Chief Executive Officer Characteristics In Relationship With Patient Experience Scores Of Hospital Value Based Purchasing

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Chief Executive O=cer Characteristics In Relationship With Chief Executive O=cer Characteristics In Relationship With

Patient Experience Scores Of Hospital Value Based Purchasing Patient Experience Scores Of Hospital Value Based Purchasing

Christina Galstian

University of Alabama at Birmingham

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CHIEF EXECUTIVE OFFICER CHARACTERISTICS IN RELATIONSHIP WITH

PATIENT EXPERIENCE SCORES OF HOSPITAL VALUE BASED PURCHASING

CHRISTINA GALSTIAN

LARRY HEARLD, COMMITTEE CHAIR

NANCY BORKOWSKI

STEPHEN O’CONNOR

RICHARD JACKSON

A DISSERTATION

Submitted to the graduate faculty of The University of Alabama at Birmingham,

in partial fulfillment of the requirements for the degree of

Doctor of Science in Health Services Administration

BIRMINGHAM, ALABAMA

201

Christina Galstian

2015

iii

CHIEF EXECUTIVE OFFICER CHARACTERISTICS IN RELATIONSHIP WITH

PATIENT EXPERIENCE SCORES OF HOSPITAL VALUE BASED PURCHASING

CHRISTINA GALSTIAN

ABSTRACT

Patient experience scores have become important indicators of value in

healthcare. This study was the first of its kind to examine CEO gender, tenure, and

education in relationship with patient experience scores and all other value based

purchasing scores, such as outcome, efficiency, clinical process of care, and total

performance scores. This study suggested that hospitals with certain types of CEOs may

perform better with respect to patient experiences and other value based purchasing

scores.

The primary analysis of this study was to examine the impact of hospital CEO

characteristics (tenure, education, gender) on patient experience scores. A supplementary

analysis examined CEO characteristics (tenure, gender, education) in relationship with all

other value based purchasing scores (outcome, efficiency, clinical process of care, and

total performance). The study controlled for a broad spectrum of organizational and

market characteristics.

Univariate, bivariate, and multivariate analysis techniques were used for the

purpose of statistical analysis. The OLS (ordinary least square) block modeling strategy

examined both primary and supplementary relationships of dependent and independent

variables. The most robust finding of this study was related to gender. Specifically, those

hospitals led by female CEOs were associated with significantly higher patient

experience scores and other value based purchasing scores.

Findings from this study open new doors for future research of CEO attributes in

the healthcare industry and will provide useful insights for the recruitment and selection

processes used by hospital boards and other executive recruiters that are interested in

hiring CEOs who will improve patient experience and other value based purchasing

scores. Therefore, the study provides important information for identifying ways to

improve patient experience.

Keywords: CEO characteristics, patient experience scores, value based purchasing,

HCAHPS, healthcare

TABLE OF CONTENTS

Page

ABSTRACT .................................................................................................................. iii

LIST OF TABLES ...................................................................................................... viii

CHAPTER

1 INTRODUCTION ..................................................................................................... 1

Study Purpose ......................................................................................................... 2

Study Significance .................................................................................................. 5

Dissertation Outline ................................................................................................ 5

2 LITERATURE REVIEW AND THEORETICAL FRAMEWORK ............................ 7

Theoretical Framework ............................................................................................ 12

Chief Executive Officer Characteristics ................................................................... 15

CEO Education ..................................................................................................... 15

Terminal vs. Non-Terminal Degrees ..................................................................... 17

Clinical Terminal vs. Non-Terminal Degrees ........................................................ 18

CEO Tenure .......................................................................................................... 20

CEO Gender (Female vs. Male CEOs) .................................................................. 21

3 METHODOLOGY .................................................................................................. 23

Research Design ...................................................................................................... 23

Study Population...................................................................................................... 23

Data Sources ......................................................................................................... 23

Measures ................................................................................................................. 25

Dependent Variable .............................................................................................. 25

Independent Variables........................................................................................... 26

Gender ............................................................................................................ 26

Tenure............................................................................................................. 27

Education ........................................................................................................ 28

Control Variables .................................................................................................. 28

Hospital Characteristics ................................................................................... 28

Market Characteristics..................................................................................... 30

Merging Data ........................................................................................................ 32

Statistical Analysis................................................................................................... 34

Page

4 ANALYSIS AND PRESENTATION OF FINDINGS ............................................. 36

Descriptive Results .................................................................................................. 39

Bivariate Results ...................................................................................................... 39

Correlation Analysis for Continuous Variables ..................................................... 39

Patient Experience Domain Scores ........................................................................ 39

Clinical Process of Care Domain Scores ............................................................... 39

Efficiency Domain Scores ..................................................................................... 40

Outcome Domain Scores....................................................................................... 40

Total Performance Scores ..................................................................................... 40

One-way ANOVA ................................................................................................... 42

Testing for Differences in the Means between Multiple Groups ............................ 42

Gender ............................................................................................................ 42

Clinical Terminal Degree ................................................................................ 43

Terminal Degree ............................................................................................. 44

Multivariate Results ................................................................................................. 45

Regression Analysis .............................................................................................. 45

Hierarchical Multiple Regression ............................................................................. 45

Patient Experience of Care Domain Scores...................................................... 46

Clinical Process of Care Domain Scores .......................................................... 49

Outcome Domain Scores ................................................................................. 50

Efficiency Domain Scores ............................................................................... 52

Total Performance Scores................................................................................ 54

5 DISCUSSION AND CONCLUSIONS .................................................................... 57

CEO Education ..................................................................................................... 57

CEO Tenure .......................................................................................................... 60

CEO Gender ......................................................................................................... 60

Control Variables .................................................................................................. 61

Limitations and Opportunities for Future Research............ ....................................... 64

Study Implications............ ....................................................................................... 65

Implications for Future Research........................................................................... 65

Implications for Practice ....................................................................................... 66

Conclusion............ ................................................................................................... 67

REFERENCES .............................................................................................................. 68

APPENDICES ............................................................................................................... 89

A INSTITUTIONAL REVIEW BOARD APPROVAL ......................................... 89

B CMS PE DATA SAMPLE PAGE ..................................................................... 91

C CHA MEMBERSHIP DIRECTORY ................................................................. 93

D HOSPITAL WEBSITE CEO PAGE .................................................................. 95

E LINKEDIN CEO PROFILE PAGE ................................................................... 97

F CA CENSUS DATA ......................................................................................... 99

vii

Page

G CENSUS DATA: MARGIN OF ERROR ........................................................ 101

H TERMINAL DEGREE LIST, UNITED STATES ........................................... 103

I CLINICAL TERMIAL DEGREE LIST ............................................................ 106

J HCAHPS SURVEY ......................................................................................... 108

viii

LIST OF TABLES

Page

1 HCAHPS Survey Questions ........................................................................................ 9

2 Study Variables, Measures, and Data Sources ........................................................... 33

3 Hospital and CEO Categorical Characteristics (N=294) ........................................... 37

4 Mean and Standard Deviations for Continuous Variables .......................................... 38

5 Correlation Matrix Continuous Variables .................................................................. 41

6 ANOVA. Dependent Variable Differences between Male and Female Groups .......... 43

7 ANOVA. Dependent Variable Differences between Clinical Terminal (CT)

And Non Clinical Terminal (NCT)........................................................................... 44

8 ANOVA. Dependent Variable Differences between Terminal (T) and Non-

Terminal (NT) ......................................................................................................... 45

9 Ordinary Least Squares (OLS) Regression Models for CEO Characteristics

(independent variable) and Patient Experience (dependent variable) ........................ 48

10 Ordinary Least Squares (OLS) Regression Models for CEO Characteristics

(independent variable) and Clinical Process of Care (dependent variable) ................ 50

11 Ordinary Least Squares (OLS) Regression Models for CEO Characteristics

(independent variable) and Outcome (dependent variable) ....................................... 52

12 Ordinary Least Squares (OLS) Regression Models for CEO Characteristics

(independent variable) and Efficiency (dependent variable) ..................................... 54

13 Ordinary Least Squares (OLS) Regression Models for CEO Characteristics

(independent variable) and Total Performance (dependent variable) ......................... 56

Chapter 1

Introduction

Patient experience has emerged in recent years as a centerpiece of efforts to

improve the U.S. healthcare system. For example, Centers for Medicare and Medicaid

Services (CMS) incentivizes or penalizes hospitals based on patient experiences during

an inpatient stay. Patient experience is derived from measuring patients' perceptions of

their hospital experiences (communication with nurses and doctors, the responsiveness of

hospital staff, the cleanliness and quietness of the hospital environment, pain

management, communication about medicines, discharge information, overall rating of

hospital, and whether or not they would recommend the hospital) (CMS, 2014).

Measuring patient experience is not a new concept in healthcare. In 2002, the

Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey,

a tool to measure patient experiences during a hospital stay was approved as part of the

Deficit Reduction Act of 2005. As a part of the Act, the final IPPS rule stipulated that

IPPS hospitals must continuously collect and submit HCAHPS data to CMS in order to

receive their full IPPS annual payment update. Those IPPS hospitals that fail to publicly

report the required quality measures, which include the HCAHPS survey, may receive an

annual payment update that is reduced by 2.0 percentage points (www.hcahpsonline.org,

2014).

This initiative was a combined effort between Agency for Healthcare Quality and

Research (AHRQ) and CMS (ahrq.gov, 2014). The purpose of HCAHPS reporting was to

promote accountability, increase efforts to improve patient centeredness, promote care

coordination, and improve patient experiences during hospital stays (IOM, 2006).

The Patient Protection and Affordable Care Act of 2010, also known as the

Affordable Care Act (ACA), (P.L. 111-148) includes HCAHPS among the measures to

be used to calculate value based incentive payments in the Hospital Value Based

Purchasing program, beginning with discharges in October 2012

(www.hcahpsonline.org). HCAHPS became part of a broader CMS strategy to promote

value based purchasing (VBP) (PPACA; Section 1003). The purpose of value based

purchasing is to offer the highest quality per each dollar spent, enhance patient

experience outcomes, and address concerns of the solvency of the U.S. healthcare system

(Porter, 2010).

Consequently, healthcare organizations are being asked to do more with less

reimbursement, less capital, and fewer resources (O'Connor, Trinh, & Shewchuk, 1995).

Value based purchasing is comprised of four key elements from 2013 through 2015 (1)

clinical process of care, (2) patient experience, (3) outcome, and (4) efficiency. Those

four elements combined create a total performance score (TPS) used by CMS to calculate

hospital incentives and penalties (cms.gov). Patient experience is weighted as 30% of the

total performance score calculation, and therefore, carries significant financial

implications for hospitals. Consequently, value based purchasing has been forcing

hospitals to reconsider their care delivery systems and leadership practices to identify

ways to provide higher value care at a lower cost (Holzer & Minder, 2011).

Study Purpose

Providing a positive patient experience is each individual’s responsibility within a

healthcare organization. CEOs, however, play an especially important role, as they are in

charge of setting the organizational vision and strategic goals, including those related to

providing a positive patient environment and patient experience.

Effective hospital CEOs are equipped with the knowledge and skills needed to

motivate and lead the process improvement initiatives after thorough organizational (e.g.,

needs, resources, cultures, skills) and environmental analysis. Their ability to execute

timely and effective change can be the driving force behind the success of an

organization (Kaufman, 2013). Given the CEO’s significant role in improving

performance, an important research question that guided this study was:

Are CEOs characteristics associated with patient experience outcomes?

Because patient experience outcomes have become important indicators of

organizational performance and can differentiate hospitals in the marketplace, efforts to

promote positive patient experience require strategic leadership, timely and effective

response to regulatory changes, and a commitment to quality and transparency (Merlino

& Raman, 2013). Patient centered leadership in the context of this study has been defined

as leadership that rests on values of patient centered care; is respectful of and responsive

to patient preferences, needs, and values; and ensures that patient values guide all clinical

decisions (IOM, 2001; Thibault, 2013). When organizations satisfy a consumer’s needs

and preferences, they are actually delivering value and/or increased perceptions of value

(Burden, 1998), which in healthcare translates into improved patient experience scores.

CMS defines patient experience as a measure of patient centeredness, delivering

value to the patient by meeting their needs and expectations (CMS, 2014). Patient

experience has also been defined as the sum of all interactions, shaped by an

organization’s culture, that influence patient perceptions across the continuum of care

(The Beryl Institute, 2014). These perceptions are recognized, understood, and

remembered by the patients based on their individual experiences. Therefore, patient

experience, in the context of this study, was defined as patient perceptions of the care

delivery experience by a hospital during an inpatient stay. .

It was the premise of this study that CEOs can provide value to their organizations

by practicing patient centered leadership and inspiring patient focused innovative

behaviors that can stimulate positive patient experiences. This includes their role as

“boundary spanners” in interpreting and defining expectations of various stakeholder

groups (e.g., government, payers, consumers) for the purpose of implementing strategies

and motivating and empowering staff to promote patient-friendly environments.

CEO attributes, such as their education, gender, and tenure, may reflect

differential skill and abilities to take on these roles and engage in these behaviors, and

thus may play an important role in cultivating a patient centered environment. For

example, MD CEOs, through extensive clinical training and education, have been taught

to practice medicine by putting their patients first, and therefore may facilitate better

patient experiences.

Despite the potentially important role of the hospital CEO, little published

research has examined whether CEO characteristics are associated with patient outcomes.

This is especially the case with patient experiences due to its relatively recent emergence

as a priority for hospitals. Therefore, the primary purpose of this study was to empirically

examine whether CEO characteristics such as gender, tenure, and education were

associated with reported patient experience outcomes.

Other value based purchasing domains (e.g., efficiency, clinical outcomes) have

been recently introduced as mandatory reporting by CMS; however, research establishing

the validity of these domain scores as hospital performance metrics is still in its infancy.

Therefore, this study included these other domains as outcomes in an exploratory,

supplementary analysis to provide preliminary evidence regarding the consistency of the

relationships between CEO characteristics and different domains of hospital performance.

Study Significance

Public reporting of patient experience scores became mandatory in 2013.

Consequently, CEOs and other hospital leaders increasingly view patient experience and

strategies for its improvement as important determinants for the future success of their

organizations (Manary, Staelin, Kosel, Schulman, & Glickman, 2013). Thus, the findings

from this study are timely and important for providing insight into ways to foster patient

centered environments and enhanced patient experience scores. The findings may also

provide useful insights for CEO recruitment and selection processes used by hospitals

and other healthcare executive recruiters. The supplementary analysis that incorporates

the additional domain scores (outcome, efficiency, clinical process of care, and total

performance) may also provide a foundation and benchmarks for future research as well

as additional insights into CEO recruitment and selection processes.

Dissertation Outline

Chapter 2 will review the existing empirical literature related to hospital patient

experience. First, it will explore the history of patient experience, its measurement, and

its effects on other outcomes of interest. Next, the chapter will review the literature on

leadership, organizational, and market characteristics correlated with patient experience.

Finally, Chapter 2 will present the study hypotheses regarding the association between

CEO characteristics and patient experience.

Chapter 3 will provide a description of the research design, sample data sources,

methods of data collection, measures, and analytic plan. Chapter 4 will present the results

of the statistical analysis; univariate, bivariate and multivariate analysis. Finally, chapter

5 will discuss the findings, study limitations, opportunities for future research, and

implications.

