GRATITUDE - Tulane University Digital Library

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GRATITUDE My education at Tulane has been one of the greatest experiences in my career. I owe a great deal to all the professors in the department of Health Systems Management. My special thanks go to all committee members, Dr. Jian Li, Dr. Joby John and especially Dr. Lizheng Shi for his input and support in guiding me through this journey. I would like to specially thank Dr. Claudia Campbell who throughout my educational endeavor at Tulane has been the ideal mentor who inspired me and taught me how to think critically and analytically in order to become a better researcher and scientist. I would also like to thank my family for their patience, my colleagues, my residents, my students and my patients for being understanding and accommodative throughout the project. Most importantly, I would also like to thank God for making all of this happen.

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TABLE OF CONTENTS

ABSTRACT ....................................................................................................................................... PAGE 5

CHAPTER 1 .................................................................................................................................................

INTRODUCTION ................................................................................................................................. PAGE 9

CHAPTER 2 .................................................................................................................................................

BACKGROUND ................................................................................................................................. PAGE 12 QUALITY REPORTING ..................................................................................................................................... 13

VALUE BASED PURCHASING ............................................................................................................................ 16 HCAHPS ..................................................................................................................................................... 19

CHAPTER 3 .................................................................................................................................................

SATISFACTION THEORIES .................................................................................................................. PAGE 22 DETERMINANTS OF SATISFACTION ........................................................................................................... 28 SATISFACTION THEORIES INTERACTION WITH DETERMINANTS................................................................................ 37

PROCESS OF CARE MEASURES AND PATIENT SATISFACTION .................................................................................... 38

CHAPTER 4 .................................................................................................................................................

CONCEPTUAL FRAMEWORK ............................................................................................................. PAGE 49 RESEARCH QUESTIONS AND HYPOTHESES ................................................................................................ 53

CHAPTER 5 .................................................................................................................................................

METHODS ......................................................................................................................................... PAGE 56 DESIGN ..................................................................................................................................................... 56 DATA SOURCES ........................................................................................................................................... 56 IRB ........................................................................................................................................................... 60 SAMPLE ..................................................................................................................................................... 60 MEASUREMENT ........................................................................................................................................... 61 DATA EVALUATION ....................................................................................................................................... 65 STATISTICAL ANALYSIS ................................................................................................................................... 66 EQUATION MODELS ...................................................................................................................................... 66

CHAPTER 6 ................................................................................................................................................. RESULTS ............................................................................................................................................ PAGE 75

OUTCOME VARIABLE ..................................................................................................................................... 75 HOSPITAL CHARACTERISTICS ........................................................................................................................... 77 HCAHPS AND PROCESS MEASURES DATA CHARACTERISTICS ................................................................................... 75 GLM REGRESSION RESULTS FOR RESEARCH QUESTION 1 ....................................................................................... 89 STRUCTURE MEASURES COEFFICIENTS ............................................................................................................ 102 GLM REGRESSION RESULTS FOR RESEARCH QUESTION 2 ..................................................................................... 106 RESULTS SUMMARY………………………………………………………………………………………………………………………………..115

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TABLE OF CONTENTS (cont.)

CHAPTER 7 ................................................................................................................................................. DISCUSSION .................................................................................................................................... PAGE 118 MODEL OF RELATIONSHIPS .......................................................................................................................... 139

LIMITATIONS ............................................................................................................................................ 139

CHAPTER 8 ................................................................................................................................................. CLINICAL IMPLICATIONS .................................................................................................................. PAGE 144 POLICY IMPLICATIONS………………………………………………………………………………………………………………….PAGE 145 FUTURE RESEARCH .......................................................................................................................... PAGE 145 CONCLUSION…………………………………………………………………………………………………………………………………PAGE 147 REFERENCES ................................................................................................................................ PAGE 150 APPENDIXES ................................................................................................................................ PAGE 169

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ABSTRACT Background:

US healthcare expenditures continue to rise although the trend is slowing down recently

1. Unfortunately despite the increase in spending, quality of health care delivery needs

improvement 2. In January of 2011, CMS proposed to implement a Hospital Value-Based

Purchasing Program (HVBP) under which value-based incentive payments will be made

in a fiscal year to hospitals that meet performance standards with respect to a

performance period for the fiscal year involved 3. For fiscal year 2014, a hospital’s

performance in VBP was based on its performance on 45% clinical process of care, 25%

outcome (mortality) and 30% experience of care. Patient experience of care is measured

by a national, standardized survey of hospital patients about their experiences during a

recent inpatient hospital stay. This is also referred to as HCAHPS (Hospital Consumer

Assessment of Healthcare Providers and Systems) 4.The Donabedian model for quality

assessment (structure, process, and outcome) provides a good framework for satisfaction

dimensions. Donabedian proposed that satisfaction was the principal outcome of the

interpersonal process of care. He also regarded "outcome" as the most important aspect

of quality of care. 17, 18. Critics have argued that associations between patients’ reports of

experiences and available measures of hospital quality and safety would add to the

credibility of HCAHPS measures 7.

There is a paucity of studies looking into the association between individual hospital

process of care quality measures and patient satisfaction. The studies showing association

mostly examined summary quality process scores instead of effects of individual process

of care scores. 6, 7, 19, 20. In addition studies on the relationship between patients’

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experiences and the quality of clinical care have had mixed results. No studies have been

done analyzing the role of each HCHAPS composite within each process of care measure

to determine global patient satisfaction.

Method:

An observational retrospective, cross-sectional design was used to analyze the

relationship between each individual hospital process of care measure for acute MI, heart

failure (HF), pneumonia (PN), SCIP (surgical care improvement project), stroke (SK),

VTE (thromboembolism), and the global patient satisfaction HCAHPS score (definitely

recommend hospital) using General Linear Modeling regression analysis using the

Donabedian framework. The data collection period is from 7/1/2012- 6/30/2013.

The sample consists of 551 hospitals in the West South Central Region consisting of the

states of Louisiana, Arkansas, Texas and Oklahoma that report HCHAPS measures in

Hospital Compare. Hospitals reporting HCAHPS data are then matched with the

American Hospital Association annual survey database to capture hospital characteristics

for bed size, ICU bed capacity, Medicare spending per beneficiary, nurse staffing,

Medicaid volume, Medicare volume, rural status, for-profit status and specialty hospital

status that are used in the analysis as adjustment factors to control for differences in

hospitals that could affect outcomes.

Results:

We expected to find a significant relationship between all individual process measures

and the percent of patients definitely recommending hospital. Instead, only a few

significant measures with a p value<0.05 were identified for individual process of care

scores in all diagnostic groups. The strongest relationships were found for discharge

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meds in stroke and myocardial infarction (dc on antithrombotic, β= 0.34 and dc on

aspirin, β=0.67 respectively), assessment of left ventricular function for heart failure (β=

1.96), cultures before antibiotics for pneumonia (β= 0.49) and infection prevention

measures for surgery SCIP (outpatient antibiotic within one hour of surgery β= 0.74,

given right antibiotic β= 0.67). These associations have also the strongest relationship to

other outcomes such as readmission, mortality and infection.

When analyzing the impact of individual HCAHPS process measures within each quality

metric group, almost all HCAHPS composites were statistically significant in

determining percent of patients definitely recommending the hospital. However, pain

control seems to be the dominant determinant for larger hospitals with greater than 100

beds. (β= 1.25 for stroke, 1.34 for VTE, 1.27 for MI, 1.27 for HF, 1.39 for pneumonia

and 1.44 for SCIP). In smaller hospitals with beds less than 100, nurse communication is

the stronger determining factor of patient satisfaction for CHF, Pneumonia and SCIP (β=

0.83, 0.95, and 0.63 respectively).

Conclusion:

Global patient satisfaction with hospital care was related to only a few significant

measures within different process of care tracts. Although some of these measures could

be relevant for policy recommendations (discharge meds for stroke/AMI, LV function for

HF, cultures before antibiotics for pneumonia and infection prevention measures for

SCIP), more needs to be done to understand the drivers behind the strengths of the

relationships of these measures with global patient satisfaction, including their

associations with other outcomes such as readmission, mortality, or infection.

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Among HCAHPS composites, pain control is most important determining factor of global

satisfaction among larger hospitals with beds>100. Additional studies need to be

performed to understand influencers of pain perception and the variability of the strengths

of the measures relationships with patient satisfaction among different bed sizes within

each process of care measure.

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CHAPTER ONE INTRODUCTION

US healthcare costs continue to grow although the trend is slowing down recently 24.

Controlling this growth has become a major policy priority. Unfortunately despite the

increase in spending, quality of health care delivery needs improvement. Moreover,

Americans report a low level of satisfaction with the health care system; in some surveys,

only 40 percent of those interviewed reported being “fairly satisfied” or “very satisfied”

with how health care is delivered in this country 2. Different approaches to quality

improvement have emerged, including the use of evidence-based medicine and clinical

practice guidelines, professional development, assessment and accountability, patient

empowerment, and total quality management 25. In January 2011, CMS proposed to

implement a Hospital Value-Based Purchasing program (HVBP) under which value-

based incentive payments will be made in a fiscal year to hospitals that meet performance

standards with respect to a performance period for the fiscal year involved 26. HVBP is

part of the Centers for Medicare & Medicaid Services’ (CMS’) long-standing effort to

link Medicare’s payment system to improve healthcare quality, including the quality of

care provided in the inpatient hospital setting. The measures initially adopted for the

program are a subset of the measures that were already adopted for the existing Medicare

Hospital Inpatient Quality Reporting Program (Hospital IQR program) 3, 27.

A hospital’s performance in HVBP was based on its performance for FY2014 on 45%

clinical process of care, 25% outcome (mortality) and 30% experience of care. Patient

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experience of care is measured by a national, standardized survey of hospital patients

about their experiences during a recent inpatient hospital stay. This is also referred to as

HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems) 28.

HVBP is funded through a reduction from participating hospitals’ Diagnosis-Related

Group (DRG) payments for the applicable fiscal year. The money that is withheld will be

redistributed to hospitals based on their Total Performance Scores (TPS). Roughly $850

million dollars in VBP incentives were paid out to participating hospitals in FY 2013.

The program was financed through a 1% across-the-board reduction in FY 2013

diagnosis-related group (DRG)-based inpatient payments to participating hospitals 5. Of

this dollar amount 30% was derived from patient satisfaction scores based on the

HCAHPS survey scores. This significant percentage of reimbursement at risk is expected

to grow with more reduction in DRG –based payments by 2017.

The HCAHPS survey, developed by the Agency for Healthcare Research and Quality,

asks patients 32 questions about their experiences in the hospital and about their

demographic characteristics. Possible responses (always, usually, sometimes, and never)

are summarized by CMS and reported in 7 domains as composites: communication with

physicians, communication with nurses, communication about medications, quality of

nursing services (responsiveness of staff), adequacy of planning for discharge, pain

management, and care transition 28. Care transition composites have been added to

HCAHPS in October 2014. Critics have argued that HCAHPS measures, which reflect

the experiences of a broader sample of patients, might provide a more representative

summary of selected aspects of care quality 29. Others have argued that associations

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between patients’ reports of experiences and available measures of hospital quality and

safety would add to the credibility of HCAHPS measures7. The Donabedian model for

quality assessment (structure, process, and outcome) provides a good framework for

satisfaction dimensions 18. Structural aspects include access, cost and physical

environment. Process aspects include technical quality, interpersonal communications,

and coordination of care 17. Outcomes aspects include satisfaction with health 30.

Donabedian proposed that satisfaction was the principal outcome of the interpersonal

process of care. He also regarded "outcome" as the most important aspect 1 7 .

There is a paucity of studies looking into the association of individual hospital process of

care quality measures and patient satisfaction 10,14,22,31 No studies have been done to

analyze the relationship between current stroke and venous thromboembolism (VTE)

process of care quality measures and patient satisfaction. In addition, no studies have

been done studying the role of each HCAHPS composite scores for each process of care

measure to determine how they predict global patient satisfaction. This gap in the

literature provides a unique opportunity to analyze the relationship between each

individual hospital process of care measure for acute MI, heart failure, pneumonia, SCIP,

stroke, VTE and global patient satisfaction HCAHPS scores using the Donabedian

framework. This study also incorporates other HCAHPS composite components into

quality elements to determine relationship with global satisfaction with care.

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CHAPTER TWO

BACKGROUND

United Stated healthcare National Health Expenditures continue to increase, growing to

$2.5 trillion in 2009, $2.6 trillion in 2010 more than three times the $714 billion spent in

1990, and over eight times the $253 billion spent in 1980 32. Controlling this growth has

become a major policy priority. There is general agreement that health costs are likely to

continue to increase in the foreseeable future. In 2008, U.S. health care spending was

about $7,681 per resident. The health share of GDP grew reached 17.3% in 2009 and is

projected to reach 19.3% in 2019. US health spending is among the highest of all

industrialized countries 24, 33, 34. It is noticeable that health spending has been growing at

historically low levels in recent years. According to the Office of the Actuary (OACT) in

the Centers for Medicare and Medicaid Services, national health spending grew by 3.9%

each year from 2009 to 2011, the lowest rate of growth since the federal government

began keeping such statistics in 1960. Estimates from the Center for Sustainable Health

Spending at the Altarum Institute suggest that the slowdown largely continued into 2012,

with health spending growing by 4.3% last year. This slowdown in health spending is a

result of broader economic factors (such as the Great Recession of 2007-2009), structural

changes in the health system that could lead to slower growth in the future as well, or

some combination of the two 24,34.

http://www.altarum.org/research-initiatives-health-systems-health-care/altarum-center-for-studying-health-spending/health-indicator-reports

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Despite the favorable slowing in spending growth, US health care quality needs

improvement. The World Health Organization Health Report 2000, ranked the U.S.

health care system only 37th overall among 191 countries, with the major deficiencies

being in the areas of health status, fairness in financial contribution, and responsiveness

to people’s expectations of the health system. Moreover, Americans report a low level of

satisfaction with the health care system; in some surveys, only 40 percent of those

interviewed reported being “fairly satisfied” or “very satisfied” with how health care is

delivered in this country 2. Different approaches to quality improvement have emerged,

including the use of evidence-based medicine and clinical practice guidelines,

professional development, assessment and accountability, patient empowerment, and

total quality management 25.

The Hospital IQR (inpatient quality reporting) Program was developed as a result of the

Medicare Prescription Drug Improvement and Modernization Act (MMA) of 2003. The

Hospital IQR Program is intended to equip consumers with quality of care information to

make more informed decisions about healthcare options. It is also intended to encourage

hospitals and clinicians to improve the quality of inpatient care provided to all patients.

Section 5001(a) of Pub. 109-171 of the Deficit Reduction Act (DRA) of 2005 provided

new requirements for the Hospital IQR Program, which built on the voluntary Hospital

Quality Initiative. The Section of the MMA authorized CMS to pay hospitals that

successfully report designated quality measures a higher annual rate to their payment

rates. Initially, the MMA provided for a 0.4% reduction in the annual market basket

update for hospitals that did not successfully report. The Deficit Reduction Act of 2005

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increased that reduction to 2.0 percentage points 35. Quality measures included in

Hospital IQR Program are used to gauge how well an entity provides care to its patients.

Measures are based on scientific evidence and can reflect guidelines, standards of care, or

practice parameters.

A quality measure converts medical information from patient records into a rate or

percentage that allows facilities to assess their performance. Process of care measures are

measures that show, in percentage form or as a rate, whether or not a health care provider

gives recommended care; that is, the treatment known to give the best results for most

patients with a particular condition. Conditions covered under process of care measures

include AMI (acute myocardial infarction), HF (heart failure), PN (pneumonia), SCIP

(surgical care improvement project) measures, SK (stroke), VTE (venous

thromboembolism), ED (emergency department throughput measures), and

immunization. Outcome measures are measures designed to reflect the results of care,

rather than whether or not a specific treatment or intervention was performed. These

include Health Care Associated Infections, thirty-day mortality for HF/MI/pneumonia,

30-day readmission for HF/MI/PN, AHRQ (Agency for Health Care Research and

Quality) measures including complication/patient safety for selected indicators and

deaths among surgical patients with serious treatable complications .Hip and knee

complications, and cost efficiency measures (Medicare spending per beneficiary) are also

included. IQR data is listed in Appendix A.

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The hospital quality of care information gathered through the hospital IQR program is

available to consumers on the Hospital Compare website:

http://www.medicare.gov/hospitalcompare. Hospital Compare currently provides

information on quality measures, which include clinical process of care, clinical outcome

measures and patient experiences of care topics. Through Hospital Compare, consumers

are able to get a better picture of the quality of care delivered at their local hospitals. The

patient experience of care is measured by a national, standardized survey of hospital

patients about their experiences during a recent inpatient hospital stay. This survey is

also referred to as HCAHPS (Hospital Consumer Assessment of Healthcare

Providers and Systems) 27.See Appendix C for all Hospital Compare measures.

Some of the quality-reporting approaches have not brought about substantial changes in

clinical practice. (E.g. legislative mandates for immunization at the state levels did not

result originally in significant improvement in immunization rates) 36 .As a result, some

purchasers, public and private employers, business coalitions, and public programs (e.g.,

Medicare and Medicaid) attempted to build quality considerations into their health care

purchasing programs. Consequently, purchasers, rather than patients, have begun to

establish themselves as the real customers within the health care delivery system, giving

them a remarkable responsibility and interest in getting value for their money 36. Such

initiatives have shown that public and private purchasers may be able to influence the

quality and costs of health care services through value-based purchasing (VBP). VBP can

be defined as “any purchasing practices aimed at improving the value of health care

services, where value is a function of both quality and cost. Value increases as quality

http://www.medicare.gov/hospitalcompare

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increases, holding expenditure constant” 37. As an example, inclusion in benefit packages

of providers perceived as being “high quality,” helped employers to retain employees and

increased employee satisfaction and productivity potentially reducing long-term health

costs 38.

In January 2011, CMS proposed to implement a Hospital Value-Based Purchasing

program (HVBP) for Medicare under section 1886(o) of the Social Security Act, under

which value-based incentive payments will be made in a fiscal year to hospitals that meet

performance standards with respect to a performance period for the fiscal year involved.

HVBP is part of the Centers for Medicare & Medicaid Services’ long-standing effort to

link Medicare’s payment system to improve healthcare quality, including the quality of

care provided in the inpatient hospital setting. The program will implement value-based

purchasing to the payment system that accounts for the largest share of Medicare

spending, affecting payment for inpatient stays in over 3,000 hospitals across the country.

Hospitals will be paid for inpatient acute care services based on the quality of care, not

just quantity of the services they provide.

The HVBP program is designed to promote better clinical outcomes for hospital patients,

as well as improve their experience of care during hospital stays. Specifically, HVBP

seeks to eliminate or reduce the occurrence of adverse events (healthcare errors resulting

in patient harm) by adopting evidence-based care standards and protocols that result in

the best outcomes for the most patients, and by re-engineering hospital processes that

improve patients’ experience of care 26. The measures initially adopted for the HVBP

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program are a subset of the measures that were already employed in the existing

Medicare Hospital Inpatient Quality Reporting Program (Hospital IQR program).

Depending on whether a hospital meets or exceeds the performance standards with

respect to the measures, the hospitals would be rewarded based on actual performance,

rather than simply reporting of data for those measures 3.

A hospital’s performance in HVBP is based on its performance according to the

following schedule:

Fiscal Year (FY) 2013: 12 clinical Process of care measures and 8 Patient Experience of

Care dimensions of the Hospital Consumer Assessment of Healthcare Providers and

Systems (HCAHPS) survey. FY 2014: 12 Clinical Process of Care measures, 8 Patient

Experience of Care dimensions (HCAHPS), three 30-Day Outcome Mortality measures

for acute Myocardial Infarction (AMI), Heart Failure (HF) and Pneumonia (PN). For FY

2015: 12 Clinical Process of Care measures, 8 Patient Experience of Care dimensions

(HCAHPS), three - 30-Day Outcome Mortality measures for Acute Myocardial Infarction

(AMI), Heart Failure (HF) and Pneumonia (PN), one Agency for Healthcare Research

and Quality (AHRQ) Composite measure (Patient Safety Indicator (PSI-90)]), one

Healthcare Associated Infection (Central Line-Associated Blood Stream Infection

(CLABSI)) and one Efficiency measure (Medicare Spending Per Beneficiary (MSPB)) 26.

VPB measures are listed in Appendix B.

HVBP will be funded through a reduction from participating hospitals’ Diagnosis-

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Related Group (DRG) payments for the applicable fiscal year. The money that is

withheld will be redistributed to hospitals based on their Total Performance Scores

(TPS). The actual amount earned by hospitals will depend on the actual range and

distribution of all eligible/participating hospitals’ TPSs. A hospital may earn back a

value-based incentive payment percentage that is less than, equal to, or more than the

applicable reduction for that program year. 1 percent reduction was applied for FY 2013.

This reduction is expected to grow by 0.25%/year till it reaches 2.0 percent in FY 2017.

A hospital’s performance in HVBP is based on measures/dimensions for the domains per

fiscal year (FY). The hospital’s TPS is composed of the following: For FY2013, 70%

clinical process of care and 30% patient experience of care. For FY2014, 45% clinical

process of care, 25% outcome (mortality) and 30% experience of care. Process of care

measures will constitute only 20% of TPS in 2015, experience of care will remain at 30%

and the rest will be comprised of outcome and efficiency measures 26.

Hospitals eligible for participation HVBP include acute care hospitals that had at least 10

cases in at least 4 of 12 clinical process of care measures and/or at least 100 completed

HCAHPS surveys. Excluded hospitals include psychiatric, rehabilitation, long-term care,

children’s or cancer hospitals. Hospitals that did not participate in hospital IQR are also

excluded. CMS estimated that more than 3000 facilities across the United States would

participate in FY 2013. Roughly $850 million dollars in VBP incentives would be paid

out to these participating hospitals in FY 2013. Money at risk is expected to grow with

more reduction in DRG-based payments by 2017 5.

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Patient satisfaction is important not only to the financial reimbursement of hospitals, but

it has been recently linked to CEO pay. In a recent retrospective observational study,

participants included 1877 CEOs at 2681 private, nonprofit US hospitals. Compensation

was associated with technology and patient satisfaction but not with processes of care,

patient outcomes, or community benefit. Hospitals with high performance on patient

satisfaction compensated their CEOs on average $51, 706 more than did those with low

performance on patient satisfaction 39.

The HCAHPS survey, developed by the Agency for Healthcare Research and Quality,

asks patients 32 questions about their experiences in the hospital and about their

demographic characteristics. Possible responses (always, usually, sometimes, and never)

to the questions are summarized by CMS and reported in 7 domains as composites:

communication with physicians, communication with nurses, communication about

medications, quality of nursing services (responsiveness of staff), adequacy of planning

for discharge, care transition, and pain management. (The care transition composite has

been recently added in October 2014: “Patients who understood their care when they left

the hospital”). CMS calculated composite ratings for the domains by averaging the

responses to each individual item within that domain. Other domains reflect individual

questions about whether the rooms were clean and whether they were quiet (possible

responses: always, usually, sometimes, and never) and two overall ratings: a global rating

of the hospital on a scale of 0 to 10, with 0 being the worst and 10 being the best a

hospital can be, and a question about whether the patient would recommend the hospital

to family and friends (possible responses: definitely yes, probably yes, probably no, and

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definitely no). The global ratings are grouped by the CMS into one of three categories, 0

to 6, 7 or 8, or 9 or 10, rather than reported individually. Data are adjusted for the method

of administration of the survey, as well as for patient demographic factors (e.g. age,

educational level, and health status) in order to adjust for nonresponse bias, as described

at www.hcahpsonline.org 6, 40. The survey is administered using a standardized protocol 2

days to 6 weeks after discharge by mail, telephone, mail with telephone follow-up, or

interactive voice recognition. A random sample of eligible patients is surveyed monthly

by the hospital or a licensed vendor and the resulting data are aggregated to produce a

rolling 12-month average. Each hospital is directed to obtain 300 completed HCAHPS

questionnaires over the yearlong reporting period. In small hospitals that are unable to

reach the target survey, all-eligible discharges are included 41-43. Hospitals that report

clinical data to CMS are eligible to participate in HCAHPS. The survey is not intended

for pediatric hospitals, psychiatric hospitals or other specialty hospitals 121. Although

HCAHPS is designed for acute-care hospitals, any hospital that is reimbursed under the

Inpatient Prospective Payment System and is eligible for the Annual Payment Update

(referred to as RHQDAPU) needs to participate in HCAHPS in order to receive full

reimbursement updates 121.

Measuring quality in health care is a subject of debate. Critics have argued that hospital

quality classification based on process measures alone may be problematic due to

imprecision arising from lower case volume at low performing hospitals and the poor

reliability of hospitals’ self-reported data. Thus, HCAHPS measures, which reflect the

experiences of a broader sample of patients, might provide a more representative

http://www.hcahpsonline.org/

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summary of selected aspects of care quality. It has been argued however that experiences

with medical care are more easily understood by patients rather than by technical process

measures. In addition, available technical process measures have limited scope and

coverage, and previous studies indicate that indicators in one domain might not reflect

quality of care in other areas 7, 29, 44. Others question the importance of patient-reported

experiences because they might reflect factors such as a patient’s general mood or

response tendencies in addition to the actual quality of care. Although case-mix

adjustment models have been developed to adjust for such factors, associations between

patients’ reports of experiences and available measures of hospital quality and safety

would add to the credibility of HCAHPS measures 7.

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CHAPTER THREE

SATISFACTION THEORIES

Patients’ satisfaction usually reflects reactions to their experience with care 45. Many

theories and concepts related to satisfaction have been described in the literature.

Expectancy-value theory of Linder-Pelz postulated that satisfaction was mediated by

personal beliefs and values about care, as well as prior expectations about care experience

(access, efficiency, cost, convenience) 46. Brennan defines satisfaction as the appraisal, by

an individual, of the extent to which the care provided has met that individual's

expectations and preferences. Normative decision theory (NDT) is used in that context to

explain the incorporation of individual expectations, values and preferences into the

decision-making processes of patient responses 47. The expectancy-disconfirmation

model in which consumers compare their expectations with service performance has been

proposed 48. In that regard, Bond and Thomas noted that increasing quality of nursing

care raises expectations 49. Modification of this model called the cognitive-affect model

was suggested, in which perception of service includes cognitive evaluation, affective

response and a direct effect on satisfaction. Feedback loops can be created affecting

patients’ behavior. These can be influenced by patient characteristics, values, beliefs and

expectations 9, 50.

Multiple models theory of Fitzpatrick and Hopkins argued that expectations were socially

mediated, reflecting the health goals of the patient and the extent to which illness and

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healthcare violated the patient’s personal sense of “self”51. Performance Theory

postulates that prior expectations do not matter; actual performance will overwhelm any

psychological response tendencies related to expectations. In that regard, overall

satisfaction will be higher (or lower) for better (or worse) clinical outcome 52. Blalock et

al. demonstrated that satisfaction would be highest for those with high psychological

well-being regardless of whether outcome is good or poor 53. Others also posit

interaction between psychological wellbeing and outcome 54. Social psychological

theories suggest that satisfaction evaluations are moderated, or even mediated, by

personal feelings of equity in the exchange, disconfirmation between desires and

outcomes55, individual preferences56, and social comparisons57, 58. According to social-

identity theory, these evaluations are moderated by demographic, situational,

environmental, and psychosocial factors 59. Further, interpretations of these factors are

moderated by individual beliefs, perceptions, and frames of reference that are affected by

cultural orientations60.

In the review by Sitzia et al. 8, 61 other psychosocial determinants such as social desirability

response can affect satisfaction. Patients may report greater satisfaction than they actually

feel because they believe positive comments are more acceptable to survey

administrators. A number of observers have also suggested that patients may be reluctant

to complain for fear of unfavorable treatment in the future. Self-interest can also bias

satisfaction scores. This theory proposes that clients are likely to perceive that

expressions of satisfaction will contribute to the continuation of the service, which in turn

will be in their own self-interest 61. A further factor is predicted by "cognitive consistency

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theory" according to which patients are likely to report they are satisfied as a way of

justifying the time and effort they have invested in their treatment 62. Likewise the

"Hawthorne Effect" postulates that the additional attention in the data collection process

and the apparent concern of the research sponsors about the patient's level of satisfaction

could likely lead to a positive perception of the service 61. Others have also shown that

respondents reporting more satisfaction with life in general, or greater confidence in the

medical care system, recorded significantly higher satisfaction with their physicians than

those who did not 9, 61.

Increased satisfaction also was related to obtaining information about what to expect 9. In

that regard, Keating et al. 63 have shown that complaints of lack of information can result

in the desire to switch physicians. Williams argued that patient expectations were the key

to understanding the reasons for expressed dissatisfaction. 64. In the "discrepancy model",

Fox and Storms65 argued that the lack of variability in satisfaction responses should

prompt a shift in focus from obtaining stability of results to understanding the conditions

under which discrepant findings can be predicted. This implies that a concentration upon

areas of expressed dissatisfaction is more valuable than obtaining consistency of

expressed satisfaction. When favorable experiences match favorable expectations

satisfaction is highest. It is also lower when negative occurrences reinforce negative

expectations or contradict positive ones 65.

