relationships between patient satisfaction, quality, outcomes and ownership type in us hospitals an...

Relationships between Patient Satisfaction, Quality, Outcomes

and Ownership Type in US Hospitals: an Empirical Study

Ediyattumangalam R. Shivaji

ittumatra uumatra ta

An Abstract

of a dissertation submitted to the Graduate School of Maharishi University

of Management in partial

fulllment of the requirements for the degree of

Doctor of Philosophy

May, 2012

Dissertation Supervisor: Dr. Bruce McCollum

1Abstract


Public concerns about rising health costs and deteriorating quality of service in the US

have become a serious issue. The Institute of Medicine (IOM)1 report brought out the need for

overhauling the US Healthcare thoroughly. This report recommended that healthcare executives

should focus on performance improvement, driven by process, data, and evidence rather than

relying on technology or working harder. Healthcare organizations face multiple objectives and

constraints, while implementing performance improvement,.

The design of the current study was nonexperimental and the study analyzed available

archival data on patient satisfaction, process of care quality measures and outcome of care

measures. The study tested nine research hypotheses about the relationships between these

measures. The study also brought out the main components contributing to patient satisfaction

and process of care quality measures.

The study used the public data on US hospitals, downloaded from the CMS database,

maintained by the Center for Medicare and Medicaid services, a federal government agency. Data

from over 4,500 hospitals were used in the analysis.

The major ndings are summarized as follows:

1. Five components of patient satisfaction were identied and the implications to hospitals

were discussed.

2. Nine research hypotheses were tested, and the evidence was mixed.

3. Mean outcome rates in Church owned hospitals were signicantly better than the other

seven groups and denitely not worse.1 IOM. (2001) Crossing the Quality Chasm: A New Health System for the 21st Century. Committee on Quality of

Health Care in America, Institute of Medicine.

Relationships between Patient Satisfaction, Quality, Outcomes and Ownership Type in US

Hospitals: an Empirical Study



A Dissertation

submitted to the Graduate School of Maharishi University of Management in partial

fulllment of the requirements for the degree of

Doctor of Philosophy

May, 2012

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Graduate School, Maharishi University of Management

Faireld, Iowa

Transcendental Meditation technique, Maharishi TM-Sidhi program, Maharishi Vedic

Approach to Health, Maharishi Ayur-Veda , Science of Creative Intelligence, Maharishi Vedic

Science, and Maharishi University of Management are registered or common law trademarks

licensed to Maharishi Vedic Education Development Corporation and used with permission.

iv

r umatra

In line with the Vedic scholastic traditions, I begin my work, humbly thanking all my teachers for

giving me the knowledge and skills that enabled me to write this dissertation.

In particular, I respectfully dedicate this work to the great teacher of these teachers, His Holiness

Maharishi Mahesh Yogi and his spiritual master Guru Dev Shankaracharya Swami Brahmananda

Saraswati.

umatra

vAbstract

Public concerns about rising health costs and deteriorating quality of service in the US have

become a serious issue. The Institute of Medicine (IOM)1 report brought out the need for

overhauling the US Healthcare thoroughly. This report recommended that healthcare executives

should focus on performance improvement, driven by process, data, and evidence rather than

relying on technology or working harder. Healthcare organizations face multiple objectives and

constraints, while implementing performance improvement,.

The design of the current study was nonexperimental and the study analyzed available archival

data on patient satisfaction, process of care quality measures and outcome of care measures. The

study tested nine research hypotheses about the relationships between these measures. The study

also brought out the main components contributing to patient satisfaction and process of care

quality measures.

The study used the public data on US hospitals, downloaded from the CMS database, maintained

by the Center for Medicare and Medicaid services, a federal government agency. Data from over

4,500 hospitals were used in the analysis.

The major ndings are summarized as follows:

1. Five components of patient satisfaction were identied and the implications to hospitals

were discussed.

2. Nine research hypotheses were tested, and the evidence was mixed.

3. Mean outcome rates in Church owned hospitals were signicantly better than the other

seven groups and denitely not worse.

1 IOM. (2001) Crossing the Quality Chasm: A New Health System for the 21st Century. Committee on Quality ofHealth Care in America, Institute of Medicine.

vi

4. Evidence was mixed for negative association between patient satisfaction and outcomes.

5. Evidence was mixed for negative association between process of care quality and outcomes.

The study found some empirical evidence for encouraging hospitals to adopt the qualities

friendship, compassion, joy of serving and equanimity advocated by the ancient Vedic

physician Charaka as the prime qualities required by healthcare professionals. The study has

many strengths such as identifying the principal components of satisfaction and quality, using the

complete CMS data on US hospitals and obtaining some empirical evidence on the relationships

between satisfaction, process-of-care quality and the outcomes. Some empirical evidence was

also obtained on the need for qualities like compassion among healthcare staff.

The study ndings are limited by the reliability of the archival data used. Statistical conclusion

validity issues were adequately controlled during testing, by adopting diagnostic techniques.

However, ambiguity of temporal precedence between outcomes and process of care quality

measures is a threat to the internal validity of testing their relationship. A subsequent larger study

requiring support from CMS is proposed.

The study ndings will assist hospitals in their performance improvement activities.

vii

Table of Contents

Copyright ii

Approval iii

Dedication iv

Abstract v

List of Tables xx

List of Figures xxv

Acronyms Used in the Dissertation xxix

1 Study Overview 1

Charakas concept of healthcare quartet. . . . . . . . . . . . . . . . . . 1

Problems of healthcare in US. . . . . . . . . . . . . . . . . . . . . . . 1

High cost. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Inefciencies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Errors and patient safety. . . . . . . . . . . . . . . . . . . . . . . . . . 2

IOM reports. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Joint Commission efforts. . . . . . . . . . . . . . . . . . . . . . . . . . 3

Background of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Performance improvement. . . . . . . . . . . . . . . . . . . . . . . . . 3

Need for Organizational change. . . . . . . . . . . . . . . . . . . . . . 4

viii

Structure - process - outcome framework. . . . . . . . . . . . . . . . . 6

Measures of healthcare quality. . . . . . . . . . . . . . . . . . . . . . . 6

Qualities of healthcare staff. . . . . . . . . . . . . . . . . . . . . . . . 7

Effects of ownership type. . . . . . . . . . . . . . . . . . . . . . . . . . 7

Statement of the Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Patient satisfaction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Improving patient satisfaction. . . . . . . . . . . . . . . . . . . . . . . 9

Process-of-care quality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Ownership type. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Purposes of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Signicance of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Denition of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Operational denitions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Patient Satisfaction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Process of care quality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Outcome measures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Constitutional denitions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

CMS data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

HCAHPS survey. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Process of care quality data. . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Outcome data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Data download. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Theoretical Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Hansmanns theory on the role of nonprot enterprise. . . . . . . . . . 17

Donabedians Structure-Process-Outcomes theory. . . . . . . . . . . . 18

Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

ix

Statistical conclusion validity issues . . . . . . . . . . . . . . . . . . . . . . . 20