Chapter 2

Literature Review and Theoretical Framework

Reporting of hospital value based purchasing measures went into effect in 2013 as

a standard for measuring hospital performance for the delivery of healthcare services

(ACA, Section 3001a). Hospital value based purchasing measures and reporting

standards were developed by the CMS, Department of Health and Human Services

(HHS), National Quality Forum (NQF), National Quality Measures Clearinghouse

(NQMC), and others.

Hospital value based purchasing had two main components called domains in

2013; the clinical process of care domain and the patient experience of care domain.

Additional domains of efficiency and outcome were added in 2014. The domains are

weighted to calculate a total performance score. In 2013, patient experience score

comprised 30% and the clinical process of care domain 70% of total performance score.

In 2014, the clinical process of care domain was 20%, the outcome domain was

30%, efficiency domain 20%, and the patient experience domain was 30% of the total

performance score. Each year the federal rule determines how each domain will be

weighted to calculate the total performance score, which in turn is used to calculate

adjustments to Medicare reimbursements for services rendered (D. M. Cosgrove et al.,

2012; Medicare.gov, 2014). Eighty-one percent of the IPPS hospitals have already

implemented and/or are implementing new processes/policies and forming new formal

positions dedicated to patient experience in order to improve reimbursements (Balbale,

2014; Batailler et al., 2014; Bertakis & Azari, 2011; Beryl Institute, 2014; Hodnik, 2012;

Kolstad & Chernew, 2009).

Patient experience has been defined as the sum of all interactions, shaped by an

organization’s culture, that influence patient perceptions across the continuum of care

(The Beryl Institute, 2014). These perceptions are recognized, understood, and

remembered by the patients based on their individual experiences. Patient experience

measures patient centeredness, whether value was delivered, and whether patient’ needs

and expectations were met (CMS, 2014). Patient experience score is measured with the

HCAHPS survey (Table 2 and Appendix J). HCAHPS was identified in Section 3001 of

the Patient Protection and Affordable Care Act of 2010 as a measure to be included in the

Hospital Value Based Purchasing (HVBP) program payments made as of Fiscal Year

2013 (CMS, 2011).

HCAHPS survey questions measure patient perceptions of care experiences by

focusing on patient interactions during healthcare encounters, whether or not certain

events or behaviors occurred, and/or how often they occurred (Long, 2012). The

HCAHPS survey (Table 1) is composed of 27 items: 18 substantive items that encompass

critical aspects of the hospital experience; four items to skip patients to appropriate

questions; three items to adjust for the mix of patients across hospitals; and two items to

support congressionally mandated reports (CMS.org).

More specifically, HCAHPS focuses on nurse communication, doctor

communication, staff responsiveness, pain management, medication communication,

discharge information, cleanliness, quietness of the hospital environment, overall rating

of the hospital, and patient willingness to recommend its services (CMS, 2014).

Furthermore, it assesses whether the patient’s visit was patient centered or not

(Tsimtsiou, Kirana, & Hatzichristou, 2014).

Table 1

HCAHPS Survey Questions

HCAHPS

Survey Questions

Nurse Communication/Care

During this hospital stay, how often did nurses treat

you with courtesy and respect?

During this hospital stay, how often did nurses listen

carefully to you?

During this hospital stay, how often did nurses explain

things in a way you could understand?

During this hospital stay, after you pressed the call

button, how often did you get help as soon as you

wanted it?

Doctor Communication/Care

During this hospital stay, how often did doctors treat

you with courtesy and respect?

During this hospital stay, how often did doctors listen

carefully to you?

During this hospital stay, how often did doctors explain

things in a way you could understand?

Staff responsiveness

During this hospital stay, did you need help from

nurses or other hospital staff in getting to the bathroom

or in using a bedpan?

How often did you get help in getting to the bathroom

or in using a bedpan as soon as you wanted?

Pain management

During this hospital stay, did you need medicine for

pain?

During this hospital stay, how often was your pain well

controlled?

During this hospital stay, how often did the hospital

staff do everything they could to help you with your

pain?

Medications Communication

During this hospital stay, were you given any medicine

that you had not taken before?

Before giving you any new medicine, how often did

hospital staff tell you what the medicine was for?

Before giving you any new medicine, how often did

hospital staff describe possible side effects in a way

you could understand?

Cleanliness & Quietness

During this hospital stay, how often were your room

and bathroom kept clean?

During this hospital stay, how often was the area

around your room quiet at night?

CMS requires hospitals to administer a minimum of 300 surveys over one

calendar year to a random sample of adult patients between 48 hours and six weeks after

discharge (www.hcapsonline.org; 2013). Completed HCAHPS are submitted to the CMS

data warehouse by hospitals. Incomplete surveys are considered invalid and removed

from the system. Unweighted and weighted domain scores, including patient experience,

range from 0 to 100 (CMS, 2014).

Care instructions before/after

discharge

After you left the hospital, did you go directly to your

own home, to someone else’s home, or to another

health facility?

During this hospital stay, did doctors, nurses or other

hospital staff talk with you about whether you would

have the help you needed when you left the hospital?

During this hospital stay, did you get information in

writing about what symptoms or health problems to

look out for after you left the hospital?

During this hospital stay, staff took my preferences and

those of my family or caregiver into account in

deciding what my health care needs would be when I

left.

When I left the hospital, I had a good understanding of

the things I was responsible for in managing my health.

When I left the hospital, I clearly understood the

purpose for taking each of my medications.

Understanding the Patient

During this hospital stay, were you admitted to this

hospital through the Emergency Room?

In general, how would you rate your overall health?

In general, how would you rate your overall mental or

emotional health?

What is the highest grade or level of school that you

have completed?

Are you of Spanish, Hispanic or Latino origin or

descent?

What is your race? Please choose one or more.

What language do you mainly speak at home?

Overall Rating of Hospital

Using any number from 0 to 10, where 0 is the worst

hospital possible and 10 is the best hospital possible,

what number would you use to rate this hospital during

your stay?

Would you recommend this hospital to your friends

and family?

As suggested by the different domains that constitute the overall total

performance score, patient experience is distinct from other aspects of hospital

performance and should be measured independently. For example, many hospitals today

measure clinical quality outcomes to meet regulatory compliance and patient/market

expectations. However, patient experience differs from clinical quality outcomes because

of its focus on subjective assessments of care processes, also viewed as abstract

expectations for quality (O'Connor, Trinh, & Scewchuk, 2001). Likewise, patient

experience differs from patient satisfaction because of its emphasis on subjective views

about hospital inpatient care processes based on experiences, perceptions, and specific

processes that are expected to occur during an inpatient hospital stay (Bleich, Özaltinb, &

Murrayc, 2009; Elliott et al., 2010; Price et al., 2014).

Because patient experience domain focuses on patient experiences and specific

care processes, patient centered care is believed to be a critical input into better patient

experience scores. In fact, a study of 69 U.S. hospitals revealed that patient centered and

compassionate care practices were significantly and positively associated with higher

patient experience scores and the likelihood of patients recommending a hospital

(McClelland, 2014).

Care that is patient centered is “respectful of and responsive to individual patient

preferences, needs, and values, and ensures that patient values guide all clinical

decisions” (IOM, 2001. p. 6). Patient centeredness enables patient access to timely and

appropriate care by skillful personnel at all levels of patient interactions, starting from the

patient’s admission to the facility through discharge and beyond. It builds compassionate

and caring relationships that bridge demographic, and economic differences, and engages

the patients in their own care; considers family values, religious beliefs, age, lifestyles,

and cultural diversities; makes them feel safe, comfortable, and cared-for (Bush, 2012; A.

M. Epstein, Zhonghe, Orav, & Jha, 2005; R. M. Epstein, Fiscella, Lesser, & Stange,

2010; Jadoo et al., 2013; Long, 2012; Tsimtsiou et al., 2014). Patient centered care

contributes to culturally sensitive communication, without which patients would feel

devalued and lacking in emotional support (Bramley, 2014; J. Chen, Koren, Munroe, &

Yao, 2014).

Importantly, although patient experience scores are used for calculating hospital

incentives, they can be used by multiple audiences. For consumers, patient experience

scores are intended to increase transparency and informed care decision making. For

hospitals and other healthcare organizations, patient experience scores might be used to

identify problems related to patient centered practices as well as reinforce and motivate

change to resolve those problems. States, federal agencies, and other regional/national

agencies could potentially use patient experience scores and related empirical evidence to

make informed policy decisions (ACA, Section 3015; ahrq.gov). Consequently,

understanding factors that influence patient experience are important for multiple health

care stakeholders. In the following section, Transformational Leadership Theory (TLT)

will be used to offer several hypotheses about why certain CEO characteristics may be

associated with better patient experience scores.

Theoretical Framework

Burns introduced the TLT in 1978. The theory suggests that leader characteristics

and behaviors can transform organizations and people to achieve better morale,

motivation, and outcomes (Burns, 1978). In 1985, Bass (1985) emphasized the

psychological components of transformational leadership and its influences on follower

motivation. According to Bass, transformational leaders are considered moral leaders

because they appeal to the values and ideals of their followers (Kuhnert, 1994; Kuhnert &

Lewis, 1987). In 1991, Covey wrote:

[T]he goal of the transformational leadership is to “transform” people and

organizations in a literal sense – to change them in mind and heart; enlarge vision,

insight, and understanding; clarify purposes; make behavior congruent with

beliefs, principles, or values; and bring about changes that are permanent, self-

perpetuating, and momentum building. (p. 287)

Consistent with these arguments, research has shown that a transformational CEO

develops and implements an organization’s vision, values, and goals. He/she motivates

positive follower behaviors through role modeling and mentoring; collaborates with

stakeholders and engages them towards achieving organizational goals, improves job

satisfaction and decreases burn out, stimulates strong stakeholder relationships and

alignment, enables compassionate and outcome driven cultures, empowers teamwork and

innovation, and implements evidence based practices and shared decision making

(Garman, Butler, & Brinkmeyer, 2006; Bass, 2008; Burns, 1978; D. Cosgrove et al.,

2012; IHI, 2014; Kaufman, 2013; Lo, Ramayah, & De Run, 2010; Luxford, Safran, &

Delbanco, 2011; Munir & Nielsen, 2009; Nielsen, Yarker, Randall, & Munir, 2009;

O'Reilly, Caldwell, Chatman, Lapiz, & Self, 2010; Resick, Weingarden, Whitman, &

Hiller, 2009; Rolfe, 2011; Sanders & Shipton, 2012; Tse, Huang, & Lam, 2013;

Vinkenburg, van-Engen, Eagly, & Johannesen-Schmidt, 2011; Wang & Howell, 2010;

Weberg, 2010). Likewise, healthcare researchers have found transformational leadership

to be positively associated with employee attitudes and intentions to follow quality and

safety practices for measurable patient outcomes (Colbert, 2008; Groves & LaRocca,

2012; Lee, Almanza, Jang, Nelson, & Ghiselli, 2013; Ling & Lubatkin, 2008).

It was the contention of this study that patient experience requires high

performing cultures that rest on values of patient centered care, cultures that are

cultivated and sustained by hospital leaders (Thibault, 2013). Research has shown that a

leader’s compassionate and patient centered behaviors can influence the degree to which

an organization is patient centered, an important determinant for patient experience

(Hartog & Belschak, 2012; O'Reilly et al., 2010). The focus of this study was on three

characteristics (education, tenure, gender) that previous research has identified as

indicators of a CEO’s transformational abilities.

Education has been identified as an important transformational leadership

attribute linked to patient centered care, in part because it enables leaders to practice

evidence based leadership (Brown & Posner, 2001; Covey, 2007; A. M. Epstein et al.,

2005; R. M. Epstein et al., 2005; Pedler, 1991). Similarly, CEO tenure is associated with

collaboration, adaptability, innovation, and trust, all of which may be important for

cultivating a culture of patient centered care. Finally, gender has been described in the

literature as a transformational characteristic that reflects differences in intuitiveness,

collaborativeness, compassion, and flexibility (Hambrick & Finkelstein, 1991, 1996;

Lewis, Walls, & Dowell, 2014; X. Luo, V. K. Kanuri, & M. Andrews, 2013b)

Thus, one could argue that hospitals that achieve high level(s) of patient

experience are likely to be led by executives/CEOs who can stimulate and sustain patient

centered values (Davis, Schoenbaum, & Audet, 2005; Latham, 2013), and CEO

characteristics that reflect transformational abilities of CEOs may be associated with

better patient experience scores.

Chief Executive Officer Characteristics

CEO Education

There is growing sentiment that healthcare leadership in the United States has to

be reconfigured to meet the needs of the reformed healthcare system to deliver timely

care in more complex care delivery systems, such as integrated care networks and

accountable care organizations (Ricketts & Fraher, 2013). The transformation of the U.S.

healthcare system requires well educated leaders (IOM, 2013). Of specific relevance for

this study, successful and effective patient experience practices have been shown to

require education that is focused on delivering value and meeting patient /family and

industry needs, with more education being associated with better patient services and

higher patient experience scores (Robert, Waite, Cornwell, Morrow, & Maben, 2014).

Research has also found CEO education to be associated with various leadership

practices and organizational outcomes, such as evidence based practices, innovation, and

improved financial performance (Bhagat, Bolton, & Subramanian, 2010; Gottesman &

Morey, 2006; Jalbert, Rao, & Jalbert, 2002).

For these reasons, the Accreditation Council for Graduate Medical Education has

placed a significant emphasis on developing formal leadership education models that

foster improved patient outcomes (Rodrigue, Seoane, Gala, Piazza, & Amedee, 2012).

Formal graduate education can produce effective and innovative leaders, lead to better

care, improve health, and lower costs (Thibault, 2014) because it supports an

understanding of the theories and practices of successful leadership strategies and

provides access to the existing body of empirical literature (Becker, 1970).

Formal graduate education can also equip CEOs with the necessary technical,

human, business, and conceptual skills (Brooks, 1994; Reilly, 2004). Specifically, formal

graduate programs often emphasize important leadership skills such as effective

communication, interpersonal skills, managing healthcare resources, and measuring and

managing quality data and activities (Brooke, Hudak, Finstuen, & Trounson, 1998).

Thus, Master’s, doctoral, and other advanced graduate degrees have been acknowledged

in the literature as important factors predictive of better workplace environments and care

delivery systems that are associated with better pain management practices, lower

medical/administrative errors, reduced adverse occurrences, lower mortality rates, and

other quality performance outcomes (Aiken, Clarke, Cheung, Sloane, & Silber, 2003;

Blegen, Vaughn, & Goode, 2001; Garman, Goebel, Gentry, Butler, & Fine, 2010;

Gillespie, Chaboyer, Wallis, & Werder, 2011; Kim, 2014; Trinkoffa et al., 2014).

Consistent with this thinking, Garman et al. (2006, 2010) illustrated that

leadership competencies are typically taught at the graduate/post graduate level.

Healthcare Leadership Alliance (HLA) is a leadership model with a “cluster of

knowledge, skills and attitudes related to role and performance,” that is taught at a

graduate level ( Garman et al., 2006; Shewchuk, O’Connor, & Fine, 2005, p. 33). HLA is

a framework developed by the consortium of the six largest healthcare associations

(ACHE, ACPE, AONE, HFMA, HIMSS, MGMA, ACMPE) that allows leaders to

establish vision, enhance organizational goals, build trust and motivation, encourage

teamwork, support diversity, promote environments where employees contribute to their

full potential, and achieve higher levels of performance and quality outcomes (Garman et

al., 2006). HLA competencies include effective communication, stakeholder relationship

management, professionalism, leadership knowledge, business management, healthcare

systems understanding, resources management, governance, strategic planning, risk

management, quality/safety management, and more (Stefl, 2008).

