of 5.1, indicating that the patient care staff’s self-efficacy
influence was a need. Specifically, 62.5% of participants believed they could use the bed
turnaround software proficiently to complete bed turnaround tasks (SQ4). Thus, the results from
the survey showed that self-efficacy influence was a need. Figure 10 shows the survey score
distribution for the motivation self-efficacy influence.

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Figure 10
Motivation Self-Efficacy Average Score Distribution (N = 40)

Interview Findings
The findings from the interviews were mixed between two patient care staff members.
Interviewee 1 detailed the tasks they were performing using the computer software stating, “It’s a
pretty quick process, it doesn’t take long.” Interviewee 2 also explained the tasks related to
computer software but did not know the name of the software, stating “…I don’t know what that
word managed software we have available to us. I just know that…” Interviewee 2 also added
that their bed management software tasks were minimal and felt they received basic minimum
training of the bed management software. The interview findings may warrant additional
research as patient care staff may not be aware of the bed management software’s full
capabilities, therefore possibly not performing the best practice for their units and not being
aware of their deficits.
10.0%
7.5%
2.5%
17.5%
12.5%
40.0%
10.0%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
1 2 3 4 5 6 7
Participant
Scores
Motivation Self-Efficacy Survey Results (SQ4)
89

Research Question 2: How Does the Organizational Culture and Context Impact Patient
Care Staff Capacity to Perform the Bed Turnaround Process?
Research Question 2 sought to explore organizational influences that affect patient care
staff performing the bed turnaround process. The accountability influence was determined as an
asset when compared with the survey score to the asset need threshold (5.1). On the other hand,
the survey score revealed that inclusion (n = 4.5), communication (n = 4.3), goals and align
incentives (n = 4.9) were determined to be needs.
The following sections provide findings and results from the document analysis, survey,
and interviews. The document analysis examined organizational influences from 26 PowerPoint
Presentations. Survey questions 5 and 6 explored the value of staff perspective and inclusion of
staff in decision making influence. the accountability influence was investigated by reviewing
survey questions 7 and 8 results. Survey questions 9 and 11 examined the communication value
influence. The goals and align incentives influences were examined by survey questions 10 and
12. Interviews provided additional insights into each of the influences.
The Hospital Neither Values Staff Perspectives on Policies, Processes and Procedures Nor
Includes Staff in the Decision-Making Process
Among numerous stakeholders involved in the patient flow process, the patient care staff
should know the bed turnaround process as they turn beds every day. This section explored
patient care staff’s perception of the assumed organizational influence focused on the hospital
valuing staff perspectives on policies, processes, and procedures and including staff in the
decision-making process. Based on the findings and results, valuing staff perspectives and
inclusion of staff in decision-making influence is a need for the organization.
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Document Analysis Findings
The staff perspective and inclusion influence was examined using the analysis of 26
University Hospital documents. Evidence of the hospital leaders' consulting staff revealed bed
turnaround policies, processes, and procedures. Document analysis suggested that University
Hospital did not consider patient care staff feedback in the decision-making process. According
to the 2019 Lean event’s attendance list, non-management patient care staff were not included in
the event to improve the bed turnaround process. The attendance list from the 2019 – 2020
Patient flow strategy group did not include non-management patient care staff. Finally, senior
leaders at the hospital comprised the 2019 clinical/operations committee. None of the documents
indicated if the senior leaders consulted staff members in the decision-making process.
Survey Results
According to the survey results, participants perceived that the hospital did not value the
perspectives of the staff and failed to include them in decision-making (M = 4.5, SD = 1.7, n =
40). The 4.5 mean score was below the asset-need threshold of 5.1, indicating that the value-
inclusion influence was a need to the organization. According to the survey, 57.5% of
participants perceived that their supervisors regularly encouraged them to provide input for the
bed turnaround process (SQ5). In addition, 47.5% of participants perceived that patient care staff
feedback or suggestions were adopted in the bed turnaround policies and procedure process
(SQ6). The survey results indicated the value and inclusion influence was a need. Figure 11
presents the survey score distribution for the cultural model value and inclusion influence.

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Figure 11
Cultural Model – Value Staff Perspectives and Inclusion in Decision Making Process Average
Score Distribution (N = 40)

Interview Findings
The interview findings were mixed regarding University Hospital valuing staff
perspectives and inclusion in decision making. Interviewee 1 stated that they felt they could talk
with nurse leaders and charge nurses about issues. Interviewee 1 further stated that the nurse
leaders and charge nurse listened to her, and she felt she could bring the topic to the regularly
scheduled meeting, saying “I could talk with the manager and discuss it with me if you know we
have a problem and then she would find out if there’s any solution or something.” Interviewee 2
did not bother to provide ideas or feedback to nurse leaders and charge nurses as Interviewee 2
felt that they did not listen. The interviewee stated that no matter how many times nurses talked
with management, “…it’s repeating the same thing to them so doesn’t seem to change.”
7.5%
2.5%
7.5%
2.5% 2.5%
0.0%
17.5%
5.0%
15.0%
10.0%
20.0%
10.0%
0%
5%
10%
15%
20%
25%
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5
Participant
Scores
Cultural Model -Value Staff Perspectives and
Inclusion in Decision Making Survey Results
(SQ5&6)
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Overall, the organizational influence of valuing staff perspective and inclusion was found
to be a need. Document analysis findings and survey results supported the inclusion influence as
a need. Interview findings were mixed with one interviewee who felt they were listened to, but
the other interviewee did not.
The Hospital Has a Culture of Professional Accountability Regarding Bed Turnaround
Responsibilities
Patient care staff are accountable for the bed turnaround process as they are the ones to
turn beds. Pekrun’s control-value theory claims emotions can affect performance if one does not
control the tasks (Pekrun, 2006). This influence explored patient care staff’s perception of
professional accountability related to bed turnaround responsibilities. Based on the findings and
results, the professional accountability influence is an asset for the organization.
Document Analysis Findings
University Hospital’s culture of professional accountability was explored by examining
26 documents. In the 30-day report after the 2019 Lean event, the Lean workgroup identified that
unclear roles in the bed management facilitation software contributed to the ineffective bed
turnaround as staff members’ roles were confusing, and they may lack the necessary access to
manage their roles responsibilities to facilitate patient flow. The 60-day report identified that not
all patient care staff had correct access to the bed management system to complete their
responsibilities. The report also stated that patient care staff were commonly confused in their
roles and responsibilities in the bed management system (University Hospital, September 5,
2019). Finally, the 90-day report informed the Lean event participants that they simplified the
roles within the bed management system and granted appropriate access to patient care staff
(University Hospital, October 10, 2019). Separately, in the August 13, 2020 document, the
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patient flow strategy committee revisited the bed management system and clarified that nurses
were responsible for performing six of the bed tasks in the system. Document analysis showed
the complexity of patient care staff roles in the bed management system caused challenges for
staff members to complete their responsibilities. The hospital made an effort to define clear roles
for nurses by August 13, 2020 to create an organizational culture of accountability. Thus, the
findings from the document analysis supported accountability as an asset.
Survey Results
The survey found the participants’ perceptions were positive on the organization’s
professional accountability culture. (M = 5.1, SD = 1.3, n = 39). The 5.1 mean score was on par
with the asset-need threshold of 5.1, indicating that the accountability influence was an asset to
the organization. According to the survey, 42.5% of participants perceived that they could not
make their own decisions about how best to complete bed turnaround tasks (SQ7). At the same
time, 79.49% of participants perceived they were held accountable by their supervisors for bed
turnaround tasks (SQ8). Figure 12 shows the survey score distribution for the cultural model
accountability influence.

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Figure 12
Cultural Model – Accountability Score Average Distribution (N = 39)

Interview Findings
Both nurses interviewed made their own decisions about how best to complete bed
turnaround tasks. Interviewee 1 said that “…just do it.” Interviewee 2 stated that “… up to the
nurse and the case manager to get this thing set up for them to leave the hospital and transport.”
The Hospital Does Not Effectively Communicate Bed Turnaround Performance Data and
Gaps with Staff
According to the literature review, communication problems are barriers to achieving
efficient bed turnaround performance (Pellicone & Martocci, 2006; Thomas & MacDonald,
2016; Tortorella et al., 2013; Rivers et al., 1998). This section explores patient care staff
perception of organizational communication in sharing bed performance data and gaps with staff.
2.6%
0.0%
2.6%
0.0% 0.0%
2.6%
15.4%
12.8%
15.4%
7.7%
28.2%
12.8%
0%
5%
10%
15%
20%
25%
30%
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5
Participant
Scores
Cultural Model - Accountability Survey Results
(SQ7 & 8)
95