In addition to the “expectancy-value” theory and discrepancy theory, equity theory and

fulfillment theory were proposed to explain satisfaction with care. Discrepancy and

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fulfillment theories define satisfaction as the perceived- but not necessarily actual-

discrepancy between what an individual desires and what occurs. Equity theories

however propose that satisfaction is perceived equity, or perceived balance of inputs and

outputs. Further, equity theory stresses the importance of evaluating one's own balance

with others' balances 66.

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TABLE A- Summary of patient satisfaction theories

THEORY DESCRIPTION COMMENTS Expectancy-Value Satisfaction mediated by expectation about

care and personal beliefs and values about care

Expectancy-disconfirmation Consumers compare expectations with service performance

Normative decision theory Appraisal of care if it meets expectations, values and preferences

Cognitive-affective model Cognitive evaluation and affective response affect satisfaction. Influenced by patients characteristics, beliefs and expectations

Increased satisfaction related to obtaining information about what to expect.

Multiple models theory Expectations socially mediated reflecting health goals of the patient

Extent to which illness violates personal sense of self

Performance Theory Satisfaction related to outcome regardless of expectations.

Psychological well-being Satisfaction highest with psychological wellbeing regardless of outcome (Blalock). Interaction between psychological wellbeing and outcome (Hudack)

Social Psychological theory Health evaluations mediated by feelings of equity in exchange between desires and outcomes, individual preferences and social comparisons.

Social identity theory Evaluations moderated by demographic, situational and psychosocial factors.

Interpretation of these factors moderated by individual beliefs and perceptions that are affected by cultural orientations

Social desirability Positive comments perceived more favorably Fear of unfavorable treatment if negative comments

Self-interest bias Expression of satisfaction will continue service in the best of self-interest

Cognitive-consistency theory Patients report satisfaction as a way of justifying time they have invested in their treatment

Hawthorne effect Concern of surveyors about patient satisfaction will likely lead to positive perception of service

Discrepancy model Satisfaction highest when favorable experiences meet favorable expectations, and lowest when negative occurrences reinforce negative expectations

Understanding conditions under which discrepant findings can be predicted. Concentration upon areas of dissatisfaction more valuable.

Fulfillment theory Satisfaction as the perceived, but not necessarily actual discrepancy between individual desires and occurrences

Also applies to discrepancy theory

Equity theory Satisfaction is the perceived equity (balance of inputs and outputs).

27

Based on the literature, there is a lack of a single widely accepted conceptual framework

for dimensions of satisfaction with healthcare. Satisfaction may address a variety of

elements of care. Healthcare quality theory of Donabedian proposed that satisfaction was

the principal outcome of the interpersonal process of care 30. He argued that the

expression of satisfaction or dissatisfaction is the patient’s judgment on the quality of

care in all its aspects, but particularly in relation to the interpersonal component of care.

The Donabedian model for quality assessment (structure, process, and outcome) provides

a good framework for satisfaction dimensions 18. Structural aspects include access, cost

and physical environment. Process aspects include technical quality, interpersonal

communications, and coordination of care. Outcomes aspects include satisfaction with

health. Priorities may differ across settings. For some health plans, the structural aspects

of quality such as access, cost, physical environment and technology are viewed

favorably 9. For inpatient care however, the interpersonal interaction particularly with

nursing care is more important 67. Patient condition can also affect satisfaction. Patients

who present with an acute complaint may prioritize outcome whereas patients with

chronic disease may prioritize the process dimensions of satisfaction 48.

Although patient satisfaction is considered as an outcome of health care, it is also

affected by the outcomes of care. In a review of over 100 papers published in the field of

satisfaction, Sitzia et al. 8, 61 argue that the major goal of patients presenting with health

problems is the resolution of those problem not satisfaction with the process. Glickman et

al. 14 examined clinical data on 6467 patients with acute myocardial infarction. Higher

patients satisfaction scores were associated with lower inpatient mortality. Clark and

colleagues identify at least one study each that independently and positively relates

28

patient satisfaction to one of these clinical outcomes: HbA1c levels, cholesterol levels,

post-surgery complications, post-surgery recovery times, risk-adjusted mortality rates,

and unexpected mortality 68. More recent data studying the relationship of outcome

measures and patient satisfaction shows a significant association, and decrease in 30-day

hospital readmission 16.

DETERMINANTS OF SATISFACTION:

Based on the theories of satisfaction and literature review, the major components

influencing satisfaction can be classified into patient factors, provider factors,

hospital/physical plant/structural factors, and quality factors. A summary of these theories

is found in table B.

Patient Factors:

A consistent determinant characteristic is patient age, with a body of evidence

suggesting that older people tend to be more satisfied with healthcare than do younger

people. This is likely related to change in expectation (older people are less likely to

criticize and have more modest expectations) 8, 61. Additionally, according to Sitzia and

Wood, greater satisfaction was also associated with lower levels of education. However

this effect was likely confounded by lower income 61. In their review of over 100 papers

published in the field of satisfaction, Sitzia and Wood found that the relationship between

satisfaction and income and “social class" was not consistent. It was also generally

found that patient gender does not affect satisfaction values 61. From the United States

29

there was evidence that whites on the whole are more satisfied than non-whites 45.

However the racial disparity was likely confounded by socio-economic status 69. In that

regard, Crow et al 9 determined that although there is a consistent positive relationship

between age and satisfaction, the evidence on other variables (gender, race, education,

income) is equivocal.

In the review by Crow et al .9, there was evidence that poorer physical health status,

disability, low quality of life, and psychological distress were associated with lower

levels of reported satisfaction patients’ ratings. Indeed, patients who perceive themselves

to be healthier may be more satisfied with life generally, and this attitude carries over to

specific episodes of care 70. Expectations of perceived improvement in health has been

determined to be a likely factor in the relationship between health status and patient

satisfaction 9,71.Patients with poor health status have been shown to have higher number

of days of hospitalization 72,73. In a recent longitudinal cohort of acute myocardial

infarction, Lee et al. examined the associations of patient-reported satisfaction with care.

Among the 1866 patients analyzed, satisfaction with care was more likely in patients who

were older, in those without depression, and in those with better functional capacity 10. In

a Dutch study, patient characteristics had statistically significant influence on patient

satisfaction. Older patients with better health status and lower education appeared to be

more satisfied with care 74.

Patient education level has been revisited with other studies suggesting a positive

relationship between lower education and satisfaction with care 75-77. Jha et al. reporting

30

data on 2429 hospitals participating in HCAHPS from July 2006 through June 2007 have

shown that hospitals with higher concentration of Medicaid patients have lower global

HCAHPS patient satisfaction ratings. Patients receiving Medicaid were utilized as a

measure of the extent to which the hospitals provide care to low-income population 6. In

that aspect, it has been suggested that individuals with higher income could experience

better healthcare quality 78.

Provider Factors:

Based on Ware framework79, components of satisfaction include interpersonal manner-

features of the way in which providers interact personally with patients (e.g. respect,

concern, friendliness, courtesy, communication, empathy). In outpatient care,

professional standards and interpersonal relationships are of prime importance to patients.

In primary care, there is evidence that patient–doctor communication and information

sharing is as important as determinants of satisfaction as access and availability 79. In a

study by Blanden et al. 80, failure of physicians to listen and form personal connections

with patients was viewed as major factor for lower patient’s satisfaction with their

physicians. Other factors include ability to have all their questions answered and

incomplete discussion of medication side effects by physicians. In questionnaires

addressed to physicians and patients, significant differences existed between patients' and

physicians' impressions about patient knowledge and inpatient care received. Improving

patient-physician communication was identified as an important step towards improving

quality of care and patient satisfaction 11. However, according to Crow et al .9, there was

31

no evidence that the gender or age of physicians or their interpersonal skills have an

effect on satisfaction.

According to Ware, continuity of care-constancy in the provider or location of care, and

availability-presence of medical care resources (e.g. enough medical facilities and

providers) are important factors in patient satisfaction 79. In a survey of 17,196 enrollees

from a large health plan in California, lack of access to specialists was significantly

related to dissatisfaction with the plan and desires to dis-enroll from the plan 81.

Satisfaction in the heterogeneous US system was also related to having insurance and a

regular source of care. In addition, patients receiving preventive services or health

promotion advice were more satisfied than those who did not. In a study of the Medicare

population, physician supply was not related to patient satisfaction. Nyweide et al. 82, in

national random survey of Medicare beneficiaries, found that patients living in areas with

more physicians per capita had perceptions of their health care that were similar to those

of patients in regions with fewer physicians.

Nursing strain and exhaustion also affected patient satisfaction 9. Experience with nursing

was most strongly correlated to patient ratings of quality of care, services and global

recommendation 83. Significant positive association has been also found between the

supply of nurses and patients evaluation of their care. Strategies towards improving

nursing supply have been suggested for quality improvement 12. Kutney-Lee et al. 13

submitted that improving nurses’ work environments, including nurse staffing, is needed

in order to improve the patient experience and quality of care. Jha et al. 6, in a study of

32

2429 hospitals reported that hospitals in top quartiles of ratio of nurses to patient-days

had better performance on HCAHPS surveys.

Quality Factors:

The Ware framework components of satisfaction include technical quality of care

(measured by the competence of providers and adherence to high standards of diagnosis

and treatment), and outcomes of care measured by the results of services (e.g.

improvements in or maintenance of health) 79. Baicker & Chandra have previously shown

that Medicare spending was inversely related to technical quality measures, and per

capita use of acute care hospitals was also negatively associated with patients’ hospitals

ratings 84, 85. In a more recent study of 2,473 acute care hospitals, Wennberg et al. 86

demonstrated an inverse relationship between greater inpatient care intensity (defined by

amount of hospitalization time and intensity of physician intervention) and patients’

ratings of their hospital experiences based on HCAHPS survey. Greater care intensity

was also associated with lower quality scores, which were also correlated with lower

patients’ ratings. Poorly coordinated care was the common link to increased intensity of

care and lower care quality.

In a study by Isaac et al. 7, data from 927 hospitals were used to examine relationships

between HCAHPS and scores of quality based on Hospital Quality Alliance (HQA) and

Patient Safety Indicators (PSIs) developed by Agency for Healthcare Research and

Quality (AHRQ). The overall global HCAHPs ratings of hospitals and willingness to

recommend hospitals had strong relationships with technical quality performance in all

33

medical conditions and surgical care. Jha et al. 6 reported data on 2429 hospitals

participating in HCAPHS from July 2006 through June 2007. Relationships of HCAHPS

measures to hospitals performances were then studied using hospital quality alliance

quality summary scores. Hospitals with higher levels of patients’ satisfaction reported

higher quality clinical care. Glickman et al. 14 examined clinical data on 6467 patients

with acute myocardial infarction. Higher patients satisfaction scores were associated with

lower inpatient mortality. Patients ‘satisfaction with their care provided important

information on the quality of acute myocardial infarction care in this study. In a recent

study by Girotra et al. 15, it was found that hospitals with consistently poor performance

on cardiac process measures also have lower patient satisfaction on average, suggesting

that these hospitals have overall poor quality of care.

Although little research exists on the relationship between process-based measures of

clinical quality and patient satisfaction with the care experience, some literature

demonstrate a significant relationship between outcome measures of clinical quality and

patient satisfaction. A comprehensive collection and evidence-based hierarchy of the

published research lists 33 studies that link patients’ satisfaction with their care

experience to clinical quality and 14 studies associating patient satisfaction with

compliance68. In their review, Clark and colleagues identify at least one study each that

independently and positively relates patient satisfaction to one of the following clinical

outcomes: clinical quality (e.g., HbA1c levels, cholesterol levels, fewer bed disability

days), chronic disease control, compliance, drug complications, quality of life, emotional

health status, mental health, physical functioning, physical health status, post-surgery

34

complications, post-surgery recovery times, risk-adjusted mortality rates, unexpected

mortality, and work effectiveness 68. More recent data studying the relationship of

outcome measures and patient satisfaction shows a significant association with a decrease

in 30-day hospital readmission 16, and lower MI mortality 14. In a recently published

study using the Hospital Compare Database, there was an inverse relationship between

patient experiences and complication rates (Hospital acquired conditions) 87.

Hospital/Physical plants/structural factors:

The Ware framework components of satisfaction include accessibility/convenience-

factors involved in arranging to receive medical care; and physical environment-features

of setting in which care is delivered (e.g. clarity of signs and directions, orderly

facilities and equipment, pleasantness of atmosphere) 79. Crow et al. 9 and Young et al.

88 found that in-hospital satisfaction in the USA has been reported to be lower in

teaching hospitals than in private hospitals. Others however have found that teaching

hospitals were more likely to have superior performance on dimensions of quality and

HCAHPS than non-teaching hospitals 89. Lehrman et al. 89 found that top hospital

performers in patient experience are most often small and rural and located in the East

South Central division. Hospital size and its effect on satisfaction was also confirmed

by Young et al. 88 who showed that among the institutional characteristics, hospital size

was significantly associated with lower satisfaction scores. Rural hospitals had

significantly higher satisfaction score than did urban hospitals. Jha et al. 6 also

demonstrated that patient satisfaction was higher among non-for-profit hospitals, non-

urban hospitals, hospitals with smaller number of beds, location in the southern US,

35

with less ICU capacity and less Medicaid patient mix.

Despite these findings, other studies showed negative association between hospital size,

rural status and patient satisfactions. Girotra et al. 15 showed that for both acute

myocardial infarction (AMI) and heart failure (HF), low-performing hospitals in quality

measures had lower annual admission volume, fewer beds, lower nurse FTE per 1000

patient days, and these hospitals were more likely to be rural, safety-net hospitals. More

than half of the low-performing AMI and HF hospitals were located in the South census

region and these hospitals scored lower on patient satisfaction. In a retrospective study

of 2761 US hospitals reporting AMI process measures to the Center for Medicare and

Medicaid Services Hospital Compare database during 2004 to 2006,low-performing

cardiac hospitals differ from better performing hospitals with regards to hospital

structure and organization. These hospitals were smaller, rural facilities and have higher

risk-adjusted mortality 90.

Wennberg et al. 19, 85 showed that patients living in regions with more hospital care

intensity (measured by amount of time spent in the hospital and the intensity of physician

intervention during hospitalization) tend to rate their inpatient experiences less favorably.

According to Wennberg19, other measures of care intensity include per capita Medicare

spending. In that regard, ICU bed capacity has been shown to be component of hospital

care intensity index 91 and a measure of technological capacity 6.

36

TABLE B- Determinants of Patient Satisfaction with Hospital Care

PATIENT FACTORS

Age Older more satisfied. Age could be proxy for expectations since older have more modest expectations (Kravitz, Young)

Association Confirmed

Race Whites more satisfied (Pascoe), but effect disappears after controlling for socioeconomic interaction

Association Not confirmed

Education Lower educated patients more satisfied but confounded by SES. (Sitzia, Hekkert,’OMalley, Rahmqvist)


Socioeconomic status (SES) Not consistent in the literature (Sitzia, Crow), Medicaid as a marker of low-income population was negatively related to satisfaction (Jha)


Poor health status, low quality of life, and psychological distress

All associated with negative satisfaction

Association confirmed

PROVIDER FACTORS

Interpersonal interaction with patients

Communication, respect, courtesy, empathy, information sharing (Ware)


Providing access to care, available medical resources


Preventive services


Nursing supply While positive association with nursing supply, negative association with nursing strain.


QUALITY FACTORS

Technical quality: adherence to standards and outcomes (Clark)


Quality scores-Process and Outcomes (Wennberg, Isaac, Glickman, Clark, Boulding, Stein)

While positively associated with satisfaction, quality scores negatively related to inpatient care intensity.


STRUCT. FACTORS

Convenience, physical environment (Ware)

Positive relationship to satisfaction Association confirmed

Teaching Lower association with satisfaction (Crow, Young), higher association (Lehrman)

Association not confirmed

Size Smaller better satisfaction (Lehrman, Young, Jha). Smaller lower satisfaction (Girotra, Popescu)


Rural status Better satisfaction (Lehrman, Young, Jha). Worse satisfaction (Girotra, Popescu)


Non-profit

Better satisfaction (Jha) Association confirmed

Care intensity (intensity of care and procedures)

Associated with lower satisfaction. Due to lower quality


37

INTERACTIONS WITH SATISFACTION DETERMINANTS.

As discussed previously patient expectations are main drivers behind satisfaction

theories, however the relationship seems quite complex. Based on the literature review,

there is a significant interplay between expectations, patients, providers, structural

characteristics and satisfaction. As mentioned in Crow et al. 9:

“Measuring satisfaction as the difference between expectations and perceptions of care experiences is complicated by the dynamic, two-way nature of the relationship between them. Experiences may cause expectations to shift, either directly as a result of information provided during the process of care, or indirectly because occurrences may alter patients’ perceptions. Equally, however, expectations may directly modify occurrences (e.g. when patients request certain treatments), or alter patients’ perceptions of them. (Pg.7)”

It has been suggested that patient demographics such as age (associated with higher

satisfaction scores) are possible proxies for patient expectations and values about health

care through which specific experiences are filtered. 92, 93. Wagner et al. 94 proposed that

expectations influence and appear to have an inverse relationship with satisfaction: if

expectations are low, satisfaction is higher; if expectations are high, perception of

satisfaction is lower. Most of the studies showed an association between satisfaction and

age, and determined that older individuals have lower expectations 61. The relationship

between other variables such as race, income and education with satisfaction however is

quite complex and inconclusive. Although it has been shown that as education and

income levels increase so does expectation level 95, prior expectations and expectations

that are adapted by experience can vary. In some, health beliefs can also affect that

relationship 9, 96.

38

While most satisfaction theories can explain the relationship between quality metrics and

satisfaction (expectancy-value, expectancy-disconfirmation, normative decision theory

multiple models theory, performance theory), the relationships with structural measures

seem more complicated such is the case with teaching and larger hospitals. Although they

are reputed to be technologically superior leading to better outcomes 97, teaching and

larger hospitals treat sicker patients with worse health status leading to worse satisfaction

in some cases70.

PROCESS OF CARE MEASURES AND PATIENT SATISFACTION

There is a paucity of studies looking into the association of hospital process of care

measures and patient satisfaction. The studies showing an association mostly examined

summary quality process scores instead of the effects of individual process of care scores.

Previous studies on the relationship between patient satisfaction with their experiences

and the quality of clinical care have had mixed results. Schneider et al. 98, in a national

sample of 233 Medicare health plans, found that enrollees in Medicare managed- care

plans that had better performance on the measures in the Healthcare Effectiveness Data

and Information Set (HEDIS quality measures) reported better experiences in obtaining

information on health plans and in dealing with customer service. However they did not

give higher global ratings of the plan. In a cross sectional population based study using

the general practice assessment survey, Rao et al. 99 studied 18 general practices in

England. There was only a weak correlation between patient assessed survey scores for

technical quality (hypertension control and vaccination), and the objective records based

39

measures of good clinical practice.

Gandhi et al. 100, in a cross-sectional chart review of patients and physicians surveys in 11

ambulatory clinic sites in the Boston-area from May 1996 to June 1997, analyzed report

card scores for five quality domains (performance on HEDIS-like measures, clinic

function, patient satisfaction, diabetes guideline compliance, asthma guideline

compliance). No association was detected between patient satisfaction and quality scores.

Chang et al. 31 studied vulnerable older patients identified by brief interviews of a random

sample of community-dwelling adults 65 years of age or older who received care in 2

managed care organizations during a13-month period. Survey questions from the

Consumer Assessment of Healthcare Providers and Systems program were used to

determine patients’ global rating of health care and provider communication. A set of 236

quality indicators, defined to measure technical quality of care given for 22 clinical

conditions was determined. Two hundred and seven quality indicators were also

evaluated using data from chart abstraction or patient interview. In a multivariate logistic

regression model that included patient and clinical factors, better communication was

associated with higher global ratings of health care. Technical quality of care was not

significantly associated with the global rating of care.

Lyu et al. 22 compared hospital patient satisfaction scores with Hospital Surgical Care

Improvement Program compliance and hospital employee safety attitudes (safety culture)

scores during a 2-year period (2009-2010). Patient satisfaction was not associated with

performance on process measures (antibiotic prophylaxis, R=0.216 [P=. 24]; appropriate

40

hair removal, R=0.012 [P=. 95]; Foley catheter removal, R=0.089 [P=. 63]; deep vein

thrombosis prophylaxis, R=0.101 [P=. 59]) .In a longitudinal cohort of 1866 acute

myocardial infarction patients, Lee et al. 10 examined the associations of patient-reported

satisfaction with care with clinical characteristics, physical and psychological function

measures, quality indicators of myocardial infarction care, and outcomes. Of the study

cohort, 1711 (91.7%) reported that they were satisfied with their overall care. Patients

who reported satisfaction with care were older, had improved physical function, and were

less likely to be depressed. Better physical function (measured by the Specific Activity

Scale) predicted higher satisfaction, after adjustment for age, sex, income, and ethnicity.

Depression was the major predictor of dissatisfaction with overall care. Quality indicators

for myocardial infarction care and clinical outcomes were not associated with patient

satisfaction. (Aspirin upon discharge, beta blocker at discharge, angiotensin-converting

enzyme inhibitor at discharge, HMG-CoA reductase inhibitor at discharge, referral to

cardiac rehabilitation, cardiac catheterization performed, percutaneous coronary

intervention performed, coronary bypass graft surgery performed, referral to cardiologist,

referral to internist, follow-up with family physician alone).

Even though the previously mentioned studies suggest no relationship between process of

care quality measures and patient satisfaction, other data support a positive association.

In a series of literature reviews, Wagner and colleagues 101 concluded that evidence-based

clinical care and well-designed chronic disease self-management programs (chronic care

model) that are patient-centered simultaneously improve disease control, patient

satisfaction, and compliance. In a random sample of patients hospitalized between

41

October 2006 and June 2007 in 2,517 U.S. hospitals, three summary scores were

calculated: one for five measures of the management of acute myocardial infarction

(AMI); a second for two congestive heart failure (CHF) measures; and a third for three

pneumonia measures. Among regions, hospitals with lower overall ratings by their

patients also tended to have lower quality measures. In addition, the percentage of

patients reporting a negative experience was positively correlated with hospital care

intensity (reflecting both the amount of time spent in the hospital and the intensity of

physician intervention during hospitalization) in all categories of the HCAHPS survey 19.

There is also evidence that per capita use of acute care hospitals may be associated with

patients’ ratings of their inpatient experiences. In California, patients using hospitals in

regions with greater care intensity such as Los Angeles tended to give lower ratings to

their hospitals than those using hospitals in regions with more conservative care patterns

such as Sacramento 85,102.

Isaac et al. 7 examined the associations between HCAHPS scores and HQA measures

(Hospital Quality Alliance) from over 900 hospitals pertaining to hospital care in 2006.

For medical conditions, they analyzed the 10 core processes of care measures that

hospitals were required to submit in order to receive their CMS fee update. For surgery,

they examined the measures that were voluntarily reported by hospitals. They calculated

summary performance measures for each quality medical condition and for surgical care

by summing the numerators of related individual measures and then dividing by the sum

of the denominators of related measures as described by Landon et al 103. For AMI,

adjusted correlations were statistically significant for seven of the nine HCAHPS

42

measures. For example, the correlation with the overall rating of the hospital was 0.53

and the correlation with adequate discharge information was 0.43 (both p< .001).

Relationships between pneumonia processes of care and HCAHPS composites were also

significant. Better CHF processes were associated with better overall rating of the

hospital and willingness to recommend the hospital but not with the other HCAHPS

measures. Relationships between surgical processes of care and the HCAHPS measures

followed a similar pattern with correlation coefficients ranging from 0.14 for

communication with doctors (p< .02) to 0.35 (p< .001) for willingness to recommend the

hospital to a friend or family member. There were consistent relationships between

patient experiences and technical quality as determined by the measures used in the HQA

program. Global measures of hospital performance, the overall rating of the hospital and

willingness to recommend the hospital, had strong relationships with better technical

performance in processes of care related to pneumonia, CHF, myocardial infarction, and

for surgical care.

Another study that examined how patients’ experiences of care in hospitals related to

HQA process measures found similar relationship: In a study 2429 hospitals participating

in HCAPHS from July 2006 through June 2007, Jha et al. 6 found that the ratio of nurses

to patient-days was a predictor of performance on the HCAHPS survey: a larger

percentage of patients in hospitals in the top quartile of the ratio of nurses to patient-days,

as compared with the bottom quartile, gave the hospital a global rating of 9 or 10 (65.9%

vs.60.5%, P<0.001 for trend). Fewer patients in for profit hospitals gave a global rating of

9 or 10 than patients in either private or public not-for profit hospitals. There was no

43

significant difference between teaching and nonteaching hospitals .The authors found that

patients’ satisfaction with care was associated with the quality of clinical care in the

hospitals for all four conditions process of care: Acute myocardial infarction, pneumonia,

congestive heart failure and surgery. The HQA scores for hospitals in the highest quartile

of HCAHPS ratings were, on average, about 2 to 4 percentage points higher than the

HQA scores for hospitals in the lowest quartile of HCAHPS ratings. The Hospital Quality

Alliance (HQA) score was defined as the percentage derived from the sum of the number

of times a hospital performed the appropriate action across all measures for that condition

(numerator) divided by the number of opportunities the hospital had to provide

appropriate care (denominator).). The score was adjusted for number of beds, academic

status, region, location, profit status, ratio of nurses to patient-days, and percentage of

patients receiving Medicaid.

Girotra et al. 15 examined patient satisfaction at hospitals that have consistently poor

performance on process measures for 2 cardiac diseases, acute myocardial infarction

(AMI) and heart failure (HF), and compared it with patient satisfaction at hospitals with

intermediate and high performance. For each hospital, the authors calculated a composite

performance score for AMI and HF performance for each year using the opportunities

scoring method 104, (dividing the total number of times each treatment was administered

(numerator) by the total number of opportunities for each therapy (denominator),

multiplied by 100). Next, they stratified hospitals into defiles based on their composite

performance scores for each year. They defined low performing hospitals as hospitals in

the bottom decile of performance for each of the 3 years, top-performing hospitals as

44

those in the top decile of performance for each year, and intermediate hospitals as all

others. Low-performing AMI and HF hospitals scored significantly lower in both global

domains of patient satisfaction, on average compared with intermediate and top-

performing hospitals. A lower ratio of nurse FTEs to patients, higher bed size, and for

profit ownership was independently associated with lower patient satisfaction.

Gesell et al. 21 examined the relationship between hospitals’ adherence to CMS clinical

process measures for heart failure treatment and their heart failure patients’ perceptions

of quality. It was retrospective database study, drawing upon data collected between the

first and second quarters of 2004 and maintained in the Press Ganey National Inpatient

Database and on the CMS Hospital Compare Web site. The satisfaction ratings of heart

failure patients and the clinical process measures for heart failure treatment for 32

hospitals were linked and analyzed. Hospital Compare then showed 17 quality measures

for heart failure, heart attack, and pneumonia treatment. This study analyzed all four of

the quality measures for heart failure treatment (percentage of patients given ACE

inhibitor for LVSD-left ventricular systolic dysfunction, percentage of patients given

assessment of LVF-left ventricular function, percentage of patients given adult smoking

cessation advice or counseling, percentage of patients given discharge instructions). Two

of the four clinical process measures (percentage of patients given assessment of LVF,

percentage of patients given discharge instructions) showed statistically significant,

moderately strong, positive correlations with a global measure of satisfaction. The other

two clinical measures (percentage of patients given an ACE inhibitor for LVSD,

percentage of patients given adult smoking cessation advice or counseling) showed no

45

statistical relationship to patient satisfaction. According to the authors, the failure to

detect a relationship between patient satisfaction and two of the clinical guidelines might

be explained by inadequate statistical power.

Glickman et al. 14 examined clinical data on 6467 patients with acute myocardial

infarction treated at 25 US hospitals participating in the CRUSADE initiative from 2001

to 2006. Patient satisfaction correlated with cardiac catheterization within 48 hours, beta-

blockers on discharge, clopidogrel on discharge, and lipid lower agents (strongest

correlation coefficient = 0.199). Shwartz et al. 20 analyzed 2005 data from a sample of

1,006 U.S. hospitals. They studied performance on five measures calculated from

publicly available data: adherence to evidence-based processes of care, risk-adjusted in-

hospital mortality, risk-adjusted efficiency, risk-adjusted readmissions, and patient

satisfaction. They calculated a composite measure of adherence by condition using the

opportunity-based weights approach (summed the numerators, summed the

denominators, and then calculated the ratio of summed numerators to summed

denominators). They also used this approach to calculate composite measure across all

fifteen-process measures for the three conditions (CHF, AMI, pneumonia). There was a

statistically significant correlation (at the p < .05 level) between the composite adherence

to process of care measure and the HCAHPS patient satisfaction measure (.33). Recently,

Tsai et al.125 in a study that included 2953 hospitals have shown that institutions with the

higher SCIP composite scores had higher HCHAPS global satisfaction scores. Kennedy

et al.135 however in a study of 171 hospitals found that large hospitals, high surgical

volume, and low mortality were associated with patient satisfaction (P < 0.001).