Beyond healthcare, research has found that higher levels of formal education are

associated with better organizational performance such as higher profits and greater

market share (Besley, Montalvo, & Reynal-Querol, 2011; Hambrick & Aveni, 1992;

Hambrick & Mason, 1984). Executives with higher levels of formal education are more

successful at managing change, facilitating organizational adaptation, and motivating

followers to improve performance (Baker, Mathis, & Stites-Doe, 2011). Higher education

level, in general, is associated with leaders’ receptiveness to change and willingness to

take risks (Wiersema & Bantel, 1992).

Likewise, Lewis et al. (2014) found that financial decision-making and strategic

behaviors vary as a function of CEO formal educational background; organizations led by

CEOs with an MBA and/or other higher degrees spend more on capital expenditures, take

on more debt, and make more diversifying acquisitions than firms led by less educated

CEOs. Kimberly and Evanisko (1981) also found that CEO education level was

positively associated with the likelihood of adopting innovative technological and

administrative strategies.

Terminal vs. Non-Terminal Degrees

While some have described the Master’s degree as the terminal degree for

healthcare management practice this study defined a terminal degree as a doctoral degree

as it is the highest academic degree approved in a given field. Terminal degrees include

research and professional doctorate degrees such as Doctor of Medicine (MD), Doctor of

Psychology (PsyD), Doctor of Philosophy (PhD), Doctor of Science (DSC), Doctor of

Education (EdD), Doctor of Public Health (DrPH), and other doctoral degrees.

Research has found that leaders with terminal degrees are associated with

transformational leadership behaviors and techniques such as inspiring, enabling,

encouraging, and modeling (Brown & Posner, 2001; Covey, 2007; Pedler, 1991).

Furthermore, graduate education which is grounded in theory, research, and utilization of

empirical literature, has shown to encourage use of evidence based leadership strategies

and has led to improved patient centered care (A. M. Epstein et al., 2005; R. M. Epstein

et al., 2005). The use of such evidence can help leaders make better decisions and

develop more effective strategies in response to changing external environments, such as

the increased emphasis on patient experience.

Therefore, it was hypothesized that:

Hypothesis 1: Hospitals led by CEOs that hold terminal degrees will be

associated with higher patient experience scores compared to those hospitals led by

CEOs with non-terminal degrees.

Clinical Terminal vs. Non-Clinical Terminal Degrees

Patient experience requirements raise another question, whether CEOs with

clinical terminal degrees or non-clinical terminal degrees can produce better patient

experience outcomes and contribute to the ongoing discussion of whether healthcare

organizations are better off run by CEOs with a clinical terminal degrees, such as MDs

(Falcone & Satiani, 2008). Most U.S. hospitals have been traditionally led by non-clinical

non-terminal degree CEOs. In 1935, 35% of U.S. hospitals were led by medical doctors,

according to the Journal of Academic Medicine (Falcone & Satiani, 2008). In 2009,

however, out of 6,500 U.S. hospitals, only 235 were led by MD executives in 2009

(Falcone & Satiani, 2008; Goodall, 2011b; Gunderman & Kanter, 2009).

CEOs with a clinical terminal degree or a non-clinical terminal degree may be

successful , however, a new breed of hospital CEO may be required to achieve better

patient centered outcomes (Schultz & Pal, 2004). Research suggests that, because clinical

CEOs spend many years in direct patient care, they may have an aptitude and desire to

communicate in ways that are more effective in promoting patient centered culture

(Mountford & Webb, 2009).

Clinical terminal degree CEOs may also cultivate more patient centered cultures

by acting as role models for their medical staff and may be more effective at attracting

gifted medical personnel (Goodall, 2011a, 2011b; Goodall, Lawrence, & Oswald, 2011;

Mäntynen et al., 2014). CEOs can also play a critical role in uniting the clinical staff and

overcoming potential resistance from physicians, patients, and other stakeholders in

implementing more patient centered delivery models (Colla, Lewis, Shortell, & Fisher,

2013).

Consistent with these arguments, a recent study by the UK National Health

Service (NHS) found that in 11 cases of attempted improvement in hospital quality

performance, organizations with stronger MD leadership were the most successful

(Fitzgerald, 2006). A 2011 study of 300 top rated hospitals found a strong positive

association between the clinical quality of a hospital and whether the CEO was an MD

(Goodall, 2011a). Another study by McKinsey and the London School of Economics

found that hospitals with the greatest MD participation in management roles scored 50%

higher on important drivers of hospital performance than those with low levels of MD

participation (Castro, Dorgan, & Richardson, 2008).

Collectively, these findings suggest that:

Hypothesis 2: Hospitals led by CEOs that hold clinical terminal degree will be

associated with higher patient experience scores compared to hospitals led by CEOs that

do not hold clinical terminal degree.

CEO Tenure

Empirical research suggests that longer CEO tenure is negatively associated with

organizational change, growth, and performance (Balkin & Gomez-Mejia, 1987; Bizjak,

Lemmon, & Naveen, 2009; D. Chen & Zheng, 2012; Finkelstein & Hambrick, 1990; Luo

et al., 2013b). In the general business literature, long tenure is considered 11 years or

longer (Henderson, Miller, & Hambrick, 2006; Lawrence & Lorsch, 1967). However, in

the hospital industry, the CEO turnover rate has increased and the average CEO tenure

has decreased dramatically (ACHE, March 2014, Report). In 2012, one study reported an

average hospital CEO tenure close to 5.5 years (Khaliq, Thompson, Walston, Saste, &

Kramer, 2012). According to Becker’s Hospital Review (2014), this number dropped

even further to 3.5 years in 2014.

Research has shown that shorter tenured and/or newly appointed CEOs are more

likely to collaborate with their teams, focus more on building trust, and are more

willing to pursue innovative strategies in comparison with longer tenured CEOs

(Finkelstein & Hambrick, 1990). CEOs learn critical knowledge early in their tenure,

which can taper off as years progress (Hambrick & Finkelstein, 1991). Shorter CEO

tenure is associated with greater adaptability, more risk taking, and readiness for

change, while longer tenure is associated with being more cautious and conservative

when making change related decisions (Gerowitz, 1998; Hitt & Tyler, 1991).

Longer tenured CEOs slowly lose their knowledge and skill development and

narrow information search, rely more on the application of previous experiences, and

knowledge to new circumstances instead of accruing new skills (Hambrick, Cheo, &

Chen, 1996; Hambrick & Finkelstein, 1991, 1996). Longer tenured CEOs become

more institutionalized, more risk averse to preserve previous gains, resistant to change,

and less aligned with customer demands and market expectations (Lewis et al., 2014;

Simsek, 2007). Similarly, longer tenure has been linked with an inability to keep up

with market expectations and be responsive to customer preferences (Henderson et al.,

2006; X. Luo, V. Kanuri, & M. Andrews, 2013a; D. Miller & Shamsie, 2001), which in

turn can negatively impact an organization’s financial performance (Luo et al., 2013b).

In summary, shorter tenure is associated with transformative behaviors that

promote change, innovation, alignment with patient needs, patient centered practices,

trust, increased employee morale, positive behaviors, collaboration, progress, risk taking,

teamwork, effective communications, and others that will potentially promote better

patient experience . Therefore, it was hypothesized that:

Hypothesis 3: Hospitals led by CEO with shorter tenure will be associated with

higher patient experience scores compared to hospitals led by longer tenured CEOs.

CEO Gender (Female vs. Male CEOs)

Women have been underrepresented in the highest levels of the healthcare

industry’s leadership. According to the American College of Healthcare Executives

(ACHE), most women are not reaching CEO positions because the healthcare industry

that has been traditionally shaped around male power and authority (Amanatullah &

Tinsley, 2013; Brescoll & Uhlmann, 2008; Engen & Willemsen, 2004; Isaac, 2011;

Lantz, 2008). Consequently, there is relatively limited literature regarding gender

attributes related to performance, with only a few studies looking at female CEO roles,

attitudes toward change, leadership styles, and other attributes (Anderson, Mclaughlin,

& Smith, 2007; Krishnan & Park, 2005; Musteen, Barker, & Baeten, 2006).

Furthermore, no research to date has examined the effects of CEO gender on patient

experience.

Women have been described as transformational leaders ( Eagly, Johannese, &

Engen, 2003), with some researchers arguing that females have better aptitude to

integrate and align organizational goals with the external environments (Prinsloo &

Barrett, 2013). Likewise, some researchers have suggested that female leaders approach

leadership differently than male CEOs and place greater emphasis on behaviors and

skills such as flexibility, intuition, and tactfulness when addressing challenging

circumstances, greater willingness to acknowledge mistakes, greater engagement in trust

building, problem solving, continuous quality improvement, collaboration, transparency,

compassion, innovation, and positive attitude toward change compared to their male

counterparts (Appelbaum, Audet, & Miller, 2003; Kark, Waismel-Manor, & Shamir,

2012; KLCM, 2014; Maniero, 1994; Paton & Dempster, 2002; Paustian-Underdahl,

Walker, & Woehr, 2014).

Females also exhibit greater levels of service orientation by placing greater

emphasis on perceptions of patient expectations for the service quality (O'Connor,

Trinh, & Shewchuk, 2000). The current researcher argued that these different emphases

would be associated with more patient centered cultures that support positive patient

experience. Thus, it was hypothesized that:

Hypothesis 4: Hospitals led by female CEOs are more likely to report higher

patient experience scores than hospitals led by male CEOs.

Chapter 3

Methodology

This chapter describes the research design, data sources, data collection, variable

operationalization, and analytic strategy.

Research Design

A cross-sectional, quantitative study was used to examine whether CEO

characteristics were associated with reported patient experience scores of CA hospitals.

Study Population

The hospital was the unit of analysis. The sample consisted of 294 California

(CA) hospitals. The decision to focus on CA hospitals was based on a combination of

pragmatic and research design considerations. The study’s use of primary data collection

for some variables, as well as limited data availability across states presented challenges

to including hospitals from multiple states. Therefore, CA hospitals were selected

because they operate across a diverse range of markets. Furthermore, the large number of

hospitals operating in CA provided a larger sample size, and thus greater power, to

examine the relationship between CEO characteristics and patient experience. The study

examined these relationships for calendar years 2013 and 2014.

Data Sources

Data for the analysis were aggregated from several sources: (1) The 2013 and

2014 American Hospital Association (AHA) Annual Surveys; (2) The California Hospital

Association (CHA) 2013 and 2014 membership directories of hospital CEOs; (3)

Individual hospital websites; (4) LinkedIn; (5) Medicare Hospital Compare website; (6)

The census bureau market characteristics data; and (7) Becker’s Hospital Review.

Medicare Hospital Compare website is a public data source. The reported data are

updated each performance period (quarter) and each calendar year in January. This study

used 2013 and 2014 patient experience data available as of January 2015.

Select hospital characteristics from the AHA Annual Survey data can be accessed

via AHA Data Viewer at www.ahadataviewer.com. The most recent data were published

in November 2014 and reflect 2013 survey results regarding organizational structure,

system affiliation, facility and services lines, beds and utilization, staffing, and expenses

(www.ahadataviewer.com, 2014).

The CHA Membership Directory is updated and published in January of each year

to represent the most current CEO information. For example, the 2014 directory

represents CEO information as of December 2013.

Market characteristics were collected primarily from the Census Bureau

(www.census.gov), which reflects data from 2009 to 2013. Most studies dealing with

market characteristics have used the census.gov data because of its small margin of error

(Appendix G).

The study used January 2015 LinkedIn hospital CEO data. LinkedIn is a

professional networking website used by various professionals and executives. LinkedIn

uses the Advanced Intrusion Detection Environment (AIDE), a directory data integrity

checker, to accurately convert written records into usable data through data entry, data

conversion, information harvesting from the web, reporting, analysis, storage, and

database backup services (LinkedIn, 2015).

The 2015 Becker’s Hospital Review CEO data were also used in this study.

Becker's Hospital Review provides hospital and leadership information and is geared

toward high-level hospital leaders (CEOs, CFOs, COOs, CMOs, CIOs, etc.). Its data are

intended for approximately 18,500 healthcare executives and is published monthly.

The most recent information published on individual hospital websites was used

to gather CEO data. Most hospitals update their website’s content on a regular basis,

especially related to CEO changes and characteristics. Consequently, the 2015 January

data were used.

Measures

Dependent Variable

The patient experience score is a continuous variable ranging from 0 to 100 and

was obtained from the 2013 and 2014 Medicare hospital compare website. Patient

experience scores were calculated by CMS from Hospital Consumer Assessment of

Healthcare Provider and System (HCAHPS) surveys completed by hospitals (CMS,

March 2011). First, CMS calculates the unweighted patient experience of care domain

score for each hospital by summing the hospital’s HCAHPS base score (0-80) and

HCAHPS consistency score (0-20). Next, CMS calculates the weighted patient

experience of care domain score for each hospital by multiplying the unweighted patient

experience of care domain score by 0.30 (CMS, 2013). CMS publicly reports the

weighted scores from four consecutive quarters annually on the Medicare Hospital

Compare website (Medicare, 2014).

Data from the website include the following information: six digit numeric

hospital provider number, hospital name, hospital physical address, Zip Code, County,

unweighted and weighted patient experience score, ranging from 0 to 100, where ‘0’ is

the lowest score possible and ‘100’ is the highest score (Appendix B). The patient

experience data were then entered into a Microsoft Excel spreadsheet.

Independent Variables

CEO characteristics were derived from primary and secondary data sources.

Secondary data sources were used to verify the accuracy of the primary sources.

The primary source was the 2013 and 2014 CHA Membership Directories. These

directories include the CEO name, contact information, education, and gender (Appendix

C). The secondary source was hospital websites, which have information regarding their

CEOs’ education/degree, tenure, and other information. Appendix D includes an example

of a hospital’s website of pertinent information. In situations where the CHA directory

and the hospital website provided inconsistent information, LinkedIn was used as a

tertiary source to adjudicate discrepancies. Appendix E includes a screenshot of a CEO’s

LinkedIn profile.

For example, if the CHA directory reported CEO gender but did not include

information regarding his/her education, the hospital’s website was consulted to confirm

the gender and determine education. Assuming the hospital website confirmed CEO

gender and provided an initial assessment of education, his/her LinkedIn profile (where

available) was consulted to confirm education. Finally, in the event these three sources

did not provide the necessary information or provided contradictory information

regarding CEO characteristics, Becker’s Hospital Review was consulted.

Gender. Each CEO has her/his name and his/her photo displayed next to the

hospital information in the CHA director. Gender was inferred based on the CEO’s name

and published photo. In situations where the name was ambiguous and the CEO photo

was missing in the directory, the hospital website was used as secondary resource.

LinkedIn CEO profiles were used as a cross-reference when the primary and secondary

sources were not clear and/or consistent. CEO gender was coded dichotomously as

female (1) and male (0).

Tenure. CEO tenure was a continuous variable ranging from 0 to 50 years,

representing time at his/her current position as the hospital’s CEO. Given the study’s

interest in CEO tenure, this variable was limited to years as CEO. For example, if he/she

worked for the hospital for 20 years, but only three years as its CEO, this study

considered tenure as three years. The primary source for CEO tenure was the hospital

website. Typically, each hospital website has a page devoted to its leadership team where

there is a short CEO biography, including how long the current CEO has been employed

as the CEO. Secondary sources for tenure were LinkedIn and Becker’s Review.