Based on the findings and results, this assumed communication influence is a need for the
organization.
Document Analysis Findings
University Hospital’s communication was examined by reviewing 26 documents. The
objective of the document analysis was finding evidence of the hospital leadership frequently
communication of bed turnaround performance data with staff members. The next paragraphs
examined the communication issues centered around the nursing staff. Nurse leads copied patient
transfer data to the bed board software and shared the data during the huddle with staff members
(University Hospital, March 11, 2019). The patient flow strategy group asked the nursing leaders
to meet with their managers by May 27, 2020, to relay the expectation of nurse leads to stay on
8:30 am patient flow huddle call for the duration of the meeting (University Hospital, May 15,
2020). This implied that nurse leads did not remain on the 8:30 am patient flow huddle call for
the meeting duration and was identified as an opportunity to change.
On June 3, 2020, the patient flow strategy called for nursing leaders to have nurse leads
join the daily patient flow huddle, suggesting that not all nurse leads stayed for the entire huddle
by the expected date of May 27, 2020. In the same document, a proposed bed assignment
workflow was included. However, the document did not indicate if the proposed bed assignment
workflow was discussed during the daily patient flow huddle. The patient flow strategy group
identified that the Microsoft Teams application was not installed on unit computers. Thus, some
nurse leads phone called into the huddle instead of logging in on Microsoft Teams. As a result, it
is unclear whether all nurse leads could view the proposed bed assignment flow.
The 2019 Lean event facilitators explained that other hospitals share real-time bed
availability data throughout the organizations to perform an efficient bed turnaround (University
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Hospital, June 13, 2019). The 30-day and 60-day report meetings indicated a new real-time bed
turnaround dashboard as an action item. However, in the 90-day report meeting, creating a new
real-time bed turnaround dashboard was on hold due to the bed management system limitation
(University Hospital, October 10, 2019).
Analysis of several other documents from September 19, 2019, July 2, 2020, October 1,
2020 meetings support that the hospital administration did not effectively communicate bed
turnaround performance and gaps with staff. The patient flow strategy group presented five
reports including the patient huddle dashboard for morning and afternoon shifts (University
Hospital, June 10, 2020). The patient flow huddle dashboard included the number of patients,
beds with staff, unavailable beds, resource hours, specific clinical notes (including COVID
diagnosis), expected volume, and other data elements. At the June 26, 2020 meeting, the patient
flow group discussed capturing data, and at the July 2, 2020 meeting, the group started to capture
data. However, neither June 26, 2020 document nor July 2, 2020 document indicated if the
information was shared with patient care staff. Another report, staffing/patient flow data
reporting, appeared on the July 9, 2020 meeting document. The patient flow strategy group
determined that “No regular KPI data sharing with clear ownership to highlight changes over
time and areas needing emphasis.” (University Hospital, October 1, 2020).
Finally, the document analysis revealed intradepartmental communication problems that
were not anticipated in the original research design. For example, data capture for patients
transferring between units was challenging. Nurse leads did not have consistent information of
patients transferring in/out of their units according to the slide 3 data review from the patient
flow strategy meeting document dated March 11, 2019. Additionally, slide four from the August
17, 2020 meeting document was devoted to patient flow communication platforms. Various
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stakeholders of the patient flow were using different communication platform contributing to
intradepartmental miscommunication. Nurse managers communicated using phone, SMS texting,
and Microsoft Teams. However, charge nurses and staff nurses had limited access to Microsoft
Teams as it was not installed in all computers on the nursing floors. Most staff nurses and EVS
workers did not have a dedicated office phone. Unlike management who were eligible for mobile
phone stipends, non-management staff nurses and EVS workers were not eligible for mobile
phone stipends which limited personal mobile phones for business use. Union nurses filed
grievance against University Hospital for asking nurses to use personal devices (University
Hospital, August 17, 2020). Some EVS workers relied on V oalte phones to communicate but due
the limited number of V oalte phones, many EVS workers did not use them. Making the
communication matter worse, the computer system to manage bed boarding (BBS, pseudonym)
was not compatible with mobile devices, and could not produce new notifications nor modified
information on timely basis. Slide 4, titled “Patient Flow Related Communication Platforms,”
listed the following communication platforms among patient care staff: phone, bed management
computer software, V oalte phone, SMS text, Microsoft Teams, and Email to SMS text. In
summary, the documents did not show University Hospital effectively provided performance
data with staff.
Survey Results
The survey results showed the participants’ perceptions were negative on the
organization’s cultural setting of communication (M = 4.3, SD = 1.7, n = 40). The 4.3 mean score
was below the asset-need threshold of 5.1, indicating that the organization’s communication
influence was a need. Half of the participants perceived that the hospital did not frequently
communicate bed turnaround performance data with staff (SQ9). Additionally, half of the
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participants perceived the hospital did not share bed turnaround progress and metrics with them
(SQ11). The survey results indicate communication influence is a need. Figure 13 shows the
survey score distribution for the cultural setting communication influence.

Figure 13
Cultural Setting – Communication Average Score Distribution (N = 40)

10.0%
0.0%
7.5%
0.0%
12.5%
7.5% 7.5% 7.5%
15.0%
2.5%
25.0%
5.0%
0%
5%
10%
15%
20%
25%
30%
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5
Participant
Scores
Cultural Setting - Communication Survey Results
(SQ9 & 11)
99

Interview Findings
Interviewee 1 was aware of management monitoring of the bed turnaround performance.
However, staff only knew a small portion of bed turnaround performance related to their specific
tasks, stating that “…the only portion I see where there’s, they say, you know, that it’s not it’s
not done, and we need to get it, get it.” Interviewee 2 simply responded to the hospital’s
communication of bed turnaround performance as “No.”
The Hospital Does Not Prioritize Improving the Bed Turnaround Process by Setting Clear
Goals and Aligning Incentives
According to the literature review, goal setting and aligning incentives positively affect
bed turnaround performance (Blanchard & Rudin, 2016; Carillo, 2006; Knarr & MacArthur
2012). This section explores patient care staff’s perception of whether the organization sets clear
goals and aligns incentives to improve the bed turnaround process. Based on the findings and
results, this assumed influence is a need for the organization.
Document Analysis Findings
University Hospital’s goal setting and aligning incentives influence was explored by
examined 26 documents to find evidence of SMART goals and bed turnaround performance
metrics. During the 2019 lean event and related documents discussed the current state (5.1 hours)
and the industry’s best practice (1.5 hours) with the stated goals: 1) improve patient flow via
proficiently using the bed management system, 2) clarify workflow, roles and responsibilities,
system management, and data and status reporting, 3) create structure and process for
sustainment and accountability (University Hospital, June 13, 2019). However, the goals lack
specific details: what metrics to monitor, success criteria of reducing turnaround time, and time
criteria, by when. The October 1, 2020 meeting document proposed developing evidence-based
100

SMART goals by January 2021 including specific metrics and criteria lacking in previous
attempts to reduce turnaround time (University Hospital, October 1, 2020). In the 30-day report,
the Lean workgroup’s goals were defined (University Hospital, July 30, 2019). Table 12 displays
the proposed goals from the 2019 Lean event.

Table 11
Lean Workgroup Tasks to Improve Bed Turnaround
Tasks
Developing role-based access to the bed management computer software

Developing a standard set of the report

Improving job aid

Developing detailed patient placement process

Allowing the conversation channel from bed management software and patient chart at same time
Providing bed management data to EVS team