Internal validity issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

External validity issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Construct validity issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Delimitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Organization of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2 Literature Review 25

Chapter Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Healthcare Costs and quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Healthcare errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Studies on HCAHPS Survey Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Studies on quality data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Effects of Ownership type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Hansmanns theory of non-prot hospitals. . . . . . . . . . . . . . . . . . . . . . . 35

Relationship of patient satisfaction, quality and outcomes . . . . . . . . . . . . . . . . . 36

Strategy for searching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3 Methodology 39


Research Design and Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Description of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Archival data retrieved. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Participants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

HCAHPS survey instrument. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Time period for the downloaded data. . . . . . . . . . . . . . . . . . . . . . . . . . 41

xData coverage. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Data preparation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Threats to validity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Statistical conclusion validity issues. . . . . . . . . . . . . . . . . . . . . . . 43

Internal validity issues. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

External validity issues. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Construct validity issues. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Analysis of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Principal component analysis (PCA). . . . . . . . . . . . . . . . . . . . . . . . . . . 47

HCAHPS data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Multivariate normality. . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Multivariate outliers. . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Linearity assumption. . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Process of care quality data. . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Multivariate normality. . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Multivariate outliers. . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Missing data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Research question 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51


Assumptions to be satised in testing. . . . . . . . . . . . . . . . . . . 53


Research hypotheses under research question 3. . . . . . . . . . . . . . . . . 54



Relationship between outcomes and hospital ownership. . . . . . . . . . . . . 58

RQ 5.1 and RQ 5.2 - relationship between outcome variables and

ownership type. . . . . . . . . . . . . . . . . . . . . . . . . 60

xi

OLS assumptions that were veried. . . . . . . . . . . . . . . . . . . . 61

Relationship between patient satisfaction and outcomes. . . . . . . . . . . . . 62

RQ 5.3 and 5.4 - Relationships of outcome variables with patient

satisfaction. . . . . . . . . . . . . . . . . . . . . . . . . . 62

Relationship between outcomes and quality. . . . . . . . . . . . . . . . . . . 63

5.5 and 5.6 Relationships of Outcome Variables with process-of-

care Quality Components. . . . . . . . . . . . . . . . . . . 64

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

4 Presentation and Analysis of Data for Research Questions 1 through 4 67


Research Question 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Principal component analysis of HCAHPS data (PCA). . . . . . . . . . . . . . . . 68

Hospital consumer assessment of healthcare providers and systems

(HCAHPS). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

Survey method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

HCAHPS Sampling methods and participants. . . . . . . . . . . . . . . 68

Survey questions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Summary statistics of HCAHPS variables. . . . . . . . . . . . . . . . . 70

Checking validity of PCA assumptions. . . . . . . . . . . . . . . . . . . . . . 74

PCA results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Interpretation of principal components of HCAHPS scores. . . . . . . . . . . . . . . 79

Applying the PCA results to hospital performance improvement. . . . . . . . . . . 81


Research Hypothesis under research question 2 . . . . . . . . . . . . . . . . . . . . 82

Testing research hypotheses with OLS regression. . . . . . . . . . . . . . . . . . . 84

Testing OLS assumptions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

Test results for research question 2. . . . . . . . . . . . . . . . . . . . . . . . 86

xii

Effect sizes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

Summary of ndings. . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

Regression diagnostic tests. . . . . . . . . . . . . . . . . . . . . . . . . 88

Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89


Process of care quality data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

Validating PCA assumptions for process of care quality data. . . . . . . . . . 93

Results from PCA of process of care quality data. . . . . . . . . . . . . . . . 93

Rotating component axes of process of care quality data. . . . . . . . . 94

Interpretation of quality components . . . . . . . . . . . . . . . . . . . 95


Process of care quality data by ownership groups in . . . . . . . . . . . 98

Research hypotheses under research question 3. . . . . . . . . . . . . . 98

Validating OLS regression assumptions for quality data . . . . . . . . . 104

Regression results for quality component 1 (heart attack/failure

related) . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

Regression results for quality component 2 (pneumonia related) . . . 109

Regression results for quality component 3 (surgical care related) . . 113

Regression results for quality component 4 smoking cessation related 115

Regression results for quality component 5 prevention related . . . . 118

Summary of Regression results for RQ-3. . . . . . . . . . . . . . . . . . 121

Research Question 4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

Validating OLS regression assumptions for research question 4. . . . . . . . 123


Regression results for quality component 1 (heart attack/failure

related) . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

Regression results for quality component 2 (pneumonia related) . . . 126

xiii

Regression results for quality component 3 (surgical care related) . . 128

Regression results for quality component 4 (smoking cessation related)132

Regression results for quality component 5 (prevention related) . . . 134

Summary of Regression results for RQ-4. . . . . . . . . . . . . . . . . . 137

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

5 Presentation and Analysis of Data for Research Question 5 140

Chapter overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

5.1 and 5.2 Relationships of outcome variables with ownership types. . . . . . . . . . . . 141

Research hypotheses to be tested. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

Validating OLS assumptions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

Comparing estimated marginal plots. . . . . . . . . . . . . . . . . . . . . . . . . . 145

Test results for heart attack. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

30-day risk adjusted mortality rate. . . . . . . . . . . . . . . . . . . . . . . . 148



30-day risk adjusted readmission rate. . . . . . . . . . . . . . . . . . . . . . . 151



Test results for heart failure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153







Test results for pneumonia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159


xiv






Summary of regression results for RQ 5.1 and RQ 5.2. . . . . . . . . . . . . . . . . 165

5.3 and 5.4 Relationships of outcome variables with patient satisfaction . . . . . . . . . 166


Testing outcomes for heart attack. . . . . . . . . . . . . . . . . . . . . . . . . . . . 167







Testing outcome variables for heart failure. . . . . . . . . . . . . . . . . . . . . . . 172







Testing outcomes for pneumonia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179





xv




5.5 and 5.6 Relationships of Outcome Variables with Process of Care Quality Components184


Testing outcomes for heart attack . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185


Regression diagnostic tests. . . . . . . . . . . . . . . . . . . . . . . . 187





Testing outcomes for heart failure. . . . . . . . . . . . . . . . . . . . . . . . . . . . 192







Testing outcomes for pneumonia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198








xvi

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205

6 Discussion and Conclusion 206


Review of ndings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207


HCAHPS survey questions. . . . . . . . . . . . . . . . . . . . . . . . . . . . 207

Implications for hospitals in improving patient satisfaction. . . . . . . . . . . 209

Improving poor satisfaction component. . . . . . . . . . . . . . . . . . 209

Expected level of performance. . . . . . . . . . . . . . . . . . . . . . . 210

Cleanliness. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211


Value based payments system for Medicare payments. . . . . . . . . . . . . . 212


Principal component analysis of quality data. . . . . . . . . . . . . . . . . . 213

Statistical tests on quality differences by ownership groups. . . . . . . . . . . 214



Research hypotheses to be tested. . . . . . . . . . . . . . . . . . . . . . 217

5.1 and 5.2 Relationships of outcome variables with ownership types. . . . . . 217

