relationships between patient satisfaction, quality, outcomes and ownership type in us hospitals an...
DESCRIPTION
Relationships Between Patient Satisfaction, Quality, Outcomes and Ownership Type in US Hospitals an Empirical StudyTRANSCRIPT
-
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
Ediyattumangalam R. Shivaji
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
Ediyattumangalam R. Shivaji
ittumatra uumatra ta
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
-
All rights reserved
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ii
c2012
Ediyattumangalam R. Shivaji
ittumatra uumatra ta
All Rights Reserved.
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.
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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
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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.
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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.
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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
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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
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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
Chapter Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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
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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
Research question 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Assumptions to be satised in testing. . . . . . . . . . . . . . . . . . . 53
Research question 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
Research hypotheses under research question 3. . . . . . . . . . . . . . . . . 54
Research question 4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
Research question 5. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
Relationship between outcomes and hospital ownership. . . . . . . . . . . . . 58
RQ 5.1 and RQ 5.2 - relationship between outcome variables and
ownership type. . . . . . . . . . . . . . . . . . . . . . . . . 60
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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
Chapter Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 Question 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
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
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Effect sizes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
Summary of ndings. . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
Regression diagnostic tests. . . . . . . . . . . . . . . . . . . . . . . . . 88
Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
Research Question 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
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
Test results for research question 3. . . . . . . . . . . . . . . . . . . . . . . . 98
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
Test results 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
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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
Regression diagnostic tests. . . . . . . . . . . . . . . . . . . . . . . . . 149
Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
30-day risk adjusted readmission rate. . . . . . . . . . . . . . . . . . . . . . . 151
Regression diagnostic tests. . . . . . . . . . . . . . . . . . . . . . . . . 152
Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
Test results for heart failure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
30-day risk adjusted mortality rate. . . . . . . . . . . . . . . . . . . . . . . . 153
Regression diagnostic tests. . . . . . . . . . . . . . . . . . . . . . . . . 155
Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
30-day risk adjusted readmission rate. . . . . . . . . . . . . . . . . . . . . . . 157
Regression diagnostic tests. . . . . . . . . . . . . . . . . . . . . . . . . 158
Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
Test results for pneumonia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
30-day risk adjusted mortality rate. . . . . . . . . . . . . . . . . . . . . . . . 159
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Regression diagnostic tests. . . . . . . . . . . . . . . . . . . . . . . . . 160
Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
30-day risk adjusted readmission rate. . . . . . . . . . . . . . . . . . . . . . . 162
Regression diagnostic tests. . . . . . . . . . . . . . . . . . . . . . . . . 164
Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
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
Research hypotheses to be tested. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
Testing outcomes for heart attack. . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
30-day risk adjusted mortality rate. . . . . . . . . . . . . . . . . . . . . . . . 167
Regression diagnostic tests. . . . . . . . . . . . . . . . . . . . . . . . . 168
Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
30-day risk adjusted readmission rate. . . . . . . . . . . . . . . . . . . . . . . 170
Regression diagnostic tests. . . . . . . . . . . . . . . . . . . . . . . . . 171
Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
Testing outcome variables for heart failure. . . . . . . . . . . . . . . . . . . . . . . 172
30-day risk adjusted mortality rate. . . . . . . . . . . . . . . . . . . . . . . . 172
Regression diagnostic tests. . . . . . . . . . . . . . . . . . . . . . . . . 174
Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
30-day risk adjusted readmission rate. . . . . . . . . . . . . . . . . . . . . . . 176
Regression diagnostic tests. . . . . . . . . . . . . . . . . . . . . . . . . 177
Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178
Testing outcomes for pneumonia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
30-day risk adjusted mortality rate. . . . . . . . . . . . . . . . . . . . . . . . 179
Regression diagnostic tests. . . . . . . . . . . . . . . . . . . . . . . . . 180
Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
30-day risk adjusted readmission rate. . . . . . . . . . . . . . . . . . . . . . . 181
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Regression diagnostic tests. . . . . . . . . . . . . . . . . . . . . . . . . 183
Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184
Summary of regression results for RQ 5.3 and RQ 5.4. . . . . . . . . . . . . . . . . 184