Resolving EVS zoning problems

Ordering more bed management phones

Improving the communication of UV cleaning robots

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However, the workgroups reported challenges in meeting some of the assigned tasks in
the 60-day report (University Hospital, September 5, 2019). The 90-day report informed that the
overall hospital's average bed turnaround time deteriorated from 5.1 hours to 5.8 hours. At the
same time, two piloted nursing units improved the average bed turnaround time from 5.1 hours
to 4.8 hours using the proposed workflow (University Hospital, October 10, 2019).
A year later, the patient flow strategy group set goals to “introduce and utilize a change
management framework to … build a foundation for change.” Furthermore, the group proposed
clarifying workflows, individual roles and responsibilities, system management, data and status
reporting, and creating communication structures and processes for patient flow activities
(University Hospital, July 16, 2020). The group continued to progress with goal setting by
forming key goals for bed turnaround (University Hospital, July 31, 2020). The goals were more
defined and expanded with process and outcome metrics, but no timeline was found (University
Hospital, August 13, 2020). According to the August 26, 2020 document, the patient flow
strategy group established a timeline to set goals between July 2020 to January 2021.
The patient flow strategy group claimed that "No defined and recorded patient flow
strategy and vision to allow for alignment throughout the organization" existed and proposed
developing data-led SMART goals (University Hospital, October 1, 2020). To do so, the group
recommended defining performance metrics and measures and identify clear ownership and
accountability. University Hospital participated in industry collaboration to improve patient flow
mentioned in the June 18 & 26, 2020 meeting document. The collaboration effort formulated the
problem statement, "Lack of structure and accountability related to patient flow at University
Hospital has led to….” inefficient bed turnaround and recommended to set goals (University
Hospital, October 28, 2020).
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Notably, the average bed turnaround time data for FY20 on October 28, 2020, was 2.2
hours, but other documents indicated 5 hours. It appeared the definition of the average bed
turnaround time used on October 28, 2020 was different from the definition of the average bed
turnaround time in other documents. Different definitions for the same term "bed turnaround
time" may confuse audiences as the definition was not consistent for the same metric.
The patient flow strategy group discussed if the organization should join an industry
vendor collaborative to optimize patient flow indicating the organization needed external help to
improve the bed turnaround process (University Hospital, Patient Flow Strategy Meeting, June
18 and 26, 2020). The patient flow strategy group identified piece-by-piece meetings,
information, and handoff as contributors to a disorderly workflow with a high volume of
duplicate and potentially unnecessary work by patient care staff (University Hospital, July 9,
2020). The patient flow group discussed how the organization could simplify, standardize, and
streamline information gathering and reporting with a few suggestions, including reducing
distractions with "No Call Zones" and other measures (University Hospital, July 9, 2020). The
patient-flow strategy group findings were well after the June 2019 Lean event with a proposed
workflow reducing duplicates and unnecessary work from 70+ steps to 21 steps. The
organization piloted the proposed workflow with two nursing units, which resulted in a reduced
bed turnaround time by October 2019. Thus, after reading the 2020 meeting documents, the new
streamlined workflow proposed in 2019 was neither thoroughly investigated nor implemented.
Finally, there was no discussion throughout the documents of bed turnaround
performance as a part of the annual performance evaluation for staff nurses and EVS workers.
The documents provided by University Hospital did not reference either plans or a program to
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align incentives to improve the bed turnaround process. Therefore, document analysis findings
support goal setting and aligning incentives as a need from the available documents.
Survey Results
The survey found the participants’ perceptions were negative on the organization’s
cultural setting of goals and aligning incentives for bed turnaround. (M=4.9, SD=1.4, n= 40). The
4.9 mean score was below the asset-need threshold of 5.1, indicating that the goals and align
incentives influence was a need to the organization. Almost half (49.5%) of participants
responded that they know the organization’s goals for bed turnaround (SQ10). According to the
survey, 55% of participants perceived their performance evaluation did not include bed
turnaround performance (SQ12). The survey results indicated that goal setting and align
incentive influence as a need. Figure 14 portrays the survey score distribution for the goal and
align incentives influence.

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Figure 14
Cultural Setting - Goals and Align Incentives Average Score Distribution (N = 40)

Interview Findings
Neither interviewee knew the hospital’s bed turnaround goal. Both interviewees
confirmed their performance evaluation did not include bed turnaround performance.
Interviewee 1 stated that “I don’t think there’s any incentive. I mean just knowing that you have
patients coming, and it has to bring more patients in, but there’s no.” However, Interviewee 1
mentioned that the hospital management evaluated nurses by patient admission metrics, so if the
bed was not available, nurses could not admit patients. When the researcher met with the hospital
leadership, the Chief Nursing Officer indicated that the hospital does not consider bed
turnaround performance in nurse performance evaluation. Overall, the goal-setting and align
incentives organizational influences were found to be needs from all research data findings and
results.
5.0%
0.0%
2.5%
0.0%
2.5% 2.5%
20.0%
5.0%
25.0%
5.0%
20.0%
12.5%
0%
5%
10%
15%
20%
25%
30%
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5
Participant
Scores
Cultural Setting - Goals and Align Incentives
Survey Results (SQ10 & 12)
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Interaction Between Organizational Influences and Stakeholder Knowledge and
Motivation
Summary of Knowledge, Motivation, and Organizational Influences’ Data
The research design uncovered the knowledge, motivation, and organizational
influences’ data. Based on the document analysis and survey findings and results, the patient care
staff's overall perception was negative except for the procedural knowledge influence and the
cultural model of accountability. Table 13 displays the results and findings of the research data
according to assets or needs.

Table 12
Asset or Need by Document Analysis and Surveys
Assumed influence Document analysis Survey
Procedural knowledge influence N/A Asset
Motivation - value N/A Need
Motivation - self-efficacy N/A Need
Cultural model - value staff perspectives, inclusion
in decision-making
Need Need
Cultural model - accountability Asset Asset
Cultural setting - communication Need Need
Cultural setting - Goals and align incentives Need Need

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The majority of influences were either overwhelmingly assets or needs with consistent
values between qualitative and quantitative data. Table 14 presents each KMO influence as either
an asset or need based on the document analysis, survey, and interview data.

Table 13
Knowledge, Motivation, and Organizational Assets or Needs as Determined by the Data
Assumed influence Asset/Need
Procedural knowledge influence Asset
Motivation - value Need
Motivation - self-efficacy Need
Cultural model - value staff perspectives, inclusion in decision-making Need
Cultural model - accountability Asset
Cultural setting - communication Need
Cultural setting - goals and align incentives Need

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Applying Clark and Estes's (2008) gap analytical framework, this study explored two
research questions. Research Question 1 examined knowledge and motivation influences related
to patient care staff performing the bed turnaround process. According to the asset/need
assessment, patient care staff possessed procedural knowledge but lacked motivation value and
self-efficacy. Research Question 2 assessed the organizational culture and context impact on
patient care staff to perform bed the turnaround process. Except for the accountability influence,
all other organizational influences revealed needs. In particular, the hospital’s cultural model of
not valuing staff perspectives and not including them in decision-making may be negatively
influencing motivation influences.
Resource issues emerged as a key organizational influence affecting patient care staff to
efficiently complete bed turnaround. Although this study found the scarceness of resources as a
possible cause of inefficient bed turnaround and presented assessing resources to achieve an
efficient bed turnaround under the organizational influence section, the study design did not ask
resource questions in surveys and interviews. Document analysis revealed a lack of actionable
reports and inadequate technology as barriers to achieving efficient bed turnaround. According to
the open-ended survey questions, ten of twenty respondents perceived the lack of resources as a
barrier to efficiently complete their bed turnround tasks. Out of ten who wrote lack of resources
as a concern or an area to improve, eight respondents stated that lack of staff was a barrier, one
respondent asking for a call bed board, and one respondent stated the bed sheet was too small for
most of the beds. In general, the respondents indicated lack of either nurse or EVS workers were
a barrier. One respondent provided a specific example of lack of staff by saying that rooms were
difficult to clean timely after 1 pm due to lack of EVS workers. Chapter 5 will discuss the
findings and results from Chapter 4. Research Question 3, and organizational practice
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recommendations in knowledge, motivation, and organizational influences are addressed in
Chapter 5.