To calculate CEO 2014 tenure, the researcher accessed CHA membership

directory published in January 2015, which reflects CEO 2014 hospital employment data.

Then, CEO appointment year was subtracted from year 2014 to get the tenure data for the

study. For example, if the CEO was appointed in 2010, the tenure was subtracted from

year 2014 and was entered as four years (2014-2010).This calculation was repeated in the

same manner for 2013 CEO tenure data; the researcher accessed the membership

directory published in January 2014, which reflects CEO employment for year 2013. If

CEO was appointed in 2010, the tenure was subtracted from year 2013 and entered as

three years (2013-2010).

An identical method was used to calculate tenure by using LinkedIn data; the

researcher accessed each CEO’s LinkedIn biography published as of January 2015, and

looked for his/her hospital appointment history. To collect 2014 tenure data, the

researcher subtracted the CEO appointment year from 2014, and to get 2013 tenure data

subtracted the appointment year from 2013. The study set a threshold for tenure at six

months; therefore, tenure below six months was excluded from data analysis.

Education. The CHA directory page lists the hospital CEO’s name and

education/degree. Terminal degree education was coded as 1 for hospitals with a CEO

with a doctoral degree (Appendix H) and 0 for all other hospitals. CEOs with all other

degrees were coded under Non-Terminal degree as (0).

Clinical Terminal vs. Non-Clinical Terminal Degree was the second educational

variable that was used to assess the relationship between education and patient experience

scores. Clinical Terminal category meant a doctoral degree/education related to patient

care services. It was categorized as Clinical Terminal (1) and Non-Clinical Terminal as

(0), (Appendix I).

Control Variables

Research, in general, has found that hospitals’ response to regulatory changes

such as the ACA vary as a function of a number of organizational characteristics such as

ownership type, teaching affiliation, system affiliation, size, geographical location, payer

composition, and others (Cook, Shortell, Conrad, & Morrisey, 1983; Kaufman, 2013).

Therefore, the study controlled for several hospital and market level factors that could

have potentially impacted the patient experience reported score.

Hospital characteristics. Hospital characteristic variables were drawn from the

AHA Annual Survey data.

Location. Every 10 years, OMB reviews and revises the criteria to define

metropolitan areas (McDermott & Emery, 2015). For the 2010 Census, to qualify as an

urban area, the territory must encompass at least 2,500 people, 1,500 of whom reside

outside institutional group quarters. According to research, urban hospitals have better

access to human and financial resources, and can offer more comprehensive services

compared to rural hospitals (Yeager et al., 2014). However, other studies suggest that

rural hospitals, especially the smaller rural hospitals, demonstrate better patient

experiences (A. M. Epstein et al., 2005; Lehrman et al., 2010). Therefore, hospitals were

coded as a dummy variable (urban=1 and rural=0). In this case, a rural hospital was

defined as any hospital that was located in a county/area with less than 2,500 people

while an urban hospital was defined as any hospital located in a county/area with more

than 2,500 people.

Ownership type. Patient experience scores vary across different types of hospital

ownership. Specifically, patient experience scores are higher in for-profit hospitals and

lower in non-profit hospitals (Lehrman et al., 2010; Siddiqui, 2014). Public non-profit

hospitals, for example, exhibit poorer patient experience outcomes mainly due to their

weak pain management practice compared to other ownership types (Gupta, Lee, Mojica,

Nairizi, & George, 2014). The ownership type was a dummy variable with (0)

representing the non-profit hospitals and (1) representing for-profit hospitals.

Teaching affiliation: For the purpose of this study, teaching hospitals were those

closely associated with medical schools, served as a practical education site for medical

students, interns, residents, fellows and other allied health personnel. Findings regarding

the relationship between teaching status and patient experience are mixed. One study

suggested that teaching hospitals were more likely to display superior performance in

patient experience scores relative to non-teaching hospitals (Lehrman et al., 2010). A

more recent study, however, revealed that teaching hospitals demonstrated lower patient

experience scores due to their bigger size, more complex structures, shortage of nursing

and other clinical staff, poor patient access, and employee burnout (Carvajal, 2014).

Teaching affiliation was coded as a dummy variable with (1) for teaching and (0) for

non-teaching hospitals.

Size. Larger hospitals are typically busier places with higher error rates related to

longer work hours and staff burnout (Rogers, Hwang, Scott, Aiken, & Dinges, 2004).

Larger hospitals have a higher percentage of patients with poor experience because of the

longer laboratory/radiology turnaround times and provider delays (Handel, French,

Nichol, Momberger, & Fu, 2014; Samina, Qadri, Tabish, Samiya, & Riyaz, 2008).

Likewise, Lehrman et al. (2010) found that the top performers on patient experience were

smaller hospitals (100 beds or fewer). Hospital size was controlled for with a continuous

variable measured as the number of beds in the facility.

Market characteristics. Research suggests that market and patient characteristics

may also influence patient experience as it is based on each individual patient’s care

expectation, culture, age, health status, insurance type, income, family size, age,

education, language, race, length of stay, admission mode, and other characteristics

(Boscardin & Gonzales, 2013; Deshpande & Deshpande, 2014; Ruigrok, Greve, &

Nielsen, 2007; Sjetne, Veenstra, & Stavem, 2007). Market level control variables were

drawn from the AHA annual survey and CA Census Bureau data.

Competition. The Herfindahl Index was used to compute market level

concentration based on the sum of county beds. The Herfindahl index, as a measure of

competition, ranges from 0 to 1. Values close to 1 show highly monopolistic markets

with no competition while values close to 0 indicate highly competitive markets.

To calculate the Herfindahl Index, each hospital’s bed count was divided by the

total number of beds in a county, yielding the percentage of the beds in the market owned

by each hospital. This value was then squared for each hospital and summed across all

hospitals in a county. For example, assume a county has two hospitals (A and B) with 50

beds each. For hospitals A and B, their respective bed counts (50) were divided by the

total number of beds in the county (100), and then squared, yielding a value of 0.25 for

each hospital The sum of these two values is 0.50 (0.25 + 0.25), which is the Herfindahl

Index for that county.

Socio-demographic characteristics. Variables were constructed from CA census

data (www.census.gov) (Appendix F). First, the researcher used the AHA databases to

identify the county where the hospital was physically located. Then, the researcher

searched the census.gov webpage by entering the county in order to get the required

socio-demographic information.

All socio-demographic characteristics were calculated by first finding the number

of residents of the specific socio-demographic group within the county of interest, and

then dividing by the total population of the county, multiplied by 100. Thus, these

variables were continuous variables ranging from 0 to 100%.

Race. Race was measured as percent White and minority. Researchers typically

consider Blacks, Asians, and Hispanics as minority groups and Whites (non-Hispanic)

as the majority group. Percent White was constructed as the total number of White (non-

Hispanic) residents in a county divided by the total number of county residents,

multiplied by 100. Percent minority was calculated in a similar manner, as the sum of

Blacks, Hispanics, and Asians in a county, divided by the total number of county

residents, multiplied by 100.

Poverty level. Poverty level was defined as the percentage of persons below the

federal poverty level (Appendix G).

Age. Age was defined as the percentage of the population that is 65 years and

over. This age group was selected since patient experience was mainly reported for

Medicare patients.

Merging Data

Microsoft Excel was used to collect and merge data sets. Initially, the Hospital

Compare patient experience data were downloaded, which contained the hospital name,

address, and related patient experience score. Then, the CEO data were manually

collected and entered into Microsoft Excel. Hospital characteristic data from the AHA

Annual Survey were then merged with the rest of the data using the hospital name.

Finally, market characteristics were merged with these data using the county name. The

merged data set was finally transferred into SPSS to conduct the proposed data analysis.

Table 2

Study Variables, Measures, and Data Sources

Dep. Variable

Measure(s)

Data Source (s)

CA Hospitals’ 2013,

2014 Patient

Experience Scores

Scores ranging

from

0 -100. 0 is the

lowest score and

100 is the highest

score

Medicare Hospital Compare 2013, 2014

Data

(http://www.medicare.gov/hospitalcompare)

Ind. Variable

Measure(s)

Data Source (s)

CEO Gender

Female (1)/Male

(0)

Primary: CHA 2013, 2014 Member

Directories

Secondary: Hospital Website

Tertiary: LinkedIn

CEO Tenure

Years as CEO in

the current position

from 0 -50 years

Primary: Hospital Website

Secondary: LinkedIn

Tertiary: Becker's Hospital Review

CEO

Education/Degree

Clinical Terminal

Degree (1) /Non-

Clinical Terminal

Degree (0)

Terminal (1)/Non-

Terminal (0)

Primary: CHA 2013, 2014 Member

Directories

Secondary: Hospital Website

Tertiary: LinkedIn

Cont. Variable

Measure(s)

Data Source (s)

Hospital Ownership

Type

For-Profit (1)/Non-

Profit (0)

AHA 2013, 2014 Databases

Hospital Location

Urban (1)/Rural (0)

AHA 2013, 2014 Databases

Hospital Size

Number of Beds as

a continuous

measure

AHA 2013, 2014 Databases

Hospital Teaching

Affiliation

Teaching(1), Non-

Teaching (0)

AHA 2013, 2014 Databases

Market Competition

Herfindahl Index (0

to 1)

AHA 2013, 2014 Databases

Race/Ethnicity

Percent White and

Minorities, 0-100%

of the total

population

U.S. Census Bureau

www.census.gov

Poverty Level

Percent population,

0-100%

U.S. Census Bureau

Age as 65+

Percent population,

0-100%

U.S. Census Bureau

Statistical Analysis

Regression analysis was used to analyze the relationships between CEO

characteristics and patient experience. Two sets of ordinary least square regressions were

used, one for each of the educational variables chosen (i.e., one model with terminal

degree as a predictor, and another with Clinical/Non-Clinical terminal degree as a

predictor). The two sets of regression models were necessary to address likely

multicollinearity between the two education variables.

The analysis included the following steps:

a. Investigated the data set via descriptive statistics to identify missing values,

departures from normality, presence of outliers and/or influential

observations, homoscedasticity, and multicollinearity among the predictors.

b. Residual analysis. Each model was rigorously tested for goodness of fit

against functional misspecifications, heteroscedasticity, and possible

multicollinearity issues using a combination of analytical tools based on the

residual terms of the regressions, including graphical methods and formal

statistical tests.

Graphical methods included:

 Plot residuals vs. predicted patient experience scores. A random

scatter plot with no outliers or influential observations and with

constant variance indicated whether there was any departure from

the conditions under which a linear regression model is known to

work well. If that is not the case, scatter plots of residuals vs. each

of the other covariates were used to detect whether any of the

predictors were responsible.

 Checked for normality using the Q-Q plot (after having solved the

other issues listed above).

Formal statistical tests included:

 Breusch-Pagan test for homoscedasticity

 Detected misspecifications and the need to transform variables by

regressing the estimated residuals against the predictors and their

squares to detect the need for more complex functional forms other

than the linear one. If all coefficients were not statistically

significant, then the model was to be correctly specified; add extra

predictor(s) or transformation of the dependent variable.

Chapter 4

Analysis and Presentation of Findings

This chapter presents the results of the data analysis used to investigate the

relationship between selected CEO characteristics and patient experience scores. The four

hypotheses tested in this study were:

Hypothesis 1: Hospitals led by CEOs that hold terminal degrees will be associated with

higher patient experience scores compared to those hospitals led by CEOs with non-

terminal degrees.

Hypothesis 2: Hospitals led by CEOs that hold a clinical terminal degree will be

associated with higher patient experience scores compared to hospitals led by CEOs that

do not hold a clinical terminal degree CEOs.

Hypothesis 3: Hospitals led by CEOs with shorter tenure will be associated with higher

patient experience scores compared to hospitals led by longer tenured CEOs.

Hypothesis 4: Hospitals led by female CEOs will be associated with higher patient

experience scores than hospitals led by male CEOs.

Descriptive Results

Tables 3 and 4 below present descriptive statistics for the study sample. The study

included 294 acute care hospitals in the state of California for the years 2013 and 2014.

The majority of the hospitals for both years were not-for-profit (76%) with the remaining

hospitals being for-profit (24%). Urban hospitals accounted for 96% of hospitals in the

sample, while rural hospitals accounted for only 4%. Teaching hospitals constituted 29%

of the study sample and 71% were non-teaching hospitals.

On average, 28% of California hospitals had female CEOs in 2014 and 30% in

2013. In 2013, over 31% of all hospital CEOs held some kind of terminal degree, 12%

had a clinical terminal degree, and 30% had some kind of a clinical degree. These

percentages declined slightly in 2014, with 29% of all California hospital CEOs having

some kind of a terminal degree, 11% having a clinical terminal degree, and 25% having

some type of clinical degree.

Table 3

Hospital and CEO Categorical Characteristics (N=294)

2013

2014

Variables

Independent Variables

Gender

Female (1)

29.9%

27.7%

Male (0)

206

70.1%

198

72.3%

Terminal Degree

Terminal (1)

31.6%

28.1%

Non- Terminal (0)

201

68.4%

197

71.9%

Clinical Terminal Degree

Clinical Terminal (1)

11.6%

10.9%

Non Clinical Terminal (0)

260

88.4%

244

89.1%

Control Variables

Hospital Ownership

For-Profit (1)

23.8%

24.1%

Non-Profit (0)

224

76.2%

208

75.9%

Hospital Location

Rural (0)

3.7%

3.3%

Urban (1)

283

96.3%

265

96.7%

Hospital Teaching

Affiliation

Teaching (1)

28.9%

29.9%

Non-Teaching (0)

209

71.1%

192

70.1%

Table 4 includes hospital and CEO characteristics for California’s hospitals for

the continuous study variables. Patient experience, on average, was 10.2 (SD=5.2) in

2013, and declined slightly in 2014 to 8.7 (SD=4.8). Similarly, the clinical process of

care domain score declined from 41.5 (SD=14.4) in 2013 to 10.4 (SD=4.8) in 2014.

Efficiency and outcome scores data were available only for 2014. On average, the

hospital efficiency domain score was 4.8 (SD=6.0) in 2014 and the outcome domain

score was 14.4 (SD=6.7). Similar to the domain scores, the total performance score was

lower, on average, in 2014 (M=38.1, SD=12.6) compared to 2013 (M=52.8, SD=15.2).

Average CEO tenure was 6.4 years in 2013 and 6.9 years in 2014. Market

competition, as measured by the Herfindahl index, was slightly higher in 2014 (.10)

compared to 2013 (.12).

Table 4

Mean and Standard Deviations for Continuous Variables

2013

2014

Variable

Mean

Std

Dev

Mean

Std

Dev

Dependent Variables

Patient Experience

287

10.2

5.2

266

8.7

4.8

Clinical Process of

Care

289

41.5

14.4

267

10.4

4.8

Efficiency Domain

267

4.8

6.0

Outcome Domain

261

14.4

6.7

Total Performance

282

52.8

15.2

267

38.1

12.6

Independent

Variables

CEO Tenure (years)

294

6.37

5.80

274

6.9

5.4

Control Variables

Number of Beds

294

249

172

274

245

161

Market Competition

294

0.12

0.24

274

0.10

0.19

Ethnicity: % Black

294

6.4%

3.4%

274

6.6%

3.5%

Ethnicity: % Hispanic

294

38%

13.4%

274

38.0%

13.4%

Ethnicity: % Asian

294

13.4%

8.2%

274

13.5%

8.2%

Ethnicity: % White

294

40.2%

14.1%

274

40%

14%

Ethnicity: Bel Poverty

294

16.0%

4.1%

274

16%

Ethnicity: % Age 65 +

293

12.7%

2.1%

274

12.7%

2.1%

Data available only for 2014

Bivariate Results

Correlation Analysis for Continuous Variables

Correlation results are presented in Table 5, which displays the correlations

between all of the continuous variables and each of the dependent variables (value based

purchasing scores) broken down by year.