46

Compliance with SCIP process measures and patient safety indicators, as well as length

of stay, did not correlate with overall satisfaction.

TABLE C- Summary of HOSPITAL Process of Care measures and patients satisfaction

SCIP AMI CHF PNEUMONIA

Lyu et al (2013): 31 hospitals NO Association with global satisfaction

Antibiotic prophylaxis, hair removal, urine cath removal, DVT prophylaxis

Kennedy et at (2014) 171 hospitals NO Association

Summary SCIP Process measures

Lee et al (2008) 1711 study cohorts: NO Association with patient satisfaction (questionnaire)

Aspirin upon discharge, beta blockers on discharge, ACE inhibitors on discharge, statins on discharge, cardiac rehab, cardiac cath, PTCA, CABG

Glickman et al (2010) 6467 patients ++ Association with global patient satisfaction

Cardiac catheterization within 48 hours, beta blockers on discharge, clopidogrel on discharge, and lipid lower agents

Wennberg et al (2009) 2517 hospitals: Lower scores associated with lower global satisfaction ratings

Summary scores Summary scores Summary scores

Isaac et al (2010) 900 hospitals: Positive association with all HCAHPS composites with quality summary scores

Summary scores Summary scores Summary scores Summary scores

Jha et al (2008) 2429 hospitals: Higher quartile summary scores associated with higher HCAHPS composite ratings

Summary scores Summary scores Summary scores Summary scores

47

Girotra et al (2012) 2467 hospitals for AMI, 3115 hospitals for CHF: Lower performing hospitals scored lower on global patients satisfaction

Composite performance score

Composite performance score

Gesell et al (2005) 32 hospitals. Positively associated with global patient satisfaction

Percent having assessment of LV function Percent given discharge instruction

Shwartz et al (2010) 1006 hospitals Positive association with global Patients satisfaction

Summary Composite measures



Tsai et al (2014) 2953 hospitals: Positive association with global HCAHPS satisfaction

Summary Composite scores

As is evident from this review of the relevant literature, most studies analyzing the

association between process of care scores and patient satisfaction mostly looked into

summary scores instead of effects of individual process of care scores. Where individual

process of care scores were used, they were based on only selected care measures (acute

MI, CHF, or SCIP surgical measures). Moreover, in these studies a limited numbers of

hospitals are included 14, 22. Other investigations as mentioned previously also showed no

relationship quality measures and patient satisfaction 10, 22, 31. No studies were done to

analyze the relationship between current stroke and venous thromboembolism (VTE)

process of care quality measures and patient satisfaction. In addition, no analyses were

done studying the role of each HCAHPS composite within each process of care measure

to determine global patient satisfaction.

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This proposed study provides a unique opportunity to analyze the relationship between

each individual hospital process of care measure and global patient satisfaction HCAHPS

score using Donabedian framework. It also incorporates other HCAHPS composite

components (communication with nurses, communication with doctors, responsiveness of

hospital staff, pain management, communication about medicines, and discharge

information, cleanliness of hospital environment and quietness of hospital environment)

into quality elements to determine relationship with global satisfaction with care.

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CHAPTER FOUR

CONCEPTUAL FRAMEWORK

The Donabedian model for quality assessment (structure, process, and outcome) provides

a good framework for satisfaction dimensions 18. The conceptual framework proposed by

Donabedian is shown in Figure 1.

Figure 1: Determinants of satisfaction based on the Donabedian framework

STRUCTURE Hospital bed size* ICU bed size* Rural/urban* RN staffing* Medicaid volume* Medicare volume* For profit status* Teaching status* Specialty Hospital status* Medicare Spending Per Beneficiary *** Cleanliness of environment** Quietness of environment**

PROCESS

Hospital Compare Measures*** Nurse communication** Doctor communication** Responsiveness of staff** Pain management** Communication about meds** Discharge information** *Data from AHA (American Hospital Association) ** Data from HCAHPS ***Data from hospital compare Process measures for AMI/HF/PN/SCIP/Stroke/VTE

stucture

process

outcome (satisfaction)

OUTCOMES Definitely recommend hospital** Hospital Rating 9-10/10**

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Structural aspects include access, cost and physical environment. "Structure" generally

refers to the organization of the institution delivering care and the conditions under

which care is provided including the medical delivery system’s fixed characteristics such

as staff number, types, qualifications and facilities. Structure is the patient/consumer’s

rating of the physical environment and physical facilities in which the service occurs.

Process aspects include technical quality, interpersonal communications, and

coordination of care. "Process" refers to the professional activities associated with

providing care such as what is done to and for the patient such as treatment. Process

measures address, for example, the patient’s rating of interpersonal interactions with

service personnel and of personnel with each other. Specific attributes include, for

example, responsiveness, friendliness, empathy, courtesy, competence, and availability

17. Outcomes aspects include satisfaction with health 30.

Donabedian proposed that satisfaction was the principal outcome of the interpersonal

process of care. He also regarded "outcome" as the most important aspect 1 7 and

stressed that an outcome is not simply a measure of health, well-being, or any other

state; it is a change in a patient's current and future health status that can be confidently

attributed to antecedent care 30, 61,105. Although some outcomes are generally

unmistakable and easy to measure (death, for example) other outcomes, not so clearly

defined, can be difficult to measure. These include patient attitudes and satisfactions,

social restoration and physical disability and rehabilitation 30,105. Donabedian suggested

that “patient satisfaction may be considered to be one of the desired outcomes of care …

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information about patient satisfaction should be as indispensable to assessments of

quality as to the design and management of health care systems” 17. Outcome-related

measures or items ask about the patient’s perception of the results of process, including

symptom reduction or resolution, improvement in functioning, or resolution of

underlying problems. He argued that the expression of satisfaction or dissatisfaction is

the patient’s judgment on the quality of care in all its aspects, but particularly in relation

to the interpersonal component of care 30.

In this study, The Structure-Process-Outcome framework is used to determine the

association of hospital quality process of care measures (acute MI/ heart

failure/pneumonia/ SCIP/stroke/VTE) with the outcome defined as Global HCAHPS

satisfaction scores. Structure measures (bed size capacity, ICU bed capacity, nurse

staffing, Medicaid volume, Medicare volume, rural status, teaching, specialty hospital

status, for-profit status and Medicare spending per beneficiary) are used as adjustment

factors. As discussed earlier, bed size capacity, rural and teaching status had various

effects on patients’ satisfaction possibly through varying influences on expectations

(larger and teaching hospitals expected to receive higher ratings of patient satisfaction,

but treat sicker patients 70,97). Medicare volume is used to reflect the age of the

population. This can also be used as indirect proxy for expectations 61, 92, 93. Medicaid

volume can be used as an adjustment factor for socioeconomic status. Rural status is

used to adjust for education and socioeconomic status, since it has been shown that rural

residents tend to lower incomes and lower college education 106,107.

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The Medicare Spending per Beneficiary (MSPB) measure evaluates hospitals’ efficiency,

as reflected by Medicare payments made during an MSPB episode, relative to the

efficiency of the median hospital. Specifically, a hospital’s MSPB measure is calculated

as the hospital’s average MSPB amount divided by the median MSPB amount across all

hospitals, where a hospital’s MSPB amount is the hospital’s average price-standardized,

risk-adjusted spending for an MSPB episode 108. The MSPB is being used in the study to

reflect hospital care intensity 19. ICU bed capacity is also used to reflect care intensity and

technological capacity 6, 91. Nurse staffing and for profit status are used as adjustment

factors since they have been shown to influence satisfaction 6. Specialty hospitals will

also be included in structure adjustment measures since they are not required to report

HCAHPS data121, and the ones that report HCAHPS do it voluntarily leading to possible

non-representative sampling. One could argue that HCAHPS components could be

interpreted as patients’ perceptions of structure, process and outcomes related to hospital

care. Incorporating HCAHPS structure and process components into the relationship

between hospital process of care measures and global satisfaction scores would be a great

addition to the literature.

The purpose of this study is analyze each individual process of care component

(Acute myocardial infarction, congestive heart failure, pneumonia, surgical care

measures, stroke, and venous thromboembolism) along with structure measures (bed

size, ICU bed capacity, rural status, RN staffing, for profit status, teaching status,

proportion Medicaid, proportion Medicare, Medicare spending per beneficiary, specialty

hospital status), and study their outcome association with global HCAHPS patient

53

satisfaction scores. Another purpose is to analyze each individual process composite

HCAHPS measure (Nurse communication, doctor communication, responsiveness of

staff, pain management, communication about meds, and discharge information) within

each process category for MI/heart failure/pneumonia/SCIP/stroke and VTE, along with

HCAHPS structure measures (cleanliness of environment, quietness of environment),

and study their outcome association with global HCAHPS patient satisfaction scores.

The study intends to address the following research questions and to test the following

research hypotheses related to patient satisfaction with hospital processes of care.

RESEARCH QUESTIONS:

1) Is there a relationship between individual hospital quality processes of care measures

for AMI, heart failure, pneumonia, SCIP, stroke, VTE, and global HCAHPS satisfaction

scores, adjusting for structure differences between hospitals?

2) Is there an association between each hospital’s HCAHPS composite process measure

within AMI, heart failure, pneumonia, SCIP, stroke, VTE, and global HCAHPS

satisfaction after adjusting for HCAHPS structure differences?

54

RESEARCH HYPOTHESES:

Ho1= There is no relationship between each individual process of care measure for

AMI/heart failure/pneumonia/SCIP/stroke/VTE and Global HCAHPS scores adjusting

for structure differences.

H1= There is a positive relationship between each individual process of care measure for

AMI/heart failure/pneumonia/SCIP/stroke/VTE and Global HCAHPS scores adjusting

for structure differences.

Ho2= There is no association between each HCAHPS process composite measure and

global HCAHPS scores within each studied quality process measure for MI/heart

failure/pneumonia/SCIP/stroke/VTE adjusting for HCAHPS structure differences.

H2= There is a significant association between each HCAHPS process composite

measure and global HCAHPS scores within each studied quality process measure for

MI/heart failure/pneumonia/SCIP/stroke/VTE adjusting for HCAHPS structure

differences.

Thus a unique opportunity exists to measure not only the effect of each process of care

measure on satisfaction, but the impact of each HCAHPS composite component within a

specific quality process on global hospital patient satisfaction. The effect of individual

structure characteristics will also be determined within each process of care type (Acute

MI, heart failure, pneumonia, SCIP, stroke and VTE). Defining the effect of stroke and

VTE quality measures on patient satisfaction has not been examined previously, and this

55

study also provides another opportunity in that regard. By determining the relationship of

each individual quality process of care measure with global patient satisfaction, policy

decisions can be made to focus on improving the weaker associations and reinforcing the

stronger relationships. In addition defining the role of each individual HCAHPS

composite within each process of care measure can determine areas of strengths and

weaknesses towards improving patient satisfaction.

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CHAPTER FIVE

METHODS

Design

This study uses an observational (non-experimental), retrospective, cross-sectional

Design to assess the association between process of care measures and patient

satisfaction. The data collection period is from 7/1/2012- 6/30/2013.

Data Sources

Samples are drawn from secondary databases. Data sources are derived from Hospital

Compare for West South Central Region, covering hospitals in Louisiana, Arkansas,

Texas and Oklahoma. The following processes of care quality scores are included:

For heart attack (MI): Percent of MI patients given aspirin upon discharge (asadc),

percent of heart attack Patients given fibrinolytic medication within 30 minutes of arrival

(TPA30min), percent who had percutaneous intervention-PCI within 90 minutes

(pci90min), number of minutes of outpatients with chest pain or MI till EKG done

(mintoekg), average number of minutes before outpatients with chest pain or possible

heart attack were transferred to another hospital ( minutestrfer), outpatients with chest

pain or possible heart attack who got aspirin within 24 hours of arrival (asa24hr),

outpatients with chest pain or possible heart attack who got drugs to break up blood clots

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within 30 minutes of arrival (outpttpa30),statins upon discharge(statindc), median time

to fibrinolysis (timetofiblys).

For heart failure (HF): percent of HF patients having evaluation of ventricular

dysfunction (lvfunction), percent given angiotensin converting enzyme inhibitors (ACEi)

or angiotensin receptor antagonists-ARBs (acearbs), percent given discharge instruction

(DCintruct). For pneumonia (PN): Percent of PN patients who had blood cultures in ER

prior to antibiotics( cxbeforeab), percent with appropriate antibiotic

selection(appropab).For Surgical Care Improvement Project measures (SCIP): Percent

surgery patients with antibiotics given within 1 hour before surgery (abonehour), percent

with antibiotic stopped within 24 hours after surgery (abdc), percent surgery patients

given the right kind of antibiotics (rightab), percent given treatment within 24 hour of

surgery to prevent blood clots (dvtproph), percent heart surgery patients with sugar

control days after surgery (sugarcont), percent surgery patients with urinary catheters

removed on first or second day after surgery (urinecath), percent surgery patients who

were taking heart drugs called beta blockers before coming to the hospital who were kept

on them (betakeep) , percent outpatients having surgery who got an antibiotic within one

hour before surgery (outpaboneh), percent outpatients having surgery who got the right

kind of antibiotic (outprightab), percent of patients having surgery who were actively

warmed in the operating room or whose body temperature was near normal (warmedor).

For blood clot prevention and treatment (VTE): percent of patients who got treatment to

prevent blood clots on the day of or day after hospital admission or surgery (preventclot),

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percent of patients who got treatment to prevent blood clots on the day of or day after

being admitted to the intensive care unit (icuprophyl), percent of patients with blood clots

who got the recommended treatment, which includes using two different blood thinner

medicines at the same time (overlap), percent of patients with blood clots who were

treated with an intravenous blood thinner and were monitored(anticmonitor), percent of

patients with blood clots who were discharged on a blood thinner medicine and received

written instructions about that medicine(dcinstruct), percent of patients who developed a

blood clot while in the hospital who did not get treatment that could have prevented it

(dvtnoproph). For stroke process of care measures (SK): percent of ischemic or

hemorrhagic stroke patients who received treatment to keep blood clots from forming

anywhere in the body within 2 days of arriving at the hospital (vteprophy), percent of

ischemic stroke patients who received a prescription for medicine known to prevent

complications caused by blood clots before discharge (dcantithrom), percent of ischemic

stroke patients with a type of irregular heartbeat who were given a prescription for a

blood thinner at discharge (afibantico),percent ischemic stroke patients who got medicine

to break up a blood clot within 3 hours after symptoms started (thrombo3hrs), percent

ischemic stroke patients who received medicine known to prevent complications caused

by blood clots within 2 days of arriving at the hospital(antithr2days), percent ischemic

stroke patients needing medicine to lower cholesterol, who were given a prescription for

this medicine before discharge (dcstatin), percent of ischemic or hemorrhagic stroke

patients or caregivers who received written educational materials about stroke care and

prevention during the hospital stay(strokeedu), percent of ischemic or hemorrhagic stroke

patients who were evaluated for rehabilitation services (rehabeval).

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HCAHPS components for West South Central region hospitals are also derived from the

Hospital Compare database. These consist of 6 composite domains: communication with

physicians (mdcomm), communication with nurses (nursecomm), communication about

medications (medexplain), quality of nursing services-responsiveness of staff-

(receivehelp), adequacy of planning for discharge (dcinfo), and pain management

(paincontrol). Possible responses for these composites: always, usually, sometimes, and

never. Only ‘always’ responses are used in the analysis. Other domains reflect individual

questions about whether the rooms were clean (roomclean) and whether they were quiet

(areaquiet). Possible responses: always, usually, sometimes, and never. Only ‘always’

responses are also used for the study. Two overall ratings are used. First a global rating of

the hospital on a scale of 0 to 10, with 0 being the worst and 10 being the best a hospital

can be. This rating is grouped by CMS into one of three categories, 0 to 6, 7 or 8, or 9 or

10. Only ‘ratings of 9-10’ are chosen (nineten). Another global rating question checks

whether the patient would recommend the hospital to family and friends) (possible

responses: definitely yes, probably yes, probably no, and definitely no). Only ‘definitely

yes’ is chosen for the analysis. (definrecomm).

The data collection period for HCAHPS and all other measures for AMI, CHF, PN, and

SCIP are between 7/1/2012-6/30/2013. Data for Stroke (SK) and VTE are available from

1/1/13-6/30/2103. 4,26,27. Data for structure/hospital characteristics are derived from AHA

(American Hospital Association) database of Census Division 7, West South Central

Region, covering hospitals in Louisiana, Arkansas, Texas and Oklahoma. These are

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derived from Annual Survey Data Base 2011 edition. These data consist of: number of

beds per hospital (beds), rural/urban (rural), RN staffing per hospital (nursing) derived

by dividing number of full time RNs (FTRN)/beds, Medicaid volume (medicaid),

determined by dividing total facility Medicaid days/total facility inpatient days

(medicaidys/inptdays), Medicare (medicare) is also determined by dividing total facility

Medicare days/ total facility inpatient days. For-profit status (profit), specialty hospital

status (specialtyhos), and teaching status (teaching) also are obtained from American

hospital Association database. The data collection period is from 01/2011-12/31/2011.

As mentioned before, domains reflecting individual questions about whether the rooms

were clean (roomclean) and whether they were quiet (areaquiet) are also utilized in

HCAHPS structural measures. These responses are also obtained from CMS Hospital

Compare database. Another structure measure, Medicare spending per beneficiary

(MSB), reflecting care intensity is also acquired from Hospital Compare.

IRB Approval

The study protocol was submitted to Tulane IRB and IRB exemption was approved

2/2014

Sample Selection

The sample is a non-probability sample consisting of all AHA hospitals in the West

South Central Region covering the states of Louisiana, Arkansas, Texas and Oklahoma,

matched with hospitals reporting HCAHPS and process measures in these states on

Hospital Compare (n=687). Critical access hospitals (CAHs) are then dropped from the

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analysis leaving total sample=551. CAH (critical access hospitals) are not required to

submit HCAHPS data. They are dropped from analysis to avoid likelihood of having a

non-representative sample CAHs that did report voluntarily. These 551 hospitals are

matched with hospital characteristics/structural measures derived from AHA annual

survey database. Reliability is checked via a second independent observer checking on

random samples of the matched data and comparing to the available data. The unit of

analysis is each individual hospital in the West South Central Region Hospital Compare

file.

Measurement

The independent variables, the constructs they measure, and the sources of the variables

are found in Tables C, D, and E. As discussed earlier, individual quality process of care

measures derived from Hospital Compare for MI, heart failure, pneumonia, SCIP and

stroke are included as independent variables in the analysis. HCAHPS process of care

measures* listed in Table C are utilized in the analysis to answer research question 2.

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TABLE C: INDEPENDENT VARIABLES – PROCESS MEASURES

CONSTRUCT

VARIABLE NAME

DESCRIPTION SOURCE

Heart attack(MI) asadc (MI1) Percent of MI patients given aspirin upon discharge Hospital compare TPA30min

(MI2) Percent of patients Given fibrinolytic medication within 30 minutes Of arrival

Hospital compare

Pci90min (MI3)

Percent who had percutaneous intervention-PCI within 90 minutes Hospital compare

mintoekg (MI4)

Number of minutes of outpatients with chest pain or MI till EKG done

Hospital compare

minutestrfer (MI5)

Average number of minutes before outpatients with chest pain or possible heart attack who were transferred to another hospital

Hospital compare

asa24hr (MI6)

Percent outpatients with chest pain or possible heart attack who got aspirin within 24 hours of arrival

Hospital compare

outpttpa30 (MI7)

Percent outpatients with chest pain or possible heart attack who got drugs to break up blood clots within 30 minutes of arrival

Hospital compare

statindc (MI8)

Percent MI patients given statins upon discharge Hospital compare

timetofiblys (MI9)

Median time to fibrinolysis Hospital compare

Heart failure( HF) lvfunction

(CHF1) Percent of HF patients having evaluation of ventricular dysfunction

Hospital compare

acearbs (CHF2)

Percent given angiotensin converting enzyme inhibitors (ACEi) or angiotensin receptor antagonists-ARBs

Hospital compare

DCinstruc (CHF3)

Percent given discharge instruction Hospital compare

Pneumonia(PN) cxbeforeab

(PN1) Percent of PN patients who had blood cultures in ER prior to antibiotics

Hospital compare

appropab (PN2)

Percent with appropriate antibiotic selection Hospital compare

Surgical Care Improvement Project measures (SCIP):

abonehour (SCIP1)

Percent surgery patients with antibiotics given within 1 hour before surgery

Hospital compare

abdc (SCIP2)

Percent with antibiotic stopped within 24 hours after surgery Hospital compare

rightab (SCIP3)

Percent surgery patients given the right kind of antibiotics Hospital compare

dvtproph (SCIP4)

Percent given treatment within 24 hour of surgery to prevent blood clots

Hospital compare

sugarcont (SCIP5)

Percent heart surgery patients with sugar control days after surgery Hospital compare

urinecath (SCIP6)

Percent surgery patients with urinary catheters removed on first or second day after surgery

Hospital compare

betakeep (SCIP7)

Percent surgery patients who were taking heart drugs called beta blockers before coming to the hospital who were kept on them

Hospital compare

outpaboneh (SCIP8)

Percent outpatients having surgery who got an antibiotic within one hour before surgery

Hospital compare

outprightab (SCIP9)

Percent outpatients having surgery who got the right kind of antibiotic

Hospital compare

warmedor (SCIP10)

Percent patients having surgery who were actively warmed in the operating room or whose body temperature was near normal

Hospital compare

Blood clot prevention and treatment (VTE)

preventclot (VTE1)

Percent of patients who got treatment to prevent blood clots on the day of or day after hospital admission or surgery

Hospital compare

icuprophyl (VTE2)

Percent of patients who got treatment to prevent blood clots on the day of or day after being admitted to the intensive care unit

Hospital compare

overlap (VTE3)

Percent patients who got recommended treatment, which includes using two different blood thinner medicines at the same time

Hospital compare

anticmonitor (VTE4)

Percent of patients with blood clots who were treated with an intravenous blood thinner and were monitored

Hospital compare

dcinstruct (VTE5)

Percent of patients with blood clots who were discharged on a blood thinner medicine and received written instructions about that medicine

Hospital compare

DVTnoproph (VTE6)

Percent of patients who developed a blood clot while in the hospital who did not get treatment that could have prevented it

Hospital compare

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STROKE ( SK) vteprophy

(SK1) Percent of ischemic or hemorrhagic stroke patients who received treatment to keep blood clots from forming anywhere in the body within 2 days of arriving at the hospital

Hospital compare

dcantithrom (SK2)

Percent of ischemic stroke patients who received a prescription for medicine known to prevent complications caused by blood clots before discharge

Hospital compare

afibantico (SK3)

Percent ischemic stroke patients with a type of irregular heartbeat who were given a prescription for a blood thinner at discharge

Hospital compare

thrombo3hrs (SK4)

Percent ischemic stroke patients who got medicine to break up a blood clot within 3 hours after symptoms started

Hospital compare

antithr2days (SK5)

Percent ischemic stroke patients who received medicine known to prevent complications caused by blood clots within 2 days of arriving at the hospital

Hospital compare

dcstatin (SK6)

Percent ischemic stroke patients needing medicine to lower cholesterol, who were given a prescription for this medicine before discharge

Hospital compare

strokeedu (SK7)

Percent of ischemic or hemorrhagic stroke patients or caregivers who received written educational materials about stroke care and prevention during the hospital stay

Hospital compare

rehabeval (SK8)

Percent of ischemic or hemorrhagic stroke patients who were evaluated for rehabilitation services

Hospital compare

HCAHPS process domains* Mdcomm* Communication with physicians (percent ‘always’) HCAHPS Nursecomm* Communication with nurses (percent ‘always’) HCAHPS Medexplain* Communication about medications (percent ‘always’) HCAHPS Receivehelp* Quality of nursing services-responsiveness of staff (percent

‘always’) HCAHPS

Dcinfo* Adequacy of planning for discharge (percent ‘always’) HCAHPS Paincontrol* Pain management (percent ‘always’) HCAHPS

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Structure measures shown in Table D are included to adjust the independent variables in

the analysis. These structural measures are chosen based on the literature as discussed

previously. HCHAPS structural domains** (room cleanliness and quietness of

environment) are also included in the analysis to address research question 2.

TABLE D- Independent Variables: STUCTURE MEASURES CONSTRUCT VARIABLE DESCRIPTION SOURCE Hospital characteristics beds Number of beds per hospital American Hospital

Association (AHA) Icubeds* Number of ICU beds per hospital AHA ICU Proportion determined by dividing icubeds/beds AHA rural Rural (yes or no) AHA FTRN* Full time registered nurses per facility AHA nursing Total RN staffing per hospital (derived by dividing

FTRN by beds) AHA

Medicaidys* Total facility Medicaid days AHA Medicardys* Total facility Medicare days AHA inptdays Total facility inpatient days AHA medicaid Proportion, determined by dividing

medicaidys/inptdays AHA

medicare Proportion,determined by dividing Medicardys/inptdays

AHA

profit Hospital for-profit status (yes or no) AHA teaching Teaching status (yes or no) AHA MSB Medicare spending per beneficiary (proportion) Hospital Compare specialtyhos Specialty hospital ( yes or no) AHA HCAHPS structure domains**

roomclean** Percent answering the rooms were clean (‘always’) HCAHPS

areaquiet** Percent answering the hospital area was quiet (‘always’)

HCAHPS

* will not be used in regression equation ** used in research question 2

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The dependent (outcome) variables shown in Table E are based on the global patient

HCAHPS satisfaction score. Only “definitely recommend” ratings are included in the

analysis.

TABLE E- Dependent Variables: OUTCOME MEASURES CONSTRUCT VARIABLE DESCRIPTION SOURCE nineten (Proportion).Percent

Global rating of hospital 9 or 10/10.

HCAHPS

definrecomm (proportion).Percent who would definitely recommend hospital to family and friends

HCAHPS

Data Evaluation Merging different data sources by hospital from Hospital Compare, and matching the

variables to the AHA national statistics obtain the necessary variables for the analysis.

Then the data are analyzed using traditional descriptive methods. These techniques

include frequencies, means, standard deviations, and normality tests such as skewness

and kurtosis. Difficulties with any of these areas are adjusted as needed by dropping

highly correlated independent variables, transforming non-normal variables, or treating

missing values by case deletion or sensitivity analysis. To see whether or not missing

data are random, a dummy variable is assigned to missing data. Missing data are coded as

1 if datum is missing and 0 if datum is not. Logistic regression model are done between

the variables and structural characteristics. If predictors are significant this implies that

missing data are not random 122.

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Statistical Analysis

The analyses will be conducted using Generalized Linear Model (GLM). GLM is

appropriate to use when the dependent variable is measured as a proportion 124.

Regression diagnostics are used to test for linearity, normality, collinearity and

homoscedasticity. Since there is a risk that outcomes can be affected by bed size,

analyses were conducted with the cluster option in the General Linear Model.

Stata R 13.1 (StataCorp, College Station TX) software is used for the analysis.