Statistical comparison of mean outcomes by owner groups. . . . . . . 218

5.3 and 5.4 Relationships of outcome variables with patient satisfaction. . . . 220

5.5 and 5.6 Relationships of outcome variables with process-of-care

quality components. . . . . . . . . . . . . . . . . . . . . . . . . . . 221

Discussion of the ndings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223

Findings relative to previous studies . . . . . . . . . . . . . . . . . . . . . . . . . . 223

Research Question 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223


xvii




5.1 and 5.2 Relationships of outcome variables with ownership types. . . . . . 229

5.3 and 5.4 Relationships of outcome variables with patient satisfaction. . . . 231

5.5 and 5.6 Relationships of outcome variables with process-of-care

quality components. . . . . . . . . . . . . . . . . . . . . . . . . . . 231

Strengths of the current study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232

Limitations of the current study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234

Data issues. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234

Threats to validity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235

Implications for hospitals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237

Recommendations for further research . . . . . . . . . . . . . . . . . . . . . . . . . . . 238

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239

7 Healthcare in light of Maharishi Vedic Science and Maharishis Vedic

approach to Total Health. 240


Glossary of Vedic terms. . . . . . . . . . . . . . . . . . . . . . . . . . 241

Maharishi Vedic science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242

Maharishi Vedic Science is the science of pure knowledge. . . . . . . . . . . . . . . 243

Unied Field and Consciousness. . . . . . . . . . . . . . . . . . . . . . 244

Comparison of modern science with Maharishi Vedic Science. . . . . . 245

Application of Maharishi Vedic Science in areas of modern science. . . . . . . . . . 247

Maharishi Vedic approach to Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250

Natural Law. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251

Ayur-Veda. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252

Maharishi Ayur-Veda and Maharishis Vedic approach to Health. . . . . 253

xviii

Empirical evidence for the efcacy of Maharishis Vedic approach

to Health. . . . . . . . . . . . . . . . . . . . . . . . . . . . 255

Unied Field Chart (UFC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260

Connections of healthcare with Pure Intelligence). . . . . . . . . . . . . 260

Connecting to Unied Field using Maharishi Vedic Technologies. . . . . 265

Connecting to Unied Field using Maharishi Vedic Technologies. . . . . 265

Connecting Unied Field to Pure Intelligence and Transcendental

Consciousness. . . . . . . . . . . . . . . . . . . . . . . . . 267

R. icho Ak-kshare Chart (RAC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269

Signicance of R. icho Ak-kshare Chart (RAC). . . . . . . . . . . . . . . 269

R. icho Ak-kshare Chart (RAC) for healthcare eld. . . . . . . . . . . . . 271

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275

Problems in US healthcare. . . . . . . . . . . . . . . . . . . . . . . . . 275

Present Research. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276

Benets from the study. . . . . . . . . . . . . . . . . . . . . . . . . . . 276

Bibliography 278

A Sample data from HCAHPS surveys 295

B Sample data from outcome variables 298

C Sample data from process of care quality measures 300

D HCAHPS Q-Q plots 303

E HCAHPS Correlations Table 304

F HCAHPS Correlation Plots 308

G HCAHPS Marginal Means Plots 309

xix

H HCAHPS survey questionnaire 310

xx

List of Tables

1 Descriptive Statistics for HCAHPS Variables . . . . . . . . . . . . . . . . . . . . . 71

2 HCAHPS Components - Explained Variance . . . . . . . . . . . . . . . . . . . . . 76

3 HCAHPS: Spearmann Test - p Values . . . . . . . . . . . . . . . . . . . . . . . . . 77

4 HCAHPS Component Loadings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

5 Contribution of the HCAHPS Variables (%) after Promax Rotation . . . . . . . . . 80

6 HCAHPS Data by Hospital Ownership Groups . . . . . . . . . . . . . . . . . . . . 82

7 Normality Testing of HCAHPS Component 1 by Ownership Groups . . . . . . . . 85

8 RQ2 : Regression Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

9 RQ2 : Robust Regression Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

10 Summary Statistics for Process of Care Quality Data . . . . . . . . . . . . . . . . . 92

11 Identifying Principal Components of Process of Care Quality Data . . . . . . . . . 94

12 Explained Variance of Quality Components after Varimax Rotation . . . . . . . . . 95

13 Quality Component Loadings after Varimax Rotation . . . . . . . . . . . . . . . . 96

14 Quality Component 1 Data by Ownership Groups . . . . . . . . . . . . . . . . . . 99





19 Normality Testing Process of Care Quality Variables in Ownership Groups . . . . . 105

20 RQ3: Regression Results for Quality Component 1 by Ownership Group . . . . . . 107

21 RQ3 : Robust Regression Results for Quality Component 1 . . . . . . . . . . . . . 109

xxi









30 RQ3 - Summary of OLS Regression Coecients on Quality Components by

Ownership Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

31 RQ4 - Regression Results for Quality Component 1 by Satisfaction Components . . 124










41 RQ4 - OLS Regression Coecients on Satisfaction Components by Quality

Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

42 Outcome Variables - Descriptives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

43 Results of Shapiro-Francia W Test for Normality Outcome Variables - . . . . . . . 142

44 Levenes Homoscedasticity Test Results for Outcome Variables - . . . . . . . . . . 145

45 RQ5 - Regression results for 30-day risk adjusted mortality rate for heart attack

by owner groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

xxii

46 RQ5 - Robust Regression Results for 30-day Risk Adjusted Mortality Rate for

Heart Attack by Owner Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

47 RQ5- Regression results for 30-day risk adjusted readmission rate for heart attack

by owner groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

48 RQ5 - Robust Regression Results for 30-day Risk Adjusted Readmission Rate for

Heart Attack by Owner Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

49 RQ5- Regression results for 30-day risk adjusted mortality rate for heart failure

by owner groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154


Heart Failure by Owner Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156

51 RQ5- Regression results for 30-day risk adjusted readmission rate for heart failure

by owner groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157


Heart Failure by Owner Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

53 RQ5- Regression results for 30-day risk adjusted mortality rate for pneumonia by

owner groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160

54 RQ5 - Robust Regression Results for 30-day Risk Adjusted Mortality Rates for

Pneumonia by Owner Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162

55 RQ5- Regression results for 30-day risk adjusted readmission rate for pneumonia

by owner groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163

56 RQ5 - Robust Regression Results for 30-day Risk Adjusted Readmission Rates

for Pneumonia by Owner Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165

57 Regression Coecients on Outcome Variables and Ownership Groups . . . . . . . . 165

58 RQ5 - OLS Regression results for 30-day risk adjusted mortality rate for heart

attack by patient satisfaction component 1 . . . . . . . . . . . . . . . . . . . . . . 167


Heart Attack with Patient Satisfaction Component 1 . . . . . . . . . . . . . . . . . 169

xxiii

60 RQ5- Regression results for 30-day risk adjusted readmission rate for heart attack

by satisfaction component 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170