Patient Experience Domain Scores

Patient experience scores exhibited a statistically significant negative correlation

with the number of beds in 2013 (r = -.196, p = .001), the percentage of Black residents (r

= -.254, p=000), and the percentage of Hispanic residents (r = -.187, p=.001). In contrast,

patient experience scores were positively correlated with market competition (r = .130,

p=.027), the percentage of White residents (r = .299, p=.000), and the percentage of

residents 65 years of age and older (r = .172, p=.004). In 2014, patient experience scores

were significantly and positively correlated only with the percentage of White residents

(r=.137, p=.026).

Clinical Process of Care Domain Scores

In 2013, clinical process of care was negatively correlated with CEO tenure (r = -

.136, p = .021), the percentage of Hispanic residents (r = -.214, p =.000), and the

percentage of residents below the federal poverty level (r = -.161, p = 006). In contrast,

clinical process of care was positively correlated with the percentage of Asian residents (r

= .150%, p = .010) the percentage of White residents (r = .134, p = .022), and the

percentage 65 years of age or older (r=.126, p=.033).

There were no significant correlations between clinical process of care and the

other continuous variables in 2014.

Efficiency Domain Scores

In 2014, hospital efficiency scores were negatively correlated with hospital size (r

= -.128, p = .037), the percentage of Hispanic residents (r = -.238, p = .000), and the

percentage of Asian residents. Efficiency score was positively correlated with the

percentage of White residents (r = .285, p = .000), market competition (r = .349, p = .000)

and the percentage of residents 65 years of age and older (r=.197, p=.001).

Outcome Domain Scores

In 2014, outcome scores were negatively correlated with market competition (r=-

.141, p=.023).

Total Performance Scores

In 2013, total performance score was negatively correlated with CEO tenure (r= -

.144, p= .016), hospital size (r= -.171, p=.004), the percentage of Hispanic residents (r= -

.192, p=.001), the percentage of residents below the federal poverty level (r= -.130,

p=.029), and the percentage of residents 65 years of age and older (r= -.118, p=.048). In

contrast, total performance score was positively correlated with the percentage of White

residents (r= .166, p=.005).

In 2014, total performance score was significantly and negatively correlated with

the percentage of Hispanic residents (r= -.173, p=.005) and the percentage of White

residents (r= -.179, p=.003). Total performance score was positively correlated with the

percentage of residents ages 65 years and older (r= .134, p=.028).

Table 5

Correlation Matrix Continuous Variables

Independent/Control Variables

Dependent Variables

CEOs

Tenure

# Beds

Market

Comp.

Ethnic% Black

Ethnic% Asian

Ethnic% Hispanic

Ethnic% White

% < Poverty

% 65 +

Patient

Experience

(2013)

.008

-.196

.130*

-.254

-.070

-.187

.299

-.103

.172

.890

.001

.027

.000

.234

.001

.000

.080

.004

287

286

Patient

Experience

(2014)

.014

-.050

.037

-.108

-.075

-.053

.137

-.069

.081

.817

.412

.547

.079

.224

.392

.026

.262

.188

266

Clinical Process

Care Domain

(2013)

-.136

-.041

.075

-.001

.150

-.214

.134

-.161

.126*

.021

.484

.202

.984

.010

.000

.022

.006

.033

289

288

Clinical Process

Care Domain

(2014)

.012

.011

-.037

-.048

.098

-.099

.055

-.119

.034

.848

.858

.548

.438

.109

.105

.369

.052

.577

267

Efficiency Domain (2013) - Not Available

Efficiency

Domain (2014)

-.089

-.128

.349

-.064

-.127*

-.238

.285

-.034

.197

.149

.037

.000

.294

.038

.000

.575

.001

267

Outcome Domain (2013) - Not Available

Outcome

Domain (2014)

.060

.062

-.141*

.073

.068

.028

-.072

-.001

-.030

.337

.317

.023

.240

.272

.651

.249

.991

.626

261

Total

Performance

(2013)

-.144

-.171

.077

-.059

.096

-.192

.166

-.130

.118*

.016

.004

.195

.322

.106

.001

.005

.029

.048

282

281

Total

Performance

(2014)

-.008

-.028

.076

-.036

-.012

-.173

.179

-.078

.135

.902

.651

.214

.558

.844

.005

.003

.202

.028

267

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

One-way ANOVA

Testing for Differences in the Means between Multiple Groups

One-way ANOVA examined equality of the population means for the value based

purchasing dependent variables across the categorical independent explanatory variables

(Tables 6-8).

Gender. On average, in 2013 (Table 6), patient experience scores were

significantly higher in hospitals with female CEOs (M=11.9, SD=5.7) compared to

hospitals with male CEOs (M=9.4, SD=4.7; F(1285)=15.74, p<0.001). The difference in

patient experience scores between hospitals with female CEOs (M=9.6, SD=4.3) and

hospitals with male CEOs (M=8.3, SD=5.0) was significant in 2014 as well F(1264)=4.3,

p<0.05).

In 2013, the clinical process of care domain scores of hospitals with female CEOs

M=45.2, SD=13.0) were significantly higher than the clinical process of care domain

scores of hospitals with male CEOs (M=39.8, SD=14.7; F(1287)=9.38, p<0.01). The

difference in clinical process of care domain scores between hospitals with female CEOs

(M=11.6, SD=3.7) and hospitals with male CEOs (M=10.0, SD=5.1) was significant in

2014 as well F(1265)=6.7, p<0.01). The average total performance scores were

significantly different between hospitals with a female CEO (M=57.3, SD=15.6) and a

male CEO (50.8, SD=14.7; F(1280)=11.6, p<0.001) in 2013. Similarly, average total

performance scores were significantly different in 2014, when hospitals with a female

CEO had an average score of 44.0 (SD=13.5) and hospitals with a male CEO had an

average score of 35.8 (SD=11.5; F (1265)=24.87, p=0.001).

In 2014, average efficiency domain scores were significantly higher in hospitals

with female CEOs (M=7.4, SD=7.01) relative to hospitals with male CEOs (M=3.7,

SD=5.28; F(1265)=25.0, p<0.001). In contrast, there was no significant difference in

outcome domain scores between hospitals with a female CEO and hospitals with a male

CEO.

Table 6

ANOVA. Dependent Variable Differences between Male and Female Groups

2013

2014

Mean

F-

value

P-

value

F-

value

P-

value

199

9.41

4.69

0.000

192

8.29

0.04

11.97

5.71

15.74

9.67

4.34

4.29

CPC

201

39.83

14.66

0.002

193

9.96

5.11

0.010

45.37

12.98

9.4

11.65

3.77

6.67

OUT

188

13.95

6.53

0.086

15.52

6.90

2.96

EFF

193

3.8

5.28

0.000

7.43

7.02

20.85

TPS

194

50.79

14.7

0.001

193

35.81

11.46

0.000

57.3

15.57

11.60

44.05

13.58

24.88

No data reported

Clinical terminal degree. There were no statistically significant differences in

performance scores between hospitals with CEOs with a clinical terminal degree and

hospitals with CEOs with a non-clinical terminal degree (Table 7). This was the case for

both 2013 and 2014.

Table 7

ANOVA. Dependent Variable Difference between Clinical Terminal (CT) and Non

Clinical Terminal (NCT)

2013

2014

Mean

10.32

3.56

0.88

9.07

6.21

0.64

NCT

253

10.12

5.34

0.023

236

8.6

4.67

0.224

CPC

44.7

14.27

0.18

10.6

4.29

0.87

NCT

255

41.09

14.36

1.9

237

10.4

4.9

0.27

OUT

15.3

7.4

0.43

NCT

232

14.3

6.6

0.615

EFF

3.3

3.8

0.15

NCT

237

4.9

6.2

2.078

TPS

55.02

14.18

0.37

37.8

10.6

0.87

NCT

248

52.5

15.26

0.796

237

38.13

12.88

0.026

No data reported

Terminal degree. In 2014 (Table 8), the average outcome domain score was

significantly higher for hospitals with CEOs with a terminal degree (M=16.1, SD=7.4)

than for hospitals with CEOs with a non-terminal degree (M=13.7, SD=6.3; F(1259) =

7.0, p<0.001). In contrast, the average efficiency score was significantly lower for

hospitals with CEOs with a terminal degree (M=3.6, SD=4.9) compared to hospitals with

a CEO with a non-terminal degree (M=5.2, SD=6.4; F(1265)=3.8, p<0.05).

Table 8

ANOVA. Dependent Variable Difference between Terminal (T) and Non-Terminal (NT)

2013

2014

Mean

10.98

5.85

0.084

6.17

0.496

197

9.80

4.7

3.012

192

8.55

4.26

0.47

CPC

39.90

15.67

0.20

9.99

5.09

0.366

198

42.24

13.73

1.62

192

10.60

4.72

0.821

OUT

16.13

7.35

0.009

189

13.72

6.27

6.975

EFF

3.64

4.87

0.05

193

5.20

6.37

3.819

TPS

52.25

17.58

0.067

38.49

12.68

0.75

194

53.09

14.10

0.184

193

37.94

12.62

0.097

No data reported

Multivariate Results

Regression Analysis

Prior to estimating the multivariate models, diagnostics were conducted to check

for normality and heteroscedasticity. Visual inspection of the plot of the estimated

residuals versus the predicted values did not show any pattern consistent with the

presence of either heteroscedasticity or model misspecification. Plotting residuals versus

predictors using P-P Plots showed no pattern indicating a lack of normality. Based on

these diagnostic tests, the analysis preceded using ordinary least square regression models

to test for the relationship between CEO characteristics and performance scores while

controlling for known confounding variables.

Hierarchical Multiple Regression

The multivariate analysis used a hierarchical, block modeling strategy.

Specifically, an initial model with only the control variables was assessed, followed by a

second model that included the control variables and the CEO predictor variables of

interest. This strategy enabled a comparison of amount of additional variance explained

by the CEO characteristics, above and beyond the control variables. The researcher

assessed the contribution of the CEO characteristics by calculating the change in the

coefficient of determination (r

) between the two models. Unstandardized beta

coefficients were used to assess the direction and significance of the relationship between

individual CEO characteristics and performance.

In total, the multivariate analysis included 20 models (i.e., four models for each of

the five dependent variables). Due to issues of multicollinearity between the education

variables, two groups of similar models were required to test all of the study hypotheses.

Specifically, one group of models was used to assess the relationship between hospital

performance and CEO characteristics using the terminal vs. non-terminal degree

distinction for education. A second group of models was used to assess the relationship

between performance and CEO characteristics using the clinical terminal vs. non-clinical

terminal degree distinction. For both groups of models, two models were estimated: a

model with only the control variables and a second model that included the CEO

characteristics.

Patient experience of care domain scores. CEO education, regardless of how it

was measured, was not significantly associated with hospital patient experience score;

thus, the analysis did not provide support for hypotheses 1 or 2. The direction and

magnitude of these relationships were similar in Model 4 where education was measured

as clinical terminal degree vs. non-clinical terminal degree. Tenure was positively

associated with patient experience scores (b = .075, p <.05), which is contrast to

hypothesis 3. Relative to hospitals with a male CEO, hospitals with a female CEO

reported higher average patient experience scores (b=1.96, p <.001), providing support

for hypothesis 4 (Table 9 - Models 2, 4).

There were several notable relationships between the control variables and patient

experience performance. The discussion below highlights those control variables that

were significant in both full models. Hospital size was negatively associated with patient

experience (b = -0.005 to -0.006, p <.001). Market competition was also negatively

associated with patient experience (b = -2.12 to -2.17, p < .05). Patient experience scores

declined, on average, from 2013 to 2014 (b=-1.44 to -1.46, p<0.001). On average, for-

profit hospitals had higher patient experience scores (b=2.23 to 2.27, p<0.05) compared

to not-for-profit hospitals.

Overall, the model covariates accounted for approximately 13.3% of the variance

in patient experience scores across hospitals. The CEO characteristics, above and beyond

the control variables, accounted for approximately 3.7% of the variation in patient

experience scores.

While the primary interest of this study was in the association between terminal

education status and hospital patient experience performance, another consideration was

whether hospitals with CEOs with clinical training, at any level, were associated with

better or worse performance. To assess this possibility, a supplemental analysis was done

by adding clinical degree vs. non-clinical degree, along with gender and tenure. The

results were consistent with the models reported in Table 2, with hospitals with female

CEOs associated with better patient experience scores (b = 2.04, p <.001).

Table 9

Ordinary Least Squares (OLS) Regression Models for CEO Characteristics (independent

variable) and Patient Experience (dependent variable)

Terminal degree vs. Non Terminal

Degree

Clinical terminal degree vs. Non

Clinical Terminal

Model 1

Model 2

Model 3

Model 4

Control only

CEO char. &

controls

Control only

CEO char. &

controls

Variable

b (SE)

B (SE)

beta (SE)

B (SE)

CEO gender

1.96(.46)***

2.04 (.46)***

CEO tenure

.075 (.037)*

.077 (.037)*

CEO Education

- CEO terminal

degree (vs. no

terminal degree)

.584 (.45)

- CEO clinical

terminal degree

(vs. non clinical

degree)

.663 (.65)

% Black

-.11.7 (17.2)

-13.47(16.9)

-.11.7 (17.2)

-13.68 (16.9)

% White

15.6 (16.3)

16.27 (16.06)

15.6 (16.3)

15.90 (16.06)

% Hispanic

7.1 (14.6)

8.2 (14.34)

7.1 (14.6)

7.7 (14.4)

% Asian

7.6 (16.05)

7.92 (15.8)

7.6 (16.05)

7.12 (15.8)

# of beds

-.006 (.001) ***

-.005 (.001) ***

-0.006 (0.001) ***

-.005 (.001)

***

% 65+

-27.5 (19.2)

-26.4 (18.9)

-27.5 (19.2)

-27.5 (18.9)

Market competition

-2.05 (1.07)

-2.12 (1.06) *

-2.05 (1.07)

-2.17 (1.06)*

Urban

.170 (1.48)

.459 (1.48)

.170 (1.48)

.328 (1.47)

Rural

Teaching

.877 (.500)

.931 (.493)

.877 (.500)

.880 (.491)

Non- Teaching

Profit

1.58 (.52)**

1.265(.516)*

1.58 (.52)**

1.232 (.516)*

Non Profit

2013

2014

-1.49 (.41) ***

-1.44 (.403)***

-1.49 (.41) ***

-1.46 (.403)***

% below poverty

.65 (7.8)

2.35 (7.7)

.65 (7.8)

2.358 (7.736)

Adjusted R

.100

.134

.100

.133

Change in R

.034

.033

*significant at p≤0.1, ** significant at p≤0.05, *** significant at p≤0.01

Clinical process of care domain scores. Longer tenure was negatively associated

with clinical process of care scores in Model 4 (b= -165, p<0.05). Compared with

hospitals with a male CEO, hospitals with a female CEO reported higher average clinical

process of care scores (b =3.51, p <.05), (Table 10, Models 2 and 4).