In this study, the first analysis tests the full, unrestricted model, using GLM to address the

first research question: “Is there a relationship between individual hospital quality

processes of care measures for AMI, heart failure, pneumonia (PN), SCIP, stroke (SK),

VTE and Global HCAHPS satisfaction scores adjusting for structure differences between

hospitals?” The first hypothesis “H1= There is a positive relationship between each

individual process of care measure for AMI/heart failure/pneumonia/SCIP/stroke/VTE

and Global HCAHPS scores adjusting for structure differences” is tested using the

following models:

Stroke ys definrecomm = βos+β1s vteprophy + β2s beds +β3s ICU +β4s rural +β5s nursing +β6s medicaid

+β7s medicare +β8s profit +β9s teaching +β10s MSB + β11s specialtyhos + εs y’s definrecomm = β’os+β’1s dcantithrom +β’2s beds +β’3s ICU +β’4s rural +β’5s nursing +β’6s medicaid

+β’7s medicare +β’8s profit +β’9s teaching +β’10s MSB + β’11s specialtyhos + ε’s y’’s definrecomm = β’’os+β’’1s afibantico +β’’2s beds +β’’3s ICU +β’’4s rural +β’’5s nursing

67

+β’’6s medicaid +β’’7s medicare +β’’8s profit +β’’9s teaching +β’’10s MSB + β’’11s specialtyhos +ε’’s y3’s definrecomm = β 3’os+ β 3’ 1s thrombo3hrs +β 3’2s beds +β 3’3s ICU +β 3’4s rural +β 3’5s nursing

+β 3’6s medicaid +β 3’7s medicare +β 3’ 8s profit +β 3’9s teaching +β 3’10s MSB + β3’ 11s specialtyhos +ε 3’s y4’ s definrecomm = β 4’os+ β 4’ 1s antithr2days +β 4’2s beds +β 4’3s ICU +β 4’4s rural +β 4’5s nursing

+β 4’6s medicaid +β 4’7s medicare +β 4’ 8s profit +β 4’9s teaching +β 4’10s MSB + β4’ 11s specialtyhos +ε 4’s y5’ s definrecomm = β 5’os+ β 5’ 1s dcstatin +β 5’2s beds +β 5’3s ICU +β 5’4s rural +β 5’5s nursing

+β 5’6s medicaid +β 5’7s medicare +β 5’ 8s profit +β 5’9s teaching + +β 5’10s MSB + β5’ 11s specialtyhos +ε 5’s y6’ s definrecomm = β 6’os+ β 6’ 1s strokeedu +β 6’2s beds +β 6’3s ICU +β 6’4s rural +β 6’5s nursing

+β 6’6s medicaid +β 6’7s medicare +β 6’ 8s profit +β 6’9s teaching +β 6’10s MSB + β6’ 11s specialtyhos +ε 6’s y7’ s definrecomm = β 7’os+ β 7’ 1s rehabeval +β 7’2s beds +β 7’3s ICU +β 7’4s rural +β 7’5s nursing

+β 7’6s medicaid +β 7’7s medicare +β 7’ 8s profit +β 7’9s teaching +β 7’10s MSB + β7’ 11s specialtyhos +ε 7’s VTE yv definrecomm = βov+β1v preventclot + β2v beds +β3v ICU +β4v rural +β5v nursing +β6v medicaid

+β7v medicare +β8v profit +β9v teaching +β10v MSB + β11v specialtyhos +εv y’v definrecomm = β’ov+β’1v icuprophyl +β’2v beds +β’3v ICU +β’4v rural +β’5v nursing +β’6v medicaid +β’7v medicare +β’8v profit +β’9v teaching +β’10v MSB + β’11v specialtyhos +ε’v y’’v definrecomm = β’’ov+β’’1v overlap +β’’2v beds +β’’3v ICU +β’’4v rural +β’’5v nursing

+β’’6v medicaid +β’’7v medicare +β’’8v profit +β’’9v teaching +β’’10v MSB + β’’11v specialtyhos

+ε’’v y3’v definrecomm = β 3’ov+ β 3’ 1v anticmonitor +β 3’2v beds +β 3’3v ICU +β 3’4v rural +β 3’5v nursing

+β 3’6v medicaid +β 3’7v medicare +β 3’ 8v profit +β 3’9v teaching +β 3’10v MSB + β3’11v specialtyhos +ε 3’v y4’ v definrecomm = β 4’ov+ β 4’ 1v dcinstruct +β 4’2v beds +β 4’3v ICU +β 4’4v rural +β 4’5v nursing

+β 4’6v medicaid +β 4’7v medicare +β 4’ 8v profit +β 4’9v teaching +β 4’10v MSB + β4’11v specialtyhos

+ε 4’v y5’ v definrecomm = β 5’ov+ β 5’ 1v DVTnoproph +β 5’2v beds +β 5’3v ICU +β 5’4v rural +β 5’5v nursing

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+β 5’6v medicaid +β 5’7v medicare +β 5’ 8v profit +β 5’9v teaching +β 5’10v MSB + + β5’11v specialtyhos

ε 5’v MI ym definrecomm = βom+β1m asadc + β2m beds +β3m ICU +β4m rural +β5m nursing +β6m medicaid

+β7m medicare +β8m profit +β9m teaching +β10m MSB + β11m specialtyhos +εm y’m definrecomm = β’om+β’1m TPA30min +β’2m beds +β’3m ICU +β’4m rural +β’5m nursing +β’6m medicaid+β’7m medicare +β’8m profit +β’9m teaching +β’10m MSB + β11’m specialtyhos +ε’m

y’’m definrecomm = β’’om+β’’1m Pci90min +β’’2m beds +β’’3m ICU +β’’4m rural +β’’5m nursing

+β’’6m medicaid +β’’7m medicare +β’’8m profit +β’’9m teaching +β’’10m MSB + β11’’m specialtyhos

+ε’’m y3’m definrecomm = β 3’om+ β 3’ 1m mintoekg +β 3’2m beds +β 3’3m ICU +β 3’4m rural +β 3’5m nursing +β 3’6m medicaid +β 3’7m medicare +β 3’ 8m profit +β 3’9m teaching +β 3’10m MSB + β113’m specialtyhos +ε 3’m

y4’ m definrecomm = β 4’om+ β 4’ 1m minutestrfer +β 4’2m beds +β 4’3m ICU +β 4’4m rural +β 4’5m nursing +β 4’6m medicaid +β 4’7m medicare +β 4’ 8m profit +β 4’9m teaching +β 4’10m MSB + β114’m specialtyhos +ε 4’m

y5’ m definrecomm = β 5’om+ β 5’ 1m asa24hr +β 5’2m beds +β 5’3m ICU +β 5’4m rural +β 5’5m nursing

+β 5’6m medicaid +β 5’7m medicare +β 5’ 8m profit +β 5’9m teaching +β 5’10m MSB + β115’m specialtyhos +ε 5’m y6’ m definrecomm = β 6’om+ β 6’ 1m outpttpa30 +β 6’2m beds +β 6’3m ICU +β 6’4m rural +β 6’5m nursing +β 6’6m medicaid +β 6’7m medicare +β 6’ 8m profit +β 6’9m teaching +β 6’10m MSB + β116’m specialtyhos +ε 6’m

y7’ m definrecomm = β 7’om+ β 7’ 1m statindc +β 7’2m beds +β 7’3m ICU +β 7’4m rural +β 7’5m nursing

+β 7’6m medicaid +β 7’7m medicare +β 7’ 8m profit +β 7’9m teaching +β 7’10m MSB + β117’m specialtyhos +ε 7’m y8’ m definrecomm = β 8’om+ β 8’ 1m timetofiblys +β 8’2m beds +β 8’3m ICU +β 8’4m rural +β 8’5m nursing +β 8’6m medicaid +β 8’7m medicare +β 8’ 8m profit +β 8’9m teaching +β 8’10m MSB + β118’m specialtyhos +ε 8’m

CHF yh definrecomm = βoh+β1h lvfunction + β2h beds +β3h ICU +β4h rural +β5h nursing +β6h medicaid

+β7h medicare +β8h profit +β9h teaching +β10h MSB + β11h specialtyhos +εh

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y’h definrecomm = β’oh+β’1h acearbs +β’2h beds +β’3h ICU +β’4h rural +β’5h nursing +β’6h medicaid+β’7h medicare +β’8h profit +β’9h teaching +β’10h MSB + β’11h specialtyhos +ε’h

y’’h definrecomm = β’’oh+β’’1h DCinstruc +β’’2h beds +β’’3h ICU +β’’4h rural +β’’5h nursing

+β’’6h medicaid +β’’7h medicare +β’’8h profit +β’’9h teaching +β’’10h MSB + β’’11h specialtyhos

+ε’’h Pneumonia yp definrecomm = βop+β1p cxbeforeab + β2p beds +β3p ICU +β4p rural +β5p nursing +β6p medicaid

+β7p medicare +β8p profit +β9p teaching +β10p MSB + β11p specialtyhos +εp y’p definrecomm = β’op+β’1p appropab +β’2p beds +β’3p ICU +β’4p rural +β’5p nursing +β’6p medicaid+β’7p medicare +β’8p profit +β’9p teaching +β’10p MSB + β’11p specialtyhos +ε’p SCIP ysc definrecomm = βosc+β1sc abonehour + β2sc beds +β3sc ICU +β4sc rural +β5sc nursing +β6sc medicaid +β7sc medicare +β8sc profit +β9sc teaching +β10scMSB + β11sc specialtyhos +εsc

y’sc definrecomm = β’osc+β’1sc abcd +β’2sc beds +β’3sc ICU +β’4sc rural +β’5sc nursing +β’6sc medicaid+β’7sc medicare +β’8sc profit +β’9sc teaching +β’10sc MSB + β’11sc specialtyhos +ε’sc

y’’sc definrecomm = β’’osc+β’’1sc rightab +β’’2sc beds +β’’3sc ICU +β’’4sc rural +β’’5sc nursing

+β’’6sc medicaid +β’’7sc medicare +β’’8sc profit +β’’9sc teaching +β’’10sc MSB + β’’11sc specialtyhos +ε’’sc y3’sc definrecomm = β 3’osc+ β 3’ 1sc dvtproph +β 3’2sc beds +β 3’3sc ICU +β 3’4sc rural +β 3’5sc nursing +β 3’6sc medicaid +β 3’7sc medicare +β 3’ 8sc profit +β 3’9sc teaching +β 3’10sc MSB + β 3’11sc specialtyhos +ε 3’sc

y4’ sc definrecomm = β 4’osc+ β 4’ 1sc sugarcont +β 4’2sc beds +β 4’3sc ICU +β 4’4sc rural +β 4’5sc nursing +β 4’6sc medicaid +β 4’7sc medicare +β 4’ 8sc profit +β 4’9sc teaching +β 4’10sc MSB + β 4’11sc specialtyhos +ε 4’sc

y5’ sc definrecomm = β 5’osc+ β 5’ 1sc urinecath +β 5’2sc beds +β 5’3sc ICU +β 5’4sc rural +β 5’5sc nursing +β 5’6sc medicaid +β 5’7sc medicare +β 5’ 8sc profit +β 5’9sc teaching +β 5’10sc MSB + β 5’11sc specialtyhos +ε 5’sc

y6’ sc definrecomm = β 6’osc+ β 6’ 1sc betakeep +β 6’2sc beds +β 6’3sc ICU +β 6’4sc rural +β 6’5sc nursing +β 6’6sc medicaid +β 6’7sc medicare +β 6’ 8sc profit +β 6’9sc teaching +β 6’10sc MSB + β 6’11sc specialtyhos +ε 6’sc

70

y7’ sc definrecomm = β 7’osc+ β 7’ 1sc outpaboneh +β 7’2sc beds +β 7’3sc ICU +β 7’4sc rural +β 7’5sc nursing +β 7’6sc medicaid +β 7’7sc medicare +β 7’ 8sc profit +β 7’9sc teaching +β 7’10sc MSB + β 7’11sc specialtyhos +ε 7’sc

y8’ sc definrecomm = β 8’osc+ β 8’ 1sc outprightab +β 8’2sc beds +β 8’3sc ICU +β 8’4sc rural +β 8’5sc nursing +β 8’6sc medicaid +β 8’7sc medicare +β 8’ 8sc profit +β 8’9sc teaching +β 8’10sc MSB + β 8’11sc specialtyhos +ε 8’sc y9’ sc definrecomm = β 9’osc+ β 9’ 1sc warmedor +β 9’2sc beds +β 9’3sc ICU +β 9’4sc rural +β 9’5sc nursing +β 9’6sc medicaid +β 9’7sc medicare +β 9’ 8sc profit +β 9’9sc teaching +β 9’10sc MSB + β 9’11sc specialtyhos +ε 9’sc

The Null Hypothesis “Ho1= There is no relationship between each individual process of

care measure for AMI/heart failure/pneumonia/SCIP/stroke/VTE and Global HCAHPS

scores adjusting for structure differences” will be rejected if all process measures within

each diagnosis show statistical significance (p<0.05) in their association with global

HCAHPS scores.

The second analysis tests the full, unrestricted model, using GLM to address the second

research question: “Is there an association between each hospital’s HCAHPS process

composite within AMI, heart failure, pneumonia, SCIP, stroke, VTE, and global

HCAHPS satisfaction adjusting for HCAHPS structure differences?” The second

hypothesis: “There is a significant association between each HCAHPS process composite

measure and global HCAHPS scores within each studied quality process measure for

MI/heart failure/pneumonia/SCIP/stroke/VTE adjusting for HCAHPS structure

differences” is addressed using GLM in these following models:

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Stroke ysk definrecomm = βo sk + β1sk Mdcomm + β2sk vteprophy + β3sk dcantithrom + β4sk afibantico + β5sk thrombo3hrs + β6sk antithr2days + β7sk dcstatin + β8sk strokeedu + β9sk rehabeval + β10sk roomclean

+ β11sk areaquiet + ξsk y’sk definrecomm = β’o sk + β’1sk Nursecomm + β’2sk vteprophy + β’3sk dcantithrom + β’4sk afibantico + β’5sk thrombo3hrs + β’6sk antithr2days + β’7sk dcstatin + β’8sk strokeedu + β’9sk rehabeval + β’10sk roomclean + β’11sk areaquiet + ξ’sk y’’sk definrecomm = β’’o sk + β’’1sk Medexplain + β’’2sk vteprophy + β’’3sk dcantithrom + β’’4sk afibantico + β’’5sk thrombo3hrs + β’’6sk antithr2days + β’’7sk dcstatin + β’’8sk strokeedu + β’’9sk rehabeval + β’’10sk roomclean + β’’11sk areaquiet + ξ’’sk y3’sk definrecomm = β3’o sk + β3’1sk Receivehelp + β3’2sk vteprophy + β3’3sk dcantithrom + β3’4sk afibantico + β3’5sk thrombo3hrs + β3’6sk antithr2days + β3’7sk dcstatin + β3’8sk strokeedu + β3’9sk rehabeval + β3’10sk roomclean + β3’11sk areaquiet + ξ3’sk y4’sk definrecomm = β4’o sk + β4’1sk Dcinfo + β4’2sk vteprophy + β4’3sk dcantithrom + β4’4sk afibantico + β4’5sk thrombo3hrs + β4’6sk antithr2days + β4’7sk dcstatin + β4’8sk strokeedu + β4’9sk rehabeval + β4’10sk roomclean + β4’11sk areaquiet + ξ4’sk y5’sk definrecomm = β5’o sk + β5’1sk Paincontrol + β5’2sk vteprophy + β5’3sk dcantithrom + β5’4sk afibantico + β5’5sk thrombo3hrs + β5’6sk antithr2days + β5’7sk dcstatin + β5’8sk strokeedu + β5’9sk rehabeval + β5’10sk roomclean + β5’11sk areaquiet + ξ5’sk VTE yvt definrecomm = βo vt + β1vt Mdcomm + β2vt preventclot + β3vt icuprophyl + β4vt overlap + β5vt anticmonitor + β6vt dcinstruct + β7vt + DVTnoproph + β8vt roomclean + β9vt areaquiet + ξvt y’vt definrecomm = β’o vt + β’1vt Nursecomm + β’2vt preventclot + β’3vt icuprophyl + β’4vt overlap + β’5vt anticmonitor + β’6vt dcinstruct + β’7vt + DVTnoproph + β’8vt roomclean + β’9vt areaquiet + ξ’vt y’’vt definrecomm = β’’o vt + β’’1vt Medexplain + β’’2vt preventclot + β’’3vt icuprophyl + β’’4vt overlap + β’’5vt anticmonitor + β’’6vt dcinstruct + β’’7vt + DVTnoproph + β’’8vt roomclean + β’’9vt areaquiet + ξ’’vt y3’vt definrecomm = β3’ovt + β3’1vt Receivehelp+ β3’2vt preventclot + β3’3vt icuprophyl + β3’4vt overlap + β3’5vt anticmonitor + β3’6vt dcinstruct + β3’7vt + DVTnoproph + β3’8vt roomclean + β3’9vt areaquiet + ξ3’vt

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y4’vt definrecomm = β4’o vt + β4’1vt Dcinfo+ β4’2vt preventclot + β4’3vt icuprophyl + β4’4vt overlap + β4’5vt anticmonitor + β4’6vt dcinstruct + β4’7vt + DVTnoproph + β4’8vt roomclean + β4’9vt areaquiet + ξ4’vt y5’vt definrecomm = β5’o vt + β5’1vt Paincontrol+ β5’2vt preventclot + β5’3vt icuprophyl + β5’4vt overlap + β5’5vt anticmonitor + β5’6vt dcinstruct + β5’7vt + DVTnoproph + β5’8vt roomclean + β5’9vt areaquiet + ξ5’vt MI ymi definrecomm = βo mi +β1mi Mdcomm +β2mi asadc +β3mi TPA30min +β4mi Pci90min + β5mi mintoekg + β6mi minutestrfer + β7mi asa24hr + β8mi outpttpa30 + β9mi statindc + β10mi timetofiblys + β11mi roomclean + β12mi areaquiet + ξmi y’mi definrecomm = β’o mi +β’1mi Nursecomm +β’2mi asadc +β’3mi TPA30min +β’4mi Pci90min + β’5mi mintoekg + β’6mi minutestrfer + β’7mi asa24hr + β’8mi outpttpa30 + β’9mi statindc + β’10mi timetofiblys + β’11mi roomclean + β’12mi areaquiet + ξ’mi y’’mi definrecomm = β’’o mi +β’’1mi Medexplain +β’’2mi asadc +β’’3mi TPA30min +β’’4mi Pci90min

+ β’’5mi mintoekg + β’’6mi minutestrfer + β’’7mi asa24hr + β’’8mi outpttpa30 + β’’9mi statindc + β’’10mi timetofiblys + β’’11mi roomclean + β’’12mi areaquiet + ξ’’mi y3’mi definrecomm = β3’o mi +β3’1mi Receivehelp +β3’2mi asadc +β3’3mi TPA30min +β3’4mi Pci90min

+ β3’5mi mintoekg + β3’6mi minutestrfer + β3’7mi asa24hr + β3’8mi outpttpa30 + β3’9mi statindc + β3’10mi timetofiblys + β3’11mi roomclean + β3’12mi areaquiet + ξ3’mi y4’mi definrecomm = β4’o mi +β4’1mi Dcinfo+ β4’2mi asadc +β4’3mi TPA30min +β4’4mi Pci90min + β4’5mi mintoekg + β4’6mi minutestrfer + β4’7mi asa24hr + β4’8mi outpttpa30 + β4’9mi statindc + β4’10mi timetofiblys + β4’11mi roomclean + β4’12mi areaquiet + ξ4’mi y5’mi definrecomm = β5’o mi +β5’1mi Paincontrol + β5’2mi asadc +β5’3mi TPA30min +β5’4mi Pci90min

+ β5’5mi mintoekg + β5’6mi minutestrfer + β5’7mi asa24hr + β5’8mi outpttpa30 + β5’9mi statindc + β5’10mi timetofiblys + β5’11mi roomclean + β5’12mi areaquiet + ξ5’mi CHF yhf definrecomm = βo hf + β1hf Mdcomm + β2 hf lvfunction + β3 hf acearbs + β4 hf DCinstruc + β5 hf roomclean + β6 hf areaquiet + ξ hf y’hf definrecomm = β’o hf + β’1hf Nursecomm + β’2 hf lvfunction + β’3 hf acearbs + β’4 hf DCinstruc + β’5 hf roomclean + β’6 hf areaquiet + ξ’ hf

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y’’hf definrecomm = β’’o hf + β’’1hf Medexplain + β’’2 hf lvfunction + β’’3 hf acearbs + β’’4 hf DCinstruc + β’’5 hf roomclean + β’’6 hf areaquiet + ξ’’ hf y3’hf definrecomm = β3’o hf + β3’1hf Receivehelp + β3’2 hf lvfunction + β3’3 hf acearbs + β3’4 hf DCinstruc + β3’5 hf roomclean + β3’6 hf areaquiet + ξ3’ hf y4’hf definrecomm = β4’o hf + β4’1hf Dcinfo + β4’2 hf lvfunction + β4’3 hf acearbs + β4’4 hf DCinstruc + β4’5 hf roomclean + β4’6 hf areaquiet + ξ4’ hf y5’hf definrecomm = β5’o hf + β5’1hf Paincontrol + β5’2 hf lvfunction + β5’3 hf acearbs + β5’4 hf DCinstruc + β5’5 hf roomclean + β5’6 hf areaquiet + ξ5’ hf Pneumonia ypn definrecomm = βo pn + β1pn Mdcomm + β2pn cxbeforeab + β3pn appropab + β4pn roomclean + β5pn areaquiet + ξ pn y’pn definrecomm = β’o pn + β’1pn Nursecomm + β’2pn cxbeforeab + β’3pn appropab + β’4pn roomclean

+ β’5pn areaquiet + ξ’ pn y’’pn definrecomm = β’’o pn + β’’1pn Medexplain + β’’2pn cxbeforeab + β’’3pn appropab +

β’’4pn roomclean + β’’5pn areaquiet + ξ’’ pn y3’pn definrecomm = β3’o pn + β3’1pn Receivehelp + β3’2pn cxbeforeab + β3’3pn appropab +

β3’4pn roomclean + β3’5pn areaquiet + ξ3’ pn y4’pn definrecomm = β4’o pn + β4’1pn Dcinfo + β4’2pn cxbeforeab + β4’3pn appropab +

β4’4pn roomclean + β4’5pn areaquiet + ξ4’ pn y5’pn definrecomm = β5’o pn + β5’1pn Paincontrol + β5’2pn cxbeforeab + β5’3pn appropab +

β5’4pn roomclean + β5’5pn areaquiet + ξ5’ pn SCIP yscp definrecomm = βo scp + β1scp Mdcomm + β2scp abonehour + β3scp abcd + β4scp rightab +

β5scp dvtproph + β6scp sugarcont + β7scp urinecath + β8scp betakeep + β9scp outpaboneh + β10scp outprightab + β11scp warmedor + β12scp roomclean + β13scp areaquiet + ξ scp y’scp definrecomm = β’o scp + β’1scp Nursecomm + β’2scp abonehour + β’3scp abcd + β’4scp rightab

+β’5scp dvtproph + β’6scp sugarcont + β’7scp urinecath + β’8scp betakeep + β’9scp outpaboneh + β’10scp outprightab + β’11scp warmedor + β’12scp roomclean + β’13scp areaquiet + ξ’ scp

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y’’scp definrecomm = β’’o scp + β’’1scp Medexplain + β’’2scp abonehour + β’’3scp abcd + β’’4scp rightab +β’’5scp dvtproph + β’’6scp sugarcont + β’’7scp urinecath + β’’8scp betakeep + β’’9scp outpaboneh + β’’10scp outprightab + β’’11scp warmedor + β’’12scp roomclean + β’’13scp areaquiet + ξ’’ scp y3’scp definrecomm = β3’o scp + β3’1scp Receivehelp + β3’2scp abonehour + β3’3scp abcd + β3’4scp rightab +β3’5scp dvtproph + β3’6scp sugarcont + β3’7scp urinecath + β3’8scp betakeep + β3’9scp outpaboneh + β3’10scp outprightab + β3’11scp warmedor + β3’12scp roomclean + β3’13scp areaquiet + ξ3’ scp y4’scp definrecomm = β4’o scp + β4’1scp Dcinfo + β4’2scp abonehour + β4’3scp abcd + β4’4scp rightab +β4’5scp dvtproph + β4’6scp sugarcont + β4’7scp urinecath + β4’8scp betakeep + β4’9scp outpaboneh + β4’10scp outprightab + β4’11scp warmedor + β4’12scp roomclean + β4’13scp areaquiet + ξ4’ scp y5’scp definrecomm = β5’o scp + β5’1scp Paincontrol + β5’2scp abonehour + β5’3scp abcd + β5’4scp rightab +β5’5scp dvtproph + β5’6scp sugarcont + β5’7scp urinecath + β5’8scp betakeep + β5’9scp outpaboneh + β5’10scp outprightab + β5’11scp warmedor + β5’12scp roomclean + β5’13scp areaquiet + ξ5’ scp

The Null hypothesis: “Ho2= There is no association between each HCAHPS process

composite measure and global HCAHPS scores within each studied quality process

measure for MI/heart failure/pneumonia/SCIP/stroke/VTE adjusting for HCAHPS

structure differences” will be rejected if all HCAHPS process measures within each

diagnosis show statistical significance (p<0.05) in their association with global HCAHPS

scores.

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CHAPTER SIX

RESULTS

After matching all AHA hospitals with Hospital Compare for hospitals reporting HCAPS,

process quality measures, and structural measures in the West South Central region, and

after dropping critical access hospitals, 551 hospitals are retained in the data set. Mean

value for the variable definrecomm is 72.37 with a median of 72. For the variable nineten,

mean value is 72.24 with a median of 72. The outcome variables also passed normality

tests, and graphical representation of data appeared normally distributed. Analysis of

definrecomm and nineten shows 92% correlation. Therefore we will use only ‘definitely

recommend’ as a measure for global satisfaction in our study.

Evaluation of missing data shows a strong relationship to bed size (i.e. more missing data

in smaller hospitals). Due to that, we conduct all our analysis based on three bed size

subgroups: beds less then100, beds between 100-200 and beds greater than 200.

We used these ranges to assess effect of small, medium and larger hospitals155 in the

interplay between process measures and patient satisfaction.

Table 1- describes the hospitals data characteristics reported by bed size after dropping

the critical access hospitals from the data. 52.4% of hospitals have fewer than 100 beds,

17.7% have 100-199 beds, and 29.7 % have more than 200 beds. Mean bed capacity is

161 for all hospitals. For small hospitals with less than 100 beds, mean bed capacity is

43. For medium sized hospitals with beds of 100-199, mean bed capacity is 142. For

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hospitals with more than 200 beds, mean bed capacity is 409. Larger hospitals seem to

have higher Medicare spending per beneficiary and a larger concentration of ICU beds

than 100 bed hospitals. A lower concentration of Medicare and a higher concentration of

Medicaid patients seem to occur in hospitals with more than 100 beds. 46% of small

(<100 beds) hospitals are for profit. This number stays at 44% among hospitals with

100-199 beds, then decreases to 26% among the greater than 200 beds hospitals. 26% of

hospitals with fewer than 100 beds are rural. This number drops to 3% if bed size is

between100 and 199 and to 0 if bed size is greater than 200. 16.6 % of hospitals with

fewer than 100 beds are specialty hospitals, 3% are specialty hospitals if beds are

between100 and 199, and 2% of large hospitals are specialty hospitals. Missing data for

the variables “beds”, “nursing”, “Medicare”, “Medicaid”, “rural”, “profit”, “teaching”,

and “specialty hospitals” were not listed in the AHA database during that time period.

For the variables “MSB” and “ICU”, the majority of missing data are in hospitals with a

less than 100- bed capacity. These hospitals also did not report MSB and ICU beds data

during that time period.

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Table 1- Hospital characteristics as reported in American Hospital Association Data after dropping critical access hospitals. Total Beds Beds<100 Beds>=100

<200 Beds>=200

Number of Hospitals (n)

551 289

98

164

Hospitals by State LA=96 AR=47 TX= 317 OK=91

LA=50 AR=17 TX=157 OK=65

LA=16 AR=15 TX=53 OK=14

LA=30 AR=15 TX=107 OK=12

Mean Bed Capacity (beds)

161 (198) n=533 (18 *, 3.2%)

43(24) n=289

142(28) n=98

409(229) n=146 (18 *, 10.9%)

Average Number for Nursing (RN/beds)

1.31(0.8) n=533 (18 *, 3.2%)

1.32(0.98) n=289

1.19(0.53) n=98

1.34(0.48) n=146 (18 *, 10.9%)

Average Medicare (Medicare days/ inpatient days)

0.5(0.17) n=533 (18 *, 3.2%)

0.52(0.18) n=289

0.5(0.14) n=98

0.45(0.14)^ n=146 (18*, 10.9%)

Average Medicaid (Medicaid days/ inpatient days)

0.15(0.12) n=533 (18 *, 3.2%)

0.12(0.10) n=289

0.18(0.11) n=98

0.20(0.11)^ n=146 (18 *, 10.9%)

Average ICU (ICU beds/ hospital beds)

0.067(0.05) n=486 (65*, 11.7%)

0.057(0.06) n=249 (40 *, 13.8%)

0.083(0.04) n=93 (5 *, 5.1%)

0.072(0.03)^ n=144 (20 *, 12%)

Average Medicare Spending per Beneficiary(MSB)

1.01(0.1) n=519 (32*, 5.8%)

0.99(0.13) n=270 (19 *, 6.5%)

1.028(0.07) n=96 (2 *, 2%)

1.031(0.06)^ n=153 (11 *, 6.7%)

For- Profit hospitals number

219(39.7%) (18 *, 3.2%)

133(46%) 44(44.8%) 42(26%) (18 *, 10.9%)

Rural hospitals number

80(14.5%) (18 *, 3.2%)

77(26.6%) 3 (3%) 0 (18 *, 10.9%)

Teaching hospitals number

83(15%) (18 *, 3.2%)

9(3.1%) 11(11.2%) 63(38.4%) (18 *, 10.9%)

Number Of specialty Hospitals

55(9.9%) (18*, 3.2%)

48(16.6%) 3 (3%) 4 (2.4%) (18*, 10.9%)

*Denote missing values. ^ Statistically significant compared to beds<100 group (p<0.05) () Number in parentheses represent standard deviation, unless it is a percentage. n=number of hospitals

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Table 2-describes HCAHPS data characteristics reported by bed size for all reporting

hospitals after dropping the critical access hospitals from the data. Most reporting

facilities are in hospitals with fewer than 100 beds (52%), 29 % of observations occur in

large hospitals where bed size is greater than 200, and 18 % are in medium-sized

hospitals with beds 100-200. Missing observations occurred in 4% of all small hospitals

(<100 beds), 2% in medium-sized hospitals (100-200 beds) and 7% of all large hospitals

(>200 beds). 50% of total missing observations occur in specialty hospitals. 4% of

missing observations are from rural hospitals. Mean percent satisfaction scores appear

higher in smaller hospitals compared to larger hospitals among all composites except for

patients definitely recommending hospital.