Heart Attack with Patient Satisfaction Component 1 . . . . . . . . . . . . . . . . . 172

62 RQ5- Regression results for 30-day risk adjusted mortality rate for heart failure

by patient satisfaction component 1 . . . . . . . . . . . . . . . . . . . . . . . . . . 173


Heart Failure with Patient Satisfaction Component 1 . . . . . . . . . . . . . . . . . 175

64 RQ5- Regression results for 30-day risk adjusted readmission rate for heart failure



Heart Failure with Patient Satisfaction Component 1 . . . . . . . . . . . . . . . . . 178

66 RQ5- Regression results for 30-day risk adjusted mortality rate for pneumonia by

patient satisfaction component 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179


Pneumonia with Patient Satisfaction Component 1 . . . . . . . . . . . . . . . . . . 181

68 RQ5- Regression results for 30-day risk adjusted readmission rate for pneumonia



Pneumonia with Patient Satisfaction Component 1 . . . . . . . . . . . . . . . . . . 184

70 Regression Coecients on Outcome Variables and Poor Satisfaction . . . . . . . . . 185

71 RQ5 - Regression results for 30-day risk adjusted mortality rate for heart attack

by quality components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186


Heart Attack with Process of Care Quality Components . . . . . . . . . . . . . . . 188

73 RQ5 - Regression results for 30-day risk adjusted readmission rate for heart attack


xxiv



75 RQ5 - Regression results for 30-day risk adjusted mortality rate for heart failure



Heart Failure with Process of Care Quality Components . . . . . . . . . . . . . . . 194

77 RQ5 - Regression results for 30-day risk adjusted readmission rate for heart failure




79 RQ5 - Regression results for 30-day risk adjusted mortality rate for pneumonia



Pneumonia with Process of Care Quality Components . . . . . . . . . . . . . . . . 201

81 RQ5 - Regression results for 30-day risk adjusted readmission rate for pneumonia



Pneumonia with Process of Care Quality Components. . . . . . . . . . . . . . . . . 204