Among the control variables, for-profit hospitals status was significantly

associated with higher clinical process of care scores (b=2.43, p<0.05) compared to not-

for-profit hospitals. Average clinical process of care scores declined from 2013 to 2014

(b=31.05 to -31.05, p<0.001), consistent with effect on patient experience scores.

Overall, the model covariates accounted for approximately 69% of the variance in

clinical process of care scores across hospitals. The CEO characteristics, above and

beyond the control variables, accounted for approximately 1.0% of the variation in

patient experience scores.

The supplemental analysis of hospitals with CEOs that held a clinical degree (vs.

non-clinical degree) showed that female CEOs (b=3.36, p <=.05), shorter tenure (b=-

.162, p<.05), for profit status (b=2.94, p<05) were positively associated with clinical

process of care scores while year (b=-30.8, p<0.01) was negatively associated with

clinical process of care scores.

Table 10

Ordinary Least Squares Regression Models for CEO Characteristics (independent

variable) and Clinical Process of Care (dependent variable)

Terminal degree vs. Non Terminal

Degree

Clinical terminal degree vs. Non

Clinical Terminal

Model 1

Model 2

Model 3

Model 4

Control only

CEO char. &

controls

Control only

CEO char. &

controls

Variable

b (SE)

B (SE)

beta (SE)

B (SE)

CEO gender

3.51(1.009)***

3.49(1.01 )***

CEO tenure

-1.54(.81)

-.165(.081 )**

CEO Education

- CEO terminal

degree (vs. non

terminal degree)

-1.561(.994)

- CEO clinical

terminal degree (vs.

non clinical degree)

1.96(1.44 )

% Black

-47.7 (37.8)

-41.84(37.47)

-47.7 (37.8)

-36.99( 37.49)

% White

-49.3(35.99)

-41.70(35.55)

-49.3(35.99)

-40.18(35.56)

% Hispanic

-61.459(32.26)

-54.44(31.88)

-61.459(32.26)

-52.65(31.88)

% Asian

-50.33(35.40)

-45.28(34.98)

-50.33(35.40)

-42.75(34.97)

# of beds

-002(.003 )

.000(.003)

-002(.003)

-.001(.003)

% 65+

-57.157(42.20)

-60.19(41.89)

-57.157(42.20)

-48.17(41.79)

Market competition

1.97(2.34)

2.02(2.34)

1.97(2.34)

2.41(2.34)

Urban

3.89( 3.27)

4.1(3.2)

3.89( 3.27)

5.18(3.53)

Rural

Teaching

.443( 1.10)

.482(1.09)

.443( 1.10)

.638 (1.08)

Non- Teaching

Profit

2.43(1.145)*

2.97(1.13)

2.43(1.145)*

2.97(1.14)***

Non Profit

2013

2014

-31.05(.90)***

-30.90(.89)***

-31.05(.90)***

-30.85(.89)***

% below poverty

-31.499(17.27)

-29.52(17.08)

-31.499(17.27)

-25.78(17.11)

Adjusted R

.685

.695

.685

.694

Change in R

.010

.009

*significant at p≤0.1, ** significant at p≤0.05, *** significant at p≤0.01

Outcome domain scores. Hospitals with CEOs with a terminal degree were

associated with higher outcome scores (b=2.291, p<0.05) compared to hospitals with

CEOs without a terminal degree (Table 11, Model 2). Notably, this association for

education was no longer significant when education was modeled as a clinical terminal

degree vs. non-clinical terminal degree. Gender and tenure were not significantly

associated with outcome scores, on average.

Overall, the model covariates accounted for approximately 4.2% of the variance

in outcome scores across hospitals. The CEO characteristics, above and beyond the

control variables, accounted for approximately 3.5% of the variation in outcome scores.

The supplemental analysis of clinical degree vs. non-clinical degree revealed that

hospitals with CEOs with a clinical terminal degree were significantly associated with

higher outcome scores (b= 3.45, p<0.001).

Table 11

Ordinary Least Squares Regression Models for CEO Characteristics (independent

variable) and Outcome (dependent variable)

Terminal degree vs. Non Terminal

Degree

Clinical terminal degree vs. Non

Clinical Terminal

Model 1

Model 2

Model 3

Model 4

Control only

CEO char. &

controls

Control only

CEO char. &

controls

Variable

b (SE)

B (SE)

beta (SE)

B (SE)

CEO gender

1.58(.94)

1.82(.94)

CEO tenure

.016(.077)

.017(.078)

CEO Education

- CEO terminal degree (vs.

no terminal degree)

2.291(.939)**

- CEO clinical terminal

degree (vs. non clinical

degree)

1.09 (1.36)

% Black

73.78(34.36)*

77.83(34.14)*

73.78(34.36)*

74.55(34.51)*

% White

57.09(32.84)

62.26(32.56)

57.09(32.84)

58.97(32.88)

% Hispanic

53.76(29.48)

59.21(29.23)*

53.76(29.48)

55.72(29.50)

% Asian

59.12(32.19)

65.26(31.94)*

59.12(32.19)

60.66(32.22)

# of beds

.004(.003)

.003(.003)

.004(.003)

.003(.003)

% 65+

-12.44(39.57)

-2.90(39.29)

-12.44(39.57)

-9.377(39.65)

Market competition

-.414(2.578)

.167(2.57)

-.414(2.578 )

-.240(2.59)

Urban

-6.64(3.30)*

-5.22(3.30)

-6.64(3.30 ) *

-1.65(1.075)

Rural

Teaching

-.158(1.006 )

.133(.999 )

-.158(1.006 )

-.121(1.00)

Non- Teaching

Profit

1.255(1.05 )

1.44(1.06 )

1.255(1.05 )

1.65(1.07)

Non Profit

-1.44 (1.06)

% below poverty

-7.00(16.43)

-1.75(16.31)

-7.00(16.43)

-3.60(16.53)

Adjusted R

.000

.025

.000

.004

Change in R

.035

.016

*significant at p≤0.1, ** significant at p≤0.05, *** significant at p≤0.01

Efficiency domain scores. Hospitals with CEOs with a terminal degree were

associated with lower efficiency scores (b=-1.55, p<0.05) compared to hospitals with

CEOs without a terminal degree. Compared to hospitals with male CEOs, hospitals with

female CEOs reported higher average efficiency scores (b =2.87 to 2.70, p <.01), (Table

12, Models 2 and 4, respectively).

Among the control variables, the number of beds (b=-008, p<0.01) was negatively

associated with efficiency scores. Market competition (b=4.14, p<0.05), teaching

affiliation (b=2.27, p<0.01), and for-profit status (b=2.53, p<0.01) were positively

associated with efficiency scores.

The model covariates accounted for approximately 22% of the variance in

efficiency scores across hospitals. The CEO characteristics accounted for approximately

6.2% of the variation in efficiency scores.

The supplemental analysis of clinical vs. non clinical education, along with

gender and tenure were consistent with the primary analysis. Female CEOs (b=2.82,

p<0.001) had overall strong positive association with efficiency scores.

Table 12

Ordinary Least Squares Regression Models for CEO Characteristics (independent

variable) and Efficiency (dependent variable)

Terminal degree vs. Non Terminal

Degree

Clinical terminal degree vs. Non

Clinical Terminal

Model 1

Model 2

Model 3

Model 4

Controls only

CEO char. &

controls

Controls only

CEO char. &

controls

Variable

b (SE)

B (SE)

beta (SE)

B (SE)

CEO gender

2.87(.74)***

2.70(.75)***

CEO tenure

-.092(.061)

-.095(.062)

CEO Education

- CEO terminal

degree (vs. no

terminal degree)

-1.55(.74)*

- CEO clinical

terminal degree (vs.

non clinical degree)

-.664(1.07)

% Black

-12.45(28.17)

-12.38(27.38)

-12.45(28.17)

-9.68(27.59)

% White

-26.97(26.85)

-24.90(26.01)

-26.97(26.85)

-22.64(26.20)

% Hispanic

-36.06(24.08)

-34.33(23.34)

-36.06(24.08)

-31.87(23.49)

% Asian

-31.19(26.29)

-30.87(25.51)

-31.19(26.29)

-27.63(25.66)

# of beds

-008(.002)***

-.008(.002)***

-008(.002)***

-.008(.002)***

% 65+

-8.33(32.089)

-11.29(31.18)

-8.33(32.089)

-6.30(31.39)**

Market competition

4.14(2.10)*

4.19(2.05)*

4.14(2.10)*

4.48(2.07)*

Urban

-2.27(2.70)

-1.85(2.64)

-2.27(2.70)

-1.35(2.66)

Rural

Teaching

2.267 (.82)***

2.25(.79)***

2.267(.82)***

2.39(.80)***

Non- Teaching

Profit

2.53(.854 )***

1.89(.83)*

2.53(.854)***

2.04(.84)

Non Profit

% below poverty

9.145(13.42)

10.54(13.03)

9.145(13.42)

11.73(13.18)

Adjusted R

.176

.232

.176

.220

Change in R

.062

.051

*significant at p≤0.1, ** significant at p≤0.05, *** significant at p≤0.01

Total performance scores. Neither education nor tenure was significantly

associated with total performance scores. Compared to hospitals with a male CEO,

hospitals with a female CEO reported higher average total performance scores (b=6.75

and 6.83, p <.001), (Table 13 - Models 2 and 4, respectively). Hospital beds (b=-.011,

p<0.001) and year 2014 (b=.14.69, p <0.001) were negatively associated with total

performance scores.

Overall, the model covariates accounted for approximately 28% of the variance in

total performance scores across hospitals. The CEO characteristics, above and beyond the

control variables, accounted for approximately 4.2 % of the variation in total performance

scores.

The supplemental analysis found that gender is significantly associated with total

performance scores.

Table 13

Ordinary Least Squares Regression Models for CEO Characteristics (independent

variable) and Total Performance (dependent variable)

Terminal degree vs. Non Terminal

Degree

Clinical terminal degree vs. Non

Clinical Terminal

Model 1

Model 2

Model 3

Model 4

Control only

CEO char. &

controls

Control only

CEO char. &

controls

Variable

b (SE)

B (SE)

beta (SE)

B (SE)

CEO gender

6.75(1.29)***

6.83(1.29)***

CEO tenure

-.162(.104)

-.168(.104)

CEO Education

- CEO terminal

degree (vs. no

terminal degree)

-.523(1.28)

- CEO clinical

terminal degree (vs.

non clinical degree)

2.16(1.83)

% Black

-18.42(48.93)

-9.69(47.92)

-18.42(48.93)

-6.42(47.86)

% White

-18.74(46.54)

-6.15(45.46)

-18.74(46.54)

-5.43(45.40)

% Hispanic

-42.97(41.71)

-30.77(40.76)

-42.97(41.71)

-29.98(40.69)

% Asian

-26.15(45.78)

-16.93(44.73)

-26.15(45.78)

-15.95(44.64)

# of beds

-.011(.004)***

-.008(.004)*

-.011(.004)***

-.009(.004)*

% 65+

-77.45(54.90)

-75.39(53.89)

-77.45(54.90)

-67.78(53.65)

Market competition

.974(3.06)

1.16(2.99)

.974(3.06)

1.4(2.98)

Urban

.089(4.23)

1.26(4.17)

.089(4.23)

1.9(4.1)

Rural

Teaching

2.12(1.42)

2.36(1.39)

2.12(1.42)

2.41(1.38)

Non- Teaching

Profit

.781(1.49)

1.83(1.47)

.781(1.49)

1.84(1.47)

Non Profit

2013

2014

-14.69(1.17)***

-14.37(1.14)***

-14.69(1.17)***

-14.35(1.14)***

% below poverty

-21.90(22.37)

-16.41(21.86)

-21.90(22.37)

-13.63(21.87)

Adjusted R

.244

.283

.244

.285

Change in R

.042

.044

*significant at p≤0.1, ** significant at p≤0.05, *** significant at p≤0.01

Chapter 5

Discussion and Conclusions

The purpose of this study was to examine the relationships between CEO

characteristics and patient experience scores of CA hospitals. Previous literature has

shown that CEO leadership matters because they are in a position to inspire proactive

employee behaviors and personal initiative (Hartog & Belschak, 2012; O'Reilly et al.,

2010; Wynia & Matiasek, 2006). However, little research has been done to examine

relationships between patient experience and CEO characteristics to find those attributes

that can motivate and inspire patient centered care. In fact, very limited research in

healthcare has explored CEO gender, education, and tenure in general. This study fills

this gap by focusing on CEO characteristics that may contribute to patient experience

scores.

The study hypotheses were based on the framework of transformational

leadership theory. This chapter presents a summary and discussion of significant findings

related to the study hypotheses. It also discusses some of the implications of the study for

healthcare leadership and other stakeholders, and future research opportunities.

CEO Education

CEO education was not significantly associated with patient experience. Thus, the

study failed to support hypotheses 2 and 3.

There are several potential explanations for this finding. It is possible that

education is not an important correlate of patient experience and therefore no relationship

was found. Another potential explanation pertains to the types of outcomes considered in

the study. Specifically, it is possible that education (specifically terminal degree) is most

impactful on objective measures such as the clinical process of care and outcome domain

scores (heart attack, acute myocardial infarction, pneumonia, heart failure, surgical care,

30 day mortality, and others), but is less impactful on subjective measures like patient

experience.

Patient experience is a subjective measure, and unlike traditional clinical

measures, it is more complex and more difficult to measure (Farrar, 2006). Patient

experience requires looking into subjective patient issues that are more challenging to

measure effectively, such as their emotional state, social issues, information, education,

anxiety, value, quality of life, respect, dignity, needs, depression, optimism, and other

factors related to patient opinions, judgement and feelings (Spurgeon, Humphreys, James,

& Sackley, 2012). CEOs with terminal degrees may be more likely to base decisions on

evidence, therefore, the effects of those decisions may be more likely to affect more

objective measures, and less likely to affect subjective measures such as patient

experience.

Consistent with this third explanation, the supplemental analysis found that CEO

terminal degree education was significantly associated with outcome and efficiency

domain scores, although the relationships were in different directions; positive

association with outcome scores and negative association with efficiency scores.

Efficiency measure was introduced for public reporting in 2014 by Medicare to assess

efficiency of resource utilization (Medicare.gov, 2015). Efficiency measures in healthcare

have caused much controversy and the use of such measures without a clearer

understanding of what they really assess may have led to unintended (negative)

consequences such as low efficiency scores, mainly because of resistance from healthcare

providers (Hussey et al., 2009). A similar explanation for these mixed results is that

outcome and efficiency scores represent different aspects of hospital performance and

may reflect different financial priorities based on their weighting in the total performance

score calculation.

Efficiency score is a claim based measure that measures the efficiency of resource

utilization and is weighted at 20% of total performance score (25% as of 2016),

(cms.gov). Outcome scores measure clinical care outcomes such as 30 day mortality,

patient safety, inpatient clinical complications, secondary infections, readmissions,

disease progression or acute exacerbations and are weighted at 30% of total performance

score (40% as of 2016) (cms.gov). Based on their weight and financial implications, it is

possible that CEOs will invest more resources toward improving outcome scores, even

potentially at the expense of efficiency score.

The supplemental study analysis also found that hospitals led by CEO with

clinical degree were associated with better outcome scores. One potential explanation for

this finding is that CEOs with a clinical backgrounds may focus more on improving

clinical measures, which is consistent with the upper echelons theory arguments that

leadership background may affect the field of vision, determine priorities and choices

(Hambrick, 2005). Therefore, those CEOs with clinical background more likely will

focus on clinical outcomes and strategies to effectively improve them.