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Chapter Five: Discussion and Recommendations
Chapter 5 begins with a discussion of the results and findings from the study. A research
approach was applied to explore the inefficient bed turnaround process using qualitative and
quantitative data collected from document analysis, surveys, and interviews. The following
section presents three recommendations for the hospital to improve the bed turnaround process
using the Clark and Estes (2008) Gap Analysis Framework. The final sections address the
limitations and delimitations of the study, followed by recommendations for future study.
Implications for equity are discussed next. Finally, Chapter 5 ends with the conclusion section.
Discussion of Results and Findings
The purpose of this section is to address key results and findings from the study. The
results and findings are grouped into assets and needs based on the research methodology
described in Chapter 3. The literature review from Chapter 2 is revisited in each subsection to
examine how the findings and results compare to this study. Although both assets and needs
subsections are discussed, the focus of this section on the identified needs. In particular, data
highlights address the following three deficit organizational influences: inclusion,
communication, and resources. The assets subsection also briefly touches on the findings and
results.
Discussion of Assets
According to the survey results and the interview findings, University Hospital’s patient
care staff possesses procedural knowledge of turning beds. The literature review from Chapter 2
contributed to the understanding of the complex nature of the bed turnaround process by
describing multiple process variations ranging from a 4-step process to a 9-step process (Brown
& Kros, 2010; Pellicone & Martocci, 2006; Tortorella et al., 2013). Unlike the literature review
describing the organizational knowledge of the bed turnaround process, the present study focuses
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on the bed turnaround procedural knowledge of two specific stakeholders: nurses, and EVS
workers. The study data support the finding that the patient care staff is knowledgeable in bed
turnaround procedures.
Document analysis finding and survey results revealed that the hospital provides a
cultural model of accountability, which is another asset. According to the literature review, the
bed turnaround process typically involves many departments, and these departments report to
different commands, contributing to additional complexity (Lovett et al., 2016; Winasti et al.,
2018). The University Hospital made numerous attempts between 2019 and 2020, as documented
in the meeting minutes to define stakeholder roles and assigned tasks, to create a culture of staff
accountability.
Discussion of Needs
The survey results indicated the patient care staff did not value their bed turnaround task
as a critical part of their job. According to Carillo (2006), the EVS workers’ morale improved at
Stony Brook University Hospital after senior leaders recognized EVS workers. The improved
morale inspired EVS workers to value their jobs and reduced turnaround time (Carillo, 2006).
Another study from the literature review found that over 80% of nurses were stressed when
demand was high and had low perceived control and received low rewards despite great effort
(Johnston et al., 2013). Pekrun’s (2006) control value theory could explain Johnston et al.
(2013)’s findings. The control value theory states that emotions, such as anxiety, can hinder
achievements and cause loss of interest and motivation when individuals do not have much
control over the tasks and outcomes and lack values (Pekrun, 2006). The survey results showed
62.5% of participants perceived the bed turnaround tasks as the most critical, and competing
priorities were barriers to completing the tasks. The interviews revealed that nurse interviewee 1
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considered admitting or giving a massage to patients were more critical tasks and would choose
them over the bed turnaround tasks.
The survey results indicated patient care staff had low self-efficacy in using the bed
turnaround software. According to the literature review, other research settings have also found
that staff members were not comfortable using bed turnaround software (Brown & Kros, 2010).
Another study attributed a low self-efficacy in software usage to inefficient bed turnaround
(Tortorella et al., 2013). This study results showed 62.5% of participants believed they could use
the bed turnaround software proficiently. Interviewee 2 did not know the name of the bed
turnaround software and stated that they received basic training only.
The document analysis findings and survey results suggested that the hospital did not
value staff perspectives, nor did they include staff in the decision-making process. Shared values
represent organizational culture (Schein, 2017), and effective organizations value employees’
input (Lewis, 2019). Health First hospitals achieved a faster bed turnaround when management
listened to staff (Blanchard & Rudin, 2016). Wentworth Douglas Hospital reduced bed
turnaround time by lowering the bed cleaning time with solutions proposed by EVS workers
during Lean Six Sigma events (Schierhorn, 2016). However, University Hospital did not accept
the EVS-specific proposal from the 2019 Lean event for lowering the bed cleaning time. The
examples from Wentworth Douglas Hospital and University Hospital suggested that hospitals
could achieve a better bed turnaround by including staff in the decision-making process. The
document analysis of 26 documents did not reveal if the management included patient care staff
perspectives in the decision-making process. According to the survey, approximately 57.5% of
participants perceived their supervisors were regularly soliciting ideas from them. Further, only
47.5% of participants indicated they witnessed staff feedback or suggestions reflected on the bed
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turnaround process. The interview findings were mixed, but interviewee 2 indicated they no
longer provided feedback to management as nothing changed when they made suggestions.
The document analysis findings and survey results indicated that University Hospital did
not communicate bed turnaround performance data and gaps with staff. The literature review
addressed different communication problems. Scholars found the bed turnaround management
to be a complex process involving many departments causing communication issues during the
transition between one department to the following department in the patient flow process
(Lovett et al., 2016; Winasti et al., 2018). When the Johns Hopkins Hospital streamlined
communication among stakeholder departments, the bed turnaround process was improved
(Kane et al., 2019). Some scholars addressed the communication between leadership and staff to
enhance patient flow (Carillo, 2006; Knarr & MacArther, 2012).
According to the document analysis, some nurse managers could not attend the daily
huddles or stay the entire time. The 2019 Lean event at University Hospital called for a real-
time bed turnaround dashboard to improve performance. Still, the hospital could not develop the
analytics due to the lack of access to the data according to the document analysis findings.
Several meetings, specifically the June 10, June 26, July 2, and July 9, 2020 meetings, discussed
and reviewed bed turnaround reports, but no meeting minutes stated if the reports were shared
with staff members. Half of the participants perceived that the organization did not
communicate bed turnaround performance metrics. Interview findings support the document
analysis that both interviewees did not see bed turnaround performance data provided by
leadership.
The document analysis findings and survey results indicated that University Hospital did
not have SMART bed turnaround goals and did not align incentives to goals. Research has
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demonstrated the link between setting goals and reducing bed turnaround. Stony Brook
University Hospital University achieved a faster turnaround time by setting goals (Carillo, 2006).
Health First hospitals also reduced bed turnaround time by setting clear goals (Blanchard &
Rudin, 2016). A 770-bed hospital improved bed turnaround when they included nursing
competencies in performance goals (Knarr & MacArthur, 2012). University Hospital
acknowledged that the hospital did not have a defined strategy and vision for patient flow and
recognized the importance of goal setting. University Hospital proposed to establish SMART
bed turnaround goals by January 2021. Almost half (49.5%) of participants knew the hospital’s
bed turnaround goals. Further, 55% of participants indicated their annual performance evaluation
did not reflect their bed turnaround performance. The interviewees did not know the
organization’s bed turnaround goals, and their evaluation did not include bed turnaround
performance.
Recommendations for Practice
The purpose of the study is to explore the University Hospital’s capacity to improve the
patient flow process by improving bed turnaround time to reduce delays in patient care.
Applying the Clark and Estes (2008) gap analysis framework as the conceptual framework, the
research examined knowledge, motivation, and organizational influences affecting the bed
turnaround process. From the results and findings in Chapter 4, three recommendations are
presented for University Hospital to improve the bed turnaround process. All three
recommendations are addressing organizational influences with one cultural model barrier and
two cultural setting barriers.