83 Regression Coecients on Outcome Variables and Quality Components . . . . . . . 204

84 Comparison of modern science with Maharishi Vedic Science. . . . . . . . . . . . . 245

85 Application of Maharishi Vedic Science to elds of study . . . . . . . . . . . . . . . 248

86 Empirical research showing eectiveness of Maharishi approach to total health . . . 256

87 HCAHPS - Sample data Page1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296

88 HCAHPS - Sample data Page2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297

89 Outcomes - Sample data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299

90 Process of care quality - Sample data Page1 . . . . . . . . . . . . . . . . . . . . . . 301

xxv

91 Process of care quality - Sample data Page2 . . . . . . . . . . . . . . . . . . . . . . 302

92 Correlation table for HCAHPS variables . . . . . . . . . . . . . . . . . . . . . . . . 305

93 Correlation table for HCAHPS variables continued . . . . . . . . . . . . . . . . . . 306

94 Correlation table for HCAHPS variables continued . . . . . . . . . . . . . . . . . . 307

xxvi

List of Figures

1 HCAHPS - Scree Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

2 Marginal Means Plot for Patient Satisfaction Component 1 . . . . . . . . . . . . . . . 83

3 Kernel Density Plot of Residuals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

4 Quality - Scree Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

5 Biplot for Quality Components 1 and 2 . . . . . . . . . . . . . . . . . . . . . . . . . 95




9 Marginal Means Plots for Quality Component 1 . . . . . . . . . . . . . . . . . . . . . 101





14 RQ3: Kernel Density Plot of Residuals for Quality Component 1 . . . . . . . . . . . . 108








xxvii



24 QQ plots for outcome variables and ownership groups . . . . . . . . . . . . . . . . . 144

25 Marginal Means Plot for Heart Attack Mortality Rate . . . . . . . . . . . . . . . . . . 145

26 Marginal Means Plot for Heart Failure Mortality Rate . . . . . . . . . . . . . . . . . . 146

27 Marginal Means Plot for Pneumonia Mortality Rate . . . . . . . . . . . . . . . . . . . 146

28 Marginal Means Plot for Heart Attack Readmission Rate . . . . . . . . . . . . . . . . 147

29 Marginal Means Plot for Heart Failure Readmission Rate . . . . . . . . . . . . . . . . 147

30 Marginal Means Plot for Pneumonia Readmission Rate . . . . . . . . . . . . . . . . . 148

31 ACPRplot for Heart Attack Mortality Rate - Satisfaction Component 1 . . . . . . . . . 168

32 RVFplot for Heart Attack Mortality Rate - Satisfaction Component 1 . . . . . . . . . . 168

33 RVFplot for Heart Attack Readmission Rate - Satisfaction Component 1 . . . . . . . . 171

34 ACPRplot for Heart Attack Readmission Rate - Satisfaction Component 1 . . . . . . . 171

35 RVFplot for Heart Failure Mortality Rate - Satisfaction Component 1 . . . . . . . . . . 174

36 ACPRplot for Heart Failure Mortality Rate - Satisfaction Component 1 . . . . . . . . 174

37 RVFplot for Heart Failure Readmission Rate - Satisfaction Component 1 . . . . . . . . 177

38 ACPRplot for Heart Failure Readmission Rate - Satisfaction Component 1 . . . . . . . 177

39 RVFplot for Pneumonia Mortality Rate - Satisfaction Component 1 . . . . . . . . . . . 180

40 ACPRplot for Pneumonia Mortality Rate - Satisfaction Component 1 . . . . . . . . . . 181

41 RVF plot for Pneumonia Readmission Rate - Satisfaction Component 1 . . . . . . . . 183

42 ACPRplot for Pneumonia Readmission Rate - Satisfaction Component 1 . . . . . . . . 183

43 RVF plot for Heart Attack Mortality Rate - Quality Components . . . . . . . . . . . . 187

44 ACPRplot for Heart Attack Mortality Rate - Quality Component 3 . . . . . . . . . . . 188

45 RVF Plot for Heart Attack Readmission Rate - Quality Components . . . . . . . . . . 190

46 ACPRplot for Heart Attack Readmission Rate - Quality Component 1 . . . . . . . . . 191

47 RVF Plot for Heart Failure Mortality Rate - Quality Components . . . . . . . . . . . . 193

48 RVF plot for Heart Attack Mortality Rate - Quality Components . . . . . . . . . . . . 196

xxviii

49 ACPRplot for Heart Failure Readmission Rate and Quality Component 1 . . . . . . . . 197

50 ACPRplot for Heart Failure Readmission Rate and Quality Component 5 . . . . . . . . 197

51 RVFplot for Pneumonia Mortality rate - Quality Components . . . . . . . . . . . . . . 200

52 RVFplot for Pneumonia Readmission Rate - Quality Components . . . . . . . . . . . . 203

53 Unied Field Chart - Complete . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261

54 Unied Field Chart - Blow-up of healthcare portion in the upper left section . . . . . . 261

55 Unied Field Chart - Blow-up of upper right section . . . . . . . . . . . . . . . . . . 266

56 Unied Field Chart - Blow-up of lower left section . . . . . . . . . . . . . . . . . . . 267

57 Unied Field Chart - Blow-up of lower right section . . . . . . . . . . . . . . . . . . 268

58 R. icho Ak-kshare Chart - Complete . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272

59 QQ plots of patient satisfaction component 1 in different owner groups . . . . . . . . . 303

60 Correlation biplots between components . . . . . . . . . . . . . . . . . . . . . . . . . 308

61 Marginal means plots of patient satisfaction components . . . . . . . . . . . . . . . . 309

xxix

Acronyms Used in the Dissertation

HCAHPS Hospital Consumer Assessment of Healthcare Providers and Systems

IOM Institute of Medicine

JCAHO Joint Commission on Accreditation of Healthcare Organization

AHRQ Agency for Healthcare Research and Quality

AMA American Medical Association

QI Quality Indicator

HHS U.S. Department of Health and Human Services

HQA Hospital Quality Alliance

AMI Acute Myocardial Infarction

CHF Congestive Heart Failure

PN Pneumonia

PSI Patient Safety Indicator

TQM Total Quality Management

MMA Medicare Prescription Drug, Improvement, and Modernization Act

CMS Centers for Medicare and Medicaid Services

MUM Maharishi University of Management

xxx

APA American Psychological Association

URL Uniform Resource Locater or Universal Resource Locater

ES Effect Size (Cohens) (in statistics)

KMO Kaiser-Meyer-Olkin statistic for sampling adequacy in PCA (in statistics)

CLES Common Language Effect Size

EFA Exploratory Factor Analysis

PCA Principal Component Analysis

OLS Ordinary Least Squares

BLUE Best Linear Unbiased Estimator

ANOVA ANalysis Of Variance

LOWESS Locally Weighted Scatterplot Smoothing

PASW Predictive Analytics SoftWare

XLSTAT Excel based statistical software from Addinsoft, inc.

Stata A general-purpose software package from StataCorp, inc.

VIF Variance Ination Factor

UFC Unied Field Chart

RAC Richo Akshare Chart

1Chapter 1

Study Overview

This chapter introduces some of the problems with the US healthcare system, its high cost

and the need to improve the quality consistent with the high standard of living. The purpose and

signicance of the study are brought out in subsequent sections. The theoretical framework,

research questions, limitations, delimitations and study assumptions are discussed. The chapter

ends with a brief description of how the study is organized.

Charakas concept of healthcare quartet. Charaka, the ancient Vedic physician of India

(Valiathan, 2007) considered healthcare as made of a quartet composed of four parts: 1) patient,

2) physician, 3) attendants and 4) treatment and compared them to four legs of an animal. The

four parts have to function together to enable the animal to move. Charaka considered that

healthcare is accomplished by the balanced functioning of all the four parts. The present day

healthcare in the US is not marked by such a balance and consequently is facing several problems.

Problems of healthcare in US. Healthcare in the US has become hugely expensive, but

the quality is not with a commensurate level of the high cost. Also, cases of medical errors,

infections acquired during hospital stay, incidents affecting patient safety and malpractice cases

that have been reported have caused public distrust that has forced the government and regulatory

bodies to advise hospitals to embark on performance and quality improvement activities.

High cost. Public concern over ever increasing health costs is rising in US. In 2008,

total national health expenditure in the US was expected to rise by 6.9%two times the rate of

2ination. Total spending was $2.3 trillion in 2007 which translates to $7681 per person. Total

healthcare spending represented 16.2 percent of GDP, representing an increase from 15.9% in

2007 (CMS, 2008). This is the highest per capita spending on healthcare in the world. With health

insurance premiums doubling every 5 years, DoBias and Evans (2006) predicted that a familys

annual costs for health insurance would be $22,000 by the year 2010. The Money magazine

(CNN, 2012) reported that a typical family of four under an employer plan, spent more than

$20,000 on healthcare in 2012, quoting the consulting rm Milliman inc. Today (2012) reported

that the US median household income at the end of 2011 was $ 51,413. This shows that the

average family in US had to spend nearly 40% of their income to meet healthcare costs in 2012.

Inefciencies. At the same time, many patients think that the quality of healthcare

services is not with a commensurate level of the high cost. The same report (CMS, 2008)

mentions Experts agree that our health care system is riddled with inefciencies, excessive

administrative expenses, inated prices, poor management, and inappropriate care, waste and

fraud. A survey conducted by ABC News, the Kaiser Family Foundation and USA Today found

that most Americans are dissatised with the healthcare system. An overwhelming 80% think that

the costs are too high, while 54% are dissatised with the quality of healthcare (Enzi, 2007).

These evidences point to an urgent need to reduce cost and enhance the quality of healthcare. An

interview conducted among healthcare opinion leaders suggested that they had a strong belief that

comprehensive strategiesincluding nancing reform, a robust information technology

infrastructure coupled with changes to work design and culture, and alignment between nancial

and clinical accountabilitycould result in a more efcient health care system (Greiner &

Starkey, 2006).

Errors and patient safety. Kohn, Corrigan, and Donaldson (2000) estimated that

between 44,000 and 98,000 preventable deaths occur every year as a result of errors in the

health care system and preventable health care-related injuries result in costs of between $17 and

3$29 billion annually . Given the preceding scenario, many policy makers have begun to question

the value that is being delivered by the U.S. health care system to the public.

IOM reports. Institute of Medicine (IOM) concluded that the American healthcare

system is in a serious state of disrepair and is in need of transformation. The full extent of the

problems with the U.S. healthcare service delivery system is outlined in a series of IOM reports

that consider the components of medical safety, quality of care, performance measurement,

quality improvement, and workforce capacity. Together these reports clearly establish that (a)

quality of care is well below the standard that the U.S. population expects and deserves, and (b)

the sources of the problems are not a lack of goodwill or right intention but rather can be found in

the fundamental construction of the healthcare system. In response, the IOM has advocated the

strategic redesign of this structure and many components of the system (Daniels, England, Page,

& Corrigan, 2005).

Joint Commission efforts. In early 1990, in response to an increasing awareness about

inefciencies in the healthcare industry, the Joint Commission, a private sector nonprot

Organization to accredit hospitals, made changes in their hospital accreditation policy, requiring

hospitals to implement performance improvement measures. Formerly, this organization was

called as the Joint Commission on Accreditation of Healthcare Organization (JCAHO).

Background of the Study

The high cost and problems of US healthcare brought out earlier in the chapter has led

hospitals to improve their performance and quality. This section briey discusses the basic issues

faced by US hospitals in implementing performance improvement techniques.

Performance improvement. The widespread concern about the high cost coupled with

low quality in healthcare caused several hospitals to implement performance improvement

activities to improve quality and cut costs. These include the use of lean, Six Sigma, operations

4research models, and a combination of these techniques. Several successful implementations in

hospitals are reported in the literature. The Joint Commission, which is responsible for accrediting

hospitals, has now made performance improvement as one of the criteria for accreditation and,

therefore, hospitals are increasingly using some form of performance improvement system.