The study failed to support hypotheses 1 and 2, CEOs with terminal degree and

clinical terminal degree education will be associated with better patient experience scores

compared to those without terminal and clinical terminal degrees, respectively.

CEO Tenure

The study found that the shorter CEO tenure was associated with lower patient

experience scores. Therefore, the study, failed to support hypothesis 3.

Previous research has already indicated that patient centered cultures are critical

to stimulate better patient experience scores and require CEOs who understand their

immediate environments and stakeholder needs (Bramley, 2014; J. Chen et al., 2014;

Latham, 2013; McClelland, 2014; Tsimtsiou et al., 2014). Therefore, CEOs who have

been in their positions for longer periods of time have greater awareness and

understanding of their environments and stakeholder needs, and therefore are more likely

to effectively promote better patient experience.

Shorter tenure, however, was significantly positively associated with higher

clinical process of care domain scores when controlled for both clinical and clinical

terminal degrees of CEOs. Consequently, shorter tenure was positively and significantly

associated with clinical process of care domain scores regardless of the CEO clinical

background. This finding may suggest that shorter tenured CEOs will more likely

improve objective measures by implementing innovative strategies (clinical process of

care) instead of subjective measures (patient experience), because objective measures are

less dependent on knowing the hospital environments and stakeholder perceptions,

preferences and expectations.

CEO Gender

The most robust finding of the study is related to gender. Specifically, those

hospitals led by female CEOs were associated with significantly higher patient

experience scores. This finding supports hypothesis 4. Supplementary analysis also found

that hospitals with female CEOs were associated with higher clinical process of care,

efficiency and total performance scores.

Previous research has suggested that female executives may be more flexible,

intuitive, collaborative, transparent, compassionate, trust building, innovative, and

transformative leaders capable of inspiring positive work environments and morale

(Appelbaum et al., 2003; Eagly et al., 2003; Eagly & Johnson, 1990; Kark et al., 2012;

KLCM, 2014; Maniero, 1994; Paton & Dempster, 2002; Paustian-Underdahl et al.,

2014). It is possible that these same qualities may be important for cultivating a patient

centered environment that can support and sustain better patient experiences, and thus,

may account for the differences between hospitals with male and female CEOs observed

in this study.

Control Variables

There were several notable findings with respect to the study’s control variables.

This section focuses on these variables significantly associated with several different

value based purchasing domain scores.

Larger hospitals, on average, reported lower patient experience, efficiency and

total performance scores. This finding is consistent with other research that smaller

hospitals report higher patient experience scores and other performance measures

(Handel et al., 2014; Lehrman et al., 2010; Rangachari, 2007; Rogers et al., 2004; Samina

et al., 2008; Sjetne et al., 2007). More generally, however, the empirical evidence has

been mixed regarding hospital size and its effects on outcomes. Some studies have

suggested that larger hospitals have the resources to implement performance

improvement initiatives (Fong et al., 2008).Other studies, however, have argued that

larger hospitals are typically busier places with higher error rates related to longer work

hours, staff burnout, longer laboratory/radiology turnaround times, and provider delays.

Hospital ownership was also associated with multiple domains scores of value

based purchasing. Specifically, for-profit hospitals were associated with better patient

experience, clinical process of care and efficiency scores. Other studies have stated that

for-profit hospitals are more likely to be proactive in meeting stakeholders’ needs in

order to increase revenues and fulfill shareholders’ profit expectation (Lehrman et al.,

2010; Wilson & Stranahan, 2000).

For-profit hospitals are also more likely to develop shared alliances with others

organizations, more likely to encourage physician involvement, have higher degree of

community orientation and better patient perception compared to nonprofits (Proenca,

Rosko, & Zinn, 2000; Shortell & Evashwick, 1981). Hence, for-profit hospitals may be

more likely to be proactive in meeting consumer objective and subjective measures, that

will in turn translate into higher patient experience scores.

High market competition was associated with significantly lower patient

experience scores, but higher efficiency scores. Some researchers have suggested that

competition is a motivating factor for restructuring and other types of organizational

changes (Dayaratna, 2013; Lamb, Smith, Weeks, & Queram, 2013). Such restructuring,

however, can cause unintended consequences in the form of low quality performance

(Barkema & Schijven, 2008; Goldman & Dudley, 2008; Kaufman, 2013; Lutfiyya et al.,

2007; J. A. Miles, 2012; R. E. Miles, Snow, Meyer, & Coleman, 1978). For example,

some hospitals reduce registered nurse (RN) hours to optimize resource utilization and

reduce costs, which negatively affected patient quality, safety, and overall satisfaction

(Person et al., 2004). Thus, it is possible that market competition could improve

efficiency while negatively affecting patient experience scores.

Finally, the study exhibited a significant decline in patient experience and all

other value based purchasing scores from 2013 to 2014. One explanation for this decline

is that the official value based purchasing reporting started in 2013 when most hospitals

were still in their early stages of the implementation process, and even Medicare did not

have uniform processes for quality data manipulation (Dooling, 2012). In fact, it was only

in 2013 that the new uniform process called Structured Data Capture (SDC) was

implemented to fully automate the data collection and dissemination processes (Garrido

et al., 2013). As such, data reporting became more refined in 2014, potentially leading to

more accurate scores that were lower than 2013.

Another potential explanation for the decline in scores between 2013 and 2014

pertains to Medicaid expansion. CA was an early adopter of ACA Medicaid expansion,

which doubled the number of Medicaid enrollees (Medicaid.gov, 2015). Consequently,

there was an influx of low income, vulnerable patients with various serious conditions in

hospitals. Research has shown that hospitals with more vulnerable populations in low

income areas tend to report lower performance scores, including patient experience

(Kang & Hasnain-Wynia, 2011). Similarly, other research has shown that safety net

hospitals report lower patient experience scores because low income patients are less

likely to rate their inpatient experiences high (Chatterjee, Joynt, Orav, & Jha, 2012;

Futurescan, 2013; Gilman et al., 2014).

Limitations and Opportunities for Future Research

This study was the first to examine the relationship between hospital CEO

characteristics and patient experience, specifically and value based purchasing

performance more generally. In doing so, the study provides important baseline insights

into the ways that hospital leadership attributes may support performance under ACA’s

value based purchasing. However, the study had a number of limitations that should be

considered when interpreting the findings and/or using study findings for future research.

First, this was a pooled, cross sectional study that used two years of data to

examine the relationships between CEO characteristics and patient experience scores.

Issues related to changes in the strength and direction of the relationships over time were

not explicitly tested as questions of change or sustained improvements in performance

could not be adequately addressed within such a short time frame. Likewise, the study

could not rule out the possibility that better performing hospitals attract a certain type of

CEO (i.e., reverse causality).

Second, the value based purchasing data were collected from secondary data

sources. Despite CMS and AHRQ rules and guidelines for data quality, integrity, and

reporting, these data could have issues with accuracy and completeness. Third, because

this study was limited to CA hospitals only, there are limits to the study’s

generalizability.

Fourth, the CEO characteristics considered in the study were collected from

various sources, and it is possible that the information published in those sources could

be inaccurate despite efforts to mitigate these issues by triangulating and verifying

characteristics across these sources to enhance their accuracy (e.g., CEO's office

submitted the wrong information about his/her background). Finally, the study focused

on only three CEO characteristics (gender, tenure, and education) and did not assess other

CEO attributes, such as leadership behaviors, skills, styles, and other background

characteristics that may also be important predictors of patient experience and other value

based purchasing scores. The consideration of these attributes in future research would

constitute an important extension of this research.

Study Implications

Patient experience scores have become important indicators of organizational

performance and can differentiate hospitals in the marketplace. Consequently, CEOs and

other hospital leaders increasingly view patient experience and strategies for its

improvement as important determinants for the future success of their organizations

(Manary et al., 2013). Findings from this study with reference to the relationships

between CEO characteristics (gender, education, and tenure) and patient experience are

timely and important for providing insight into ways to improve patient experience.

Implications for Future Research

The study’s statistical analysis indicated that several CEO characteristics were

significantly associated with patient experience scores. Even so, these characteristics and

other model covariates did not account for a substantial amount of variation in the study’s

dependent variables, indicating that other factors must account for variation between

hospitals. Hence, future researchers may want to consider other relevant explanatory

variables that may impact patient experience such as CEO behaviors or personality traits.

Future researchers could also examine CEO degrees from CAHME (Commission on

Accreditation of Healthcare Management Education) accredited programs, compared to

CEOs who do not have degrees from CAHME accredited programs.

Similarly, organizational characteristics such as system affiliation, funding

sources, board composition, hospital rating, organizational cultures, and years in

business, may be important considerations for future research. Patient characteristics such

as gender, education, citizenship status, cultural background, religion, health status,

inpatient utilization, risk, marital status, immigration status, and other relevant

characteristics may also potentially impact patient experience.

The study focused on hospitals in California. Future researchers can build upon

the study’s findings by including a larger sample of hospitals, including and especially

hospitals from other states. A larger sample may help improve the statistical power and

precision of the study’s relationships. Including hospitals from other states can also

enable researchers to control for or identify state-level differences in these relationships.

There is also an opportunity to study these relationships longitudinally (Kraemer,

1994; S. A. Miller, 1998). Longitudinal studies are better suited to identifying changes

over time, may increase statistical power and provide more rigorous tests of potential

cause and effect relationships (Mednick, Griffith, & Mednick, 1981).

Implications for Practice

Patient experience is forcing hospitals to reconsider their care delivery systems to

provide higher value and improve patient experience. Such considerations require

strategic leadership committed to quality and transparency (Holzer & Minder, 2011;

Merlino & Raman, 2013). This study suggests that different types of CEOs may be

associated with patient experience scores. Such findings may be important information

for hospital boards and CEOs when hiring executives, particularly those focused on

improving performance in the areas of patient experience and other value based

purchasing scores.

Likewise, the findings of this study may have implications for executive recruiters

when trying to identify and/or develop hospital leaders who can develop and implement

strategies that promote patient experience and value based purchasing more broadly.

Conclusion

The Affordable Care Act was designed to reduce CMS payments by increasing

value (Orszag & Emanuel, 2014; CBO). The findings of this study suggest that hospitals

with certain types of CEOs may perform better with respect to patient experience.

Therefore, the study may provide important information for identifying ways to improve

the patient experience in hospitals.

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APPENDIX A

INSTITUTIONAL REVIEW BOARD APPROVAL

APPENDIX B

CMS PE DATA SAMPLE PAGE

Medicare Hospital Compare Data

Provider #/ Hospital Name Address City State Zip County Unweighted PE

Weighted PE

050002

ST ROSE

HOSPITAL

27200

CALAROGA AVE

HAYWARD

94545

ALAMEDA

22. 0

6.60

050006

ST JOSEPH

HOSPITAL

2700 DOLBEER

EUREKA

95501

HUMBOLDT

15.0

4.50

050007

PENINSULA

MEDICAL

CENTER

1501

TROUSDALE

DRIVE

BURLINGAME

94010

SAN MATEO

49.0

14.70

050008

CALIFORNIA

PACIFIC

MEDICAL CTR-

DAVIES CAMPUS

HOSP

45 CASTRO

STREET

SAN FRANCISCO

94114

SAN FRANCISCO

16.0

4.80

050009

QUEEN OF THE

VALLEY

MEDICAL

CENTER

1000 TRANCAS

NAPA

94558

NAPA

21.0

6.30

050013

ST HELENA

HOSPITAL

10 WOODLAND

ROAD

SAINT HELENA

94574

NAPA

69.0

20.70

050014

SUTTER

AMADOR

HOSPITAL

200 MISSION

BLVD

JACKSON

95642

AMADOR

32.0

9.60

050016

MARIAN

REGIONAL

MEDICAL

CENTER,

ARROYO

GRANDE

345 S HALCYON

ARROYO

GRANDE

93420

SAN LUIS

OBISPO

35. 0

10.50

050017

MERCY

GENERAL

HOSPITAL

4001 J ST

SACRAMENTO

95819

SACRAMENTO

25.00

7.50

050018

PACIFIC

ALLIANCE

MEDICAL

CENTER

531 W COLLEGE

LOS ANGELES

90012

LOS ANGELES

19.0

5.70

050022

RIVERSIDE

COMMUNITY

HOSPITAL

4445 MAGNOLIA

AVENUE

RIVERSIDE

92501

RIVERSIDE

14.0

4.20

050024

PARADISE

VALLEY

HOSPITAL

2400 EAST 4TH

NATIONAL CITY

91950

SAN DIEGO

21.0

6.30

APPENDIX C

CHA MEMBERSHIP DIRECTORY

APPENDIX D

HOSPITAL WEBSITE CEO PAGE

LINDA BRADLEY - CEO, CHAIR OF THE GOVERNING BOARD

Linda Bradley, RN/JD serves as Chief Executive Officer of Centinela Hospital

Medical Center, a position she has held since May, 2010. During her tenure she has been

instrumental in assisting Centinela to achieve quality distinctions including the

Distinguished Hospital Award – Clinical Excellence in 2010, 2011, and 2012, top

ranking in the nation and the state for stroke care, and Truven Healthcare Analytics

(formerly Thomson Reuters) Top 100 Hospital for 2012.

Linda completed her RN studies in San Bernardino, California and later went on to earn

her Juris Doctor degree, graduating from Thomas Jefferson School of Law in San Diego,

magna cum laude. She is a sought-after and accomplished educator of physicians, nurses,

other clinical practitioners, and assists in creating practical implementation strategies to

achieve organization strategic results in this complex and ever-changing healthcare

landscape.

Serving on the original Board of the California Hospital Patient Safety Organization

(CHPSO), her commitment to quality care and patient safety is unquestionable. She

comes to Centinela Hospital with decades of healthcare delivery experience, including

serving on the CHA and UHA Boards. Additionally she has educated thousands of

healthcare providers throughout California as faculty for CHA.

Linda is a native Southern Californian where she continues to reside with her husband.

In her current position, she provides day-to-day operational leadership for the hospital;

interfaces with community stakeholders and is an integral part of Prime Healthcare

Services senior management team. She states, "Prime Healthcare Services has provided

me with an amazing opportunity to continue the legacy of Centinela Hospital Medical

Center, which has been providing hospital services to surrounding communities since

1924. I am honored to be part of the Centinela team. The dedication and commitment of

the staff and physicians in providing our patients the highest quality of care is truly

humbling. I look forward to continuing to build upon the success of the hospital and to

provide an even greater commitment to the communities that we serve."