Table 2- Hospitals reporting HCAHPS characteristics in Hospital Compare Data by bed size and hospitals missing based on number of hospitals in the AHA database after dropping critical access hospitals from data.

All hospital bed sizes (min-max)

Hospitals with beds<100 (min-max)

Hospitals with beds >=100 <200 (min-max)

Hospitals with beds>=200 (min-max)

Percent patients who reported that nurses always communicated well.

79.5(55-98) N=525 (26*, 4.7%)

81.5(55-98) N=277 (12*, 4.1%)

77.3(67-88) N=96 (2*, 2%)

77.3%(65-94)^ N=152 (12 *, 7.3%)

Percent patients who reported that physicians always communicated well

83.6(71-98) N=525 (26 *, 4.7%)

85.8(71-98) N=277 (12*, 4.1%)

81.7(75-92) N=96 (2 *, 2%)

81.0(74-94)^ N=152 (12 *, 7.3%)

Percent patients who reported that they always receive help as soon as they wanted

68.8%(45-99)) N=525 (26 *, 4.7%)

73.3(48-99) N=277 (12*, 4.1%)

64.7(51-85) N=96 (2 *, 2%)

63.2(45-88)^ N=152 (12 *, 7.3%)

79

Percent patients who reported that their pain was always well controlled

72.4(51-98) N=525 (26 *, 4.7%)

74.1(51-98) N=277 (12*, 4.1%)

70.6(60-85) N=96 (2 *, 2%)

70.6(62-84)^ N=152 (12 *, 7.3%)

Percent patients who reported that staff always explained about medicines.

65.4(34-98) N=525 (26 *, 4.7%)

67.7(34-98) N=277 (12*, 4.1%)

62.7(55-71) N=96 (2 *, 2%)

62.8(52-83)^ N=152 (12 *, 7.3%)

Percent patients who reported that their room and bathroom were always clean

73.4(46-95) N=525 (26 *, 4.7%)

76.3(46-95) N=277 (12*, 4.1%)

70.6(60-85) N=96 (2 *, 2%)

70.1(57-87)^ N=152 (12 *, 7.3%)

Percent patients who reported that the area around their room was always quiet at night.

68.6(46-97) N=525 (26 *, 4.7%)

72.4(46-97) N=277 (12*, 4.1%)

65.0(52-81) N=96 (2 *, 2%)

63.9(51-91)^ N=152 (12 *, 7.3%)

Percent patients who reported that they were given information about what to do during their recovery at home

84.5(61-97) N=525 (26 *, 4.7%)

85.2%(61-97) N=277 (12*, 4.1%)

83.7(76-92) N=96 (2 *, 2%)

83.9(76-92)^ N=152 (12 *, 7.3%)

Percent patients who gave their hospital a rating of 9 or 10 on a scale from 0 to 10

72.2(32-97) N=525 (26 *, 4.7%)

73.6(32-97) N=277 (12*, 4.1%)

69.7%(53-90) N=96 (2 *, 2%)

71.3(48-91)^ N=152 (12 *, 7.3%)

Percent patients who reported YES they would definitely recommend the hospital

72.3(37-98) N=525 (26 *, 4.7%)

72.6(37-98) N=277 (12*, 4.1%)

70.3(49-93) N=96 (2 *, 2%)

73.3(49-93) N=152 (12 *, 7.3%)

Numbers represent mean percent (min-max) N=number of hospitals *Denotes missing observations. ^ Statistically significant compared to beds<100 group (p<0.05)

Table 3 reports stroke data derived from Hospital Compare. There are significant missing

observations for stroke patients. Most of observations occur in hospitals with beds >200.

Most missing observations occur in beds <100. Very few total observations are noticed

within SK3 and SK4. Among missing observations, 10-20% are from specialty hospitals,

80

20-30% are from rural hospitals. In most measures, there is noticeable improvement in

mean stroke quality percent scores as hospital bed size increases.

Table 3- Stroke Data as reported in Hospital Compare. All hospital beds

(min-max) Beds<100 (min-max)

Beds>=100 <200(min-max)

Beds>=200 (min-max)

Percent stroke patients who received treatment to keep blood clots from forming within 2 days of arriving at the hospital (SK1)

89.9(29-100) N=274 (277*, 50%)

85.4(42-100) N=43 (246 *, 85%)

88.8(29-100) N=85 (13 *, 13%)

91.9(50-100)^ N=146 (18 *, 10.9%)

Percent ischemic stroke patients who received a prescription for medicine known to prevent complications caused by blood clots before discharge (SK2)

97.7(64-100) N=278 (273*, 49.5%)

96.1(80-100) N=44 (245 *, 84.7%)

97.4(64-100) N=87 (11 *, 11%)

98.3(83-100)^ N=147 (17 *, 10.3%)

Percent ischemic stroke patients with a type of irregular heartbeat who were given a prescription for a blood thinner at discharge (SK3)

95.2(73-100) N=60 (491 *, 89%)

0 N=0 (289 *, 100%)

95.8(86-100) N=6 (92 *, 94%)

95.1(73-100) N=54 (110 *, 67%)

Percent ischemic stroke patients who got medicine to break up a blood clot within 3 hours after symptoms started (SK4)

69.6(0-100) N=44 (507 *, 92%)

0 N=0 (289*, 100%)

33.5(0-94) N=6 (92*, 94%)

75.3(0-100) N=38 (126*, 77%)

Percent ischemic stroke patients who received medicine known to prevent complications caused by blood clots within 2 days of arriving at the hospital (SK5)

97.2(75-100) N=268 (283*, 51%)

96.4(82-100) N=38 (251*, 87%)

96.8(75-100) N=84 (14*, 14.2%)

97.6(76-100)^ N=146 (18*, 10.9%)

81

Percent ischemic stroke patients needing medicine to lower cholesterol, who were given a prescription for this medicine before discharge (SK6)

90.3(36-100) N=266 (285*, 51.7%)

81.3(36-100) N=38 (251*, 87%)

89.7(45-100) N=82 (16*, 16.3%)

92.9(59-100)^ N=146 (18*, 10.9%)

Percent ischemic or hemorrhagic stroke patients or caregivers who received written educational materials about stroke care and prevention during the hospital stay (SK7)

85.5(0-100) N=227 (324*, 59%)

76.4(16-100) N=16 (273*, 94%)

84.4%(0-100) N=68 (30*, 30.6%)

87.0(18-100)^ N=143 (21*, 13%)

Percent ischemic or hemorrhagic stroke patients who were evaluated for rehabilitation services (SK8)

95.2(46-100) N=282 (269*, 49%)

91.9(64-100) N=48 (241*, 83%)

93.9(46-100) N=87 (11*, 11%)

97.0(80-100)^ N=147 (17 *, 10.3%)

Numbers represent average percent scores (min-max) N= number of hospitals *denote missing observations ^ Statistically significant compared to <100 beds group (p<0.05)

Table 4 reports VTE data derived from Hospital Compare. There are significant missing

observations within VTE, most are concentrated in hospitals with beds less than 100.

Most of observations occur in hospitals with beds >200, except for VTE1 (patients who

got treatment to prevent blood clots on the day of or day after hospital admission or

surgery), where majority of observations occur in beds<100. Very few total observations

are noticed for VTE4 and VTE6. Among missing observations, 15-30 % are from

specialty hospitals, 20-35% are from rural hospitals (except variable VTE1, where 55%

of missing values are from specialty hospitals). In most measures, there is noticeable

improvement in VTE mean quality percent scores as hospital bed size increases.

82

Table 4- VTE Data as reported in Hospital Compare. All Beds Beds<100 Beds>=100 Beds>=200 <200 (Min-max) (Min-max) (Min-max) (Min-max) Percent patients who got treatment to prevent blood clots on the day of or day after hospital admission or surgery (VTE1)

75.4 (0-100) N=522 (29*, 5.2%)

68.6 (0-100) N=269 (20 *, 6.9%)

82.9 (20-100) N=96 (2*, 2%)

82.7(44-100)^ N=157 (7 *, 4.2%)

Percent patients who got treatment to prevent blood clots on the day of or day after being admitted to the intensive care unit (ICU) (VTE2)

88.1(19-100) N=360 (191 *, 34.6%)

82.7(19-100) N=114 (175 *, 60.5%)

89.3(43-100) N=93 (5*, 5.1%)

91.4(67-100)^ N=153 (11*, 6.7%)

Percent patients with blood clots who got the recommended treatment, which includes using two different blood thinner medicines at the same time (VTE3)

90.5(36-100) N=266 (285 *, 51.7%)

86.5(36-100) N=44 (245 *, 84.7%)

90.7(37-100) N=76 (22*, 22.4%)

91.6(54-100)^ N=146 (18 *, 10.9%)

Percent patients with blood clots who were treated with an intravenous blood thinner, and then were checked to determine if the blood thinner was putting the patient at an increased risk of bleeding (VTE4)

97.1(21-100) N=109 (442 *, 80.2%)

100(0-100) N=4 (285 *, 98.6%)

97.6(85-100) N=18 (80*, 81.6%)

96.8(21-100) N=87 (77 *, 46.9%)

Percent patients with blood clots who were discharged on a blood thinner medicine and received written instructions about that medicine (VTE5)

81.9(0-100) N=238 (313 *, 56.8%)

81.1(18-100) N=30 (259*, 89.6%)

85.5(0-100) N=67 (31*, 31.6%)

80.4(0-100) N=141 (23 *, 14%)

Percent patients who developed a blood clot while in the hospital who did not get treatment that could have prevented it (VTE6)

9.4(0-47) N=80 (471 *, 85.4%)

0 N=0 (289 *, 100%)

2.6(0-8) N=3 (95 *, 97%)

9.7(0-47) N=77 (87 *, 53%)

Numbers represent mean percent (min-max) N= number of hospitals *denote missing observation ^statistically significant compared to <100 beds group (p<0.05)

83

Table 5 reports AMI data derived from Hospital Compare. There are significant missing

observations within MI, most are concentrated in beds<100. Most of observations occur

in hospitals with beds >200 for measures MI1, MI3 and MI8. For MI4 and MI6, most

observations occur in beds<100. Very few total observations are noticed for MI5, MI7

and MI9. There are no observations for MI2 (Percent of heart attack patients given

fibrinolytic medication within 30 minutes of arrival). Among missing observations, 10-20

% are from specialty hospitals, 20-30% from rural hospital.

Average percentage scores are higher in beds> 200 hospitals in most AMI measures.

Table 5- MI Data as reported in Hospital Compare. All Beds Beds<100 Beds>=100 Beds>=200 <20 (Min-max) (Min-max) (Min-max) (Min-max) Percent of Heart Attack Patients Given Aspirin at Discharge (MI1)

98.1(58-100) N=268 (283*, 51.3%)

95.9(58-100) N=45 (244*, 84.4%)

97.8(72-100) N=79 (19*, 19.3%)

98.9(79-100)^ N=144 (20*, 12.1%)

Percent of Heart Attack patients given fibrinolytic medication Within 30 Minutes Of Arrival (MI2)

0 N=0 (551*, 100%)

0 N=0 (289*, 100%)

0 N=0 (98 *, 100%)

0 N=0 (164 *, 100%)

Percent of Heart Attack Patients Given PCI Within 90 Minutes Of Arrival (MI3)

93.8(41-100) N=197 (354*, 64.2%)

94.1(82-100) N=16 (273*, 94.4%)

92.2(43-100) N=46 (52 *, 53%)

94.3(41-100) N=135 (29 *, 17.6%)

Average number of minutes before outpatients with chest pain or possible heart attack got an ECG (MI4)

9.0(0-60) N=225 (326*, 59.1%)

9.24(0-60) N=152 (137 *, 47.4%)

8.3(1-26) N=46 (52 *, 53%)

9.0(2-17) N=27 (137 *, 83.5%)

84

Average number of minutes before outpatients with chest pain or possible heart attack were transferred to another hospital (MI5)

83.1(28-306) N=30 (521*, 94.5%)

101(28-306) N=18 (271 *, 93.7%)

59(30-130) N=5 (93 *, 94.8%)

52.5(30-98)^ N=7 (157 *, 95.7%)

Percent of outpatients with chest pain or possible heart attack who got aspirin within 24 hours of arrival (MI6)

94.4(71-100) N=225 (326*, 59.1%)

93.9(71-100) N=152 (137*, 47.4%)

95.5(76-100) N=46 (52 *, 53%)

95.3(73-100) N=27 (137*, 83.5%)

Percent of outpatients with chest pain or possible heart attack who got drugs to break up blood clots within 30 minutes of arrival (MI7)

54.3(6-92) N=16 (535*, 97%)

45.8(6-75) N=9 (280*, 96.8%)

65.1(15-92) N=7 (91 *, 92.8%)

0 N=0 (164 *, 100%)

Percent of heart Attack Patients Given a Prescription for a Statin at Discharge (MI8)

96.5(36-100) N=268 (283*, 51.3%)

93.0(41-100) N=45 (244*, 84.4%)

95.7(36-100) N=79 (19 *, 19.3%)

98.1(83-100)^ N=144 (20 *, 12.2%)

Median Time to Fibrinolysis in minutes (MI9)

38.2(18-134) N=16 (535*, 97%)

45.3(27-134) N=9 (280 *, 96.8%)

29.1(18-62) N=7 (91 *, 92.8%)

0 N=0 (164 *, 100%)

Numbers represent average percent scores (min-max), except in MI9, MI5 and MI4 where numbers represent minutes. N= number of hospitals *denote missing observations ^ statistically significant compared to beds<100 group (p<0.05)

Table 6 reports CHF data derived from Hospital Compare. There are significant missing

observations, all concentrated in beds<100. There are however less missing observations

compared to MI, stroke and VTE. In CHF1 and CHF3, most observations occur in beds<

100, although there are significant observations for beds>100 among all CHF measures.

Among missing observations, 15-40 % are from specialty hospitals, 10-25% from rural

hospitals. Average percentage scores are higher in beds> 200 hospitals in all CHF

measures

85

Table 6 - CHF Data as reported in Hospital Compare. All hospital beds<100 beds>=100 beds>=200 Beds <200 (Min-max) (Min-max) (Min-max) (Min-max) Percent of Heart Failure Patients Given an Evaluation of Left Ventricular Systolic (LVS) Function (CHF1)

94.8(0-100) N=430 (121*, 22%)

88 (0-100) N=184 (105 *, 36%)

98.6(76-100) N=96 (2 *, 2%)

99.52(94-100)^ N=150 (14 *, 8.5%)

Percent of Heart Failure Patients Given ACE Inhibitor or ARB for Left Ventricular Systolic Dysfunction (CHF2)

95(17-100) N=356 (195 *, 35%)

91.5(17-100) N=117 (172*, 59%)

96.5(76-100) N=92 (6*, 6%)

96.9(81-100)^ N=147 (17 *, 10.3%)

Percent of Heart Failure Patients Given Discharge Instructions (CHF3)

91(6-100) N=423 (128*, 23%)

86.6(6-100) N=177 (112 *, 38.7%)

93.6(44-100) N=96 (2 *, 2%)

94.72(47-100)^ N=150 (14 *, 8.5%)

Numbers represent average percent scores (min-max) N= number of hospitals *denote missing observations ^ statistically significant compared to beds<100 group (p<0.05) Table 7 reports pneumonia data derived from Hospital Compare. There are significant

missing observations, all concentrated in hospitals with beds size less than 100. There are

however less missing observations compared to MI, stroke or VTE. Most observations

occur in hospitals with less than 100 beds, although there are significant observations for

beds greater than 200. Among missing observations 5-15% are from rural hospitals and

35-40% are from specialty hospitals. Average percentage scores are higher in beds> 100

hospitals in all pneumonia measures.

86

Table 7 - Pneumonia Data as reported in Hospital Compare. All hospital beds<100 beds>=100 beds>=200 Beds <200 (Min-max) (Min-max) (Min-max) (Min-max) Percent of Pneumonia Patients Whose Initial ER Blood Culture Was Performed Prior to Administration Of First Dose Of Antibiotics (PN1)

96.7(55-100) N=423 (128*, 23.2%)

94.9(55-100) N=178 (111*, 38.4%)

98.2(92-100) N=95 (3*, 3%)

97.9(76-100)^ N=150 (14*, 8.5%)

Percent of Pneumonia Patients Given the Most Appropriate Initial Antibiotic(s) (PN2)

92.6(16-100) N=434 (117*, 21.2%)

88.9(16-100) N=190 (99*, 34.2%)

95.2(74-100) N=95 (3*, 3%)

95.8(80-100)^ N=149 (15*, 9.1%)

Numbers represent average percent scores (min-max) N= number of hospitals *denote missing observations ^ statistically significant compared to beds<100 group (p<0.05)

Table 8 reports SCIP data derived from Hospital Compare. There are significant missing

observations within SCIP, all concentrated in beds<100. Most of observations occur in

hospitals with beds <100 and beds>200, except in SCIP5 (Heart surgery patients whose

blood sugar (blood glucose) is kept under good control in the days right after surgery),

where, as expected, most observations occur in beds>200. Among missing observations,

10-20 % are from specialty hospitals and 40-50% from rural hospitals. Average

percentage scores are higher in beds> 200 hospitals in all SCIP measures.

87

Table 8 -SCIP Data as reported in Hospital Compare. All hospital beds<100 beds>=100 beds>=200 Beds <200 (Min-max) (Min-max) (Min-max) (Min-max) Percent surgery patients who were given an antibiotic at the right time (within one hour before surgery) (SCIP1)

97.5(6-100) N=433 (118*, 21%)

95.9(6-100) N=187 (102*, 35%)

98.8(88-100) N=94 (4*, 4%)

98.8(83-100)^ N=152 (12*, 7%)

Percent surgery patients whose preventive antibiotics were stopped at the right time (within 24 hours after surgery) (SCIP2)

96.6(0-100) N=430 (121*, 22%)

95.7(0-100) N=185 (104*, 36%)

97.0(69-100) N=93 (5 *, 5%)

97.5(89-100)^ N=152 (12 *, 7%)

Percent surgery patients who were given the right kind of antibiotic to help prevent infection (SCIP3)

97.9(6-100) N=432 (119*, 21.5%)

96.9(6-100) N=186 (103*, 35%)

98.2(59-100) N=94 (4*, 4%)

99(94-100)^ N=152 (12 *, 7%)

Percent patients who got treatment at the right time (within 24 hours before or after their surgery) to help prevent blood clots (SCIP4)

96.2(6-100) N=443 (108*, 19.6%)

94.6(6-100) N=195 (94*, 32.5%)

97.1(77-100) N=94 (4 *, 4%)

97.6(89-100)^ N=154 (10 *, 6%)

Percent heart surgery patients whose blood sugar (blood glucose) is kept under good control in the days right after surgery (SCIP5)

95.8(77-100) N=186 (365*, 66.2%)

95.1(85-99) N=13 (276*, 95.5%)

96.1(77-100) N=42 (56*, 57%)

95.8(81-100) N=131 (33*, 20%)

The percent of surgery patients whose urinary catheters were removed on the first or second day after surgery (SCIP6)

95.1(33-100) N=404 (147*, 26.6%)

93.54(33-100) N=159 (130*, 45%)

95.7(68-100) N=92 (6 *, 6%)

96.3(79-100)^ N=153 (11 *, 6.7%)

88

Percent surgery patients who were taking beta blockers before coming to the hospital who were kept on them (SCIP7)

95.8(46-100) N=382 (169*, 30.6%)

94.3(46-100) N=140 (149 *, 51.5%)

95.7(59-100) N=90 (8*, 8%)

97.3(81-100)^ N=152 (12 *, 7.2%)

Percent outpatients having surgery who got an antibiotic at the right time - within one hour before surgery (SCIP8)

96.7(45-100) N=402 (149*, 27%)

95.9(45-100) N=159 (130*, 45%)

96.9(79-100) N=90 (8*, 8%)

97.3(67-100)^ N=153 (11*, 6.7%)

Percent outpatients having surgery who got the right kind of antibiotic (SCIP9)

96.6(47-100) N=401 (150*, 27.2%)

96.3(47-100) N=158 (131*, 45.3%)

95.6(47-100) N=90 (8*, 8%)

97.3(80-100) N=153 (11 *, 6.7%)

Percent patients having surgery who were actively warmed in the operating room or whose body temperature was near normal (SCIP10)

99.4(32-100) N=452 (99*, 18%)

98.7(32-100) N=203 (86 *, 29.7%)

99.9(98-100) N=95 (3 *, 3%)

99.9(98-100)^ N=154 (10*, 6%)

Numbers represent average percent scores (min-max) N= number of hospitals *denote missing observations ^statistically significant compared to beds<100 group (p<0.05)

In the following section, General linear Modeling (GLM) will be used to analyze the

association between the outcome variable definrecomm (percent patients definitely

recommending hospital) and different process of care measures for stroke, VTE, MI,

CHF, pneumonia and SCIP. Evaluation of missing data shows a strong relationship to

bed size (i.e. more missing data in smaller hospitals). Due to that, we conduct our

analysis based on three bed size subgroups: beds less then100, beds between 100-200 and

beds greater than 200. We use these ranges to assess effect of small, medium and larger

hospitals155 in the interplay between process measures and patients satisfaction

89

In the GLM between definrecomm (definitely recommend hospital) and individual

process of care measures for stroke (shown in Table 9), only the variable SK2 (percent of

ischemic stroke patients who received a prescription for medicine known to prevent

complications caused by blood clots before discharge) is statistically significant in

beds>200. In beds<100, the small sample size limits the interpretation of findings for

this bed size. Recall that in stroke, most of observations occur in beds>200. Within all

hospital beds, percent of ischemic stroke patients who received a prescription for

medicine known to prevent complications caused by blood clots before discharge (SK2)

has the higher significance influencing patients definitely recommending hospital

(coefficient =0.37), although stroke patients who received treatment to keep blood clots

from forming within 2 days of arriving at the hospital (SK1) show some influence

towards patient satisfaction but with a lower magnitude (coefficient =0.09).

Table 9- Coefficients determining relationship between outcome variable (percent that definitely recommend hospital) and individual process of care measures for STROKE, after adjusting for structure measures (beds ,nursing , ICU , Medicare, Medicaid ,MSB, for profit , rural, teaching, specialty hospital) using General Linear Modeling. Total hospital

beds

Beds <100

Beds >=100 <200

Beds >=200

Percent stroke patients who received treatment to keep blood clots from forming within 2 days of arriving at the hospital (SK1)

0.09* n=262

n=40^

-0.004 n=82

0.12 n=140

Percent ischemic stroke patients who received a prescription for medicine known to prevent complications caused by blood clots before discharge (SK2)

0.37* n=263

n=40^

0.08 n=83

0.34* n=140

90

Percent ischemic stroke patients with a type of irregular heartbeat who were given a prescription for a blood thinner at discharge (SK3)

-0.009 n=60

n<10

n<10

-0.01 n=54

Percent ischemic stroke patients who got medicine to break up a blood clot within 3 hours after symptoms started (SK4)

-0.01 n=43

n<10

n<10

-0.01 n=37^

Percent ischemic stroke patients who received medicine known to prevent complications caused by blood clots within 2 days of arriving at the hospital (SK5)

0.2 n=257

n=35^

-0.009 n=82

0.15 n=140

Percent ischemic stroke patients needing medicine to lower cholesterol, who were given a prescription for this medicine before discharge (SK6)

0.09 n=254

n=34^

0.04 n=80

0.12 n=140

Percent ischemic or hemorrhagic stroke patients or caregivers who received written educational materials about stroke care and prevention during the hospital stay (SK7)

0.02 n=219

n=14^

0.02 n=66

0.04 n=139

Percent ischemic or hemorrhagic stroke patients who were evaluated for rehabilitation services (SK8)

0.06 n=267

n=44^

-0.06 n=83

-0.01 n=140

Numbers represent coefficient. *Denote statistical significance (p<0.05) n = number of observations (hospitals) ^ denote observations too few for significance

91

When studying the relationship between definrecomm (percent who definitely

recommend hospital) and individual process of care measures for VTE shown in Table

10, only a weak association (coefficient =0.06) is found with VTE1 (percent patients who


surgery) in beds<100. No associations are detected in larger hospitals or among all

hospitals.

Table 10- Coefficients determining relationship between outcome variable (percent that definitely recommend hospital) and individual process of care measures for VTE, after adjusting for structure measures (beds ,nursing , ICU , Medicare, Medicaid ,MSB, profit , rural, teaching, specialty hospital) using General Linear Modeling. Total beds

Beds <100

Beds >=100 <200

Beds >=200

Percent patients who got treatment to prevent blood clots on the day of or day after hospital admission or surgery (VTE1)

0.04 n=457

0.06* n=224

0.006 n=91

0.07 n=142

Percent patients who got treatment to prevent blood clots on the day of or day after being admitted to the intensive care unit -ICU (VTE2)

0.07 n=335

0.1 n=106

-0.007 n=89

0.04 n=140

Percent patients with blood clots who got the recommended treatment, which includes using two different blood thinner medicines at the same time (VTE3)

-0.04 n=252

n=40^

-0.015 n=73

0.023 n=139

92

Percent patients with blood clots who were treated with an intravenous blood thinner, and then were checked to determine if the blood thinner was putting the patient at an increased risk of bleeding (VTE4)

-0.009 n=105

n=0

n=17

-0.005 n=85

Percent patients with blood clots who were discharged on a blood thinner medicine and received written instructions about that medicine (VTE5)

0.007 n=227

n=29^

-0.014 n=64

0.021 n=134

Percent patients who developed a blood clot while in the hospital who did not get treatment that could have prevented it (VTE6)

-0.001 n=78

n=0

n<10

-0.004 n=75

Numbers represent coefficient. *Denote statistical significance (p<0.05) n = number of hospitals (observations.) ^ denote observations too few for significance

In Acute MI, shown in Table 11, for bed size greater than 200, 3 of 9 measures are

positively associated with definrecomm (patients definitely recommending hospital). The

strongest association occurs with percent of heart attacks patients given aspirin upon

discharge (MI1). Each percent increase in MI patients given aspirin upon discharge is

associated with 0.67% increase in patients definitely recommending hospitals. This

association remains in hospitals with 100-200 beds capacity but of a lesser magnitude

(coefficient = 0.52). The other associations in large hospitals occur with percent of

patients given statins upon discharge and percent of heart attack patients given PCI

within 90 minutes (coefficients =0.48 and 0.13 respectively). No other relationship

93

between definrecomm and process of care measures for MI is detected in the100-200 bed

size or in smaller (<100 beds) hospitals. Among all bed sizes, MI1 (aspirin upon

discharge) shows the strongest significance influencing patient satisfaction (coefficient

=0.32). MI8 (statins upon discharge) is also significantly associated with patients

definitely recommending hospital but with a lower magnitude (coefficient =0.23).