Need for Organizational change. Implementation of performance improvement

programs in a hospital requires diffusion of innovation. It involves an organizational change,

affecting the employees at many levels. Implementing any organizational change is a key event

and has to be systematically done to be successful. This is particularly true of lean sigma and

quality implementation since these techniques represent a transformational change in the

organizations way of thinking and approaching problems. While implementing such activities,

hospitals have to balance between several conicting objectives, holding the personnel and

procedures together to support the implementation.

Similar balancing and holding together activities happen in the human body under by the

powers of intelligence. Even at the level of cells, intelligence exists in the cellular wall that

maintains the chemical stability of the cell contents, effectively ltering out unwanted, harmful

chemicals from penetrating. Nader (2000) considered that this holding together and supporting

quality of intelligence is represented by Charaka Samhita of Ayurveda, the ancient Vedic Science

of medicine and health and is expressed in the physiology of the cell nucleus. This will be

discussed Chapter 7, viewing healthcare in the light of Maharishi Mahesh Yogis Vedic approach

R1to health.

.1 Transcendental Meditation technique R, Maharishi TM-Sidhi program, Maharishi Vedic Approach to Health,

Maharishi Ayur-Veda , Science of Creative Intelligence, Maharishi Vedic Science, and Maharishi University of

Management are registered or common law trademarks licensed to Maharishi Vedic Education Development

Corporation and used with permission.

5In contrast to manufacturing industries, applying performance improvement methods in

hospitals has some unique characteristics. It is difcult to dene quality in healthcare as in other

types of industries. In manufacturing systems, quality is the totality of features and

characteristics of a product or service that bear on its ability to satisfy stated or implied needs

(ISO, 1986). Need is assumed to refer to what customers need. However, in healthcare, customers

(patients and their families) are only aware of their short term requirements, but may not be of

possible long term effects on their health.

Also, healthcare is not an exact science, and the human body is also not a standardized

mechanism. Human body is exceedingly complex, and it is difcult to predict how each person is

likely to react to a treatment. Certain chronic pains and diseases may be beyond modern medical

science. Nevertheless, patients may be unwilling to accept this limitation but demand a quick

cure.

Sometimes, human mind may cause psychosomatic illnesses that are difcult to diagnose

or treat. Sometimes, habitual drug seekers come to hospitals for getting narcotic drugs and

complain loudly about dissatisfaction when they are denied such medication.

The adage a little knowledge is a dangerous thing applies particularly well to health.

Sometimes, patients because of partial knowledge insist on a course of treatment and the medical

staff may nd it difcult to explain the likely problems. There are instances when patients insist

on getting antibiotics and getting dissatised with quality if their request is not accepted, because

of possible side effects.

Sometimes, patients do not change their unhealthy habits and lifestyles but keep blaming

the healthcare system for lack of desired improvements in health.

These are some of the problems in dening quality in Healthcare. The Institute of

Medicine (IOM) has dened quality as the degree to which health service for individuals and

populations increase the likelihood of desired health outcomes and are consistent with current

professional knowledge (Schuster, McGlynn, & Brook, 2005).

6Structure - process - outcome framework. Quality can be evaluated on the

structureprocessoutcomes framework (Donabedian, 1988) and this classic framework is widely

used and quoted in published research on evaluating healthcare quality (e.g., Birkmeyer, Dimick,

& Birkmeyer, 2004). Structural quality evaluates health system characteristics. Process quality

assesses interactions between clinicians and patients. Outcomes offer evidence about changes in

patients health status. All the three dimensions can provide valuable information for measuring

quality. The normative structural qualities for hospitals are monitored and controlled by the

various accreditation, government and consumer agencies.

Measures of healthcare quality. This study uses Donabedians

structure-process-outcome framework to measure the quality of healthcare as follows:

1. Structural qualitypatient perception of the quality of healthcare is measured by patient

satisfaction scales. The patient perceived structural and process quality is a key indicator of

hospital performance. This study uses the publicly available Hospital Consumer

Assessment of Healthcare Providers and Systems (HCAHPS) satisfaction ratings as

indicating a hospitals structural quality.

2. Process qualityquality indicators (QIs) were formulated by the Agency for Healthcare

Research and Quality (AHRQ) concerning various aspects of healthcare (prevention,

inpatient, safety and pediatrics). A hospitals performance on these indicators measures a

hospitals process-of-care quality. Though AHRQ has developed a set of patient safety

indicators (PSIs), the patient safety data are outside the scope of this study. This is because

the patient safety indicators are not publicly available. Hospitals keep data on safety

incidents and indicators as condential. Also, interpreting the safety incidents requires deep

medical knowledge and has to done on a case by case basis. In 2003, U.S. Department of

Health and Human Services (HHS) established a national program Hospital Quality

Alliance (HQA) to collect data on key measures of hospitals management of three common

medical conditions: Acute Myocardial Infarction (AMI), Congestive Heart Failure (CHF),

7and Pneumonia (PN). The HQA data provide hospitals with performance benchmarks and

can be used to guide quality improvement. The percentage of cases a hospital treated as

recommended measures its process quality. This study uses the HQA data for

process-of-care quality data.

3. Outcomeoutcomes offer evidence of patients health status after treatment. This study

considers the following published measures for comparison: Risk adjusted 30 day mortality

rates from heart attack, heart failure and pneumonia.

Qualities of healthcare staff. Healthcare is delivered by humans and, therefore, their

qualities and ways of approaching the patients, determine the quality of healthcare. Often, this is

ignored in healthcare management research. The importance of empathy of healthcare

practitioners towards patients has been brought out by Epstein and Hundert (2002) and Larson

and Yao (2005). The role of the healthcare practitioners qualities has been emphasized by

Charaka in his monumental work Charaka Samhita : friendship, compassion, joy in serving, and

equanimity. Charaka recommended that, at the time of selecting students, only those who show

signs of possessing these qualities should be selected. The instructors should encourage their

students to develop these qualities during their education. In a similar vein, Larson and Yao

(2005) have recommended regular training during medical education in making conscious efforts

to develop their empathetic abilities. Chapter 7 of the dissertation will discuss this aspect and

connections with Maharishi Vedic Science.

Effects of ownership type. Hospitals may be owned by prot making corporations or

government or not- for-prot organizations. According to the theory developed by Hansmann

(1980), any differences among ownership types should vanish under managed care. Managed care

plans like Medicare pay hospitals on a prospective basis, and there is risk sharing between the

plan and the provider. Over time, Hansmann argued that only the most efcient hospitals will

thrive and survive. Consequently, the incentives to manage a hospital efciently provided by

managed care will reduce the nonoptimal behavior of all hospitals. However, a few studies have

8reported signicant differences between ownership types (e.g., Baker et al., 2000). The current

study assessed the relationships between patient satisfaction, process quality, patient outcomes

and hospital ownership type.