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Recommendation 1: Create an Inclusive Culture Valuing Nurses and EVS Workers
Perspectives in the Decision-Making Process
Document analysis did not reveal if the management included the staff members in the
decision-making process. Survey data showed that, 57.5% of participants perceived that their
management asked for suggestions. Also, only 47.5% of participants perceived the bed
turnaround process reflects their feedback. Interview findings were mixed with one interviewee
stating that their nursing manager listened. Still, another interviewee supported the survey and
document analysis that staff’s perspectives were not reflected in the bed turnaround processes.
The literature review provided examples of improving bed turnaround when hospitals
value staff perspectives and include them in the decision-making process. Knarr and MacArthur
(2012) observed a reduction in the patient flow time when decision-making included staff nurses
as they did not need to wait for nursing management to make decisions. Wentworth Douglas
Hospital cut bed turnaround time by reducing the cleaning time when implementing EVS
workers' proposals during the Lean Six Sigma events (Schierhorn, 2016). Based on the study
findings and literature review, the first recommendation is for University Hospital to expand the
daily huddles to solicit ideas from the staff and reflect the best practices on the bed turnaround
process.
According to the documents, University Hospital holds daily huddles. Instead of creating
new processes to solicit employees’ inputs, University Hospital should offer a cultural setting of
daily meetings that include management and staff members. Document analysis focused on the
daily huddles from the management participation perspective. It is recommended that University
Hospital dedicates one day out of the week as a forum for staff members to bring their own
ideas, best practices, and lessons learned without judgments. To galvanize staff participation in
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the forum, the hospital should delegate the meeting facilitation to staff members on a rotation
basis, so every staff member has a chance to lead the meeting. According to the document
analysis, senior management expected nurse leads to lead the huddles. Further, it is
recommended that the organization highlights any process updates reflecting staff suggestions to
encourage more input from staff.
Recommendation 2: Develop a Communication Plan That Frequently Shares Bed
Turnaround Goals with Nurses and EVS Workers
The document analysis revealed several communication issues starting with the nursing
managers not fully engaging in the daily huddles. They did not stay for the entire meeting or only
joined via phone rather than video conferencing. Next, the document analysis found that
University Hospital did not have a defined patient flow strategy and vision. According to the
survey, 50% of participants perceived the organization did not frequently communicate bed
turnaround performance metrics, and 49.5% knew the organization’s goals. The interview
participant data supported the lack of goals and the communication issue. The literature review
provided examples of how communication and sharing goals improved bed turnaround. Johns
Hopkins Hospital established a centralized command center to coordinate communication to
enhance the bed turnaround process (Kane et al., 2019). Stony Brook University Hospital
improved bed turnaround time by setting goals (Carillo, 2006), and Health First hospitals also
improved bed turnaround time by setting goals (Blanchard & Rudin, 2016). Based on the study
findings and literature review, the second recommendation is for University Hospital to develop
a bed turnaround communication plan with frequent communication from senior leaders to
nurses and EVS workers to remind them of the goals and provide performance metrics.
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Again, University Hospital can utilize the standing daily huddles to communicate unit-
specific bed turnaround performance metrics with staff members. The performance metrics
should deliver essential information quickly without cognitive overload. The unit-specific
performance metrics are more relevant to patient care staff than the overall hospital performance
metrics. It is recommended that direct supervisors share their own unit performance data with
their staff members to facilitate two-way communication between management and staff. Knarr
and MacArthur (2012) found open communication between management and staff was the key to
improved patient flow. In addition, senior leaders should communicate annual bed turnaround
goals and overall hospital performance with stakeholders once per month.
Recommendation 3: Provide More Resources to Nurses and EVS Workers to Support
Efficient Bed Turnaround
The document analysis revealed that the lack of actionable patient flow reports hindered
an efficient bed turnaround. Furthermore, the document analysis identified incoherent
communication platforms and inadequate technology as barriers for patient care staff in
improving bed turnaround. Half of the open-ended survey participants (n = 20) stated that
resource constraints were the barriers they faced, especially not enough staffing. The literature
review provided examples of resources that commonly contribute to the efficient bed turnaround
process. Walker et al. (2016) found actionable bed information could improve the bed allocation
process. Addressing the nursing shortage was one way to combat a slow bed turnaround from a
survey of 75 hospitals (Walker et al., 2016). Tortorella et al. (2013) promoted mobile devices to
improve the communication process to improve the bed turnaround time. Hopman et al. (2005)
observed the quality of cleaning improved with additional training for EVS workers. Based on
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the study findings and literature review, the third recommendation is for University Hospital to
develop actionable reports and provide robust technology to improve the bed turnaround process.
Staffing increase is an obvious way to address the patient care staff’s labor resource
issue. However, due to financial considerations, increasing staffing is not recommended. Instead,
the resource recommendation focuses on providing actionable reports and robust technology to
improve the bed turnaround process. With that said, University Hospital should expand the
capacity of the bed management system to overcome the lingering data limitation problem
identified in the October 10, 2019 meeting minutes. After the data are available, University
Hospital should support qualified data analysts to develop actionable reports based on the needs
identified by both management and patient care staff. The actionable reports will be displayed on
the nursing floors with electronic boards such as large TV monitors displaying bed status in real-
time. It is recommended that University Hospital purchases more Voalte phones to improve the
cleaning time as recommended in the documents. Further, University Hospital should install
Microsoft Teams on all computers in the nursing units so stakeholders can join the daily huddles
via video conferencing to engage more with other meeting participants.
Integrated Recommendations
Clark and Estes's (2008) gap analytics framework was used to examine knowledge,
motivation, and organizational influences affecting bed turnaround performance. Further, the gap
analysis revealed each influence as either an asset or a need. From the research findings, three
recommendations were presented to University Hospital to improve the bed turnaround
performance. McKinsey’s (1980) 7S framework examined how patient care staff at University
Hospital can work with other entities within the organization to integrate the recommendations
(Waterman et al., 1980).
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According to Singh (2013), organizations may apply McKinsey’s 7S model to analyze
their effectiveness via aligning seven internal aspects of an organization: strategy, structure,
systems, shared values, style, staff, and skills. It is recommended that University Hospital
designs a patient flow strategy with the first three elements of the McKinsey’s 7S model
(strategy, structure, and systems). The hospital should establish a structure with clearly outlined
roles and responsibilities based on best practices. For example, the hospital could implement a
streamlined workflow to remove wasted steps formulated and proposed during the 2019 Lean
event and further refined in the 2020 workflow proposal. Furthermore, the organization could
develop actionable reports and provides robust technology.
Moving on to the four soft elements of the McKinsey 7S model (style, staff, skills, and
shared value), the hospital leaders should practice transformational leadership style and create a
culture of no judgment when patient care staff provides their perspectives. University Hospital
can manage staffing more effectively by being flexible and responsive to individual staff
member’s needs. The organization should improve patient care staff’s skills related to bed
turnaround tasks. Lastly, University Hospital can bring all elements together under the shared
organizational value of excellent patient care, providing the proper care at the right time in the
right setting. To provide the proper care, right time, and the right setting, it is recommended that
University Hospital sets a bold goal to eliminate any delay in care due to the inefficient bed
turnaround process.
The research suggested that patient care staff possess the procedural knowledge to
conduct their bed turnaround tasks, but they are not motivated. Historically, University Hospital
has been a hierarchical organization with a traditional leadership style. Singh (2013) suggested
organizations move away from traditional leadership based on the hierarchical power structure to
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transformational leadership. By adopting transformational leadership, University Hospital would
motivate employees to perform more than their job tasks and strive for goals.
Recommendation 2 addressed communication. Singh (2013) used the term grapevine
communication to describe informal communication that could be destructive. Expanding the
existing daily huddles to improve communication requires less effort than creating another
communication venue. University Hospital’s daily huddles are conducted with in-person and
Microsoft Teams to engage face-to-face interactions among participants. Optimizing the daily
huddles will subdue grapevine communication.
To integrate these three recommendations, the University Hospital should use
Mckinsey’s 7S framework to analyze seven internal elements ready for the implementation. It is
recommended that University Hospital implements an efficient bed turnaround process by
utilizing the critical elements: strategy, structure, systems, style, staff, and skills. To achieve an
efficient bed turnaround process, the researcher recommends University Hospital incorporate the
following components: including staff in the decision-making process, valuing staff’s
perspectives, developing a communication plan to frequently share bed turnaround goals, and
providing more resources to patient care staff. Most importantly, University Hospital should
align the critical elements by the stated shared value of excellent patient care, which is the
hospital's mission statement. The mission statement is the superordinate shared value among all
stakeholders driving the change.
University Hospital should create leading indicators to monitor the progress of the bed
turnaround initiative. According to Kirkpatrick’s Four Levels of Training Evaluation, leading
indicators guide the organization to the goal by showing the path from the current state to the
desired state (Kirkpatrick & Kirkpatrick, 2016). It is recommended that University Hospital
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applies the leading indicator concepts from Kirkpatrick by creating internal leading indicators to
measure the performance of bed turnaround process. The internal leading indicators should be
drilled down to the task level by individual nursing floor and nursing unit level to identify the
causes of inefficiency. In addition to the internal leading indicators, University Hospital should
frequently monitor the external leading indicators, especially patient satisfaction survey results
regarding access of care, delay of care, and wait time. Just as important as monitoring the leading
indicators, University Hospital should share the leading indicator performance with all
stakeholders.
Limitations and Delimitations
Theofanidis and Fountouki (2018) stated a limitation as a potential weakness that is out
of the researcher’s control and delimitation as the limitation intentionally imposed by the
researcher. The study had several limitations, especially with the COVID-19 pandemic, as the
participants were patient-facing hospital workers. According to University Hospital, 1,109
healthcare workers from the health system were infected with COVID-19 since March 2019,
causing a high stress level among the study participants (internal report, April 13, 2021).
University Hospital had 1,200 nurses working in the beginning of the research. The research
design expected 117 sample participants for the survey and 10 to 12 volunteers for the interview.
However, only forty completed the survey and two volunteered for the interview. Therefore, the
research sample may not represent the entire population.
The small number of interviews, only two interviews, limited the quality of this research.
Since document analysis did not reveal information for knowledge influence, interviews could
provide a more in-depth understanding of patient care staff’s knowledge of effectively turning
beds. Also, document analysis was not used for the motivation influences because interviews
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could reveal nuanced understanding of patient care staff’s valuing the bed turnaround tasks and
self-efficacy of using the bed boarding management software.
Another limitation that was the interviewees did not remove their names before the Zoom
interview. This resulted in the Zoom transcripts included the interviewee names and
compromised the confidentiality of interviewees. Since the interviewees had their names
available in Zoom, I replaced the interviewee’s name with “interviewee” during the Zoom
transcript review before using coding by hand. Both interviewees joined the Zoom session using
the phone without a video camera on. Thus, the researcher did not see the interviewees.
Because I hold a leadership position at University Hospital, protecting the confidentiality
of the hospital was a limitation, even with the use of a pseudonym. I secured the research data in
a reasonable manner by the research community including password protected computer with
antivirus and VPN protections. However, the data protection will not eliminate potential security
issues, including hacking and other malicious attacks by cyber threats, which have increased
tremendously since the COVID-19 pandemic (Richardson & Mahle, 2020).
Another limitation of the research was that due to the COVID-19 pandemic, University
Hospital was no longer fully occupied (September Internal Census, 2020). Due to a lower patient
population, the desire to reduce bed turnaround existed, but the pressure to do so weakened
compared to the pre-COVID-19 pandemic pressure to turn beds fast was weakened. At the
beginning of the interviews, I instructed the interviewees to put themselves in the pre-COVID-19
pandemic time period when they answered the questions. The COVID-19 pandemic impacted
staffing, including absenteeism, paid time off, sick days, and leave of absence. The high-stress
level of patient care staff may worsen when they need to cover another staff’s shift due to
staffing issues (nursing manager from a hospital, personal communication, July 20, 2020). The
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high-stress level of patient care staff may have influenced the survey's response rate and
interview and influence the quality of responses. The interview time was shorter than a typical
one-hour interview to accommodate the patient care staff’s availability.
The study also faced several delimitations were associated with the COVID-19 pandemic.
An important delimitation was the length of the interview was no more than 30 minutes based on
the feedback from multiple nurses during the pilot interviews conducted during my Inquiry II
class. When I contacted several nurses in California and out of state, I learned that they did not
want to spend a long time for the interview and did not want to interview after work hours. Due
to the limited time and personal bias working at University Hospital during the COVID-19
pandemic, I offered the interviewees two weeks to review the Zoom transcripts and provide
feedback. I planned to review the interviewees' feedback and conduct a follow-up Zoom session
or email for clarification. However, feedback received after two weeks from the notice was not
considered. The timing of feedback was clearly explained during the interview and in the thank
you email. I explained the two weeks feedback timing clearly in the beginning of the interview
and in the thank you email. Neither interviewee provided feedback. The research design called
for 117 patient care staff as a sample size focusing on the two busiest nursing units that should
benefit from an efficient bed turnaround process. Out of the N = 117 proposed sample size, 100
are nurses. With 1,200 nurses, 100 nurses would not reach data saturation. However, due to the
COVID-19 pandemic, I decided to include only two nursing units.
Recommendations for Future Research
Future research should examine the effectiveness of collaboration among stakeholders in
person, which could not be done during the COVID-19 pandemic. Researchers would gain
personal experience to conduct in-person observation of nursing daily huddles, EVS daily
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huddles, and the nursing floor. Also, there are more ways to recruit research participants post-
pandemic, including the ability to post research posters in hospital elevators and break rooms. In-
person access to the hospital and patient care staff would likely result in data collection not
possible within the delimitations of this study.
Further research should examine the effectiveness of the new bed assignment workflow
after it is implemented. It would be worthwhile to explore why the proposed streamlined
workflow did not get implemented in 2019 and investigate which stakeholder group resisted
implementing the workflow: staff members, the management, or another stakeholder group.
Also, future research may benefit from comparing the 2019 proposed workflow to the 2020
proposed workflow and investigating how they differ or resemble may reduce duplication and
eliminate unnecessary steps. Future studies may expand the research to explore the mixed results
of the organization’s professional accountability culture, including the impact on the staff who
are held accountable to complete bed turnaround tasks but cannot make their own decisions
about how to best complete the tasks.
Future studies may expand the research to examine the apparent disconnect between the
daily patient flow huddle reports and patient care staff’s response to not receiving bed turnaround
performance data. The researcher recommends observing the daily huddle meeting to understand
what information is being shared with patient care staff and how they are sharing. The document
analysis stated that the organization is sharing data, but patient care staff responses were negative
from the survey and further validated by interviews. Finally, future research should expand the
number of hospitals to include multiple hospitals, especially a hospital system with numerous
hospitals. Expanding the scope of future research would enable exploration of how an individual
hospital's unique organizational culture affects the patient care staff's ability to turn beds.
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Implications for Equity
A slow bed turnaround matters not just for the hospital’s bottom line and reputation but
matters to the patients who are waiting for the beds. Going back to Chapter 1, a court case was
described in which a patient passed away due to a prolonged transfer to a hospital. The other
hospital was equipped to provide the care she needed to survive but the hospital did not have
available intensive care unit beds. The treating physician realized that the scheduled transfer was
canceled due to a lack of available beds, and they reached out to two other hospitals in the area to
see if they could offer life-saving services. However, the other hospitals did not have beds
available. In the court case, the patient died. Inefficient bed turnaround is among a myriad of
causes for the access to care issue. For example, CDC found that a lack of inpatient beds was one
of the main culprits for ambulance diversion (Burt & McCaig, 2006). From the cardiovascular
patient outcome research for Medicare patients, Shen and Hsia (2016) suggested that the lack of
hospital beds in high-minority hospitals could contribute to the higher ambulance diversion for
black patients that resulted in poor outcomes in comparison to white patients.
Access to care barriers disproportionally affect racial and ethnic minority groups based
on the structural racism in U.S. (Flores et al., 2016; Rhee et al., 2019; Snowden, 2012; Tung et
al., 2019). For example, City of Los Angeles, the second most populous city of United States
with four million people, lost 925 beds since 2017 due to hospital closures. The following four
intercity hospitals served predominately racial and ethnic minority groups in the city of Los
Angeles but have closed since 2017: 204-bed Olympia Medical Center (closed on March 31,
2020), 212-bed Silver Lake Medical Center (closed in 2019 and converted to a mental health
facility), 128-bed Pacific Alliance Medical Center, (closed on November 30, 2017), 381-bed St.
Vincent Medical Center (closed on January 24, 2020). The recent Los Angeles hospital closures
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could increase ambulance diversion as CDC found about a half of intercity hospitals divert
ambulances (Burt & McCaig, 2006). Hospital closures affect remaining hospitals as they fill in
that service gap (Lawrence et al., 2019). In Rhode Island, researchers observed an increased
rates of ED volume, length of stay, and leaving the hospital without being seen in neighboring
hospitals due to a hospital closure (Lawrence et al., 2019). Therefore, the recent hospital closures
in Los Angeles could impact populations who live in the underserved areas disproportionally
with potential for poor patient outcomes. Efficient bed turnaround alone cannot resolve access to
care barriers for marginalized groups, but it would make more beds available without adding
new beds to an already stressed healthcare system.
Conclusion
As hospitals face an unpredictable marketplace, enormous financial pressures, and
government regulations, a renewed focus on their mission of providing care will reinforce the
trust that patients place in them. One way to provide more care and save more lives is by
reducing delays in accessing care. This study found several organizational opportunities for
University Hospital to achieve efficient bed turnaround: inclusion, communication, and
resources. From the research and grounded by literature, the study recommends University
Hospital value staff perspectives and include staff in the decision-making process for bed
turnaround, develop a communication plan to share bed turnaround performance data and goals
with staff, and provide adequate resources to achieve efficient bed turnaround. Hospitals
implementing an efficient patient flow can improve patient outcomes and save lives through a
faster bed turnaround.