Table 11- Coefficients determining relationship between outcome variable (percent that definitely recommend hospital) and individual process of care measures for MI, after adjusting for structure measures (beds ,nursing , ICU , Medicare, Medicaid ,MSB, profit , rural, teaching, specialty hospital) using General Linear Modeling. Total

beds

Beds <100

Beds >=100 <200

Beds >=200

Percent of Heart Attack Patients Given Aspirin at Discharge (MI1)

0.32* n=253

n=40^

0.52* n=76

0.67* n=137

Percent of Heart Attack Patients Given Fibrinolytic Medication Within 30 Minutes Of Arrival (MI2)

n=0

n=0

n=0

n=0

Percent of Heart Attack Patients Given PCI Within 90 Minutes Of Arrival (MI3)

0.07 n=190

n=15^

0.07 n=44

0.13* n=131

Average number of minutes before outpatients with chest pain or possible heart attack got an ECG (MI4)

-0.09 n=201

-0.22 n=135

-0.12 n=44

0.22 n=22^

Average number of minutes before outpatients with chest pain or possible heart attack were transferred to another hospital (MI5)

-0.008 n=28^

-0.01 n=17^

n<10

n<10

94

Percent outpatients with chest pain or possible heart attack who got aspirin within 24 hours of arrival (MI6)

0.05 n=202

0.18 n=136

0.04 n=44

0.04 n=22^

Percent outpatients with chest pain or possible heart attack who got drugs to break up blood clots within 30 minutes of arrival (MI7)

0.06 n=16^

n<10

n<10

n=0

Percent heart Attack Patients Given a Prescription for a Statin at Discharge (MI8)

0.23* n=253

0.2 n=40^

0.14 n=76

0.48* n=137

Median Time to Fibrinolysis (MI9)

0.02 n=16^

n<10

n<10

n=0

Numbers represent coefficient. *Denote statistical significance (p<0.05) n = observations. ^ denote observations too few for significance When GLM is done between definrecomm (percent definitely recommend hospital) and

individual process of care measures for CHF (adjusting for beds, nursing, ICU, Medicare,

Medicaid, MSB, for profit, rural, teaching and specialty hospital status), various

associations are detected at different bed sizes. The results are presented in Table 12. In

larger hospitals (>200 beds), 2 out of 3 measures are related to percent of patients

definitely recommending the hospital. However, the variable percent of heart failure

patients given an evaluation of left ventricular systolic function (CHF1) is the most

strongly associated predictor of satisfaction (definrecomm). For each percent increase in

HF patients having evaluation of ventricular dysfunction there is a 1.96% increase in

95

patients definitely recommending hospital. Only percent given angiotensin converting

enzyme inhibitors or angiotensin receptor antagonists (CHF2) is associated with

definrecomm in beds 100-200 (coefficient 0.42). For hospitals with beds <100, there are

weaker relationships with CHF1 and percent given discharge instructions (CHF3)

(coefficient=0.15 and 0.1 respectively). All three measures are associated with percent

definitely recommending hospital among all beds. Strongest association in this group

remains with percent given evaluation of left ventricular function (coefficient=0.18).

Table 12- Coefficients determining relationship between outcome variable (percent that definitely recommend hospital) and individual process of care measures for CHF, after adjusting for structure measures (beds, nursing , ICU , Medicare, Medicaid ,MSB, profit , rural, teaching, specialty hospital) using General Linear Modeling.

Total Beds

Beds <100

Beds >=100 <200

Beds >=200

Percent of Heart Failure Patients Given an Evaluation of Left Ventricular Systolic Function (CHF1)

0.18* n=391

0.15* n=161

0.29 n=91

1.96* n=139

Percent of Heart Failure Patients Given ACE Inhibitor or ARB for Left Ventricular Systolic Dysfunction (CHF2)

0.16* n=333

0.07 n=107

0.42* n=87

0.37* n=139

Percent of Heart Failure Patients Given Discharge Instructions (CHF3)

0.1* n=385

0.10* n=155

0.02 n=91

0.04 n=139

Numbers represent coefficient.

96

*Denote statistical significance (p<0.05) n = observations.

In pneumonia, shown in Table 13, only patients who had blood cultures in the ER prior to

antibiotics (PN1) are more likely to definitely recommend with a 0.49% increase

satisfaction for each percent increase in cultures before antibiotics among large hospitals

with more than 200 beds. A weaker relationship persists among all hospital beds

(coefficient 0.28). Among all beds, all pneumonia measures are associated with percent

patients definitely recommending hospital. There is a notable weaker relationship with

percent given the most appropriate initial antibiotics (coefficient=0.08). No other

statistically significant associations are seen in hospitals less than 100 beds or in medium

sized hospitals (100-200 beds).

97

Table 13- Coefficients determining relationship between outcome variable (percent that definitely recommend hospital) and individual process of care measures for PNEUMONIA, after adjusting for structure measures (beds ,nursing , ICU , Medicare, Medicaid ,MSB, profit , rural, teaching, specialty hospital) using General Linear Modeling. Total

Beds

Beds <100

Beds >=200 <200

Beds >=200

Percent of Pneumonia Patients Whose Initial ER Blood Culture Was Performed Prior to Administration Of First Dose Of Antibiotics (PN1)

0.28* n=388

0.21 n=159

0.65 n=90

0.49* n=139

Percent of Pneumonia Patients Given the Most Appropriate Initial Antibiotic(s) (PN2)

0.08* n=397

0.07 n=168

0.14 n=90

0.19 n=139

Numbers represent coefficient. *Denote statistical significance (p<0.05) n = observations.

When GLM is conducted between definrecomm (percent definitely recommending

hospital) and individual SCIP measures (adjusting for beds, nursing, Medicare, Medicaid,

ICU, MSB, teaching, rural, for profit and specialty hospital variables), significant

associations are found in 4 out of 10 variables in hospitals with more than 200 beds

(SCIP5, SCIP6, SCIP7 and SCIP8). See Table 14 below. The strongest association

occurs in beds>200 with SCIP6 and SCIP8 variables. For each percentage increase in

surgery patients with urinary catheters removed on first or second day after surgery and

for each percent increase in outpatients receiving antibiotics within one hour of surgery,

there is a 0.39-0.35 % increase in patients who definitely recommend the hospital

98

respectively. In large hospitals with more than 200 beds, blood sugar control is

significantly associated with patient satisfaction (coefficient=0.33). In hospitals with 100-

200 beds, 4 out of 10 measures are associated with definrecomm. The strongest

relationship occurs with SCIP8. For each percent increase in outpatients having surgery

who got an antibiotic within one hour before surgery, there is a 0.74 % increase in

patients definitely recommending hospital. For inpatients, the strongest association

occurs with SCIP3 (Percent of surgery patients given the right antibiotic), with a

coefficient =0.67.In small hospitals with fewer than 100 beds, 7/10 SCIP measures have a

statistically significant association with definrecomm. Strongest relationship occurs with

SCIP2 (antibiotic stopped within 24 hours after surgery -coefficient = 0.35). For

outpatients, SCIP8 variable was strongly related to patient satisfaction (coefficient =

0.34). Among all hospital beds, SCIP8 for outpatients (Outpatients having surgery who

got an antibiotic at the right time - within one hour before surgery) and SCIP2 for

inpatient (surgery patients whose preventive antibiotics were stopped within 24 hours

after surgery) measures are of the most significance towards patient satisfaction

(coefficients of 0.36 and 0.31 respectively).

99

Table 14- Coefficients determining relationship between outcome variable (percent that definitely recommend hospital) and individual process of care measures for SCIP, after adjusting for structure measures (beds ,nursing , ICU , Medicare, Medicaid ,MSB, profit , rural, teaching, specialty hospital) using General Linear Modeling. Total

Beds

Beds <100

Beds >=100 <200

Beds >=200

Percent surgery patients who were given an antibiotic at the right time (within one hour before surgery) (SCIP1)

0.22* n=387

0.24* n=157

0.07 n=89

0.53 n=141

Percent surgery patients whose preventive antibiotics were stopped at the right time (within 24 hours after surgery) (SCIP2)

0.31* n=384

0.35* n=155

0.31 n=88

0.09 n=141

Percent surgery patients who were given the right kind of antibiotic to help prevent infection (SCIP3)

0.24 n=386

0.19 n=156

0.67* n=89

0.8 n=141

Percent patients who got treatment at the right time (within 24 hours before or after their surgery) to help prevent blood clots (SCIP4)

0.02 n=396

0.02 n=164

0.07 n=90

0.28 n=142

Percent heart surgery patients whose blood sugar (blood glucose) is kept under good control in the days right after surgery (SCIP5)

0.21 n=179

n=12

0.26 n=39

0.33* n=128

Percent of surgery patients whose urinary catheters were removed on the first or second day after surgery (SCIP6)

0.2* n=366

0.15* n=137

0.34* n=88

0.39* n=141

100

Percent surgery patients who were taking beta blockers before coming to the hospital who were kept on them (SCIP7)

0.15* n=349

0.14* n=121

0.35* n=86

0.32* n=142

Percent outpatients having surgery who got an antibiotic at the right time - within one hour before surgery (SCIP8)

0.36* n=359

0.34* n=131

0.74* n=86

0.35* n=142

Percent outpatients having surgery who got the right kind of antibiotic (SCIP9)

0.13 n=358

0.28* n=130

-0.04 n=86

0.07 n=142

Percent patients having surgery who were actively warmed in the operating room or whose body temperature was near normal (SCIP10)

0.20* n=403

0.23* n=171

-1.1 n=90

-0.02 n=142

Numbers represent coefficient. *Denote statistical significance (p<0.05) n = observations. ^ denote observations too few for significance

Table 15 presents the results of estimated relationships between individual structure

characteristics (MSB, profit, rural, teaching, beds, specialty hospital status, nursing,

Medicare, Medicaid, ICU) and the percent of patients definitely recommending hospital

using the General Linear Model for the individual process measures for stroke, VTE,

AMI, CHF, pneumonia and SCIP. Among most process of care models of patients

recommending the hospital, for profit hospitals are negatively associated with percent of

patients definitely recommending hospital. Larger bed size is positively associated with

definrecomm for 22 out of 38 measures, with only two associations in SCIP. 30 out of 38

measures in hospitals with a higher Medicaid concentration are negatively associated

101

with percent of patients definitely recommending hospital. Medicare concentration is also

negatively associated with patients definitely recommending hospital in 23 of 38

measures, but the magnitude of the association is weaker than with Medicaid. ICU bed

capacity is also negatively associated with definrecomm in 14 out of 38 process of care

measures. Most of ICU negative association with patients definitely recommending

hospital occurs in SCIP measures. Nursing supply is positively associated with

definrecomm among most process measures. Overall, no statistically significant

relationship is detected with Medicare spending per beneficiary (MSB), rural location, or

teaching status with hospitals for most measures. Specialty hospital status is strongly

positively associated with percent patients definitely recommending hospital across

measures for stroke, VTE, CHF, MI, pneumonia and SCIP.

102

Table 15- Coefficients for ‘structure’ measures in the GLM regression used to control for differences between hospitals that might influence or confound the relationship between the outcome variable (definitely recommend hospital) and individual process variables for Stroke, VTE, AMI, CHF, Pneumonia and SCIP, among all hospital bed sizes. Process measures

MSB profit rural teaching beds nursing medicare medicaid ICU Spec. Hosp.

SK1 10 -4.7* -5.1 -0.62 0.005* 3.02* -9 -13.5* 2.42 - SK2 2.7 -5.1* -1.1 -1.3 0.005* 2.4* -12.9* -15.2* -0.25 22.14* SK3 3.08 -7.5* - -0.22 0.001 2.12 -10 -8.9 -12.8 - SK4 17.9 -5.85 - -2.45 0.005* 2.9 -7.1 -28.9 -23.6 - SK5 8.72 -4.2* -0.61 -0.65 0.005* 3.02* -11.6* -18.07* 4.6 - SK6 3.92 -4.6* 0.71 -1.02 0.005* 2.18* -13.04* -16.27* -2.83 - SK7 4.9 -4.9* 0.21 -1.06 0.005* 2.18* -13.4* -18.4* -1.45 - SK8 6.09 -4.6* -4.64 -0.92 0.005* 3* -12.8* -15* -0.91 22.32* VTE1 2.2 -2* -2.6 0.67 0.004* 3.6* -6.5 -23.6* -31.9* 6.94* VTE2 8.9 -3.5* -5.5* 0.76 0.005* 3.4* -6.7 -20* -12 15.8* VTE3 5.5 -3.2* -3.5 -0.43 0.004* 2.9* -18.4* -26.8* -11.2 19.5* VTE4 9.09 -5.4* 5.45* -1.19 0.005* 0.027 -9.8 -21.64* 7.75 - VTE5 3.9 -3.9* 3.7* -2.7 0.004* 2.54* -13.1* -26.13* -8.1 19.3* VTE6 -3.87 -4.7* - -0.48 0.003* -0.05 -1.29 -13.54 -11.18 - MI1 2.3 -3.8* 3.3 -0.22 0.004* 2.3* -10.7* -19.7* -10 17.5* MI2 - - - - - - - - - - MI3 5.1 -4.9* 3.4* -1.6 0.004* 1.76* -9.8* -16.6* -14.2 21.7* MI4 5.51 -3.5* -2.07 4.11 0.002 4.31* -7.55 -21.84* -18.4 - MI5 - - - - - - - - - - MI6 3.78 -3.6* -1.73 3.98 0.003 4.22* -7.53 -21.76* -18.5 - MI7 - - - - - - - - - - MI8 -2.2 -4* 3.4* -0.34 0.0036 2.15* -10.6* -21.8* -11.1 17* MI9 - - - -- - - - - - CHF1 3.2 -2.9* -1.62 0.19 0.005* 3.37* -9* -22.5* -18.3 14* CHF2 6 -3.4* -0.17 0.03 0.005* 3.6* -12.8* -28.8* -22* 14* CHF3 4.7 -2.9* -3.2* 0.308 0.005* 3.8* -8 -21.1* -18.3* 11.9* PN1 5.9 -2.8* -1.32 0.74 0.0047* 3.58* -10.6* -22.6* -20.6* 14* PN2 6.7 -2.6* -1.8 0.44 0.0045* 3.86* -9.3* -19.4* -21.8* 12.4* SCIP1 5.1 -1.7* -1.37 0.59 0.0028 3.3* -10.5* -26.9* -31.6* 7.5* SCIP2 4.3 -1.6 -1.23 0.55 0.0016 3.36* -9.5* -26.5* -31.7* 7.23* SCIP3 4.1 -1.8* -2.5 0.48 0.0027 3.3* -10.2* -26.1* -29.8* 7.5* SCIP4 4.6 -1.8* -2.7 0.76 0.0039* 3.3* -10.3* -28.15* -28.2* 7.4* SCIP5 -11.9 -4.2* 2.4 -0.92 0.0024 1.77 -7.3 -19.7* -9.4 15.2* SCIP6 2.3 -2.2* 0.49 0.82 0.0025 3.1* -10.34* -27.6* -27.2* 7.6* SCIP7 0.51 -1.9* 2.7 0.49 0.0019 3.05* -12.5* -28.7* -26.2* 7.2* SCIP8 3.5 -2.6* -0.02 0.38 0.002 3.24* -15.47* -28.5* -26.4* 6.4* SCIP9 0.93 -2.2* -1.16 0.42 0.003 3.2* -15.5* -30.8* -25* 6.5* SCIP10 6.2 -1.4 -3.17 0.96 0.0038* 3.38* -9.75* -28.13* -30.2* 7.4* Numbers = coefficients. *Denote statistical significance (p<0.05).

103

When structural measures were analyzed in large hospitals with more than 200 beds

(Table 16), the statistically significant negative relationships between patients satisfaction

and ICU capacity, Medicare and Medicaid concentration are no longer present.

Moreover, the association with nursing supply and definrecomm is no more evident. The

only remaining significant relationships in large hospitals are with for-profit status

(negative) and number of beds (positive). Hospital specialty status still shows significant

positive association with patients definitely recommending hospitals within SCIP.

104

Table 16- Coefficients for ‘structure’ measures in the GLM regression used to control for differences between hospitals that might influence or confound the relationship between the outcome variable (definitely recommend hospital) and individual process variables for Stroke, VTE, AMI, CHF, Pneumonia and SCIP, among hospitals with beds larger than 200. Process measures

MSB profit rural teaching beds nursing medicare medicaid ICU Spec. Hosp.

SK1 -3.09 -6.6* 0 -0.87 0.006* 0.34 -0.68 -10.5 3.35 0 SK2 -5.89 -6.6* 0 -1.27 0.006* 0.59 -0.73 -10.2 4.49 0 SK3 0.16 -7.5* 0 -0.2 0.003 3.08 -10.5 -5.06 -28.5 0 SK4 16.09 -7.06* 0 -1.9 0.005 1.27 -2.33 -18.6 -10.9 0 SK5 -5.3 -6.27* 0 -1.16 0.006* 0.73 -2.32 -11.9 6.11 0 SK6 -6.06 -6.68* 0 -1.12 0.005* 0.18 -1.29 -10.66 3.52 0 SK7 -3.9 -6.72* 0 -1.05 0.005* 0.61 -1.03 -9.84 5.45 0 SK8 -4.77 -6.2* 0 -1.13 0.006* 0.72 -3.27 -12.7 5.98 0 VTE1 -4.3 -6.8* 0 -1.11 0.006* 0.48 0.34 -9.3 2.1 10* VTE2 -5.05 -6.45* 0 -1.05 0.006* 0.577 -2.12 -11.77 5.18 0 VTE3 -1.11 -6.43 0 -1.45 0.006* 0.69 -2.48 -10.43 1.73 0 VTE4 9.38 -5.67* 0 -1.33 0.0056* -0.715 -4.95 -10.79 -5.82 0 VTE5 -1.62 -6.9* 0 -1.57 0.006* 0.7 -2.4 -8.5 5.04 0 VTE6 0.63 -5.2* 0 -0.51 0.003* -0.25 -0.82 -12.2 -19.3 0 MI1 1.55 -6.88* 0 -1.03 0.0053* 0.075 -2.51 -8.82 0.34 0 MI2 - - - - - - - - - - MI3 -0.15 -7.45* 0 -162 0.005* 0.235 -2.61 -6.3 3.2 0 MI4 - - - - - - - - - - MI5 - - - - - - - - - - MI6 - - - - - - - - - - MI7 - - - - - - - - - - MI8 -0.99 -7.32* 0 -1.3 0.005* 0.02 -0.89 -8.05 2.3 0 MI9 - - - -- - - - - - - CHF1 -2.3 -7.31* 0 -1.1 0.005* -0.23 -0.6 -7.84 -1.73 0 CHF2 -1.76 -7.18* 0 -1.02 0.005* -0.12 -3.1 -10.09 -0.73 0 CHF3 -0.6 -6.43* 0 -1.29 0.0063* 0.64 -2.95 -10.69 1.85 0 PN1 -5.2 -6.95* 0 -0.91 0.0057* 0.73 -4.3 -9.2 2.1 0 PN2 -1.16 -6.8* 0 -1.23 0.0059* 0.89 -2.39 -9.62 0.85 0 SCIP1 -1.8 -6.8* 0 -1.37 0.006* 0.77 -3.18 -9.2 3.27 7.59* SCIP2 -0.62 -6.5* 0 -1.72 0.006* 0.76 -2.2 -9.6 2.07 8.4* SCIP3 0.21 -6.95* 0 -1.2 0.006* 0.53 -3 -8.4 2.5 7.96* SCIP4 -4.06 -6.9* 0 -1.16 0.0061* 0.44 -1.7 -10.3 5.9 9.19* SCIP5 -0.89 -7.64* 0 -1.7 0.0054* 0.68 -2.8 -6.7 1.39 0 SCIP6 -8.06 -6.91* 0 -1.15 0.006* 0.7 -2.5 -10.98 -1.79 4.79 SCIP7 -3.6 -6.85* 0 -1.14 0.006* 0.25 -2.7 -10.9 6.45 10.2* SCIP8 -3.34 -7.47* 0 -1.39 0.0057* 0.42 -1.8 -7.1 8.5 8.07* SCIP9 -3.79 -6.53* 0 -1.16 0.006* 0.70 -2.5 -11.6 5.8 8.6* SCIP10 -4.6 -6.3* 0 -1.14 0.0061* 0.72 -2.6 -11.6 6.3 8.7* Numbers = coefficients. *Denote statistical significance (p<0.05).

105

The following tables (17-22) analyze the relationship between global patient satisfaction

scores and individual HCAHPS composites (nursing communication, doctors

communication, responsiveness of staff, pain management, communication about

medications, adequacy of discharge planning) for individual process measures associated

with stroke, VTE, AMI, CHF, pneumonia and SCIP, adjusting for HCAHPS structure

measures (room clean, area quiet).

Table 17 shows the GLM regression results predicting the percent of patients who will

definitely recommend hospital based on each individual stroke process HCAHPS

measure adjusting for HCAHPS structure measures (rooms clean, area quiet). There is a

significant positive relationship between HCAHPS composites and percent of stroke

patients definitely recommending hospital. The strongest association is with pain

control among all beds (except beds<100 due to very few observations). In largest

hospital group, for each percent increase in patients who reported that their pain is

"Always" well controlled, there is a 1.25% increase in percent of patients who will

definitely recommend hospital. The second strongest association with the percent who

will definitely recommend the hospital is with nurse communication. Having fewer than

30 observations in small hospitals prevented statistical assessment of stroke measures in

this bed size range.

106

Table 17- Coefficients determining relationship between outcome variable (definitely recommend hospital), and each individual HCAHPS composite measure within stroke process measures adjusting for HCAHPS structure characteristics (room clean, area quiet) using General Linear Modeling. Total beds

Beds<100

Beds>=100 <200

Beds>=200

Patients who reported that nurses always communicated well.

1.11* n=224

n=16^

0.88* n=68

1.15* n=140

Patients who reported that physicians always communicated well

0.75* n=224

n=16^

0.37 n=68

0.94* n=140

Patients who reported that they always receive help as soon as they wanted

0.46* n=224

n=16^

0.57* n=68

0.44* n=140

Patients who reported that their pain was always well controlled

1.21* n=224

n=16^

1.14* n=68

1.25* n=140

Patients who reported that staff always explained about medicines.

0.68* n=224

n=16^

0.30* n=68

0.69* n=140

Patients who reported that they were given information about what to do during their recovery at home

0.86* n=224

n=16^

0.93* n=68

0.78* n=140

n=number of hospitals Numbers represent coefficients *Denote statistical significance ^ Very low observations for significant interpretation.

107

In Table 18, GLM regression results are reported for the outcome variable (definitely

recommend hospital) and each individual process HCAHPS measure for VTE adjusting

for HCAHPS structure measures (rooms clean, area quiet). There is a significant positive

relationship between HCAHPS composites and percent of patients definitely

recommending hospital within VTE. As with stroke, the strongest association for

individual process measures is with pain control among all hospitals and groups (except

for bed size of less than 100 due to very few observations). In the largest hospitals with

beds>200, for each percent increase in patients who reported that their pain is "Always"

well controlled, there is a 1.34% increase in patients who will definitely recommend the

hospital. The second strongest association of patients who will definitely recommend the

hospital is with nurse communication as with stroke patients.

108

Table 18- Coefficients determining relationship between outcome variable (definitely recommend hospital), and each individual HCAHPS composite measure within VTE process measures adjusting for HCAHPS structure characteristics (room clean, area quiet) using General Linear Modeling.

Total beds

Beds<100

Beds>=100 <200

Beds>=200


1.08* n=230

n=30^

0.89* n=65

1.14* n=135


0.54* n=230

n=30^

0.04 n=65

0.84* n=135


0.44* n=230

N=30^

0.49* n=65

0.41* n=135


1.16* n=230

n=30^

1.37* n=65

1.34* n=135


0.73* n=230

n=30^

0.66* n=65

0.73* n=135


0.96* n=230

N=30^

0.94* n=65

0.95* n=135


109

In Table 19, GLM regression results are reported for the outcome variable (definitely

recommend hospital) and each individual process HCAHPS measure for MI adjusting for

HCAHPS structure measures (rooms clean, area quiet). There is a significant positive

relationship between HCAHPS composites and percent of patients definitely

recommending hospital within AMI. Strongest association is with pain control among all

beds (except beds<100 due to very few observations). As with stroke and VTE, the

second strongest association is with nurse communication.

Table 19- Coefficients determining relationship between outcome variable (definitely recommend hospital), and each individual HCAHPS composite measure within AMI process measures adjusting for HCAHPS structure characteristics (room clean, area quiet) using General Linear Modeling.

Total beds

Beds<100

Beds>=100 <200

Beds>=200


1.08* n=194

n=16^

0.65* n=46

1.18* n=132


0.70* n=194

n=16^

-0.1 n=46

0.88* n=132


0.48* n=194

n=16^

0.37* n=46

0.47* n=132


1.14* n=194

n=16^

1.08* n=46

1.27* n=132


0.74* n=194

n=16^

0.47* n=46

0.74* n=132

110


0.83* n=194

n=16^

0.53 n=46

0.81* n=132


In Table 20, GLM regression results are reported between outcome variable (definitely

recommend hospital) and each individual process HCAHPS measure along with process

measures for CHF, adjusting for HCAHPS structure measures (rooms clean, area quiet).

There is a significant positive relationship between HCAHPS composites and percent of

patients definitely recommending hospital within CHF. Strongest association is with pain

control among all beds, except in beds<100 where nurse communication seems to have

the stronger relationship with definrecomm. In beds>200, for each percent increase in

patients who reported that their pain is "Always" well controlled, there is a 1.27 %

increase in patients who will definitely recommend hospital.

111

Table 20- Coefficients determining relationship between outcome variable (definitely recommend hospital), and each individual HCAHPS composite measure within CHF process measures adjusting for HCAHPS structure characteristics (room clean, area quiet) using General Linear Modeling.

Total beds

Beds<100

Beds>=100 <200

Beds>=200


0.90* n=350

0.83* n=117

1.03* n=92

1.11* n=141


0.15 n=350

0.18 n=117

0.32 n=92

0.72* n=141


0.22* n=350

0.28* N=117

0.56* n=92

0.39* n=141


0.84* n=350

0.53* N=117

1.38* n=92

1.27* n=141


0.41* n=350

0.38* n=117

0.31 n=92

0.61* n=141


0.87* n=350

0.78* N=117

1.22* n=92

0.76* n=141

n=number of hospitals Numbers represent coefficients *Denote statistical significance

112

Table 21 shows the GLM regression results predicting the percent of patients who will

definitely recommend hospital based on each individual pneumonia process HCAHPS

measure adjusting for HCAHPS structure measures (rooms clean, area quiet). There is a

significant positive relationship between HCAHPS composites and percent of pneumonia

patients definitely recommending hospital. The strongest association is with pain

control among beds>100 .In largest hospital group, for each percent increase in patients

who reported that their pain is "Always" well controlled, there is a 1.39% increase in

percent of patients who will definitely recommend hospital. In small bed hospitals (less

than 100), nurse communication dominates in predicting percent recommending

hospitals.

Table 21- Coefficients determining relationship between outcome variable (definitely recommend hospital), and each individual HCAHPS composite measure within PNEUMONIA process measures adjusting for HCAHPS structure characteristics (room clean, area quiet) using General Linear Modeling.

Total beds

Beds<100

Beds>=100 <200

Beds>=200


0.92* n=409

0.95* n=173

0.99* n=95

1.21* n=141


0.23* n=409

0.47* n=173

0.37 n=95

0.74* n=141


0.15* n=409

0.29* N=173

0.52* n=95

0.45* n=141

113


0.78* n=409

0.62* N=173

1.24* n=95

1.39* n=141


0.37* n=409

0.38* n=173

0.26 n=95

0.72* n=141


0.9* n=409

0.88* N=173

1.16* n=95

0.89* n=141


In Table 22, GLM regression results are reported between outcome variable (definitely

recommend hospital) and each individual process HCAHPS measure along with process

measures for SCIP, adjusting for HCAHPS structure measures (rooms clean, area quiet).

There is a significant positive relationship between HCAHPS composites and percent of

SCIP patients definitely recommending hospital. The strongest association is with pain

control among all beds, except in beds<100 where nurse communication has a stronger

relationship in determining patients definitely recommending hospital. In beds>200, for

each percent increase in patients who reported that their pain is "Always" well controlled,

there is a 1.44 % increase in patients who will definitely recommend hospital.

114

Table 22- Coefficients determining relationship between outcome variable (definitely recommend hospital), and each individual HCAHPS composite measure within SCIP process measures adjusting for HCAHPS structure characteristics (room clean, area quiet) using General Linear Modeling.

Total beds

Beds<100

Beds>=100 <200

Beds>=200


0.83* n=344

0.63* n=117

0.8* n=85

1.21* n=142


0.40* n=344

0.40* n=117

0.23 n=85

0.75* n=142


0.24* n=344

0.23* N=117

0.47* n=85

0.43* n=142


0.85* n=344

0.39* N=117

1.01* n=85

1.44* n=142


0.43* n=344

0.26* n=117

0.34 n=85

0.72* n=142


0.7* n=344

0.41 N=117

0.66 n=85

0.93* n=142


115

RESULTS SUMMARY

Significant findings are detected from the descriptive statistics. Smaller hospitals (less

than 100 beds) constitute the majority of observations reporting HCAHPS data. In

addition most of the HCAHPS mean composite scores also seem higher in smaller

hospitals. Larger hospitals however appear to have more Medicaid and less Medicare

patients. These hospitals also have more ICU beds and higher Medicare Spending per

Beneficiary. Larger hospitals also have lower ratio of for profit hospitals compared to

smaller ones. Rural and specialty hospitals are mostly distributed in smaller less than 100

beds hospitals, constituting 26% and 17% of these hospitals accordingly. Teaching

hospitals are mostly located among larger hospitals.