Statement of the Problem

The pressing needs to improve patient satisfaction and quality in healthcare in US have

been emphasized both by the government and the public. This section briey discusses the

problems that may be faced by the hospitals in attempting to improve patient satisfaction and

quality.

Patient satisfaction.

Improving customer satisfaction is the goal of all quality management concepts. Total

Quality Management (TQM) concepts guide us by focusing on quality and customer satisfaction.

TQM concepts believe that customers ultimately dene quality, and if customers are satised with

a product or a service, it is of high quality and adds value to customers. Monitoring patient

satisfaction has become a standard operating procedure in most healthcare organizations,

especially with the implementation of public reporting of patient satisfaction ratings for hospitals.

Higher patient satisfaction is expected to lead to higher customer volume per market research

literature that suggests that customer satisfaction leads to customer loyalty, e.g.(Hallowell, 1996).

Patient satisfaction is a complex construct and its measurement and interpretation differ widely

with the demography of patients, nature of ailment, patients level of education, income and

maturity and many more such confounding variables.

Despite this, patient satisfaction ratings have become valuable for hospitals for the

following reasons.

1. HCAHPS ratings are publicly published and regularly updated on the Medicare web site.

Low ratings could affect the corporate image and funding of hospitals.

92. Hospitals use the HCAHPS ratings to set corporate goals, monitor the performance,

identify areas for improvement, and in quality assurance-type activities.

3. Marketing places a strong emphasis on customer satisfaction.

4. Many hospitals routinely outsource services such as emergency services to outside agencies

and use the patient ratings as a measure of performance in these contracts.

5. Patient satisfaction is taken as a quality measure by accreditation agencies. It can be tied to

quality metrics, including length of stay, patient safety indicators and core measures

6. Medicare reimbursements to hospitals are being linked to patient satisfaction scores from

HCAHPS surveys from scal year 2008. Hospitals need to monitor their HCAHPS ratings,

to avoid possible reduction in their Medicare payments.

7. Increased patient satisfaction may be associated with mental satisfaction and a feeling of

wellness that could help recovery. Satised patients are more likely to react positively and

subsequently benet to a greater extent from their treatment. This is supported by empirical

evidence as reported by Guldvog (1999).

Improving patient satisfaction. Because of the importance of patient satisfaction,

hospitals face the problem of how to increase patient satisfaction. The current national average

reported in HCAHPS survey report for patients giving a high overall rating of 9-10 to a hospital

on their visit is 65%. Patients who responded that they would denitely recommend the hospital

to their friends and relatives, averaged 68%. If a hospital is reported to score below the national

average, it runs the risk of a cut in the government Medicare payments with the new performance

based payments system. Also, since patient satisfaction ratings are publicly reported, patients

may opt for a hospital with better rating, and this could reduce the patient load of the hospital.

Hospitals need to identify the areas to focus on to improve patient satisfaction and this study

attempts to answer this by using a factor analysis to determine the dimensions of patient

satisfaction.

10

Process-of-care quality.

Though it is difcult to dene or quantify quality in healthcare, it was found necessary to

do so to assess and improve a hospitals performance. Technical process quality refers to the

appropriateness of the treatment. Poor quality can mean too much care with unnecessary tests,

medications or procedures, or too little care with omitting appropriate tests or procedures or

wrong care with procedures or medications that should not have been given. This would be

difcult to measure, and hospitals may have to do an evaluation in selected cases for quality

assurance.

Another way to measure process quality is to determine whether the provided care meets

professional standards. This is done using the quality indicators (QIs) prescribed by AHRQ.

HQA uses a subset in accordance with government guidelines. Using the HQA data, Jha, Orav,

Zhonghe, and Epstein (2007) found that higher quality is associated with lower risk adjusted

mortality rates. Several other studies seem to conrm this. However, Isaac and Jha (2008)

reported inconsistent and usually poor associations between the patient safety indicators and

HQA quality measures. There have been no empirical studies published on the association

between patient satisfaction and process of care quality measured by QIs. This study attempts to

nd if such a relationship exists and its nature. The current study tested the research hypothesis

that hospitals compromise quality in favor of higher patient satisfaction.

Ownership type.

Hospitals may be government or privately owned for prot or owned by not-for-prot

voluntary organizations or church groups. Mobley suggested that church-owned hospitals

consider that their chief mission is to provide indigent care and that it takes precedence over

nancial performance (1997). Thomson Reuters surveys US hospitals and lists the top hundred

hospitals every year (Reuters, 2010). From this site, I listed the hospitals that repeat more than ten

times since their rst such list in 1992. There are nine such hospitals and all of them are voluntary

11

nonprot hospitals and two of them are owned by church groups. This points to the strong

inuence of ownership on hospital performance.

A few studies have tried to study the relationships between ownership type and patient

satisfaction and quality, but these had limited scope (e.g., Baker et al., 2000; Eggleston, Shen,

Lau, Schmid, & Chan, 2008). The present study aims at nding how patient satisfaction, quality

and outcomes vary across ownership types. Particularly, if signicant differences are found in

favor of hospitals run by church groups, this would bring out the connection between spirituality

in organizations and performance. Heaton, Schmidt-Wilk, and Travis (2004) suggest that

spirituality can be used in managing change. Change management is required for carrying out

performance improvement in hospitals.

Purposes of the Study

The rst purpose of the study is to identify the main dimensions of patient satisfaction

empirically by analyzing the ratings published by the HHS obtained from HCAHPS survey

questionnaire. The HCAHPS survey questionnaires have ten measures:

Six summary measures constructed from two or three survey questions. These measuressummarize how well nurses and doctors communicate with patients, how responsive

hospital staffs are to patients needs, how well hospital staffs help patients manage pain,

how well the staff communicates with patients about medicines, and, whether key

information is provided at discharge.

Two individual items address the cleanliness and quietness of patients rooms

Two global items report patients overall rating and whether they would recommend thehospital to family and friends.

The second purpose is to analyze the process-of-care quality ratings also published by

HHS to determine the principal dimensions of quality.

12

Third purpose is to test the relationship between patient satisfaction, quality and

outcomes. The outcome data are published by HHS as risk adjusted 30 day mortality and

readmission rates for heart attack, heart failure and pneumonia.

Fourth purpose is to test the relationships between hospital ownership type with patient

satisfaction, quality and outcomes.

Signicance of the Study

The study will help hospitals to identify the principal dimensions of patient satisfaction on

which to focus during their performance improvement programs. Researchers such as Jha, Orav,

Zheng, and Epstein (2008) assumed that the percentage of patients who rated the hospital in the

highest category (9 or 10 on a scale of 0 to 10) is the primary indicator of patient satisfaction.

This approach needs validation because this has not been tested to be a principal dimension of

satisfaction.

Also, hospitals would like to identify the main dimensions of patient satisfaction so that

they can rank the tasks and focus on the main items rst in improving patient satisfaction. These

become the low hanging fruits to achieve demonstrable results, emphasized by lean Six Sigma

techniques. Going by over-all rating without knowing what factors contribute to it is not useful to

hospitals, planning to improve their ratings.