5.5 and 5.6 Relationships of Outcome Variables with Process of Care Quality Components184
Research hypotheses to be tested. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
Testing outcomes for heart attack . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
30-day risk adjusted mortality rate. . . . . . . . . . . . . . . . . . . . . . . . 185
Regression diagnostic tests. . . . . . . . . . . . . . . . . . . . . . . . 187
Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
30-day risk adjusted readmission rate. . . . . . . . . . . . . . . . . . . . . . . 189
Regression diagnostic tests. . . . . . . . . . . . . . . . . . . . . . . . . 190
Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
Testing outcomes for heart failure. . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
30-day risk adjusted mortality rate. . . . . . . . . . . . . . . . . . . . . . . . 192
Regression diagnostic tests. . . . . . . . . . . . . . . . . . . . . . . . . 193
Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194
30-day risk adjusted readmission rate. . . . . . . . . . . . . . . . . . . . . . . 194
Regression diagnostic tests. . . . . . . . . . . . . . . . . . . . . . . . . 196
Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196
Testing outcomes for pneumonia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198
30-day risk adjusted mortality rate. . . . . . . . . . . . . . . . . . . . . . . . 198
Regression diagnostic tests. . . . . . . . . . . . . . . . . . . . . . . . . 200
Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200
30-day risk adjusted readmission rate. . . . . . . . . . . . . . . . . . . . . . . 201
Regression diagnostic tests. . . . . . . . . . . . . . . . . . . . . . . . . 203
Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
Summary of regression results for RQ 5.5 and RQ 5.6. . . . . . . . . . . . . . . . . 204
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Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
6 Discussion and Conclusion 206
Chapter Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206
Review of ndings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207
Research question 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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
Research Question 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
Value based payments system for Medicare payments. . . . . . . . . . . . . . 212
Research Question 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
Principal component analysis of quality data. . . . . . . . . . . . . . . . . . 213
Statistical tests on quality differences by ownership groups. . . . . . . . . . . 214
Research Question 4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
Research Question 5. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
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
Research Question 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224
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Research Question 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225
Research Question 4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227
Research Question 5. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229
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
Chapter Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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
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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
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H HCAHPS survey questionnaire 310
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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
15 Quality Component 2 Data by Ownership Groups . . . . . . . . . . . . . . . . . . 99
16 Quality Component 3 Data by Ownership Groups . . . . . . . . . . . . . . . . . . 99
17 Quality Component 4 Data by Ownership Groups . . . . . . . . . . . . . . . . . . 100
18 Quality Component 5 Data by Ownership Groups . . . . . . . . . . . . . . . . . . 100
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
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22 RQ3: Regression Results for Quality Component 2 by Ownership Group . . . . . . 110
23 RQ3 : Robust Regression Results for Quality Component 2 . . . . . . . . . . . . . 112
24 RQ3: Regression Results for Quality Component 3 by Ownership Group . . . . . . 113
25 RQ3 : Robust Regression Results for Quality Component 3 . . . . . . . . . . . . . 116
26 RQ3: Regression Results for Quality Component 4 by Ownership Group . . . . . . 116
27 RQ3 : Robust Regression Results for Quality Component 4 . . . . . . . . . . . . . 119
28 RQ3: Regression Results for Quality Component 5 by Ownership Group . . . . . . 119
29 RQ3 : Robust Regression Results for Quality Component 5 . . . . . . . . . . . . . 121
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
32 RQ4 : Robust Regression Results for Quality Component 1 . . . . . . . . . . . . . 126
33 RQ4 - Regression Results for Quality Component 2 by Satisfaction Components . . 126
34 RQ4 : Robust Regression Results for Quality Component 2 . . . . . . . . . . . . . 129
35 RQ4 - Regression Results for Quality Component 3 by Satisfaction Components . . 129
36 RQ4 : Robust Regression Results for Quality Component 3 . . . . . . . . . . . . . 132
37 RQ4 - Regression Results for Quality Component 4 by Satisfaction Components . . 132
38 RQ4 : Robust Regression Results for Quality Component 4 . . . . . . . . . . . . . 134
39 RQ4 - Regression Results for Quality Component 5 by Satisfaction Components . . 135
40 RQ4 : Robust Regression Results for Quality Component 5 . . . . . . . . . . . . . 137
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
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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
50 RQ5 - Robust Regression Results for 30-day Risk Adjusted Mortality Rate for
Heart Failure by Owner Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
51 RQ5- Regression results for 30-day risk adjusted readmission rate for heart failure
by owner groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
52 RQ5 - Robust Regression Results for 30-day Risk Adjusted Readmission Rate for
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
59 RQ5 - Robust Regression Results for 30-day Risk Adjusted Mortality Rate for
Heart Attack with Patient Satisfaction Component 1 . . . . . . . . . . . . . . . . . 169
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60 RQ5- Regression results for 30-day risk adjusted readmission rate for heart attack