APPENDIX E

LINKEDIN CEO PROFILE PAGE

APPENDIX F

CA CENSUS DATA

100

Agoura Hills (city), California

Want more? Browse data sets for Agoura Hills (city)

People QuickFacts

Agoura

Hills

California

Population, 2013 estimate

20,681

38,332,521

Population, 2012 estimate

20,589

37,999,878

Population, 2010 (April 1) estimates base

20,330

37,253,959

Population, percent change, April 1, 2010 to

July 1, 2013

1.7%

2.9%

Population, percent change, April 1, 2010 to

July 1, 2012

1.3%

2.0%

Population, 2010

20,330

37,253,956

Persons under 5 years, percent, 2010

4.4%

6.8%

Persons under 18 years, percent, 2010

24.1%

25.0%

Persons 65 years and over, percent, 2010

11.3%

11.4%

Female persons, percent, 2010

50.7%

50.3%

White alone, percent, 2010 (a)

84.3%

57.6%

Black or African American alone, percent,

2010 (a)

1.3%

6.2%

American Indian and Alaska Native alone,

percent, 2010 (a)

0.3%

1.0%

Asian alone, percent, 2010 (a)

7.5%

13.0%

Native Hawaiian and Other Pacific Islander

alone, percent, 2010 (a)

0.1%

0.4%

Two or More Races, percent, 2010

3.6%

4.9%

Hispanic or Latino, percent, 2010 (b)

9.5%

37.6%

White alone, not Hispanic or Latino, percent,

2010

78.6%

40.1%

Living in same house 1 year & over, percent,

2008-2012

92.2%

84.2%

Foreign born persons, percent, 2008-2012

16.6%

27.1%

Language other than English spoken at home,

pct age 5+, 2008-2012

19.7%

43.5%

High school graduate or higher, percent of

persons age 25+, 2008-2012

95.5%

81.0%

101

APPENDIX G

CENSUS DATA: MARGIN OF ERROR

102

Source: U.S. Census Bureau, 2009-2013; 5-Year American Community Survey

California Total Estimate Margin of Error

Total population

37,659,181

***

SEX AND AGE

Male

49.7%

+/-0.1

Female

50.3%

+/-0.1

Under 5 years

6.7%

+/-0.1

5 to 17 years

17.8%

+/-0.1

18 to 24 years

10.5%

+/-0.1

25 to 44 years

28.1%

+/-0.1

45 to 54 years

13.9%

+/-0.1

55 to 64 years

11.1%

+/-0.1

65 to 74 years

6.4%

+/-0.1

75 to 84 years

3.7%

+/-0.1

85 years and over

1.7%

+/-0.1

Median age (years)

35.4

+/-0.1

RACE AND HISPANIC OR LATINO

ORIGIN

One race

95.7%

+/-0.1

White

62.3%

+/-0.1

Black or African American

6.0%

+/-0.1

American Indian and Alaska Native

0.8%

+/-0.1

Asian

13.3%

+/-0.1

Native Hawaiian and Other Pacific

Islander

0.4%

+/-0.1

Explanation of Symbols:

1. An '**' entry in the margin of error column indicates that either no sample observations or too few

sample observations were available to compute a standard error and thus the margin of error. A statistical

test is not appropriate.

2. An '-' entry in the estimate column indicates that either no sample observations or too few sample

observations were available to compute an estimate, or a ratio of medians cannot be calculated because one

or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.

3. An '-' following a median estimate means the median falls in the lowest interval of an open-ended

distribution.

4. An '+' following a median estimate means the median falls in the upper interval of an open-ended

distribution.

5. An '***' entry in the margin of error column indicates that the median falls in the lowest interval or

upper interval of an open-ended distribution. A statistical test is not appropriate.

6. An '*****' entry in the margin of error column indicates that the estimate is controlled. A statistical

test for sampling variability is not appropriate.

7. An 'N' entry in the estimate and margin of error columns indicates that data for this geographic area

cannot be displayed because the number of sample cases is too small.

8. An '(X)' means that the estimate is not applicable or not available.

103

APPENDIX H

TERMINAL DEGREE LIST, UNITED STATES

104

D.Ac., D.Acu

Doctor of

Acupuncture

D.G.S.

Doctor of Geological

Science

D.A.O.M.

Doctor of

Acupuncture and

Oriental Medicine

D.H.A.

Doctor of Health

Administration

Dr.AP

Doctor of Anesthesia

Practice

D.H.S.

Doctor of Health and

Safety

D.A.S

Doctor of Applied

Science

D.H.Ed

Doctor of Health

Education

D.Arch

Doctor of Architecture

D.H.L.

Doctor of Hebrew

Literature/Letters

D.A., Art.D.

Doctor of Arts

D.H.Sc.

Doctor of Health

Science

D.A.T.

Doctor of Athletic

Training

D.H.S.

Doctor of Hebrew

Studies

Au.D

Doctor of Audiology

D.Hum.Litt., L.H.D.,

Litt.D., LTD

Doctor of Humane

Letters

D.B.A.

Doctor of Business

Administration

D.I.T.

Doctor of Industrial

Technology

J.C.D.

Doctor of Canon Law

D.I.T.

Doctor of Information

Technology

D.Chem.

Doctor of Chemistry

S.J.D., J.S.D.

Doctor of Juridical

Science

D.C.

Doctor of Chiropractic

J.D.

Doctor of

Jurisprudence

D.C.N.

Doctor of Clinical

Nutrition

L.P.D., D.L.P.

Doctor of Law and

Policy

D.C.L.

Doctor of

Comparative Law

Doctor of Civil Law

D.L.S.

Doctor of Library

Science

D.C.S.

Doctor of Computer

Science

D.Litt. et Phil.

Doctor of Literature

and Philosophy

D.C.J.

Doctor of Criminal

Justice

Dmgt.

Doctor of Management

D.Crim

Doctor of Criminology

D.M.H.

Doctor of Medical

Humanities

D.M.D.

Doctor of Dental

Medicine

D.M.Sc.

Doctor of Medical

Science

D.D.S.

Doctor of Dental

Surgery

M.D.

Doctor of Medicine

D.Des.

Doctor of Design

D.Min., D.M.

Doctor of Ministry

Ed.D.

Doctor of Education

D.M.L.

Doctor of Modern

Languages

D.Emg.

Doctor of Engineering

D.Mus, Mus.Doc.

Doctor of Music

D.E.Sc., Sc.D E.

Doctor of Engineering

Science

D.M.A., A.Mus.D.

Doctor of Musical Arts

D.Env.

Doctor of

Environmental

Science and

Engineering

D.M.E.

Doctor of Musical

Education

105

D.F.A.

Doctor of Fine Arts

D.P.M.

Doctor of Podiatric

Medicine

D.F.

Doctor of Forestry

D.P.T., D.Th.P.

Doctor of Practical

Theology

D.M.T.

Doctor of Music

Therapy

D.P.S.

Doctor of Professional

Studies

D.N.

Doctor of Naprapathic

Medicine

Psy.D

Doctor of Psychology

N.D., N.M.D.

Doctor of

Naturopathic

Medicine

D.P.A.

Doctor of Public

Administration

D.N.P.

Doctor of Nursing

Practice

D.P.H.

Doctor of Public

Health

D.O.M., O.M.D.

Doctor of

Occupational Therapy

D.Rec., D.R.

Doctor of Recreation

O.D.

Doctor of Optometry

Rh.D.

Doctor of

Rehabilitation

D.O.M., O.M.D.

Doctor of Oriental

Medicine

D.Sc., Sc.D.

Doctor of Science

D.O.

Doctor of Osteopathic

Medicine

D.Sc.D.

Doctor of Science in

Dentistry

D.PC

Doctor of Pastoral

Counseling

D.Sc.H.

Doctor of Science and

Hygiene

Pharm.D

Doctor of Pharmacy

L.Sc.D.

Doctor of the Science

of Law

Ph.D

Doctor of Philosophy

D.S.Sc.

Doctor of Social

Science

D.P.E.

Doctor of Physical

Education

D.S.W.

Doctor of Social Work

D.P.T.

Doctor of Physical

Therapy

D.S.M.

Doctor of Sacred

Music

S.T.D.

Doctor of Sacred

Theology

Th.D.

Doctor of Theology

D.V.M.

Doctor of Veterinary

Medicine

D.B.H.

Doctor of Behavioral

Health

D.D.

Divinitatis Doctor

S.D.

Doctor of Science

D.A.S.

Doctor of Applied

Science

D.Ch.

Doctor of Surgery

O.T.D

Doctorate of

Occupational Therapy

DScPA

Doctor of science

physician assistant

D.P.C.

Doctor of Professional

Counseling

Dr.PH

Doctor of Public

Health

106

APPENDIX I

CLINICAL TERMINAL DEGREE LIST

107

Au.D

Doctor of Audiology

D.O.

Doctor of Osteopathic

Medicine

D.Sc.H.

Doctor of Science and

Hygiene

D.P.T.

Doctor of Physical

Therapy

D.C.

Doctor of Chiropractic

M.D.

Doctor of Medicine

D.D.S.

Doctor of Dental

Surgery

D.P.M.

Doctor of Podiatric

Medicine

D.M.D.

Doctor of Dental

Medicine

D.S.W.

Doctor of Social Work

O.T.D.

Doctorate of

Occupational Therapy

Dr.AP

Doctor of Anesthesia

Practice

O.D.

Doctor of Optometry

D.N.

Doctor of Naprapathic

Medicine

D.N.P.

Doctor of Nursing

Practice

Sc.D.

Doctor of Medical

Science

Rh.D

Doctor of

Rehabilitation

D.Ac., D.Acu

Doctor of

Acupuncture

D.A.O.M., D.O.M.,

O.M.D

Doctor of

Acupuncture and

Oriental Medicine

D.C.N.

Doctor of Clinical

Nutrition

D.B.H.

Doctor of Behavioral

Health

D.M.T.

Doctor of Music

Therapy

D.D.S.

Doctor of Dental

Surgery

DScPA

Doctor of Science

Physician Assistant

D.H.Sc.

Doctor of Health

Science

Psy.D

Doctor of Clinical

Psychology

D.M.Sc.

Doctor of Medical

Science

D.Sc.

Doctor of Science

D.Sc.D.

Doctor of Science in

Dentistry

DCh

Doctor of Surgery

D.P.C.

Doctor of Professional

Counseling

Pharm.D.

Doctor of Pharmacy

108

APPENDIX J

HCAHPS SURVEY

109

SURVEY INSTRUCTIONS

♦ You should only fill out this survey if you were the patient during the hospital stay

named in the cover letter. Do not fill out this survey if you were not the patient.

♦ Answer all the questions by checking the box to the left of your answer.

♦ You are sometimes told to skip over some questions in this survey. When this

happens you will see an arrow with a note that tells you what question to answer

next, like this:

Yes

No If No, Go to Question 1

You may notice a number on the survey. This number is used to let us know if

you returned your survey so we don't have to send you reminders.

Please note: Questions 1-25 in this survey are part of a national initiative to measure the

quality of care in hospitals. OMB #0938-0981

110

Please answer the questions

in this survey about your stay

at the hospital named on the

cover letter. Do not include

any other hospital stays in

your answers.

YOUR CARE FROM

NURSES

1. During this hospital stay,

how often did nurses treat you

with courtesy and respect?

1Never

2Sometimes

3Usually

4Always

2. During this hospital stay,

how often did nurses listen

carefully to you?

1Never

2Sometimes

3Usually

4Always

3. During this hospital stay,

how often did nurses explain

things in a way you could

understand?

1Never

2Sometimes

3Usually

4Always

4. During this hospital stay,

after you pressed the call

button, how often did you get

help as soon as you wanted

it?

1Never

2Sometimes

3Usually

4Always

9I never pressed the call

button

111

YOUR CARE FROM

DOCTORS

5. During this hospital stay,

how often did doctors treat

you with courtesy and

respect?

1Never

2Sometimes

3Usually

4Always

6. During this hospital stay,

how often did doctors listen

carefully to you?

1Never

2Sometimes

3Usually

4Always

7. During this hospital stay,

how often did doctors explain

things in a way you could

understand?

1Never

2Sometimes

3Usually

4Always

THE HOSPITAL

ENVIRONMENT

8. During this hospital stay,

how often were your room and

bathroom kept clean?

1Never

2Sometimes

3Usually

4Always

9. During this hospital stay,

how often was the area

around your room quiet at

night?

1Never

2Sometimes

3Usually

4Always

YOUR EXPERIENCES IN THIS

HOSPITAL

10. During this hospital stay, did you

need help from nurses or other

hospital staff in getting to the

bathroom or in using a bedpan?

1Yes

2NoIf No, Go to Question

11. How often did you get help in

getting to the bathroom or in using a

bedpan as soon as you wanted?

1Never

2Sometimes

3Usually

4Always

12. During this hospital stay, did you

need medicine for pain?

1Yes

2No If No, Go to Question

13. During this hospital stay, how

often was your pain well controlled?

1Never

2Sometimes

3Usually

4Always

14. During this hospital stay, how

often did the hospital staff do

everything they could to help you

with your pain?

1Never

2Sometimes

3Usually

4Always

112

15. During this hospital stay,

were you given any medicine

that you had not taken

before?

1Yes

2No If No, Go to

Question 18

16. Before giving you any new

medicine, how often did

hospital staff tell you what the

medicine was for?

1Never

2Sometimes

3Usually

4Always

17. Before giving you any new

medicine, how often did

hospital staff describe

possible side effects in a way

you could understand?

1Never

2Sometimes

3Usually

4Always

WHEN YOU LEFT THE

HOSPITAL

18. After you left the hospital,

did you go directly to your

own home, to someone else’s

home, or to another health

facility?

1Own home

2Someone else’s home

3Another health

facility If Another, Go to

Question 21

19. During this hospital stay,

did doctors, nurses or other

hospital staff talk with you

about whether you would

have the help you needed

when you left the hospital?

1Yes

2No

20. During this hospital stay, did you

get information in writing about

what symptoms or health problems

to look out for after you left the

hospital?

1Yes

2No

OVERALL RATING OF

HOSPITAL

Please answer the following

questions about your stay at the

hospital named on the cover letter.

Do not include any other hospital

stays in your answers.

21. Using any number from 0 to 10,

where 0 is the worst hospital

possible and 10 is the best hospital

possible, what number would you

use to rate this hospital during your

stay?

00 Worst hospital possible

11

22

33

44

55

66

77

88

99

1010 Best hospital possible

113

22. Would you recommend

this hospital to your friends

and family?

1Definitely no

2Probably no

3Probably yes

4Definitely yes

UNDERSTANDING YOUR

CARE WHEN YOU LEFT

THE HOSPITAL

23. During this hospital stay,

staff took my preferences and

those of my family or

caregiver into account in

deciding what my health care

needs would be when I left.

1Strongly disagree

2Disagree

3Agree

4Strongly agree

24. When I left the hospital, I

had a good understanding of

the things I was responsible

for in managing my health.

1Strongly disagree

2Disagree

3Agree

4Strongly agree

25. When I left the hospital, I

clearly understood the

purpose for taking each of my

medications.

1Strongly disagree

2Disagree

3Agree

4Strongly agree

5I was not given any

medication when I left the

hospital

ABOUT YOU

There are only a few

remaining items left.

26. During this hospital stay,

were you admitted to this

hospital through the Emergency

Room?

1Yes

2No

27. In general, how would you

rate your overall health?

1Excellent

2Very good

3Good

4Fair

5Poor

28. In general, how would you

rate your overall mental or

emotional health?

1Excellent

2Very good

3Good

4Fair

5Poor

29. What is the highest grade or

level of school that you have

completed?

18th grade or less

2Some high school, but did

not graduate

3High school graduate or GED

4Some college or 2-year

degree

54-year college graduate

6More than 4-year college

degree

114

30. Are you of Spanish, Hispanic or Latino origin or descent?

1No, not Spanish/Hispanic/Latino

2Yes, Puerto Rican

3Yes, Mexican, Mexican American, Chicano

4Yes, Cuban

5Yes, other Spanish/Hispanic/Latino

31. What is your race? Please choose one or more.

1White

2Black or African American

3Asian

4Native Hawaiian or other Pacific Islander

5American Indian or Alaska Native