Similarly, the study will identify the main dimensions of process quality. Further, the

study will evaluate the relationships between patient satisfaction and quality on the

comprehensive Centers for Medicare and Medicaid Services (CMS) data while other researchers

have been analyzing a subset of the data (E.g. Jha et al. (2008)). This will help hospitals towards

global optimization instead of sub optimization in favor of improving patient satisfaction. The

relationships between patient satisfaction, quality, and outcome variables have been studied by

some researchers on a subset of data e.g. Jha, Orav, Zhonghe, and Epstein (2007). This study will

do so on the complete data using factor analysis to isolate variables that have a signicant impact.

13

The study will evaluate the relationships between patient satisfaction, quality, outcome

variables and ownership group. This has not been studied at length. K. White and Ozcan (1996)

showed that church owned hospitals were more efcient than secular nonprot hospitals, using a

California sample. However, Thornlow and Stukenborg (2006) reported inconsistent relationship

between ownership type and quality of care showing conicting study ndings. This study will

analyze the relationship using the comprehensive CMS data.

Denition of Terms

Operational denitions.

Patient Satisfaction.

Patient satisfaction is a construct to measure the patients perception of the healthcare

service quality. In terms of Donabedians structure-process-outcomes (1988) framework for

assessing healthcare quality, patient satisfaction measures the patient perception of structural and

process qualities of healthcare. While there are many available instruments to measure patient

satisfaction, HCAHPS is the most frequently used instrument for hospital comparisons. CMS

(2010) gives the standards used in HCAHPS.

Process of care quality.

A way to measure process quality is to determine whether the provided care meets

professional standards. This assessment is done by creating a list of quality indicators that

describe a process of care that should occur for a particular type of patient or clinical

circumstance and then evaluating whether the patients care was consistent with the indicators.

AHRQ has formulated a very large number (nearly 500) of quality indicators (QIs) concerning

various aspects of healthcare such as prevention, inpatient, safety and pediatrics (AHRQ, 2011).

Out of these, the following were adopted for hospital comparison by CMS in consultation with

hospitals and Joint Commission:

14

Seven measures relating to heart attack care.

Four measures relating to heart failure care.

Six measures relating to pneumonia care.

Eight measures relating to surgical care improvement project.

Three measures relating to asthma care for children only.

Outcome measures.

HHS publishes Outcome of Care Measures showing the medical status of patients with

certain conditions after receiving hospital care. The death rates give the percentage of patients

died within 30 days of their hospitalization. The rates of readmission focus on whether patients

were hospitalized again within 30 days. These rates show whether a hospital is doing its best to

prevent complications, and teach patients at discharge. The hospital death rates and rates of

readmission are based on Medicare patients. For fair comparison, these rates are risk-adjusted

by CMS, to correct for factors that are beyond the control of hospitals such as age, gender and

preexisting health condition. When the rates are risk-adjusted, it helps make comparisons fair.

CMS compares an individual hospitals rates with the national averages, for rating hospitals as

better, worse or not different. Shaughnessy (2002) gives details of the outcome measures and

the model used for risk adjustment.

Constitutional denitions.

Certain technical terms and expressions used in the dissertation are described here.

CMS data.

CMS is the Centers for Medicare & Medicaid Services, a federal government organization

that manages the Medicare and Medicaid programs.

15

HCAHPS survey.

CMS and the Agency for Healthcare Research and Quality (AHRQ) developed the

HCAHPS survey questionnaire. It is a core set of questions that hospitals can combine with a

customized group of hospital-specic items if the order of the questions is not changed, and

hospital-tailored questions are added at the end. The National Quality Forum, established to

standardize healthcare-quality measurement and reporting, formally endorsed HCAHPS in May

2005.

Originally, the conceptual framework of the survey drew from the following domains of

quality health care proposed in the IOM report Crossing the Quality Chasm: A New Health

System for the 21st Century (IOM, 2001):

1. Respect for patients values

2. Attention to patients preferences and expressed needs

3. Coordination and integration of care

4. Patient information, communication and education

5. Physical comfort

6. Emotional support

7. Involvement of family and friends

8. Transition and continuity of care

9. Access to care

After pilot tests, the original set of questions was simplied. Two domains (1, and 7) were

dropped because these two are difcult to measure, and not fully under a hospitals control.

16

Process of care quality data.

Based on the Medicare Prescription Drug, Improvement, and Modernization Act (MMA)

of 2003, the HHS established a program to collect data on key measures of hospitals management

of three common medical conditions: acute myocardial infarction (AMI), congestive heart failure

(CHF), and pneumonia. Hospitals participating in this, constitute the Hospital Quality Alliance

(HQA). The HQA data provide hospitals an opportunity to compare their performance against

national averages and their own targets. Although there are other programs for rating hospitals on

quality, HQA has become the largest and most comprehensive program with the participation of

most US hospitals. The HQA data is also published by CMS in their website.

Outcome data.

To improve the quality of nations hospitals, HHS was mandated to make outcome and

quality measures publicly available. Therefore, HHS publicly reports risk-standardized 30-day

mortality measures and readmission rates for AMI, HF and PN patients. Mortality within 30 days

can be strongly inuenced by hospital care. Readmissions are also strongly inuenced by hospital

care and represent expensive, adverse events for patients and are often preventable. These

standardized measures were developed by a team of experts from Yale and Harvard universities

and endorsed by National Quality Forum.

Data download.

HCAHPS data is updated quarterly and can be downloaded from the website:

http://www.medicare.gov. The downloaded data covered 4,460 hospitals in US, in 50

states, Washington DC and Puerto Rico, in 2010. Basic information about the hospitals such as

address, county, ownership type is included. The downloaded HCAHPS data had been updated by

Medicare organization in March,2010. The downloaded data on process-of-care measures &

HCAHPS patient surveys were collected during the period July, 2008 to June, 2009. The

17

mortality and readmission outcome measures data downloaded are for the period July, 2005 to

June, 1008. The downloaded data covered the following:

1. Process of care and outcome Quality measures, 28 in number covering heart attack, heart

failure, pneumonia, surgical care improvement and childrens asthma care.

2. Mortality measures, (6 in number) cover hospital 30-day death and readmission rates for

heart attack, heart failure and pneumonia.

3. Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) Survey

results. This survey measures 29 indicators of patient satisfaction in response to 22

questions. The HCAHPS Patient Satisfaction Surveys cover several aspects of patient

perception of healthcare given to them.

Theoretical Framework

Two widely used theories in healthcare research are used to in this study. These are as

follows:

Hansmanns theory on the role of nonprot enterprise. Hansmann brought out the

theory of the role of nonprot enterprise to explain the difference between not-for-prot and for

prot hospitals. Hansmann divided nonprots loosely into two broad categories: donative

nonprots, which derive a substantial portion of their income from grants or donations, and

commercial nonprots which derive most or all of their income by selling their services directly

to consumers. However, with the trend towards managed care in the United States, Hansm

relationships between patient satisfaction, quality, outcomes and ownership type in us hospitals an...

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