Looking into individual Hospital Compare process measures, it is noticeable that mean

percent scores for each measure are higher among larger hospitals. In stroke, VTE and

MI, most of the observations occur among larger hospitals. Increase in smaller hospitals

reporting for CHF, pneumonia and SCIP is noticeable with some having more

observations then larger hospitals. Missing data remains problematic across all hospital

compare measures, however there are less missing data among SCIP, pneumonia and

heart failure compared to stroke, VTE and MI. Rural hospitals and specialty hospitals

constitute 20-30% of missing data. Most of these data are also concentrated in hospitals

smaller than 100 beds.

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GLM was then done to study the association of individual process of care measures for

stroke, VTE, MI, CHF, pneumonia and SCIP, and patients definitely recommending

hospital adjusting for structure measures (MSB, for- profit status, rural status, teaching,

beds, nursing, Medicare, Medicaid, ICU and specialty hospital status). Only 2 out of 8

measures are significant in stroke, with strongest relationship pertinent to antithrombotic

on discharge. No measures are significant in VTE among all bed sizes. In heart attack,

only 2 out of 9 measures are positively associated with patients definitely recommending

hospital among all beds, strongest relationship pertinent to aspirin given upon discharge.

In heart failure and pneumonia, all measures are positively associated with patient

satisfaction among all beds, with the strongest relationship occurring with evaluation of

left ventricular function in CHF and blood cultures before antibiotics in pneumonia.

Among all beds, 6 out of 10 SCIP measures are positively related to patients definitely

recommending hospital. Within inpatients strongest relationship occurs with stopping

antibiotics within 24 hours of surgery and among outpatients strongest association occurs

with receiving antibiotics at the right time.

The coefficients for structure measures in the GLM regression between the outcome

variable (definitely recommend hospital) and individual process variables for stroke,

VTE, AMI, CHF, pneumonia and SCIP are then analyzed. Among all beds, for-profit

status, Medicare, Medicaid and ICU are negatively associated with the outcome variable,

whereas nursing, bed size and specialty hospital status are positively related. Among

larger hospitals (>200 beds), only for-profit status remained negatively associated with

117

percent definitely recommending hospital. Only specialty hospital status and bed size

remain positively associated with the outcome variable in larger hospitals.

GLM regression was then done to analyze the relationship between global patient

satisfaction scores and individual HCAHPS composites (nursing communication, doctors

communication, responsiveness of staff, pain management, communication about

medications, adequacy of discharge planning) for individual process measures associated

with stroke, VTE, AMI, CHF, pneumonia and SCIP. Among larger hospitals, all

HCAHPS composites are significantly positively associated with percent patients

definitely recommending hospital. Pain control however seems to be the dominant

HCAHPS composite predicting global patient satisfaction in larger hospitals. In small

hospitals (<100 beds), and within heart failure, pneumonia and SCIP, nurse

communication is the dominant HCAHPS composite determining percent patients

definitely recommending hospital.

In the following chapter we will analyze all the above-mentioned findings in detail, and

try to understand drivers and common factors behind these relationships. Clinical and

policy implications will then be suggested towards improving patient satisfaction with

hospital care. In that regard, more research will be recommended towards that goal.

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CHAPTER SEVEN

DISCUSSION

There is a paucity of studies looking into the association of hospital process of care

measures with patient satisfaction. Most of the studies examined summary quality

process scores instead of the effects of individual process of care scores 19,7,6,15,20,125

No studies have been done analyzing the association of stroke and VTE hospital compare

process measures with satisfaction scores. GLM regression was used to answer our

research question 1: “Is there a relationship between individual quality processes of care

measures for AMI, heart failure, pneumonia, SCIP, stroke, VTE, and global HCAHPS

satisfaction scores, adjusting for structure differences between hospitals?” We did not

find support to reject the null hypothesis Ho1= “There is no relationship between each

individual process of care measure for AMI, heart failure, pneumonia, SCIP, stroke, VTE

and Global HCAHPS scores adjusting for structure differences” because significant

process measures were not associated with the outcome (patients definitely

recommending hospital).

In stroke, 2 out of 8 process measures are related to global patient satisfaction. The

strongest association is with the percent of ischemic stroke patients who received a

prescription for medicine known to prevent complications caused by blood clots before

discharge. This is also the only significant relationship between stroke process measures

and patients definitely recommending hospital in larger hospitals (>200 beds).

119

Association between Hospital Compare stroke measures and patient satisfaction has not

been previously studied. Scant research has been done to compare various stroke

processes and patient satisfaction. Reker et al.126, in a cohort of 288 stroke veterans in

2002, studied compliance with AHRQ process stroke guidelines (patient and family

education, baseline assessments, discharge planning, family involvement, monitoring of

patient progress, management of impairments, multidisciplinary evaluation, prevention of

complications, prevention of recurrent stroke, goal setting, treatment plan) and patient

satisfaction using a stroke- specific instrument. Process of care in their study was

positively and significantly associated with greater patient satisfaction.

The positive relationship between percent of ischemic stroke patients who received a

prescription for medicine known to prevent complications caused by blood clots

(antithrombotic) before discharge and definrecomm in our study seems to be in agreement

with findings on the components of AHRQ stroke process measures in the Reker’s

analysis (discharge planning, prevention of recurrent stroke, treatment plan).

Antithrombotic therapy with antiplatelet agents is known to prevent strokes. In a meta-

analysis of 287 studies involving 135,000 patients in comparison of antiplatelet therapy

versus control, allocation to antiplatelet therapy reduced the combined outcome of any

serious vascular event by about one quarter, non-fatal myocardial infarction was reduced

by one third, non-fatal stroke by one quarter, and vascular mortality by one sixth140. In

our inquiry two processes were associated with patients definitely recommending hospital

in stroke (percent of stroke patients receiving medications to prevent strokes upon

discharge and percent stroke patients who received treatment to keep blood clots from

120

forming within 2 days of arriving at the hospital). As mentioned previously health

outcomes including mortality, problem resolution, readmissions and complications have a

strong effect on patient satisfaction 14, 68, 16,8,61. It has been suggested that individual

stroke measures are associated with improved outcomes150 (improved functional ability,

decreased risk of blood clots, decreased risk of recurrent strokes). Fonarow in a study of

stroke “Get with the Guidelines "summary composites in 2003-2009 (anti-thrombotics

/lipid lowering/smoking cessation/dysphagia/rehab evaluation and education), showed

that there was some improvement in hospital length of stay and in hospital mortality

when these composites were implemented159. There are limited studies however

analyzing all stroke quality measures as group with outcomes, or looking into influences

comparing individual processes towards outcomes. It is possible that the lack of other

stroke process associations with patient satisfaction is due to the absence of a true

relationship between different stroke process measures and outcomes. In a prospective

study of consecutive patients with acute stroke, process was measured by use of the

Royal College of Physicians Stroke Audit Package, and outcomes were measured by

range of disability, health status, handicap, and independence measures, as well as

mortality. There was evidence for a relationship between some process variables and

outcomes at hospital discharge, but the relationships were generally weak. Only 3 out of

six process measures were significant towards functional independence measure of

outcome. None of the process variables remained associated with outcomes after 12

months. The link between stroke process and outcome was not straightforward138. In a

retrospective cohort study that included patients with an ischemic stroke or transient

ischemic attack (TIA), the combined outcome included in-hospital mortality, discharge to

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hospice, or discharge to a skilled nursing facility. Seven processes of stroke care were

evaluated: fever management, hypoxia management, blood pressure management,

neurologic evaluation, swallowing evaluation, deep vein thrombosis (DVT) prophylaxis,

and early mobilization. Only 3 of these processes of care, swallowing evaluation, DVT

prophylaxis, and treating all episodes of hypoxia with supplemental oxygen, were

independently associated with an improvement in the combined outcome after adjustment

139. Thus the lack of solid evidence linking stroke process measures with clinical

outcomes could explain the lack of relationship between the stroke Hospital Compare

measures and patient satisfaction.

In our study we are unable to reject the null hypothesis associated with VTE processes.

Among all beds there is no relationship between patient satisfaction and VTE measures.

Only a weak association is detected (coefficient=0.06) between percent of patients who


surgery in small hospitals (beds<100) and satisfaction. It is established that VTE

prophylaxis reduces risks of venous thromboembolism151 but there are no studies looking

into the association of VTE measures and patient satisfaction. The lack of VTE process

association with patient satisfaction scores could be due to the lack of an underlying

clinical relationship between VTE processes as a group and outcomes. In a study by

Johnbull et al. 127, using Hospital Compare SCIP VTE measures, and AHRQ PSI (patient

safety indicator) VTE outcomes; average annual prophylaxis performance was linked to

the 2-year VTE rate for each hospital (from July 1, 2009, to June30, 2011). The PSI-12

was adjusted for age, sex, diagnosis-related group, and modified comorbidity index.

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Simple linear regression was used to test for an association between VTE prophylaxis

and VTE rate. There were 3040 hospitals with complete prophylaxis and VTE data. The

median risk adjusted VTE rate was 4.13 per 1000 surgical discharges. Prophylaxis

performance was not associated with VTE rate (P = .13) on linear regression. In a review

within the American College of Physicians VTE guidelines, Qaseem et al.160 have shown

that VTE prophylaxis was not associated with reduced risk of mortality. In our study, we

found no relationship between VTE measures and patient satisfaction (outcome measure)

in all hospitals or large hospitals with more than 200 beds.

In acute MI, only 3 out of 9 measures in large hospitals and 1 out of 9 measures in

medium sized hospitals with 100 to 200 beds are associated with patient satisfaction. The

strongest correlation occurs with percent of MI patients given aspirin upon discharge.

The second strongest association is detected with percent of MI patients given a statin

upon discharge. In the literature, one study looked into individual process of care

measures for AMI and there was no relationship between aspirin upon discharge, beta

blockers on discharge, ACE inhibitors on discharge, statins on discharge, cardiac rehab,

cardiac catheterization, PTCA or CABG, and patient satisfaction 10. Glickman et al. 14

examined clinical data on 6467 patients with acute myocardial infarction treated at 25 US

hospitals participating in the CRUSADE initiative from 2001 to 2006. Patient satisfaction

correlated with cardiac catheterization within 48 hours, beta-blockers on discharge,

clopidogrel on discharge, and lipid lower agents (strongest correlation coefficient =

0.199). Other studies looking into summary scores of AMI process measures did find a

positive association with patient satisfaction 19,7,6,15,20.

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It is intriguing that measures related to discharge medications such as aspirin on

discharge and statins on discharge seem most strongly associated with global patient

satisfaction scores. In a study by Bradley et al. 128, the authors assessed hospital

performance in the CMS/JCAHO AMI core process measures using 2002-2003 data from

962 hospitals and they correlated these measures with 30-day mortality rates. All

measures were associated with decrease mortality, the strongest correlation however

occurred with aspirin upon discharge variable (coefficient =-0.18). In a more recent study

that included all patients discharged from Massachusetts General Hospital between 1 July

2004 and 31 December 2007 with a principle diagnosis of acute myocardial infarction

(AMI), heart failure (HF) or pneumonia (PN), hospital data were linked with state

administrative data to determine mortality and readmissions. Non-adherence to aspirin on

discharge was linked to highest hazard rate of 90-day mortality. 129. Also in the review

by the Antithrombotic Trialists' Collaboration 140 it was found that allocation to

antiplatelet therapy reduced non-fatal myocardial infarction by one third. Thus, it is very

likely that because of their stronger effect on other outcomes like mortality, these

discharge processes of care measures also have the most significant impact on patient

satisfaction.

In Heart Failure, two measures out of 3 are associated with patient satisfaction in large

hospitals with beds >200 (percent having evaluation of ventricular heart function, percent

given ACE inhibitors or ARBs), one out of 3 heart failure measures is associated with

patient satisfaction in medium sized hospitals (100-200 beds) (given ACE inhibitors or

124

ARBs), and two out of 3 measures are related to satisfaction in small hospitals with beds

<100(evaluation of ventricular heart function, percent heart failure patients given

discharge instruction) . Among all hospitals, all 3 measures are related to satisfaction.

The strongest relationship (coefficient =1.96) is found in hospitals with more than 200

beds for the percent of HF patients having an evaluation of ventricular dysfunction.

Gesell et al. 21, in a study of only 32 hospitals have shown that among HF measures, only

the percent of patients having had assessment of ventricular function and percent of

patients given discharge instructions were associated with patient satisfaction. Other

studies utilizing summary composite measures for HF showed significant positive

relationships with patient satisfaction 86, 7, 6, 15, and 20. In a study of 22,750 Medicare

patients with heart failure between March 2003 and December 2004, mortality and

cardiovascular readmission at 1 year and adherence to hospital-level process measures

were analyzed. Only assessment of left ventricular function was statistically associated

with cardiovascular readmissions130. Likewise in another cross-sectional study of hospital

care between January 1 and December 31, 2004, for acute myocardial infarction, heart

failure, and pneumonia, ten process performance measures included in Hospital Compare

were compared with hospital risk-adjusted mortality rates. In HF, assessment of LV

function was associated with lower 30 day and 1 year mortality 131. In a study by Shahian

et al. 129, non-adherence to LV assessment in the setting of HF showed the strongest

relationship to 365-days mortality. Thus, it is very likely by its demonstrated effect on

clinical outcomes, such mortality and readmission to the hospital, LV function

assessment for HF patients is likely to have the most significant impact on patient

satisfaction.

125

In pneumonia, patients admitted to hospitals with more than 200 beds and among all

hospitals, the percent of patients who had blood cultures in the ED prior to administration

of antibiotics is statistically and positively associated with global patient satisfaction.

Percent of pneumonia patients given the most appropriate initial antibiotic is weakly

associated with patients definitely recommending hospital among all bed sizes, but the

relationship is of a lower magnitude (coefficient=0.08). In review of the literature,

summary process of care measures in pneumonia have been shown to be positively

associated with patient satisfaction19, 7, 6, 20. Lee et al. 23 studied 2076 patients hospitalized

with pneumonia from 32 emergency departments. They used multilevel logistic

regression modeling to assess independent associations between patient outcomes and the

performance of 4 individual processes of care for pneumonia (assessment of oxygenation,

blood cultures, and rapid initiation [<4 hours] and appropriate selection of antibiotic

therapy). Mortality was not significantly associated with either individual or cumulative

process measures in multivariable models. In a study by Shahian et al.129, time-weighed

non adherence to blood culture prior to antibiotic therapy had the strongest relationship to

increased hazard rate of readmission at 90 and 365 days (1.06 and 1.04 respectively). The

lack of consistency in pneumonia process measure relationships with other outcomes

such as mortality, as shown in Lee et al.23 and their association with readmission in the

Shahian et al.129 study could possibly explain the relationship of the ‘cultures before

antibiotics administration’ measure in large hospitals with beds>200 with patient

satisfaction, and lack of relationships in smaller bed size hospitals in our analysis.

126

Among different bed sized hospitals, SCIP measures differ in their influences on the

percent of patients who definitely recommend the hospitals. In hospitals with more than

200 beds, 4 out of 10 SCIP measures are significantly associated with patient satisfaction.

Strongest relationships occur with percent surgery patients with urinary catheters

removed on first or second day after surgery for inpatients (coefficient=0.39) and percent

outpatients who received antibiotics within one hour or surgery for outpatients

(coefficient = 0.35). In hospitals with 100 to 200 beds, 4 out of 10 measures are

significantly associated with patient satisfaction. The strongest relationships in this bed

size range occur with percent having surgery that got an antibiotic within one hour before

surgery for outpatients (coefficient= 0.74), and the percent surgery patients given the

right kind of antibiotics for inpatients (coefficients = 0.67). In small hospitals with fewer

than 100 beds, 7 out of 10 measures are significantly associated with patient satisfaction.

The strongest relationship in this bed size occurs with percent whose antibiotic was

stopped within 24 hours after surgery for inpatients (coefficient =0.35) and percent that

received antibiotics within one hour of surgery for outpatients (coefficient=0.34).

Lyu et al.22, in a study of 31 hospitals, found no association between patient satisfaction

and individual SCIP measures (antibiotic prophylaxis, hair removal, urine catheter

removal, DVT prophylaxis). Other studies found a significant relationship between

summary SCIP measures and patient satisfaction 7, 6,125. To analyze the possible effect of

SCIP process measures on outcomes influencing patient satisfaction, additional SCIP

process and outcome studies were reviewed. In a cross-sectional study of 189 hospitals,

relationships between SCIP measures and outcomes (30-day overall morbidity, serious

127

morbidity, surgical site infections [SSI], and mortality) were collected from January 1,

2008, through December 31, 2008. Of the 16 correlations, 15 demonstrated non-

significant associations with risk-adjusted outcomes. The exception was the relationship

between SCIP-2 (appropriate antibiotics administration) and SSI (p = 0.004). SCIP-1

(antibiotics administered one hour before incision) demonstrated an intriguing but non-

significant relationship with SSI (p =0.08) and overall morbidity (p =0.08). Adherence to

SCIP-2 was a significant predictor of risk-adjusted SSI (p <0.0001) and overall morbidity

(p <0.0001) 132.

It might be reasonable to assume based on these findings that the effect of SCIP measures

on patient satisfaction could be related to a decrease in infectious complications. Stulberg

et al. 120 in a retrospective cohort study between July 1, 2006 and March 31, 2008, of

405,720 patients from 398 hospitals found that SCIP adherence measured through a

global all-or-none composite infection prevention score was associated with a lower

probability of developing a postoperative infection. However, adherence reported on

individual SCIP measures was not associated with a significantly lower probability of

infection in this study. Another study by Wang et al. found that targeting complete

compliance with SCIP infection prevention measures was not associated with additional

reductions in infection outcomes following hip replacement 133. Awad 134, in a review of

the literature in 2012, concluded that whereas there is some evidence that compliance

with the SCIP measures decreases SSIs, this has not been corroborated by large-scale

national studies. This was also reinforced by the fact that numerous studies demonstrate

that the Surgical Care Improvement Program (SCIP) has been ineffective.

128

It is very likely that by their effect on other outcomes (such as mortality and

readmission), SCIP processes of care measures have the most significant impact on

patient satisfaction. The strongest SCIP measures affecting patient satisfaction in our

data may have important impacts on decreasing infectious complications. They include

urine catheter removal within 48 hours, outpatient antibiotics within one hour of surgery,

right antibiotic administration, and preventive antibiotics discontinued within 24 hours of

surgery. However, the effect of individual SCIP measures on outcomes and satisfaction

is not fully established. It is not clear that the potential effect of SCIP processes of care

on the surgical infection rate is the sole clinical outcome that influences of patient

satisfaction. This argument has been raised by Kennedy et al.135 who in a study of 171

hospitals found that large hospitals, high surgical volume, and low mortality were

associated with patient satisfaction (P < 0.001). Compliance with SCIP process measures

and patient safety indicators, as well as length of stay, did not correlate with overall

satisfaction. Additionally, according to Kennedy et al.135, the presence of surgical

complications or increased readmission was not found to affect patient satisfaction.

Favorable surgical outcomes in their study were not consistently associated with high

HCAHPS scores. The authors concluded that factors outside of surgical outcomes could

be influencing patients’ perceptions of their care. More studies need to be done to

understand the various relationships among SCIP measures among different hospital bed

sizes and their associations with outcomes including patient satisfaction.

In previous studies, nursing supply has shown a positive relationship with satisfaction,

129

whereas Medicaid, for-profit status, and care intensity were negatively related6. Studies

have differed on the relationship between teaching status, bed size and rural status on

patient satisfaction. Moreover, in these studies, these relationships were not stratified

among different hospital bed sizes. 6,12,89,15,90,70,86 .In our research a significant positive

association between nursing, bed size, specialty hospital status and patient satisfaction

was detected among all process of care quality measures. There were also significant

negative relationships between for profit status, Medicaid, Medicare status, and ICU

(reflecting care intensity) and patient satisfaction. Although Medicare reflects older

patients who are expected to have better satisfaction 92, 88, these patients are generally

sicker with poorer health status and this could be correlated with lower satisfaction 9, 70, 71

In large hospital (beds >200), it is very interesting to note the disappearance of the

relationship between nursing, Medicare, Medicaid and ICU variables and percent of

patients definitely recommending hospitals. It is possible that a saturation point of

nursing supply, Medicare, Medicaid or ICU beds has been reached at a certain bed level

(less than 200), beyond which the impact of these variables is not of a significant

magnitude towards satisfaction. In hospitals with beds>200, only bed size was positively

associated, and for profit status was negatively associated with patient satisfaction.

Specialty hospital status remained significantly positively related to patients definitely

recommending hospital within SCIP. It is not clear why for profit hospitals perform

worse in patient satisfaction. It has been suggested that the patient population seen at for-

profit hospitals might differ in expectations, from the population seen at not-for profit 6.

As discussed previously, expectations influence and appear to have an inverse

relationship with satisfaction: if expectations are low, satisfaction is higher; if

130

expectations are high, perception of satisfaction is lower94

GLM regression was also used to answer our research question 2: “Is there an association

between each HCAHPS composite process measure within AMI, heart failure,

pneumonia, SCIP, stroke VTE, and global HCAHPS satisfaction adjusting for HCAHPS

structure differences?” .We did find enough support to reject the null hypothesis Ho2=

“There is no association between each HCAHPS process composite measure and global

HCAHPS scores within each studied quality process measure for MI, heart failure,

pneumonia, SCIP, stroke and VTE adjusting for HCAHPS structure measures” for large

hospitals with beds>200. The strongest associations in large hospitals were detected

between the percent of patients who report that pain was ‘always’ well controlled and

percent who definitely recommend hospital for the diagnoses of stroke, VTE, acute MI in

all bed sizes, and in hospitals with beds >100 for CHF, pneumonia and SCIP. In small

hospitals for CHF, pneumonia and SCIP, strongest relationship occurred between percent

who reported that nurses always communicated well and the percent who would

definitely recommend hospital. Pain control came second in this setting.

There is a paucity of studies looking into the association of individual HCAHPS

composite measures with global satisfaction measures. In cross-sectional observational

data, Elliot et al.136 determined whether the contributions of patients experiences on

HCHAPS composite measures with their overall hospital satisfaction ratings vary by

hospitalization type based on medical diagnoses. They calculated the simultaneous partial

correlations of 7 HCAHPS composite scores with overall hospital rating, controlling for

131

patient demographics. Nurse communication was most important overall, with a 0.34

average partial correlation. Discharge information was the least important overall to

patient hospital ratings, with an average partial correlation of 0.05. The importance of

composite scores varied by hospitalization types. Pain management had a partial

correlation of 0.23 with overall ratings for infectious diseases, but no correlation with

overall ratings for nervous system surgery.

In a study by Otani et al.137, the authors utilized data collected between January 2007 and

June 2008 from 32 hospitals. The patient satisfaction survey included the Consumer

Assessment of Healthcare Providers and Systems, Hospital version questionnaire items.

Two-stage multiple linear regression analyses were conducted with control variables

(age, gender, perceived health, education and race). It was found that patients' highest

priority was to be treated with courtesy and respect by nurses and physicians. Clark et

al.12 have a found significant positive association between nurses supply and patient

evaluation of their hospital care. Nurse supply was also positively associated with other

areas such as pain management, emotional and spiritual care, discharge process, tests and

treatment and overall satisfaction. Jha et al.6 in a study of 2429 hospitals reported that

hospitals in top quartiles of ratio of nurses to patient-days had better performance on

HCAHPS surveys compared to the hospitals in the bottom quartiles. In the same study,

within supplementary appendix 4, the correlation matrix among individual domains of

patient experience showed that global satisfaction score ‘definitely recommend hospital’

had the strongest correlation with communication with nurses(R=0.7). This was followed

by pain control (R=0.69) and then by discharge instructions (R=0.6). Wolosin et al.142,

132

investigated how domains of patient satisfaction in hospitals (via a commercially

available survey) predict HCAHPS global rating scores. Patient satisfaction with rooms,

physician care, and meals were also significant predictors of the HCAHPS overall rating,

but nursing care in their study was clearly the most important.

In view of the above findings, a correlation matrix between the outcome variable

definrecomm (definitely recommend hospital) and other HCAHPS composites: nurse

communication (nursecomm), doctor communication (mdcomm), receive help when

needed (receivehelp), pain control (paincontrol) , explanation about meds (medexplain),

discharge information(dcinfo), rooms kept clean (roomclean), areas kept quiet

(areaquiet) was examined. Results are found in Table 23.

Table 23- Correlation matrix between definrecomm and all other HCAHPS composites

As evident from the matrix on the data, the strongest correlation occurs between

definrecomm and nursecomm (R=0.67), followed by paincontrol (R=0.61) then dcinfo

(R=0.57). These results are comparable to the findings of the correlation matrix produced

in Jha et al.6

133

Our investigation however was unique in identifying the roles of each HCAHPS

composite for each process of care measures for stroke, VTE, AMI, CHF, pneumonia and

SCIP and analyzing the relationships of each HCAHPS composite with global patient

satisfaction. Pain control was the most important determinant for all measures in larger

hospitals with more than 100 beds. In hospitals with fewer than 100 beds, nurse

communication was the most important determinant in CHF, pneumonia and SCIP

patients. No other study in the literature has looked into these factors within these process

measures. It is interesting to note that pain control has the strongest association with

global patient satisfaction for process of care measures in our report, although in other

studies nurse communication had the strongest relationship to patients definitely

recommending hospital. We have suggested in our analysis of our first research question

that process of care measures affect patient satisfaction by influencing health outcomes.

Thus, it is very feasible that by affecting or reflecting patients’ outcomes or their

experience of their outcomes, pain control would strongly influence patient satisfaction.

In that regard, it has been shown that pain as a factor contributes to the development of

post-operative pulmonary complications among the elderly population after abdominal

surgery It has been also determined that pain could be a symptom of complications

related to hospitalization, surgery or any procedure suggestive of a poorer outcome146 147.

To further understand the role of pain management in the patient’s experience, a study of

4349 patients examined the relationship between patients' perceptions of pain control

during hospitalization and their overall satisfaction with care. Patient satisfaction was

134

more strongly correlated with the perception that caregivers did everything they could to

control pain than with pain actually being well controlled 143. In another pilot study of 88

patients, the authors used a 14-question survey from a questionnaire developed by the

American Pain Society to assess patient pain control and overall satisfaction with the

institution's pain management strategies. There was no association between pain intensity

score and patient satisfaction with overall pain management 144. It appears that

perception of pain control could be of greater importance than actual pain or pain

intensity control. Since pain control is of higher significance in our analysis towards

global patient satisfaction, it will be of great interest in future studies to comprehend

whether actual pain control or whether other determinants affecting patients’ perception

of pain management within stroke, VTE, AMI, CHF, pneumonia and SCIP affect global

patient satisfaction. That research should also include analysis of health outcomes such as

medical adverse events, surgical complications, readmissions, mortality or other

outcomes that are related to pain.

Our study is limited with missing data, although critical access hospitals (CAH) were

dropped from analysis. Before dropping critical access hospitals (n=136), 16.4% of

hospitals had missing HCAHPS data. However there are still 4.7% missing data among

these hospitals after dropping CAH. Significant missing data also remain in hospitals

reporting process measures in Hospital Compare. In stroke for example, in SK1 (Stroke

patients who received treatment to keep blood clots from forming within 2 days of

arriving at the hospital), there were 60% missing observations before dropping CAH.

However, there are still 50% missing observations after dropping CAH. Understandably,

135

stratifying information within different bed sizes helped us to identify the determinants of

missed data that are mostly concentrated in small hospitals with fewer than 100 beds.

We also had the opportunity to analyze large hospitals with beds>200 where there were

little missing data.

When studying effects of individual HCAHPS measures within hospital process

measures, significant amount of missing data limited interpretation of satisfaction in

smaller hospitals for stroke, VTE and MI. However for CHF, pneumonia and SCIP, with

a large enough sample size to assess hospital with less than 100 beds, there was a clear

distinction of the influence of pain control in large hospitals (beds>200) and nurse

communication in small hospitals (beds<100). This difference is also clearly shown in

tables 15 and 16 where the coefficients for ‘structure’ measure in the GLM regression

between outcome variable (definitely recommend hospital) and individual process

variables for stroke, VTE, AMI, CHF, pneumonia and SCIP were analyzed. The roles of

nursing, Medicare, Medicaid and ICU bed capacity were not evident in large hospitals

compared to small hospitals. The effect of bed size was also factored when looking into

the association of each process of care measure with patient satisfaction. Changes in the

level and strengths of relationships of these process measures were obvious among

hospitals of different sizes. More research needs to be done to understand the decreasing

role of nursing communication with increasing bed size. Since the great majority of rural

and specialty hospitals are concentrated in small hospitals, understanding other unique

characteristics among these hospitals will help to better appreciate their influence on

patient satisfaction.

136

It is also important to study other unmeasured confounders, such as cultural differences

and other contributors to patients’ perceptions of care among different hospital sizes.

These confounders can influence the relationships between process of care measures or

individual HCAHPS composites towards patients definitely recommending hospital.