by satisfaction component 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
61 RQ5 - Robust Regression Results for 30-day Risk Adjusted Readmission Rate for
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
63 RQ5 - Robust Regression Results for 30-day Risk Adjusted Mortality Rate for
Heart Failure with Patient Satisfaction Component 1 . . . . . . . . . . . . . . . . . 175
64 RQ5- Regression results for 30-day risk adjusted readmission rate for heart failure
by patient satisfaction component 1 . . . . . . . . . . . . . . . . . . . . . . . . . . 176
65 RQ5 - Robust Regression Results for 30-day Risk Adjusted Readmission Rate for
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
67 RQ5 - Robust Regression Results for 30-day Risk Adjusted Mortality Rate for
Pneumonia with Patient Satisfaction Component 1 . . . . . . . . . . . . . . . . . . 181
68 RQ5- Regression results for 30-day risk adjusted readmission rate for pneumonia
by patient satisfaction component 1 . . . . . . . . . . . . . . . . . . . . . . . . . . 182
69 RQ5 - Robust Regression Results for 30-day Risk Adjusted Readmission Rate for
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
72 RQ5 - Robust Regression Results for 30-day Risk Adjusted Mortality Rate for
Heart Attack with Process of Care Quality Components . . . . . . . . . . . . . . . 188
73 RQ5 - Regression results for 30-day risk adjusted readmission rate for heart attack
by quality components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
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74 RQ5 - Robust Regression Results for 30-day Risk Adjusted Readmission Rate for
Heart Attack with Process of Care Quality Components . . . . . . . . . . . . . . . 191
75 RQ5 - Regression results for 30-day risk adjusted mortality rate for heart failure
by quality components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
76 RQ5 - Robust Regression Results for 30-day Risk Adjusted Mortality Rate for
Heart Failure with Process of Care Quality Components . . . . . . . . . . . . . . . 194
77 RQ5 - Regression results for 30-day risk adjusted readmission rate for heart failure
by quality components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
78 RQ5 - Robust Regression Results for 30-day Risk Adjusted Mortality Rate for
Heart Attack with Process of Care Quality Components . . . . . . . . . . . . . . . 198
79 RQ5 - Regression results for 30-day risk adjusted mortality rate for pneumonia
by quality components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
80 RQ5 - Robust Regression Results for 30-day Risk Adjusted Mortality Rate for
Pneumonia with Process of Care Quality Components . . . . . . . . . . . . . . . . 201
81 RQ5 - Regression results for 30-day risk adjusted readmission rate for pneumonia
by quality components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
82 RQ5 - Robust Regression Results for 30-day Risk Adjusted Readmission Rate for
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
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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
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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
6 Biplot for Quality Components 1 and 3 . . . . . . . . . . . . . . . . . . . . . . . . . 97
7 Biplot for Quality Components 1 and 4 . . . . . . . . . . . . . . . . . . . . . . . . . 97
8 Biplot for Quality Components 1 and 3 . . . . . . . . . . . . . . . . . . . . . . . . . 98
9 Marginal Means Plots for Quality Component 1 . . . . . . . . . . . . . . . . . . . . . 101
10 Marginal Means Plots for Quality Component 2 . . . . . . . . . . . . . . . . . . . . . 102
11 Marginal Means Plots for Quality Component 3 . . . . . . . . . . . . . . . . . . . . . 102
12 Marginal Means Plots for Quality Component 3 . . . . . . . . . . . . . . . . . . . . . 103
13 Marginal Means Plots for Quality Component 3 . . . . . . . . . . . . . . . . . . . . . 103
14 RQ3: Kernel Density Plot of Residuals for Quality Component 1 . . . . . . . . . . . . 108
15 RQ3: Kernel Density Plot of Residuals for Quality Component 2 . . . . . . . . . . . . 111
16 RQ3: Kernel Density Plot of Residuals for Quality Component 3 . . . . . . . . . . . . 114
17 RQ3: Kernel Density Plot of Residuals for Quality Component 4 . . . . . . . . . . . . 118
18 RQ3: Kernel Density Plot of Residuals for Quality Component 5 . . . . . . . . . . . . 120
19 RQ4: Kernel Density Plot of Residuals for Quality Component 1 . . . . . . . . . . . . 125
20 RQ4: Kernel Density Plot of Residuals for Quality Component 2 . . . . . . . . . . . . 128
21 RQ4: Kernel Density Plot of Residuals for Quality Component 3 . . . . . . . . . . . . 131
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22 RQ4: Kernel Density Plot of Residuals for Quality Component 4 . . . . . . . . . . . . 133
23 RQ4: Kernel Density Plot of Residuals for Quality Component 5 . . . . . . . . . . . . 136
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
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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
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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
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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
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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
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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).
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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),
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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.
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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
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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.
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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.
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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:
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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.
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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.
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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
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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