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149

Appendix A: Survey Protocol
Question Open
or
Closed
?
Level of
Measurement
(nominal,
ordinal,
interval,
ratio)
Response options (if
close-ended)
RQ Concept being
measured (from
emerging
conceptual
framework)
1. I can execute
bed turnaround
tasks
independently.
Closed Ordinal • Strongly
agree
• Agree
• Somewhat
agree
• Neither agree
nor disagree
• Somewhat
disagree
• Disagree
• Strongly
disagree
1 Procedural
Knowledge
2. I know how to
utilize resources
available to me
to complete bed
turnaround
tasks.
Closed Ordinal • Strongly
agree
• Agree
• Somewhat
agree
• Neither agree
nor disagree
• Somewhat
disagree
• Disagree
• Strongly
disagree
1 Procedural
Knowledge
3. Among my
responsibilities,
bed turnaround
tasks are the
most critical
tasks.
Closed Ordinal • Strongly
agree
• Agree
• Somewhat
agree
• Neither agree
nor disagree
• Somewhat
disagree
• Disagree
• Strongly
disagree
1 Motivation:
Value
150

4. I believe I am
able to
effectively use
the bed
management
computer
software
proficiently to
complete bed
turnaround
tasks.
Closed Ordinal • Strongly
agree
• Agree
• Somewhat
agree
• Neither agree
nor disagree
• Somewhat
disagree
• Disagree
• Strongly
disagree
1 Motivation:
Self-Efficacy
5. My supervisor
regularly
encourages me
to provide input
for the bed
turnaround
process.
Closed Ordinal • Strongly
agree
• Agree
• Somewhat
agree
• Neither agree
nor disagree
• Somewhat
disagree
• Disagree
• Strongly
disagree
2 Organization:
Cultural Model
Influence 1
6. I have seen
feedback or
suggestions
being adopted to
the bed
turnaround
policies and
procedures.
Closed Ordinal • Strongly
agree
• Agree
• Somewhat
agree
• Neither agree
nor disagree
• Somewhat
disagree
• Disagree
• Strongly
disagree
2 Organization:
Cultural Model
Influence 1
7. I can make my
own decision
about how to
best complete
bed turnaround
tasks.
Closed Ordinal • Strongly
agree
• Agree
• Somewhat
agree
• Neither agree
nor disagree
• Somewhat
disagree
2 Organization:
Cultural Model
Influence 2
151

• Disagree
• Strongly
disagree
8. I am held
accountable by
my supervisor
for bed
turnaround
tasks.
Closed Ordinal • Strongly
agree
• Agree
• Somewhat
agree
• Neither agree
nor disagree
• Somewhat
disagree
• Disagree
• Strongly
disagree
2 Organization:
Cultural Model
Influence 2
9. The organization
frequently
communicates
bed turnaround
performance
data with staff.
Closed Ordinal • Strongly
agree
• Agree
• Somewhat
agree
• Neither agree
nor disagree
• Somewhat
disagree
• Disagree
• Strongly
disagree
2 Organization:
Cultural Setting
Influence 1
10. The organization
has clear goals
for reducing bed
turnaround.
Closed Ordinal • Strongly
agree
• Agree
• Somewhat
agree
• Neither agree
nor disagree
• Somewhat
disagree
• Disagree
• Strongly
disagree
2 Organization:
Cultural Setting
Influence 2
11. The
organization’s
progress and
metrics related
to achieving its
bed turnaround
Closed Ordinal • Strongly
agree
• Agree
• Somewhat
agree
2 Organization:
Cultural Setting
Influence 2
152

are shared with
me.
• Neither agree
nor disagree
• Somewhat
disagree
• Disagree
• Strongly
disagree
12. My performance
evaluation
includes bed
turnaround
performance.
Closed Ordinal • Strongly
agree
• Agree
• Somewhat
agree
• Neither agree
nor disagree
• Somewhat
disagree
• Disagree
• Strongly
disagree
2 Organization:
Cultural Setting
Influence 2
13. What is your
gender?
Closed Nominal • Male
• Female
• Prefer to
describe:
• Prefer not to
answer
Demographic
14. What is your
race?
Closed Nominal • White
• Black or
African
American
• American
Indian and
Alaska
Native
• Asian
• Native
Hawaiian
and Other
Pacific
Islander
• Hispanic or
Latino or
Latinx
• Some other
race
Demographic
153