Moreover, these confounders could play a key role in pain perception determining

satisfaction among larger hospitals compared to small ones. It is quite possible that

perception of quality of care also influences the way patients evaluate their responses

within all quality measures for stroke, VTE, MI, CHF, Pneumonia or SCIP.

It is established that confirmation or disconfirmation of expectations for a hospital is the

result of comparing the perceptions of current performance of the hospital with the

expectations. Depending on the confirmation or disconfirmation of expectations by

patient perceptions of the current hospital experience, patients are either satisfied or

dissatisfied 96, 161. In addition, perceived quality is the evaluation of a hospital experience

as determined by perceptions of the hospital performance 162. Perceived quality is also

influenced by expectations. Thus, patient expectations determine both the evaluation (i.e.,

perceived quality) and the response (i.e., satisfaction) to that evaluation of the health care

provider's performance 96. John has demonstrated that expectations are derived from

previous hospital experiences and these patient expectations influence perceptions of

current experience. Patient satisfaction consequently is the result of confirmation or

disconfirmation of these patient expectations 96. It is therefore important in future studies

to understand drivers behind previous and current patient experiences and their role

towards expectations and perception of care. This will help clarify the interplay between

quality process and outcome measures and patients satisfaction with hospital care.

137

It is of interest to find in our data the strong positive relationship between specialty

hospital status and patients definitely recommending hospital. Barro et al.156 have shown

that specialty hospitals have lower mortality and readmission rates compared to all other

hospitals. In another study of hospitals in 6 states, it has been shown that specialty

hospitals had lower risk-adjusted thirty-day mortality rates. These hospitals also had

higher patient satisfaction 157. Concerns have been raised however that specialty hospitals

data could be biased since some of these hospitals would select less medically

complicated patients 158.

Girotra et al. 15 showed that for both acute myocardial infarction (AMI) and heart failure

(HF), low-performing hospitals in quality measures had lower annual admission volume,

fewer beds, lower nurse FTE per 1000 patient days, and these hospitals were more likely

to be rural. It was also shown that low-performing cardiac hospitals were smaller, rural

facilities and these have higher risk-adjusted mortality90. Joynt et al.152 indicated that

patients who were discharged from small hospitals also had higher readmission rates than

those discharged from large hospitals. It was also shown by Joynt et al. that rural

hospitals had fewer clinical capabilities, worse measured processes of care, and higher

mortality rates for patients with AMI, CHF, or pneumonia153. In US hospitals higher

condition-specific performance on process measures was associated with lower risk-

adjusted mortality for Acute MI, heart failure and pneumonia154.

138

Better hospital quality process measures were also associated with improved

outcomes150, 138,128,130,131,149,129,132. (Functional ability, complications, readmissions,

mortality). It is also determined that health outcomes including mortality, problem

resolution, readmissions and complications have a strong effect on patient satisfaction 14,

68, 16,8,61. Larger sized hospitals in our data have higher mean scores on process measures.

Accordingly as anticipated for these larger hospitals, those with process measures having

highest impact on outcomes would have a greater impact on patient satisfaction. Indeed

in our regression data within research questions 1 and 2, the coefficients determining the

strengths of the relationships between process of care measures and satisfaction are

greater in magnitude in larger sized hospitals.

As discussed previously, a major determinant affecting global satisfaction is patients

‘expectations. This raises the potential for uncontrolled selection bias, which may affect

the results 113. In that regard, Heckman correction 114 was used in research question two,

for pneumonia process measures in the relationships between nursecomm, paincontrol

and definrecomm. The variable medicare was used in the selection equation, since

medicare reflects older patients who are expected to have better satisfaction 92, 88. Using

Heckman’s approach in this setting did not show evidence of selection bias (Mills p value

0.97 for nursecomm and 0.99 for paincontrol , not significant 114). See Appendix D for

Stata output of Heckman selection models.

139

Based on the above discussion, a proposed model on the relationship between process of

care measures, HCAHPS composites and Global patient satisfaction is outlined below.

Model for relationships between process of care measures, HCAHPS composites and

Global patient satisfaction:

140

In the above model, it is proposed that associations between process of care measures for

stroke, MI, CHF, pneumonia, SCIP, and other outcome measures (readmission, mortality

or infection) influence the relationships between individual process of care measures and

global patient satisfaction. (VTE not included in model since there are no associations

except for a small relationship in small hospitals with beds<100). Moreover, pain control

and nurse communication are the strongest determinants of patient satisfaction when

analyzed within individual process of care measures for stroke, VTE, acute MI, CHF,

pneumonia or SCIP.

LIMITATIONS

The study is limited by the number of various missing data from different components of

process of care measures limiting sample size for the analysis. Since there was a

relationship between missing data and bed size, we stratified hospitals in our analysis by

bed sizes (<100, 100-200, and >200 beds). The cross sectional design has several

significant weaknesses or threats to internal validity. One weakness is the potential for

uncontrolled selection bias, which may affect the results 113. In that aspect, Heckman

correction was applied as discussed previously. Additionally, because this is a cross-

sectional analysis, there is a significant possibility for endogeneity among the

independent variables. This problem arises when the specified model contains an omitted

variable that, because of its omission, is a part of the error term. If the omitted variable is

correlated with any of the independent predictor variables, the estimates produced would

141

be biased and inconsistent 115. This is especially problematic in the second research

question where expectations for example could influence individual HCAHPS

composites and Global HCAHPS scores.

Other sources of bias could be related to unmeasured confounders, such as cultural

differences and other factors affecting patients’ perceptions of care. These confounders

can influence the relationships between process of care measures or individual HCAHPS

composites towards patients definitely recommending hospital. It is quite possible that

perception of quality of care also influences the way patients evaluate their responses

within all quality measures for stroke, VTE, MI, CHF, Pneumonia or SCIP. Some portion

of the differences observed between for-profit hospitals and not-for profit hospitals for

example may also reflect confounding; the patient population seen at for-profit hospitals

might differ in important ways, including expectations, from the population seen at not-

for profit hospitals6.

Major data collection was done with survey instruments, leading to the possibility that

respondents differ in some important ways than non-respondents. Satisfaction surveys are

plagued with low response rate often less than 50%. Demographics, utilization, patterns,

and health status differ between respondents and non-respondents 8. Since less satisfied

patients are also less likely to respond, and more satisfied patients are more likely to

respond caution must be taken when interpreting the results of studies with satisfaction

rate below 80%. 48,116. Response bias may also significantly impact the results of patient

satisfaction surveys, leading to overestimation of the level of satisfaction in the patient

142

population overall 117.All of the data used in this study comes from secondary databases,

which also suffer from similar other limitations. Weaknesses are inherent to the cross-

sectional design. In that setting, even if associations are found, there are limitation in

establishing temporal relationships between predictors and outcome 118.

The limitations of the performance measures used in the analysis are well known,

including concerns about the quality of the input data and the adequacy of (or lack of)

risk adjustment 20. Adherence to process measures are self-reported by hospitals and not

subjected to independent external validation; some have voiced concern over whether

hospitals might be manipulating their results to enhance their apparent quality 15, 90. The

HQA program only examines process measures across a few conditions, and although

these conditions are common and a source of major morbidity and mortality, they make

up only 15 percent of hospital admissions. In addition, the ability to fully account for

differences in underlying risk among patients might be limited 89,119. Process measures

are not a definitive criterion standard for quality of care. Some studies suggested that

some of these measures (SCIP) may not be a total surrogate marker of quality and have

shown that they do not correlate with patient outcomes 22,120.

The present study reports findings from hospitals in the West South Central Region that

may not be representative to generalize to other hospitals. The analysis was only limited

to 1 year of data for all process of care measures, but only for 6 months of data for VTE

and stroke measures (newest measures reported). Although it was a cross section study,

143

structure data were from previous year (data was only available for that period, and it is

presumed that no major changes in hospital characteristics have occurred in a year

period). Our data represent a snapshot of patients’ experiences, and it will be critical to

understand the ways in which these scores change over time and the factors that underlie

their improvement 6.

144

CHAPTER EIGHT

CLINICAL IMPLICATIONS

Significant implications can be made from this study. They include that the associated

process of care variables with global patient satisfaction have been shown to be of most

relevance towards improving outcomes (mortality and readmissions), except in SCIP

where these strong associations are of importance to prevent surgical infections. For

stroke and AMI, the strongest associations to global patient satisfaction occur with

medications given on discharge. In research question 2 looking into the association of

HCAHPS composites with global patient satisfaction within process measures for stroke,

VTE, MI, CHF, pneumonia and SCIP, pain control is most important determinant of

patient satisfaction in larger hospitals among all these process measures. In small

hospitals with beds<100, nurse communication is the most important determinant of

global patient satisfaction in CHF, pneumonia and SCIP. The relationships between

Medicare volume, Medicaid volume, RN staffing and ICU bed capacity (reflecting care

intensity) with global patient satisfaction seem to dissipate in large hospitals (beds>200)

145

POLICY IMPLICATIONS

This study also has important policy implications. These include that in stroke and AMI,

focusing on discharge medications (DC anti-thrombotics and aspirin on discharge) could

be of primary importance towards improving patient satisfaction. In heart failure,

concentrating on assessment of LV function metric can ensure improved global patient

satisfaction. In pneumonia, cultures before antibiotics metric would be of core emphasis

towards improving satisfaction. In SCIP, in the outpatient setting, antibiotics

administration within one hour of surgery seems to be fundamental for global patient

satisfaction. Discontinuation of urine catheter is the most important variable in large

hospitals to ensure patient satisfaction. Giving the right antibiotics and discontinuation of

antibiotics within one hour of surgery are more important in medium sized hospitals

(beds100-200) and small hospitals (beds<100) respectively. Focusing on pain control

likely has the strongest impact towards satisfaction in larger hospitals (>100 beds). In

small hospitals with beds <100, focusing on nurse communication likely has the strongest

influence towards satisfaction within SCIP, pneumonia and CHF.

FUTURE RESEARCH

The relationship of nurse communication and pain control with global satisfaction in

smaller and larger beds hospitals, and the associations of structure factors

(Medicare/Medicaid/ICU/beds/nursing) with satisfaction in smaller beds and

disappearance of these links in >200 beds is intriguing. These are remarkable findings

146

especially since the smaller hospitals in our study tend to be more rural, with larger

Medicare population, and less ICU bed capacity. More research needs to be done to

identify other unique characteristics of the smaller hospitals compared to larger hospitals

in order to understand the differences in relationships between process of care measures

and patient satisfaction. It is possible that a saturation point of nurse staffing, Medicare

volume or ICU bed capacity has been reached at a certain level within hospital size range

of 100-200 beds, beyond which the impact of these variables is not of a significant

magnitude with regard to patient satisfaction. Determining this point and associated

factors would be a great value to the literature.

Perception of pain control seems of greater importance than pain intensity control, and

pain control appears to be of higher significance towards global patient satisfaction in

larger hospitals. Moreover, perception of quality of care could also influence the way

patients evaluate their responses within all quality measures for stroke, VTE, MI, CHF,

pneumonia or SCIP. As a result, it will be of great interest in future studies to look into

the determinants affecting patients’ perception of care within stroke, VTE, AMI, CHF,

pneumonia and SCIP, and patients perception of satisfaction within all HCAHPS

composites, mainly pain control. Patient satisfaction is the result of confirmation or

disconfirmation of patient expectations. These expectations are derived from previous

hospital experiences and they also influence perceptions of current experience 96. It is

therefore important in future studies to understand drivers behind previous and current

patient experiences and their role towards expectations and perception of care.

147

Determining the role of additional unmeasured confounders such as socio-cultural factors

within this framework will be a great addition to the literature.

More research including prospective studies is needed to compare each individual

process measure within each quality metric (stroke/VTE/pneumonia/CHF/MI/SCIP) and

its association with other outcomes, especially in view of the strong relationships

between outcome measures and patient satisfaction. These investigations will expand our

understanding of the major drivers behind stronger and weaker relationships within each

process care category.

Since a new HCAHPS composite related to care transition has been introduced recently

(Patients who understood their care when they left the hospital), it will be of great interest

to study the effect of this composite towards global patient satisfaction within all process

measures for stroke, VTE, MI, CHF, pneumonia and SCIP. Lastly, since our study was

limited to hospitals in South Central region. Research to include all hospitals in the US

will be helpful to generalize the findings

CONCLUSION

This study provides a unique opportunity to analyze the relationships between each

individual hospital process of care measure for acute MI, heart failure (HF), pneumonia

(PN), SCIP (surgical care improvement project), stroke (SK) ,VTE , and the global

patient satisfaction HCAHPS score( definitely recommend hospital) using Generalized

Linear Model regression analysis within the Donabedian framework. This study also

148

incorporates other HCAHPS composite components into quality elements to determine

relationship with global satisfaction with care.

We expected to find significant associations between all individual process measures and

percent of patients definitely recommending hospital. Instead, only few significant

measures were identified within different process of care scores. Although some of these

measures could be relevant for policy recommendations (discharge meds for Stroke/AMI,

LV function for HF, cultures before antibiotics for pneumonia and infection prevention

measures for SCIP), it is prudent to note that more needs to be done to understand the

drivers behind the strengths of the relationships of these measures with global patient

satisfaction, including their associations with other outcomes such as readmission,

mortality of infection. More also needs to be done to determine the generalizability of our

findings to other US hospital.

When analyzing the impact of individual HCAHPS process measures within each quality

metric, almost all HCAHPS composites were significant in determining percent of

patients definitely recommending hospital. However, pain control seems to be the

dominant determinant for beds>100. More needs to be done to understand influencers of

pain perception and outcomes with patient satisfaction.

It is very interesting to note the variability of the strengths of process and structure

measures relationships with patient satisfaction within different bed sizes. Larger

hospitals in our data have more care intensity and they also have higher mean percent

149

scores on process measures .The relationship with nurse supply with satisfaction seems to

disappear with large hospitals (beds >200). It is possible that a saturation point has been

reached at this level beyond which the impact of nursing is not of a significant magnitude

towards satisfaction. Same situation occurs in research question 2, where in larger

hospitals the dominance of nurse communication impact on global satisfaction yields to

pain control in CHF, pneumonia and SCIP.

Our study was unique in analyzing stroke and VTE hospital compare measures with

satisfaction. In addition, our study was distinctive in stratifying relationships among

different bed sizes, and in measuring the relationships of each HCAHPS composite

within each hospital compares process with global satisfaction.

More investigations to include all US hospitals need to be undertaken to understand the

various factors and determinants of process and structure measures within different

hospital bed sizes and their relationships with outcomes including patient satisfaction.

Towards this end, policy recommendations will be more valid towards improving

satisfaction with care.

150

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APPENDIX A HOSPITAL INPATIENT QUALITY REPORTING MEASURES (IQR)

171

Measures Requiring Web-based Hospital Data Entry

172

Measure Information Obtained from Claims-Based Data

Measures Removed from the Hospital IQR Program

173

APPENDIX B Value Based Purchasing Clinical Process of Care Domains

Measure ID*

Measure Description FY 2013

FY 2014

FY 2015

AMI-7a Fibrinolytic Therapy Received Within 30 Minutes of Hospital Arrival

Yes Yes Yes

AMI-8a Primary PCI Received Within 90 Minutes of Hospital Arrival

Yes Yes Yes

HF-1 Discharge Instructions Yes Yes Yes

174

PN-3b Blood Cultures Performed in the Emergency Department Prior to Initial Antibiotic Received in Hospital

Yes Yes Yes

PN-6 Initial Antibiotic Selection for CAP in Immunocompetent Patient

Yes Yes Yes

SCIP-Card-2 Surgery Patients on a Beta Blocker Prior to Arrival That Received a Beta Blocker During the Perioperative Period

Yes Yes Yes

SCIP-Inf-1 Prophylactic Antibiotic Received Within One Hour Prior to Surgical Incision

Yes Yes Yes

SCIP-Inf-2 Prophylactic Antibiotic Selection for Surgical Patients Yes Yes Yes SCIP-Inf-3 Prophylactic Antibiotics Discontinued Within 24 Hours

After Surgery End Time Yes Yes Yes

SCIP-Inf-4 Cardiac Surgery Patients with Controlled 6AM Postoperative Serum Glucose

Yes Yes Yes

SCIP-Inf-9 Postoperative Urinary Catheter Removal on Post Operative Day 1 or 2

No Yes Yes

SCIP-VTE-1 Surgery Patients with Recommended Venous Thromboembolism Prophylaxis Ordered

Yes Yes No

SCIP-VTE-2 Surgery Patients Who Received Appropriate Venous Thromboembolism Prophylaxis Within 24 Hours Prior to Surgery to 24 Hours After Surgery

Yes Yes Yes

* Measure name: AMI = Acute Myocardial Infarction HF =

Heart Failure PN = Pneumonia SCIP = Surgical Care Improvement Project

VBP Efficiency Domains

Measure ID Measure Description FY

2013 FY 2014

FY 2015

MSPB Medicare Spending Per Beneficiary No No Yes

VBP Patient Experience of Care for the Hospital Consumer Assessment of Healthcare Providers and Systems Survey

175

(HCAHPS) Domains.

Measure Description FY 2013 FY 2014 FY 2015 Communication with Nurses Yes Yes Yes Communication with Doctors Yes Yes Yes Responsiveness of Hospital Staff Yes Yes Yes Pain Management Yes Yes Yes Communication about Medicines Yes Yes Yes Cleanliness and Quietness of Hospital Environment Yes Yes Yes Discharge Information Yes Yes Yes Overall Rating of Hospital Yes Yes Yes

VBP Outcome Domains

Measure ID** Measure Description FY 2013

FY 2014

FY 2015

MORT-30-AMI Acute Myocardial Infarction (AMI) 30-Day Mortality Rate

No Yes Yes

MORT-30-HF Heart Failure (HF) 30-Day Mortality Rate No Yes Yes MORT-30 PN Pneumonia (PN) 30-Day Mortality Rate No Yes Yes AHRQ Composite (PSI-90)

Complication/Patient safety for selected indicators (Composite)

No No Yes

HAI Central Line-Associated Blood Stream Infection (CLABSI)

No No Yes

** Measure names: MORT = Outcome Mortality

Measure AHRQ = Agency for Healthcare Research and Quality PSI = Patient Safety Indicators HAI = Healthcare Associated Infection.

APPENDIX C Hospital Compare Measures in the study

Hospital Measures Current Data Collection Period

176

From Through

Survey of patients' experiences (HCAHPS)

Patients who reported that their nurses "Always", “Usually”, or “Sometimes/Never” communicated well

7/1/2012 6/30/2013

Patients who reported that their doctors "Always”, “Usually”, or “Sometimes/Never” communicated well

7/1/2012 6/30/2013

Patients who reported that they "Always”, “Usually”, or “Sometimes/Never” received help as soon as they wanted

7/1/2012 6/30/2013

Patients who reported that their pain was "Always”, “Usually”, or “Sometimes/Never” well controlled

7/1/2012 6/30/2013

Patients who reported that staff "Always”, “Usually”, or “Sometimes/Never” explained about medicines before giving it to them

7/1/2012 6/30/2013

Patients who reported that their room and bathroom were "Always”, “Usually”, or “Sometimes/Never” clean

7/1/2012 6/30/2013

Patients who reported that the area around their room was "Always”, “Usually”, or “Sometimes/Never” quiet at night

4/1/2012 3/31/2013

Patients who reported “Yes” or “No” that they were given information about what to do during their recovery at home

7/1/2012 6/30/2013

Patients who gave their hospital a rating of “9 or 10,” “7 or 8,” or “6 or lower” on a scale from 0 (lowest) to 10 (highest)

7/1/2012 6/30/2013

Patients who reported they would “Definitely,” “Probably” or “Not” recommend the hospital

7/1/2012 6/30/2013

Timely & effective care

Heart attack care

177

Timely heart attack care

Average number of minutes before outpatients with chest pain or possible heart attack who needed specialized care were transferred to another hospital

7/1/2012 6/30/2013

Average number of minutes before outpatients with chest pain or possible heart attack got an ECG

7/1/2012 6/30/2013

Outpatients with chest pain or possible heart attack who got drugs to break up blood clots within 30 minutes of arrival

7/1/2012 6/30/2013

Outpatients with chest pain or possible heart attack who got aspirin within 24 hours of arrival

7/1/2012 6/30/2013

Heart attack patients given fibrinolytic medication within 30 minutes of arrival

7/1/2012 6/30/2013

Heart attack patients given PCI within 90 minutes of arrival

7/1/2012 6/30/2013

Effective heart attack care

Heart attack patients given aspirin at discharge 7/1/2012 6/30/2013

Heart attack patients given a prescription for a statin at discharge

7/1/2012 6/30/2013

Heart failure care

Effective heart failure care

178

Heart failure patients given discharge instructions

7/1/2012 6/30/2013

Heart failure patients given an evaluation of left ventricular systolic (LVS) function

7/1/2012 6/30/2013

Heart failure patients given ACE inhibitor or ARB for left ventricular systolic dysfunction (LVSD)

7/1/2012 6/3102013

Pneumonia care

Effective pneumonia care

Pneumonia patients whose initial emergency room blood culture was performed prior to the administration of the first hospital dose of antibiotics

7/1/2012 6/30/2013

Pneumonia patients given the most appropriate initial antibiotic(s)

7/1/2012 6/30/2013

Surgical care

Timely surgical care

Outpatients having surgery who got an antibiotic at the right time (within one hour before surgery)

7/1/2012 6/30/2013

Surgery patients who were given an antibiotic at the right time (within one hour before surgery) to help prevent infection

7/1/2012 6/30/2013

Surgery patients whose preventive antibiotics were stopped at the right time (within 24 hours after surgery)

7/1/2012 6/30/2013

Patients who got treatment at the right time (within 24 hours before or after their surgery) to help prevent blood clots after certain types of surgery

7/1/2012 6/30/2013

Effective surgical care

179

Outpatients having surgery who got the right kind of antibiotic

7/1/2012 6/30/2013

Surgery patients who were taking heart drugs called beta blockers before coming to the hospital, who were kept on the beta blockers during the period just before and after their surgery

7/1/2012 6/30/2013

Surgery patients who were given the right kind of antibiotic to help prevent infection

7/1/2012 6/30/2013

Heart surgery patients whose blood sugar (blood glucose) is kept under good control in the days right after surgery

7/1/2012 6/30/2013

Surgery patients whose urinary catheters were removed on the first or second day after surgery

7/1/2012 6/30/2013

Patients having surgery who were actively warmed in the operating room or whose body temperature was near normal by the end of surgery

7/1/2012 6/30/2013

Emergency department care

Timely emergency department care

Average time patients spent in the emergency department, before they were admitted to the hospital as an inpatient

7/1/2012 6/30/2013

Average time patients spent in the emergency department, after the doctor decided to admit them as an inpatient before leaving the emergency department for their inpatient room

7/1/2012 6/30/2013

Average time patients spent in the emergency department before being sent home

7/1/2012 6/30/2013

Average time patients spent in the emergency department before they were seen by a healthcare professional

7/1/2012 6/30/2013

180

Average time patients who came to the emergency department with broken bones had to wait before receiving pain medication

7/1/2012 6/30/2013

Percentage of patients who left the emergency department before being seen

1/1/2012 6/30/2012

Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival

7/1/2012 6/30/2013

Preventive care

Patients assessed and given influenza 10/1/2012 3/31/2013

vaccination

Patients assessed and given pneumonia vaccination 7/1/2012 6/30/2013

Children's asthma care

Effective children's asthma care

Children who received reliever medication while hospitalized for asthma

7/1/2012 6/30/2013

Children who received systemic corticosteroid medication (oral and IV medication that reduces inflammation and controls symptoms) while hospitalized for asthma

7/1/2012 6/30/2013

Children and their caregivers who received a home management plan of care document while hospitalized for asthma

7/1/2012 6/30/2013

Stroke care

Timely stroke care

181

Ischemic stroke patients who got medicine to break up a blood clot within 3 hours after symptoms started

1/1/2013 6/30/2013

Ischemic stroke patients who received medicine known to prevent complications caused by blood clots within 2 days of arriving at the hospital

1/1/2013 6/30/2013

Ischemic or hemorrhagic stroke patients who received treatment to keep blood clots from forming anywhere in the body within 2 days of arriving at the hospital

1/1/2013 6/30/2013

Effective stroke care

Ischemic stroke patients who received a prescription for medicine known to prevent complications caused by blood clots before discharge

1/1/2013 6/30/2013

Ischemic stroke patients with a type of irregular heartbeat who were given a prescription for a blood thinner at discharge

1/1/2013

6/30/2013

Ischemic stroke patients needing medicine to lower cholesterol, who were given a prescription for this medicine before discharge

1/1/2013 6/30/2013

Ischemic or hemorrhagic stroke patients or caregivers who received written educational materials about stroke care and prevention during the hospital stay

1/1/2013 6/30/2013

Ischemic or hemorrhagic stroke patients who were evaluated for rehabilitation services

1/1/2013 6/30/2013

Blood clot prevention and treatment

Blood clot prevention

182

Patients who got treatment to prevent blood clots on the day of or day after hospital admission or surgery

1/1/2013 6/30/2013

Patients who got treatment to prevent blood clots on the day of or day after being admitted to the intensive care unit (ICU)

1/1/2013 6/30/2013

Patients who developed a blood clot while in the hospital who did not get treatment that could have prevented it

1/1/2013 6/30/2013

Blood clot treatment

Patients with blood clots who got the recommended treatment, which includes using two different blood thinner medicines at the same time

1/1/2013 6/30/2013

Patients with blood clots who were treated with an intravenous blood thinner, and then were checked to determine if the blood thinner was putting the patient at an increased risk of bleeding

1/1/2013 6/30/2013

Patients with blood clots who were discharged on a blood thinner medicine and received written instructions about that medicine

1/1/2013 6/30/2013

Pregnancy and delivery care

Percent of newborns whose deliveries were scheduled too early (1-3 weeks early), when a scheduled delivery was not medically necessary

1/1/2013 6/30/2013

Readmissions, complications and deaths

30-Day outcomes: Readmission and death rates

Rate of readmission for heart attack patients 7/1/2009 6/30/2012

Death rate for heart attack patients 7/1/2009 6/30/2012

Rate of readmission for heart failure patients 7/1/2009 6/30/2012

183

Death rate for heart failure patients 7/1/2009 6/30/2012

Rate of readmission for pneumonia patients 7/1/2009 6/30/2012

Death rate for pneumonia patients 7/1/2009 6/30/2012

Rate of readmission after hip/knee surgery 7/1/2009 6/30/2012

Rate of readmission after discharge from hospital (hospital-wide)

7/1/2011 6/30/2012

Surgical complications

Rate of complications for hip/knee replacement patients 7/1/2009 3/31/2012

Serious complications

7/1/2010

6/30/2012

Deaths among patients with serious treatable complications after surgery

7/1/2010 6/30/2012

Healthcare-associated infections

Central line-associated bloodstream infections (CLABSI) 7/1/2012 6/30/2013

Catheter-associated urinary tract infections (CAUTI) 7/1/2012 6/30/2013

Surgical site infections from colon surgery (SSI: Colon)

7/1/2012

6/30/2013

Surgical site infections from abdominal hysterectomy (SSI: Hysterectomy)

7/1/2012 6/30/2013

Staphylococcus aureus (or MRSA) Blood Infections (Antibiotic-resistant blood infections)

1/1/2013 6/30/2013

Clostridium difficile (or C.diff.) Infections 1/1/2013 6/30/2013

Other Readmission Measure

184

American College of Cardiology percutaneous coronary intervention (PCI) readmission measure

1/1/2010 11/30/2011

Use of Medical Imaging

Outpatients with low back pain who had an MRI without trying recommended treatments first, such as physical therapy

1/1/2011 12/31/2011

Outpatients who had a follow-up mammogram, ultrasound, or MRI of the breast within 45 days after a screening mammogram

1/1/2011 12/31/2011

Outpatient CT scans of the chest that were “combination” (double) scans

1/1/2011 12/31/2011

Outpatient CT scans of the abdomen that were “combination” (double) scans

1/1/2011 12/31/2011

Outpatients who got cardiac imaging stress tests before low-risk outpatient surgery

1/1/2011

12/31/2011

Outpatients with brain CT scans who got a sinus CT scan at the same time

1/1/2011 12/31/2011

Medicare Payment

Spending per hospital patient with Medicare 1/1/2012 12/31/2012

Number of Medicare Patients

Number of Medicare patients treated 10/01/2011 9/30/2012

Structural Measures

Cardiac Surgery Registry 1/1/2012 12/31/2012

Stroke Care Registry 1/1/2012 12/31/2012

Nursing Care Registry 1/1/2012 12/31/2012

Multispecialty Surgical Registry 1/1/2012 12/31/2012

General Surgery Registry

1/1/2012 12/31/2012

185

HIT Measures Able to receive lab results electronically 1/1/2012 6/30/2012 Able to track patients’ lab results, tests, and referrals electronically

1/1/2012 6/30/2012

APPENDIX D Stata output of Heckman selection models used in discussion section

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