15. Years of
experience in your
role (overall
experience in your
role, not specific to
the current
position).
Closed Ordinal • <1 year
• 1-3 years
• 4-5 years
• 6-10 years
• >11 years
Demographic
16. What are the
obstacles, if any,
you face in
completing your bed
turnaround tasks
efficiently?
Open 3 Organization
17. What
recommendations, if
any, do you have to
improve bed
turnaround tasks?
Open 3 Organization

154

Appendix B: Spanish Language Survey
Pregunta Opciones de respuesta (si son cerradas)
1. Puedo ejecutar tareas de cambio de
cama de forma independiente.
• Totalmente de acuerdo
• De acuerdo
• Parcialmente de acuerdo
• Ni de acuerdo ni en desacuerdo
• Algo en desacuerdo
• No estoy de acuerdo
• Muy en desacuerdo
2. Sé cómo utilizar los recursos
disponibles para completar las tareas de
cambio de cama.
• Totalmente de acuerdo
• De acuerdo
• Parcialmente de acuerdo
• Ni de acuerdo ni en desacuerdo
• Algo en desacuerdo
• No estoy de acuerdo
• Muy en desacuerdo
3. Entre mis responsabilidades, las tareas
de cambio de cama son las más críticas.
• Totalmente de acuerdo
• De acuerdo
• Parcialmente de acuerdo
• Ni de acuerdo ni en desacuerdo
• Algo en desacuerdo
• No estoy de acuerdo
• Muy en desacuerdo
4. Creo que puedo utilizar eficazmente el
software informático de gestión de
camas de forma competente para
completar las tareas de cambio de
cama.
• Totalmente de acuerdo
• De acuerdo
• Parcialmente de acuerdo
• Ni de acuerdo ni en desacuerdo
• Algo en desacuerdo
• No estoy de acuerdo
• Muy en desacuerdo
5. Mi supervisor me anima regularmente a
proporcionar información para el
proceso de cambio de cama.
• Totalmente de acuerdo
• De acuerdo
• Parcialmente de acuerdo
• Ni de acuerdo ni en desacuerdo
• Algo en desacuerdo
• No estoy de acuerdo
• Muy en desacuerdo
6. He visto que se han adoptado
comentarios o sugerencias para el
• Totalmente de acuerdo
• De acuerdo
• Parcialmente de acuerdo
155

proceso de procedimientos y políticas
de cambio de cama.
• Ni de acuerdo ni en desacuerdo
• Algo en desacuerdo
• No estoy de acuerdo
• Muy en desacuerdo
7. Puedo tomar mi propia decisión sobre
cómo completar mejor las tareas de
cambio de cama.
• Totalmente de acuerdo
• De acuerdo
• Parcialmente de acuerdo
• Ni de acuerdo ni en desacuerdo
• Algo en desacuerdo
• No estoy de acuerdo
• Muy en desacuerdo
8. Mi supervisor me hace responsable de
las tareas de cambio de cama.
• Totalmente de acuerdo
• De acuerdo
• Parcialmente de acuerdo
• Ni de acuerdo ni en desacuerdo
• Algo en desacuerdo
• No estoy de acuerdo
• Muy en desacuerdo
9. La organización comunica con
frecuencia al personal los datos sobre el
rendimiento del cambio de cama.
• Totalmente de acuerdo
• De acuerdo
• Parcialmente de acuerdo
• Ni de acuerdo ni en desacuerdo
• Algo en desacuerdo
• No estoy de acuerdo
• Muy en desacuerdo
10. Conozco los objetivos de la
organización para el cambio de cama.
• Totalmente de acuerdo
• De acuerdo
• Parcialmente de acuerdo
• Ni de acuerdo ni en desacuerdo
• Algo en desacuerdo
• No estoy de acuerdo
• Muy en desacuerdo
11. Comparto conmigo el progreso y las
métricas de la organización
relacionados con el logro de su cambio
de cama.
• Totalmente de acuerdo
• De acuerdo
• Parcialmente de acuerdo
• Ni de acuerdo ni en desacuerdo
• Algo en desacuerdo
• No estoy de acuerdo
• Muy en desacuerdo
12. Mi evaluación de desempeño incluye el
desempeño de cambio de cama.
• Totalmente de acuerdo
• De acuerdo
• Parcialmente de acuerdo
• Ni de acuerdo ni en desacuerdo
156

• Algo en desacuerdo
• No estoy de acuerdo
• Muy en desacuerdo
13. Cuál es tu género? • Masculino
• Hembra
• No binaria / tercer género
• Prefiero no responder
14. Cuál es tu raza? • Blanca
• Negra o afroamericana
• Indígena americano y nativo de Alaska
• Asiática
• Nativo de Hawái y otras islas del Pacífico
• Hispana o latina o latinx
• Alguna otra raza
15. Años de experiencia en su puesto
(experiencia general en su puesto, no
específica del puesto actual).
• <1 año
• 1-3 años
• 4-5 años
• 6-10 años
• >11 años
16. Cuáles son los obstáculos, si los hay,
que enfrenta para completar las tareas
de cambio de cama de manera
eficiente?

157

17. Qué recomendaciones, si las hay, tiene
para mejorar las tareas de cambio de
cama?

Appendix C: Interview Protocol
Introduction to the Interview:
Good morning or afternoon. My name is Julius Hahn. I want to thank you for taking time
out of your busy schedule to meet with me and agreeing to participate in my study by answering
some questions. This interview will take approximately 30 minutes. I am currently enrolled in a
doctoral program at USC and am conducting a study on efficient bed turnaround. The purpose of
this interview is to explore and examine the bed turnaround process at your hospital. I am not
here as an employee of this organization or to make a professional assessment or judgment of
your performance as a leader. I would like to emphasize that today I am only here as a researcher
collecting data for my study. The information you share with me will be placed into my study as
part of the data collection. Your name will not be disclosed to anyone or anywhere outside the
scope of this study and will be known only to me specifically for this data collection. While I
may use a direct quote from you in my research, I will not provide your name specifically and
will remove any potentially identifying information. I will gladly provide you with a copy of my
transcript upon request.
158

Your participation is entirely voluntary. You may skip any questions you don’t want to
answer and you may stop this interview at any time. I will record the interview to help me
capture all of your responses accurately and completely. This recording will not be shared with
anyone outside the scope of this project. If you would like me to stop recording at any point, I
will do so. The recording will be transferred to my password-protected files on a cloud file
storage account. I will be using a third party such as otter.ai to transcribe the recording and all
files will be returned to me upon finalization of the transcription. The recording and all other
data will then be destroyed three years after this research is published.
With that, do you have any questions about the study before we get started? If not, please
review and keep the information sheet. I would like your permission to begin the interview. May
I also have your permission to record this conversation? Thank you.

Interview
Questions
Potential Probes RQ Key Concept
Addressed
Q Type
(Patton)
1. Can you
please walk me
through your
process for
turning beds?
What steps did you take to
complete the bed assignment
quickly?

What resources are available to
you to complete bed turnaround
tasks?

What kind of bed turnarounds
would require more than one
person to complete and how do
you go about getting that
assistance?

What feedback was helpful for
you to improve your bed
turnaround tasks?

Who is assigning your work
assignment? How do you notify
RQ1 Knowledge
Influence
Experience
#1
159

the other team member after
you complete your assignment?
How do you transition to the
next work assignment?

Tell me about time when you
observed/ watched another
patient care staff perform
turnaround beds? What did you
learn when you observe
someone performing bed
turnaround? This could be
either positive experience (that
you learn from watching others)
or negative experience (that you
saw another person performing
bed turnaround in incorrect
way)

On a typical day, how many
beds do you turnaround?

What day is the busiest day,
and what day is the slowest day
of the week?

What training did you receive?
How does job aid, training, if
any, help you do your job?
How do you learn new skills at
work? How you preferred to
learn?
2. Compared
to other duties
you perform
during your
shift, how
important are
the bed
turnaround
tasks to you?
Tell me about time, if any, a
patient or family member
thanked for turning beds?

RQ1 Motivation
Influence: Value

Feeling #1
3. What
emotion comes
to mind when
you receive a
new bed
How do you feel alert, happy,
nervous, anxious, stressed,
angry, energetic, sad, frustrated,
etc.?

RQ1 Motivation
Influence: Value
Feeling #2
160

turnaround
assignment?

Have you ever, if any,
consciously or unconsciously
delay the bed turnaround tasks?
4. How
comfortable
are you with
using the bed
management
software
application?

What training did you receive?

What would help you to
improve your technical skills
using the bed turnaround
software application?

Do you consistently update the
bed management software
when you are performing the
bed turnaround tasks?

What challenges have you
faced using the software
application?

how much time are you given
to complete a bed turnaround
task in the computer
application?
RQ1 Motivation
Influence: Self-
Efficacy
Feeling #3
5. In what
ways, if any,
does your
organization
incorporate
your input in
the bed
turnaround
process?

What channels are available for
you to provide feedback to the
organization regarding the bed
turnaround process?

Tell me, if any, your feedback
was adopted to the bed
turnaround process? If so, tell
me, what was it?

Tell me if any of your team
member’s feedback was
adopted to the bed turnaround
process?

When you have concerns or
new ideas, how do you let your
management know?
RQ2 Organization Model
Influence1: The
hospital needs to
value staff
perspectives on
policies, processes,
and procedures, and
include staff in the
decision-making
process.
Opinion #1
6. How much
decision
making, if any,
is delegated to
you regarding
What is your sense of
accountability for bed
turnaround tasks?

RQ2 Organization Model
Influence2: The
hospital needs to
have a culture of
professional
Opinion #2
161

how you
conduct the
bed turnaround
process?

What tasks if any, should be
delegated to you?

How much flexibility do you
have when you perform job
tasks? How do you prioritize
your work? How do you
determine what to do and when
to do?

What decisions you need to get
from your supervisor?
accountability
regarding bed
turnaround
responsibilities.
7. Let’s turn
our attention to
goals and
tracking bed
turnaround
performance…
Tell me your
department’s
internal target
for your bed
turnaround
tasks?

Tell me if you heard bed
turnaround goals from other
hospitals you worked in the
past.

RQ2 Organizational
Setting Influence2:
The hospital needs
to prioritize
improving the bed
turnaround process
by setting clear
goals and aligning
incentives
Opinion #4
8. How do you
get updates
from your
organization, if
any, on bed
turnaround
performance
and/or
analytics?
How often does this occur?

Do you find the content of these
meetings relevant? If not, why?

RQ2 Organizational
Setting Influence1:
The hospital needs
to effectively
communicate bed
turnaround
performance data
and gaps with staff.
Opinion #3
9. What
incentives, if
any, does your
organization
offer to turn
beds
efficiently?
In addition to your own
personal incentives (e.g.,
feeling proud), what incentives
would entice you to do your
assigned tasks more quickly?

If your organization does not
offer incentives, what
incentives do you think will
motivate you to more
effectively complete your bed
turnaround tasks?
RQ2 Organizational
Setting Influence2:
The hospital needs
to prioritize
improving the bed
turnaround process
by setting clear
goals and aligning
incentives

Opinion #5
162

Does your organization
includes bed turnaround
performance in your
performance review?
10. What do
you think
would help
you or the
organization to
turn beds more
efficiently, if
anything?
Tell me more about your
recommendations. How will
they help you do your job?
RQ3 Recommendations
from staff
Opinion #6

The conclusion to the Interview
Thank you very much for your time. During the COVID-19 pandemic, taking the time for
this interview must be difficult. Do you have any comments or questions before closing this
interview?

163

Appendix D: Document Analysis Protocol
The researcher does not have firsthand knowledge of the documents but expects
PowerPoint Presentations and meeting minutes for regularly held hospital operation meetings
will be available.
The documents that will be analyzed for this study include the following:
1. PowerPoint presentation
2. Meeting minutes

164

Appendix E: Information Sheet for Exempt Research

STUDY TITLE: Facilitating Hospital Patient Flow: An Exploratory Analysis on Reducing
Patient Bed Turnaround Time
PRINCIPAL INVESTIGATOR: Choong-Yop Julius Hahn
FACULTY ADVISOR: Jennifer Phillips, D.L.S.
You are invited to participate in a research study. Your participation is voluntary. This
document explains information about this study. You should ask questions about anything that is
unclear to you.
PURPOSE
The purpose of this study is to explore and understand University Hospital’s efficient
patient flow process using the gap analysis model to examine influences that impact the bed
turnaround time. We hope to learn the bed turnaround process and identify organizational
practices in the areas of knowledge, motivation, and organizational influences. You are invited as
a possible participant because your roles as patient care staff with responsibilities include bed
165

turnaround. The researcher has received approval from the COO of your organization to conduct
this study.
PARTICIPANT INVOLVEMENT
Participants will be asked to fill out a survey that will be sent to their work email address.
Participants can complete the survey via computers, tablets, or smartphones. Participants have
three weeks to complete the survey. Also, the research seeks 12 volunteers for a 30-minute
interview for further discovery of the bed turnaround process. Zoom will be used for interviews,
and participants can decline to be recorded and refuse to answer any questions.
If you decide to take part, you will be asked to complete the survey and volunteer for the
interview listed below:
Survey
• A survey link will be sent to your work email address.
• You can complete the 17-question survey within three (3) weeks from the receiving date.
• The survey can be completed using a computer, tablet, or mobile phone.
Interview
• A 30-minute interview will be conducted via Zoom.
• The recording will only be turned on with your explicit permission.
• Your identity will not be shared with the organization.
• You have an option to mask your name in the Zoom session to further protect your identity.
• You do not have to turn on your video as an option to preserve confidentiality.
• If you do not mask your name, the researcher will replace your name with “Interviewee” in
the Zoom transcript.
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• The Zoom transcript will be shared with you within one week after the interview for
feedback.
• You have two weeks to provide feedback to be reflected in the study.
• It is important to note that you can stop the interview at any time or refuse to answer
specific questions.
PAYMENT/COMPENSATION FOR INTERVIEW PARTICIPATION
You will receive a $10 gift card for your time for the interview. You do not have to
answer all of the questions in order to receive the card. The card will be given upon completion
of the interview.
CONFIDENTIALITY
The members of the research team and the University of Southern California Institutional
Review Board (IRB) may access the data. The IRB reviews and monitors research studies to
protect the rights and welfare of research subjects. When the results of the research are published
or discussed in conferences, no identifiable information will be used. Information will be housed
in the researcher’s personal computer, which is secured in their home office. The computer is
protected with a login credential. Data files and analysis files will be password protected to add
additional security.
The survey and interview data will be destroyed after the study is published. You have
the right to review/edit the Zoom transcript. You will receive the interview transcript via email
and will have two weeks to respond if you have any feedback. The Zoom recording will be
erased after the study is published. Your personal identities will be disguised via declaring your
name as “Interviewee” when you log in to the Zoom session. If not, the researcher will replace
your name with “Interviewee” in the Zoom transcript.
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INVESTIGATOR CONTACT INFORMATION
If you have any questions about this study, please contact Julius Hahn at
choongyh@usc.edu or Jennifer Phillips, D.L.S. at jlp62386@usc.edu.
IRB CONTACT INFORMATION
If you have any questions about your rights as a research participant, please contact the
University of Southern California Institutional Review Board at (323) 442-0114 or email
irb@usc.edu.

Abstract (if available)

Abstract Hospitals are striving to achieve efficient bed turnaround to increase revenues, improve patient satisfaction, and most importantly, improve patient outcomes. The bed turnaround process is deceptively simple on the surface but is a complex process requiring collaboration among multiple stakeholders. The purpose of this study was to explore the knowledge, motivation, and organizational influences affecting bed turnaround at a hospital located in the western U.S. The hospital attempted to improve its bed turnaround time without much success. The study conducted document analysis of 26 patient flow related documents created between March 2019–October 2020. In addition, the study conducted surveys and interviews with patient care staff represented by a sample of nurses and environmental services workers. The results and findings indicated that the patient care staff possessed the procedural knowledge to perform their job functions, and the hospital provided a cultural model of accountability. However, the patient care staff did not perceive that the hospital valued their perspectives or included them in the decision-making process. Also, the patient care staff did not perceive that the hospital communicated bed turnaround goals or provided enough resources to support efficient bed turnaround. Further research is needed to evaluate the effectiveness of the new bed assignment workflow and the impact of relentless communication sharing bed turnaround performance and goals with staff members.

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Asset Metadata

Creator Hahn, Choong-Yop Julius (author)

Core Title Facilitating hospital patient flow: an exploratory analysis on reducing patient bed turnaround time

School Rossier School of Education

Degree Doctor of Education

Degree Program Organizational Change and Leadership (On Line)

Degree Conferral Date 2021-08

Publication Date 08/04/2021

Defense Date 07/13/2021

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag bed turnaround,OAI-PMH Harvest,patient flow

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Phillips, Jennifer (committee chair), Murphy, Don (committee member), Ott, Maria (committee member)

Creator Email Choongyh@usc.edu,LADozenCare@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC15676564

Unique identifier UC15676564

Legacy Identifier etd-HahnChoong-9992

Document Type Dissertation

Format application/pdf (imt)

Rights Hahn, Choong-Yop Julius

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given.

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu