Pay-For-performance's Impact on Overall Quality of Care for Acute Myocardial Infarction Patients

UNIVERSITY OF CALIFORNIA

Los Angeles

Pay-for-Performance’s Impact

on Overall Quality o f Care

for Acute Myocardial Infarction Patients

A dissertation submitted in partial satisfaction of the

requirements for the degree Doctor o f Philosophy

in Health Services

by

Mikele Mariah Bunce

2007

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

UMI Number: 3272270

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The dissertation of Mikele Mariah Bunce is approved.

Charles Corbett

Paul Torrens• ' ...

Robert Kaplan, Committee Chair

University o f California, Los Angeles

2007

ii


DEDICATION

To Donald Randy Bunce and Diana Carter Bunce

for inspiring me to go after my dreams and for believing that I could achieve them

and to Cameron Carter Bunce for his love and support.

iii


TABLE OF CONTENTS

Section Page Number

I. Introduction 1

II. Background 4

a. Sub-Optimal Elealth Care Quality 4

b. United States’ Reimbursement Systems for Hospitals 6

c. Quality Metrics 7

d. Premier Hospital Quality Incentive Demonstration 8

e. Hospital Quality Alliance 9

f. Scripps Health 10

III. Literature Review 12

a. Process and Outcome Measures 12

b. Acute Myocardial Infarction Hospital Process Measures 13

c. Pay-for-Performance 19

IV. Hypotheses 23

V. Conceptual Model 25

VI. Data 26

a. Main Analysis 26

b. Additional Analysis 27

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VII. Data Sources 29

VIII. Data Elements 31

a. Dependent Variables 36

b. Predictor Variables 36

i. Pay-for-Performance Scores 37

ii. Patient Demographics 39

iii. Hospital Characteristics 39

iv. Patient’s Medical Condition 40

v. Treatment 40

vi. Patient’s Behavior 41

IX. Study Design 42

X. Statistical Methods 45

a. Study Aim 1 45

b. Study Aim 2 47

XI. Results 50

a. Patient Characteristics 50

b. Pay-for-Performance Process Measure Analysis 59

i. Scripps Performance Results 59

ii. Premier, Inc. and Scripps Health Performance 66

c. Process Outcomes Link Analysis 70

i. Survival Analysis Mortality Results 70

ii. Survival Analysis Times Series Mortality 72Results

v


iii. Logistic Regression Mortality Results 75

iv. Logistic Regression Times Series Mortality 75Results

v. Logistic Regression Morbidity Results 77

vi. Logistic Regression Times Series Morbidity 77Results

d. Covariates’ Impact on Outcomes Analysis 78

i. Test for Proportional Hazards 78

ii. Covariate Survival Analysis Results 79

e. Pay-for-Performance Outcomes Analysis 82

i. Mortality Pre and Post Intervention Results 82

ii. Morbidity Pre and Post Intervention Results 85

XII. Discussion 86

a. Pay-for-Performance’s Impact on Process Measures 86

b. Process-Outcomes Link 94

c. Covariates’ Impact on Outcomes 97

d. Pay-for-Performance’s Impact on Outcomes 102

e. Summary 104

XIII. Limitations 106

XIV. Attachments 114

a. Premier Hospital Quality Incentive Demonstration 114

b. Hospital Quality Initiative 116

c. Conceptual Model 121

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d. Variable Coding 122

e. Covariates Frequencies Over Time 126

f. Covariates Included in Stepwise Survival Analysis on Total 134 Population

g. Covariates Included in Time Series Stepwise Survival 137 Analysis

XV. References 143

LISTS OF FIGURES

Figure Page Number

I. All Applicable P4P Measure Compliance Over Time 59

II. Aspirin at Arrival Compliance Over Time 62

III. Beta Blocker at Arrival Compliance Over Time 63

IV. ACEI or ARB for LVSD Compliance Over Time 63

V. Smoking Cessation Advice/Counseling Compliance Over Time 64

VI. Aspirin at Discharge Compliance Over Time 64

VII. Beta Blocker at Discharge Compliance Over Time 65

VIII. Thrombolytic Agent Within 30 Minutes Compliance Over Time 65

IX. PCI Within 120 Minutes Compliance Over Time 66

X. Premier, Inc. and Scripps Health Weighted Average AMI 68Process Measure Compliance Over Time

XI. 30-Day Mortality Rates Over Time 83

XII. 90-Day Mortality Rates Over Time 83

XIII. 180-Day Mortality Rates Over Time 84

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XIV. 30-Day Readmission Rates Over Time 86

LISTS OF TABLES

Table Page Number

I. Variable Sources 29

II. Conceptual Domains, Theoretical Variables, Empirical 31Variables, and Prediction on Outcomes

III. Variable Frequencies for Non-Continuous Variables for 52Total Patient Population

IV. Variable Means for Continuous Variables for Total Patient 56Population

V. Means of Age and Hospital Characteristics at Each 58Observation Time Period

VI. Logistic Regression Results of Scripps Process Measure Scores 60Before and After HQA

VII. Premier, Inc., Scripps Health, and Other National HQA 68Participants’ Process Measure Scores

VIII. Differences in Performance Measure Scores Between Scripps 69Health, Premier, Inc., and Other National HQA ParticipantsBefore and After P4P and P4R

IX. Scripps Health and Premier, Inc. Performance Before and After 69P4P

X. Scripps Health and Premier, Inc. Performance Before and After 70P4R

XI. Survival Analysis Results for Regressors o f Interest in Total 70Population

XII. All Applicable and Recommended P4P Variable Survival 71Analysis Results

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XIII. Time Series Survival Analysis Results for Regressors o f Interest 73

XIV. Alive/Dead at 30 Days Outcome Results for Regressors of 75Interest in Total Population

XV. Alive/Dead at 30 Days Time Series Outcome Results for 76Statistically Significant Regressors o f Interest in Total Population

XVI. Readmission in 30 Days Outcome Results for Regressors o f 77Interest in Total Population

XVII. Readmission in 30 Days Outcome Results for Statistically 78Significant Regressors o f Interest in Total Population

XVIII. Test for Proportional Hazards Results 79

XIX. Covariate Hazard Functions 81

XX. Logistic Regression Results o f 30-Day Mortality Before and 84After HQA

XXI. Logistic Regression Results of 90-Day Mortality Before and 84After HQA

XXII. Logistic Regression Results of 180-Day Mortality Before and 85After HQA

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ACKNOWLEDGEMENTS

I would like to take this opportunity to thank and acknowledge a number o f people who

helped make this research come to fruition. First and foremost, I would like to thank

my dissertation committee: Dr. Robert Kaplan, Dr. Jack Needleman, Dr. Paul Torrens,

and Dr. Charles Corbett for their guidance, patience, and motivation. I would also like

to extend thanks to Dr. Ninez Ponce and Dr. Tom Rice at the UCLA School o f Public

Health.

Scripps Health has been extremely supportive o f my research endeavors. I could not

have completed this dissertation without the encouragement and aide o f Dr. Brent

Eastman, Barbara Price, Chris Van Gorder, and Mindi Matson. The cardiologists in the

Scripps Health system were invaluable in helping create the conceptual model for this

research and in providing clinical expertise, in particular, Dr. Paul Teirstein, Dr. Eric

Topol and Dr. Paul Phillips.

My friends and family have been by my side throughout the whole process. I want to

express my thanks to them for making this journey easier along the way.

x


VITA

August 9, 1977

1999

1999

1999- 2001

2002 - 2005

2003

2005 - present

Bom, Stanford, California

B.A., Human Biology Stanford University Stanford, California

Quality Data Coordinator Palo Alto Medical Foundation Palo Alto, California

Cancer Center Consultant; JCAHO/PI Analyst UCSF Medical Center San Francisco, California

Consultant; Senior Consultant Sinaiko Healthcare Consulting Los Angeles, California

M.P.H., Health Services University o f California, Los Angeles Los Angeles, California

Director, Quality Scripps Health San Diego, California

PUBLICATIONS AND PRESENTATIONS

Bunce, Mikele. “Clinical Guidelines: Increased Quality of Care at the Expense of Clinical Autonomy?” Journal o f Health Care Compliance. 2005; 7(3):50-52.

Bunce, Mikele M. “Innovative Approaches Help Improve the Managed Care Trifecta.” Managed Healthcare Executive. 2005; 15(6):42-44.

Bunce, Mikele and Richard Sinaiko. “HIPAA: The Next Phase. Myths and Realities of the Electronic Transaction and Code Set Standards.” Physicians Practice. 2003; 13(8):67-70.

xi


Sinaiko, Richard E, Mikele M Bunce, Neal Eigler, Saibal Kar, Sepideh S Farivar, Emma C Wollschlager. “Drug-Eluting Stent Use May Negatively Impact the Economic Helath of a Hospital: A Single-Center Case Study.” Supplement to Journal o f American College o f Cardiology - Abstracts o f Original Contributions. 2004; 43(5):402A-403A.

Toloui, Omid B and Mikele M Bunce. “Are Individual or Group Incentives Best?” Cardiology Practice Options. Oct 2005; 6-7.

xii


ABSTRACT OF THE DISSERTATION

Pay-for-Performance’s Impact

on Overall Quality of Care

for Acute Myocardial Infarction Patients

by

Mikele Mariah Bunce

Doctor of Philosophy in Health Services

University o f California, Los Angeles, 2007

Professor Robert Kaplan, Chair

Background: Pay-for-performance (P4P) is a methodology where financial incentives

are given to healthcare providers for the provision of high quality patient care.

However, there is limited research on whether P4P programs improve patient

outcomes. Methods: Compliance with eight acute myocardial infarction (AMI) metrics

used in the Hospital Quality Alliance (HQA) precursor to the Centers for Medicare &

Medicaid Services (CMS) Values Based Purchasing (i.e., P4P) plan as well as patient

mortality were analyzed using patient level data for 3,954 patients discharged from

Scripps Health hospitals from 2003 to 2005. Three observational time periods of six

months of data before participation in the HQA were compared to three observational

xiii


time periods of six months of data after participation in the HQA within a time series

study design. Multivariate survival analyses and logistic regressions were performed to

determine whether an improvement in process measure compliance and/or patient

mortality could be attributed to participation in the HQA. Results: Compliance with

providing all applicable P4P measures improved from 60-72% before HQA to 75-86%

after HQA. Similarly, 30-day mortality improved from 11-13% before HQA to 8-9%

after HQA. Regression discontinuity analyses with time series process measure and

outcome data identified that neither the slope nor the intercept of performance after

participation in the HQA was statistically significantly different than before

participation in the HQA using a p-value o f 0.05. Conclusion: For a hospital system

already improving compliance with AMI process measures and patient outcomes,

participation in the HQA did not change that pattern of improvement. One cannot

conclude that the HQA was the catalyst for improvement. However, mortality did

improve after participation in the HQA, indicating that any potential unintended

consequences o f participation in the HQA did not have a significant negative impact on

outcomes. Further research is required to determine whether other changes (e.g.,

increased staffing ratios, treatment modality used) could be attributed to the change in

performance metric compliance and the change in outcomes. Further research is also

recommended to determine whether the HQA had early or lagged effects on process

measures and outcomes not identified in this study.

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In t r o d u c t io n

According to the Institute of Medicine’s report Crossing the Quality Chasm: A

New Health System for the 21st Century, “health care today harms too frequently and

fails to deliver its potential benefits” therefore “the American health care delivery

system is in need o f fundamental change.”1 Although healthcare professionals aim to

provide high quality patient care, recent reports on the staggering number o f medical

errors and the failure to deliver the best available care confirm that not only is high

quality care not always received, but poor quality care has resulted in unnecessary

deaths.2,3

The healthcare industry is no different than other industries in that it is driven

by money. In order to survive, provider organizations must be mindful of

reimbursement. However, the dominant healthcare payment systems in the United

States (US) do not reward quality care and can often provide incentives against

providing high quality care.4

Pay-for-performance (P4P) systems were developed as a mechanism to align

financial incentives for providing high quality care. The American Medical

Association (AMA) defines pay for performance as “a method of linking pay to a

measure o f individual, group, or organizational performance, based on an appraisal

system. These types of bonus incentive schemes are based on the idea that work

output, determined by some kind of measuring system, varies according to effort and

that the prospect of increased pay will motivate improved performance.”5

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There are three methodologies for P4P programs: competitive bonus payment;

payment at risk; and quality tiered networks. Competitive bonus payments are

awarded to top performers in a group of providers and bottom performers may or may

not receive less compensation. In payment at risk models, a percentage o f revenue is

withheld by the payor until a review of quality scores is conducted. Providers who do

not meet quality targets lose the percentage at risk. In quality-tiered networks,

consumers are incented to select high quality providers by offering discounted co

payments. Consumers who prefer lower scoring hospitals on quality measures must

pay higher co-payments.6 Reimbursement is allocated based upon providers’ scores on

specific quality metrics as identified by the particular P4P program.

There are a few dominant P4P programs in the US for hospitals. The Premier

Hospital Quality Incentive Demonstration is a true P4P program, where top tiered

providers are paid higher reimbursement rates and bottom tiered providers are paid

lower reimbursement rates. However, the Premier Hospital Quality Incentive

Demonstration is limited to Premier, Inc. hospitals.

Another program geared towards hospitals is the Centers for Medicare &

Medicaid Services (CMS) Hospital Quality Alliance program. Section 501(b) of the

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

established a financial incentive for hospitals to report on the quality of inpatient care

they provide to patients.7 Hospitals began voluntarily reporting this data to CMS by

July 1, 2004. Every hospital in the US has the ability to participate. The Hospital

Quality Alliance pays hospitals that do not report quality data to CMS lower

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reimbursement rates than hospitals that do provide performance data. This program is

currently a pay-for-reporting reimbursement system, but is amalgamating into a pay-

for-performance reimbursement system over time.8

The literature suggests that if you measure something, it will improve.9,10 This

notion has also been substantiated through some of the P4P literature. A literature

review showed that more often than not, P4P programs had their intended effect of

* • 11 12 13improving scores on the measures that dictate payment. ’ ‘ However, literature is

sparse on whether outcomes were actually improved through these P4P programs.

“Despite the proliferation of pay-for-performance programs, they are largely

untested.”14

This research’s first aim is to determine whether P4P leads to improved

process measure scores. The second aim of the research is to determine whether

increased process measure scores lead to improved outcomes within the context of a

P4P program. In other words, the second study aim tries to answer the question of

whether overall quality o f care (as evidenced through outcomes) is improved while

process measure scores are improved or whether the effect o f P4P is a zero sum

game.15 In a zero sum game situation, the process measures that are rewarded through

P4P may improve but the overall quality of care (outcomes) remains the same because

other measures affecting outcomes that are not tied to reimbursement have decreased.

It is possible for limited resources to be reallocated to the performance measures that

impact reimbursement at the potential detriment to the overall quality o f care

provided.

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This study will analyze the change in eight P4P indicators for acute myocardial

infarction (AMI) patients before and after the Hospital Quality Alliance was

implemented and will analyze correlations between these measures and AMI patient

outcomes (mortality). Data from five hospital campuses within the Scripps Health

system in San Diego, California from January 1, 2003 to December 31, 2005 was used

to conduct this research.

B a c k g r o u n d

Sub-Optimal Health Care Quality

The US performs more poorly than most countries in many measures o f health

care quality.16 The reports o f substandard healthcare are numerous. Haley et al

estimated that two million patients suffer hospital-acquired infections each year.17

While patients may believe that they are coming to a hospital to have their sickness

cured or managed, some patients’ conditions become worse in the hospital.

An article by Robert Langreth reported that three percent or more o f hospital

patients are hurt by medical errors and that one in 300 patients die from such mistakes.

Comparing this figure to the fact that in US aviation only one in five million flights

ends in a deadly accident is disturbing. Langreth further notes that 24% of people say

they or a family member have been harmed by a medical error. The same article

reports that 90,000 people die o f hospital-acquired infections annually and that more

than half of these deaths may be preventable. In addition, 180,000 elderly outpatients

die or are seriously injured by drug toxicity, where half of these incidents may be

4


preventable.18

According to the Institute of Medicine’s report To Err is Human: Building a

Safer Health System, “when extrapolated to the over 33.6 million admissions to US

hospitals in 1997, the results of [a] study in Colorado and Utah imply that at least

44,000 Americans die each year as a result of medical errors. The results of the New

York Study suggest the number may be as high as 98,000. Even when using the lower

estimate, deaths due to medical errors exceed the number attributable to the 8th-

leading cause o f death. More people die in a given year as a result of medical errors

than from motor vehicle accidents (43,458), breast cancer (42,297), or AIDS

(16,516).”2 The 2003 National Committee for Quality Assurance (NCQA) report titled

“The State o f Health Care Quality” estimates that there are more than 57,000 deaths

per year attributable to failure to deliver recommended care.19 Regardless o f the exact

number of avoidable deaths, the projected numbers are astounding and provide

impetus for healthcare delivery reform.

Even if hospitals/physicians are not contributors to further illness, healthcare

professionals may not be treating patients as well as they could. McGlynn et al

determined that patients only receive 54.9% of recommended care overall and that

quality can range from receiving 78.7% of recommended care for patients with senile

cataracts to 10.5% of recommended care for patients with alcohol dependence.

Specifically for heart attack patients, W oolf determined that 39% to 55% of patients

did not receive needed medications which resulted in 37,000 avoidable deaths.21 There

are numerous studies like these that aim to quantify not only unnecessary morbidity

5


and mortality but a lack o f ideal healthcare being currently provided in the US. While

researchers may not be in agreement about how wide the gap or chasm is between

current healthcare and ideal healthcare, there is little debate over the fact that a gap

does exist.

United States’ Reimbursement Systems for Hospitals

The past and current healthcare payment systems have created incentives to

overutilize services (e.g. FFS) and underutilize service (e.g. capitation). Current

reimbursement systems expect high quality care rather than pay for it.

Medicare reimburses hospitals on a prospective basis. Each patient case is

categorized into a diagnosis related group (DRG). One of the important components

that determines Medicare’s payment rate is cost of care as determined by hospital cost

reports. Medicare uses a cost-based reimbursement system, where theoretically higher

cost services should receive higher payment in future years. In some instances,

Medicare’s reimbursement system actually rewards poor care, for example, if a patient

acquires an infection during admission, reimbursement may be higher than if the

patient had not acquired a nosocomial infection.4

Another example o f Medicare’s reimbursement system acting as a disincentive

to provide better care is in the case o f a patient needing multiple vessel percutaneous

coronary intervention. Flospital reimbursement is higher if a patient receives one drug-

eluting stent at one time and then receives a second drug-eluting stent during a second

procedure at least a few days later. If the patient has both stents deployed during the

6


same procedure the quality of care for the patient is better due to a reduced risk of

complications and less recovery time, however, the staging o f stent deployment has

been determined to be one of the negative consequences of the low reimbursement rate

for angioplasty using drug-eluting stents.22

Fee-for-service payment methodologies reimburse higher rates for services

with higher charges. Fee-for-service reimbursement may therefore incent providers to

furnish more services than necessary to reap greater financial reward.

Capitation and case rate payments put a hospital at risk. A lump sum is

prospectively paid to a hospital for coverage of a patient’s care for a certain period of

time. If the cost of providing care to that patient is less than the payment received,

then the hospital makes a profit. If the cost o f care is greater than the payment

received, then the hospital loses money. Hence, capitation and case rate payments

result in increased profit to providers if fewer services are rendered. These

reimbursement methods can create an incentive towards underutilization o f diagnostic

and treatment services.

Quality Metrics

Organizations such as the Agency for Healthcare Research and Quality

(AHRQ) and the National Quality Forum (NQF) develop quality metrics by reviewing

evidence-based literature for clinical conditions. When there is a very high degree of

consensus regarding metrics that positively correlate with improved clinical outcomes,

the organization develops a proposal metric. Various constituents and the general

7


public (through posting on the organizations’ websites) are asked to give feedback

upon proposed metrics until the organization decides that the metric is ready for

“finalized” form or whether the metric is too controversial to use. AHRQ’s National

Quality Measures Clearinghouse then tracks the use of the measure, the extent of

measure testing that has been conducted, and the evidence for reliability/validity

testing.23

There are a variety o f categories of quality metrics: structural measures;

process measures; outcome measures; access to care measures; and experience

measures. Most quality metrics for clinical conditions are related to process rather than

clinical outcomes. The reason that process measures are often chosen is because o f the

feasibility o f collecting process measures and because these process measures have

been deemed through evidence-based medicine to be positively correlated with

improved outcomes.

Premier Hospital Quality Incentive Demonstration

The Premier Hospital Quality Incentive Demonstration is a P4P program

between CMS and Premier, Inc. Premier, Inc. is a collaborative o f not-for-profit

hospitals across the nation, of which, a total of 274 hospital members have chosen to

participate in the Demonstration project.

Each hospital reports inpatient performance data on 34 quality measures in the

clinical areas of heart attack, heart failure, pneumonia, coronary artery bypass graft

(CABG), and hip and knee replacements. Top performers in each clinical condition

8


(top 50%) are recognized as such at the website www.cms.hhs.gov. Additionally, the

top two deciles o f performers are given a financial bonus by CMS. By the third year of

the Demonstration, hospitals that were in the bottom two deciles of performance in

year one receive lower reimbursement rates from CMS if their performance has not

increased from the baseline level. For more information, see Attachment I.

Hospital Quality Alliance

The Hospital Quality Alliance is a collaboration of the CMS, the American

Hospital Association, the Federation of American Hospitals, and the Association of

American Medical Colleges and is a component o f the larger CMS Hospital Quality

Initiative (see Attachment II). The goal o f the Hospital Quality Alliance is “to improve

the quality of care provided by the nation’s hospitals by measuring and publicly

reporting on that care.”

The Hospital Quality Alliance is a voluntary initiative. However, hospitals that

currently choose not to participate receive a market basket minus 2.0% reimbursement

from CMS. Hospitals that do participate submit data on various process measures

which then get reported on a public website www.hospitalcompare.hhs.gov. The

public posting of scores is expected “to improve the quality of care and the ability of

consumers to make informed healthcare choices.” 24 The Hospital Compare website

currently reports 21 measures across the following clinical conditions: heart attack;

heart failure; pneumonia; and surgical infection prevention. Hospitals participating in

the Hospital Quality Alliance submitted their first data by July 1, 2004 on a “starter

9


http://www.cms.hhs.gov

http://www.hospitalcompare.hhs.gov

set” of 10 measures. The 21 measures currently posted on Hospital Compare include

the “starter set” and are scheduled to continue to grow over time.

Scripps Health

Scripps Health is a private, not-for-profit, community-based health care

delivery network that includes four licensed acute-care hospitals located on five

campuses in San Diego County. There is a distance of three to 41 miles between each

campus.

Scripps was founded in 1924 by Ellen Browning Scripps. The flagship

hospital, Scripps Memorial Hospital La Jolla, has 372 licensed beds and is one of the

county’s six designated trauma centers and the only Magnet Hospital in San Diego

County. Scripps Memorial Hospital La Jolla’s payor mix is approximately 28%

Medicare, 3% MediCal, 60% commercial, 6% other governmental payors and self pay

(including no pay), and 3% other (including workers compensation).

Scripps Memorial Hospital Encinitas joined the Scripps Health system in 1978.

This hospital provides services to patients located in San Diego’s North County and

has 140 acute-care licensed beds. Scripps Memorial Hospital Encinitas’s payor mix is

approximately 38% Medicare, 17% MediCal, 38% commercial, 6% other

governmental payors and self pay, and 1 % other.

Scripps Green Hospital joined the Scripps Health system in 1991. Scripps

Green Hospital has 173 acute-care licensed beds. Scripps Green Hospital is unique

because it shares its campus with Scripps Clinic, which houses a large medical group,

10


so all physicians who admit patients at Scripps Green Hospital are part o f the Scripps

Clinic Medical Group. Scripps Green Hospital’s payor mix is approximately 54%

Medicare, 0% MediCal, 41% commercial, 3% other governmental payors and self pay,

and 2% other.

Scripps Mercy Hospital has two campuses, one in the Hillcrest area and one in

Chula Vista. The Scripps Mercy Hospital San Diego (Hillcrest) location of Scripps

Mercy Hospital was founded in 1890 and is San Diego’s oldest hospital as well as its

only Catholic medical center. It joined the Scripps system in 1995. Scripps Mercy

Hospital San Diego has 700 licensed beds. In October, 2004 Scripps Mercy Hospital

expanded to include Scripps Mercy Hospital Chula Vista which has another 183 acute-

care licensed beds. Because of its locations and charitable mission, Scripps Mercy

Hospital renders a lot of uncompensated care as evidenced by the high percentage of

other governmental and self pay patients which includes no-pay patients. Scripps

Mercy Hospital San Diego’s payor mix is approximately 31% Medicare, 22%

MediCal, 32% commercial, 12% other governmental payors and self pay, and 3%

other. Scripps Mercy Hospital Chula Vista’s payor mix is approximately 39%

Medicare, 28% MediCal, 17% commercial, 16% other governmental payors and self

pay, and 0% other.

The Scripps Health system employs over 10,000 individuals, is affiliated with

over 2,600 physicians and operates 11 clinics, an ambulatory surgery center, a home

health center, and various other support services.

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L it e r a t u r e R e v ie w

Process and Outcomes Measures

Hospital performance measures began being collecting in the 1850’s by

Florence Nightingale. Nightingale collected data on the number of deaths per 1,000

sick patients before and after commencement of sanitary improvements were

conducted. The resulting data suggest that sanitary improvements may have been the

cause o f the drastic reduction in the number o f observed deaths during that time.25

Collecting performance data gives individuals the knowledge with which to make

improvements and then monitor the success o f the improvement activities.

Some performance metrics measure outcomes, such as those used by Florence

Nightingale, and others measure process. While the goal of performance improvement

activities is often to improve patient outcomes, if outcomes alone are measured, it may

be difficult to determine which activities should be implemented to affect a change in

outcomes. When certain processes are determined to be causal factors of improved

outcomes, the tracking of these process measures can more easily guide providers to

the activities on which they should focus. For example, Tu and Cameron conducted a

survey of physicians at Ontario hospitals about whether acute myocardial infarction

‘report cards’ were useful for assessing and improving the quality of care. Survey

respondents noted that process of care measures such as post-infarction beta-blocker

and angiotensin-converting enzyme inhibitor use, and cardiac procedure waiting times

were the most useful data, and outcomes data (e.g. 30-day and one-year risk adjusted

AMI mortality rates) the least useful of the many performance measures published in

12


the report card.26 If process measures are indeed correlated with outcomes, then

knowing ones scores on these measures can guide improvement activities which

should result in improved outcomes.

Unfortunately, not all process measures are good predictors of outcomes. A

study by Griffith, Knutzen, and Alexander compared structure and process measures

used by the Joint Commission on Accreditation of Healthcare Organizations (JCAHO)

to hospital performance measures derived from Medicare. The results of the study

indicated that JCAHO’s measures were generally not correlated with outcome

measures.27

Other studies have tried to determine whether collecting hospital performance

measures improve quality of care, however, these studies do not address outcomes.

For instance, a Williams et al article notes that after JCAHO implemented

standardized performance measures, consistent improvements in process o f care

measures for AMI, heart failure, and pneumonia were observed over a two-year

period. Hospitals were successful in improving process measures, however, the

authors o f this study did not determine whether improved process measures scores

were correlated with improved outcomes. Improving outcomes is the intended goal of

these metrics but is often assumed rather than verified.

Acute Myocardial Infarction Hospital Process Measures

Evidence-based medicine suggests that the eight process measures for acute

myocardial infarction (AMI) used in the Hospital Quality Alliance are correlated with

13


improved outcomes. According to the AHRQ’s National Quality Measures

Clearinghouse, each of the eight AMI process measures may lead to reduced mortality

and morbidity, yet is underperformed or underutilized with AMI patients . 2 9

1. Percent o f patients without aspirin contraindications who received aspirin within 24

hours before or after hospital arrival and

2. Percent o f patients without aspirin contraindications who are prescribed aspirin at

hospital discharge

A number o f meta-analyses have been conducted that show that patients who

have had an AMI or who had had acute or prior vascular disease have reduced

mortality and incidence of stroke and recurrent MI when the patient is treated with

• • 30 ,31 ,32,33aspirin. ’ ’ ’

The American College of Chest Physicians recommend in their guideline for

thrombolysis and adjunctive therapy in AMI treatment, that patients with AMI/ST-

elevated myocardial infarction (STEMI) be given aspirin at the initial healthcare

evaluation and then indefinitely thereafter. 3 4 This recommendation received a Grade

1 A which means that the magnitude of benefits, risk, burdens, and costs is certain and

that randomized controlled trails on the subject generate consistent results.

The American College o f Cardiology/American Heart Association

(ACC/AHA) recommends that patients with STEMI receive aspirin for initial

T Streatment, for ongoing treatment, and for secondary prevention. All three of these

14


recommendations are rated as Class I which denotes situations in which interventions

are effective or useful based on evidence and/or consensus.

The European Society o f Cardiology also agrees that patients with AMI should

receive aspirin as noted in their recommendations regarding platelet inhibitor therapy

in patients with AMI . 3 6 They gave this recommendation a rating o f Grade 1 indicating

that it is a situation in which the benefits of the intervention clearly outweigh the

burden, costs, and risks.

3. Percent of patients without beta blocker contraindications who received a beta

blocker within 24 hours after hospital arrival and

4. Percent of patients without beta blocker contraindications who are prescribed a beta

blocker at hospital discharge

There have been many meta-analyses that have concluded that patients with an

37 33 38 39AMI who receive beta blockers have a reduced risk o f mortality. ’ ‘ 7 Meta

analyses have also determined that beta-blockers are effective for the secondary

prevention of coronary events . 3 9 ,4 0

The ACC/AHA recommends with a Class I rating that patients with STEMI

receive prompt administration of oral beta-blockers unless contraindicated. The

ACC/AHA also recommends with a Class I rating that beta-blockers should be

continued indefinitely unless contraindicated for secondary prevention management of

patients with STEMI. 3 5

15


The European Society of Cardiology agrees that patients with STEMI receive

beta-blockers for acute treatment, to prevent reinfarction, to improve survival, and for

primary prevention of sudden cardiac death . 4 1 Both recommendations for initial

treatment and secondary prevention received a Class I rating.

5. Percent o f patients with left ventricular systolic dysfunction (LVSD) and without

both angiotensin converting enzyme inhibitor (ACEI) and angiotensin receptor blocker

(ARB) contraindications who are prescribed an ACEI or ARB at hospital discharge

Meta-analyses by Latini et al and the ACE Inhibitor Myocardial Infarction

Collaborative Group both determined that early ACEI therapy reduced 30-day

mortality compared to those who received a placebo 4 2 ,4 3 A meta-analysis by

Domanski et al also showed that ACEIs given in patients with AMI reduced mortality,

cardiovascular death, and sudden cardiac death as compared with patients who

received a placebo . 4 4

Lee et al conducted a meta-analysis which showed that for patients with high-

risk AMI, the use of ARBs compared with the use o f ACEIs made no difference in

mortality rates 4 5

The ACC/AHA recommends that patients with STEMI should be prescribed an

ACEI at the time o f hospital discharge for long-term management unless

contraindicated. The ACC/AHA also recommends that patients with STEMI with left

ventricular ejection fraction (LVEF) of less than 40% or patients who have clinical or

radiological signs o f heart failure and who are intolerant of ACEIs should be

16


prescribed an ARB at the time of hospital discharge for long-term management. 3 5 Both

o f these recommendations received a Class I rating.

Similarly, the European Society o f Cardiology made the Class I

recommendation that patients with AMI beyond the first 24 hours who have left

ventricular dysfunction (defined as LVEF less than 45%) or overt heart failure should

receive an ACEI . 4 6

6 . Percent of patients with history o f smoking cigarettes who are given smoking

cessation advice or counseling during the hospital stay

A meta-analysis by Wilson et al determined that smoking cessation reduces

mortality in patients who have had a myocardial infarction (MI) . 4 7 Similarly, a meta

analysis by Critchley and Capewell determined that patients with coronary artery

disease (CAD) who quit smoking have a lower risk of death as compared to patients

with CAD who continue to smoke . 4 8

A Houston et al retrospective analysis showed that compared with those who

did not receive smoking cessation counseling, smokers who did receive inpatient

counseling had lower rates of 30-day, 60-day, and two-year mortality . 4 9 A

retrospective analysis conducted by Rea et al determined that for patients who have

had an MI, smoking was associated with an elevated risk for recurrent coronary

events . 5 0 Further, a guideline developed by the European Society o f Cardiology notes

that the most effective o f all secondary prevention measures for patients who have had

a STEMI is smoking cessation 4 1

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7. Percent of patients receiving percutaneous coronary intervention (PCI) during the

hospital stay with time from hospital arrival to PCI of 120 minutes or less

Multiple meta-analyses have showed that in patients with an AMI, primary

PCI reduces short-term mortality, reinfarction, recurrent ischemia, and stroke

compared to patients who receive thrombolysis . 5 1 ,5 2 ,5 3

Zeymer et al determined that in patients with AMI undergoing primary PCI

who have cardiogenic shock, a longer symptom onset to PCI time is associated with

increased mortality . 5 4 A nonrandomized prospective study by Cannon et al showed

that for patients with AMI undergoing primary percutaneous transluminal coronary

angioplasty (PTCA) (a form of PCI), a door-to-balloon inflation time o f greater than

two hours is associated with increased in-hospital mortality . 5 5

Until 2007, the Hospital Quality Alliance measured compliance with PCI

within 1 2 0 minutes from hospital arrival, however, the standard has since changed to

measuring PCI within 90 minutes of hospital arrival. Guidelines such as those

developed by the ACC/AHA recommend that patients with STEMI undergoing

primary PCI should have the procedure performed as quickly as possible, aiming for a

door-to-balloon time of 90 minutes or less . 3 5

8 . Percent of patients receiving thrombolytic therapy during the hospital stay and

having a time from hospital arrival to thrombolysis of 30 minutes or less.

Meta-analyses conducted by Lau et al and Antman et al both show that the use

of thrombolytic therapy is associated with a reduction in mortality for patients with

18


33 39 • •AMI. ’ The Fibrinolytic Therapy Trialists’ Collaborative Group also found a large

mortality reduction between days two to 35 for patients receiving thrombolysis . 5 6

The ACC/AHA and the European Society of Cardiology both give Class I

recommendations for the use o f thrombolytics unless contraindicated for STEMI

patients . 3 5 ,4 1

The American College of Chest Physicians recommends that for patients who

receive fibrinolytic therapy, the goal should be 30 minutes from hospital arrival or first

contact with the patient until administration . 3 4

Pay-for-Performance

P4P programs are relatively new in the healthcare industry, although they are

rising in number. While there is growing interest in this area, there is little published

research on P4P in health care and there are only a few studies which demonstrate that

P4P leads to improved quality of care . 5 7 Most articles on P4P are explanatory. There

are not many research studies that show the rates o f compliance with quality indicators

before and after P4P programs. In fact, Dudely et al conducted a literature search on

performance-based payment in the healthcare industry and only identified eight

randomized controlled trials published on this topic. “The eight trials o f performance-

based payment were neither consistent in their design of the independent variable (the

financial incentive offered) nor comparable in terms of their dependent variable (the

performance indicator measured ) . ” 11 Dudley et al found that all of these randomized

controlled trials were aimed to incent individual physicians, a group of providers, or

19


pharmacists. In these eight studies with a total o f ten dependent variables, six

dependent variables were “positive” which means that there was an effect in the

desired direction, and four were negative . 11 These studies show that there is no clear

consensus as to whether P4P is successful in achieving its desired outcomes.

A more recently published review of empirical studies on financial incentives

designed to improve healthcare by Petersen et al affirmed the Dudley et al findings

that P4P generates positive yet inconsistent desired effects. In the Petersen et al

analysis of 17 eligible studies addressing financial incentives’ effect on quality of care,

none of the studies were based on hospital performance. Five o f the six studies of

physician-level financial incentives, seven of the nine studies of provider group-level

financial incentives, and one of the two studies o f the payment system-level financial

incentives found partial or positive effects on process or access measures. Four studies

58suggested unintended effects o f incentives.

Other non-randomized controlled studies about P4P programs geared towards

physicians have shown to have some benefits. One study noted that P4P helped the

health plan Wellpoint improve immunization rates from 21% to 58% and pap smear

rates from 79% to 85%.59 A New York P4P program showed that financial incentives

for physicians coupled with other care management tools led to improved scores on

five out of six process measures and two out o f three outcome measures . 6 0 The

Integrated Healthcare Association’s P4P program for California physicians rates

physicians on three domains: clinical measures; patient experience; and information

technology (IT) adoption. Results from year two compared to year one show that 87%

20


of physician groups improved their clinical measure scores by an average of 5.3%, that

65% of physician groups improved their patient experience average performance, and

that 34% of physician groups who reported no IT capability in 2003 received partial or

10full credit for IT adoption in 2004. Year three results also showed increases in the

number of patients receiving cervical cancer screenings, diabetes tests, and childhood

immunizations compared to year two . 61

There are not as many articles about how well P4P works in the hospital

setting. One such study, the Premier Hospital Quality Incentive demonstration,

showed that this P4P program accomplished its intended results. “Quality of care

improved in all of the five clinical areas for which quality was measured. Composite

quality scores improved between the first and last quarter o f the first year o f the

demonstration: from 87% to 91% for patients with acute myocardial infarction (heart

attack); from 65 to 74% for patients with heart failure; from 69% to 79% for patients

with pneumonia; from 85% to 90% for patients with coronary artery bypass graft; and

13from 85% to 90% for patients with hip and knee replacement.”

A study in China, while structured differently than the P4P programs aimed

towards improving quality, found that P4P contributed significantly to the increase in

hospital service revenue and hospital cost recovery, which were the aims of the

project. However, this program also showed that when the bonus system switched

from a weaker incentive to a stronger one, there was an increase in unnecessary care . 6 2

Not all reviews of P4P programs conclude that the program has produced its

intended benefit. A study by Rosenthal, Frank, Li, and Epstein concluded that “paying

21


clinicians to reach a common, fixed performance target may produce little gain in

quality for the money spent and will largely reward those with higher performance at

baseline . ” 5 7 In an article by Sipkoff, American Medical Association Secretary John H.

Armstrong, MD took a harder stance, although it was not supported by any data,

stating that “some so-called pay-for-performance initiatives are a lose-lose proposition

for patients and their doctors. The only benefit is to health plans. Done right, these

programs can improve medical care; done wrong, they can harm patients.”

Furthermore, a review of empirical literature conducted by Rosenthal and Frank

concluded that there is little evidence to support the effectiveness o f paying for quality

in healthcare . 6 4

The research that has been conducted thus far on pay-for-performance

programs for healthcare providers does address whether scores on P4P measures have

improved. However, studies that look at whether the improved scores on the P4P

process measures do indeed result in improved outcomes have been scarce. The

assumption is that based upon evidence-based literature, outcomes should improve,

but research on P4P programs has not conclusively proved that assumption to be

correct.

Fonarow et al used the ACC/AHA performance measures to test the

relationship between heart failure P4P metrics and outcomes. They found that none of

the five performance measures was significantly associated with reduced early

mortality risk and only one measure was associated with 60 to 90 day post-discharge

mortality or rehospitalization. The authors concluded that additional measures and

22


better methods for identifying and validating heart failure performance measures may

be needed to improve care of heart failure patients . 6 5

The evidence shows that improvements on the eight AMI process measures

used by the Hospital Quality Alliance should result in improved outcomes. However,

the evidence for this assumption was not gathered from P4P programs. If everything

else is held constant and process measures improve, outcomes improve according to

the literature. However, what if other factors are not held constant? Pay-for-

performance models financially reward scores on certain measures. By rewarding

some quality indicators and not others, one can assume that the measures that are

rewarded may increase, but one cannot assume that the measures that are not rewarded

will remain constant. If some quality measures that are not rewarded decrease because

of the increased emphasis and allocation o f resources on the measures that are

financially rewarded, then what effect does that decrease have on overall quality? The

question remains unanswered as to whether the net effect of P4P programs is positive

(i.e. decreased mortality/increased outcomes) or whether the net affect o f P4P

programs is constant or is correlated with worse outcomes.

H y p o t h e s e s

Specific Aim 1 - To determine the relationship between participation in a pay-for-

performance program and scores on process measures.

23


While a literature review suggests that P4P programs generally result in their

intended outcomes, it is not consistently the case. My research aims to determine

whether AMI process measure scores improved due to participation in P4P programs.

Hypothesis 1 a): Process measure scores will not improve due to

participation in pay-for-performance.

Hypothesis 1 b): Process measure scores will improve due to

participation in pay-for-performance.

Specific Aim 2 - To determine whether pay-for-performance programs succeed in

improving the overall quality o f patient care as evidenced through the outcome

measure o f mortality.

While evidence-based medicine shows that improved scores on process

measures can improve patient outcomes, the data collected thus far on pay-for-

performance programs only suggests that pay-for-performance programs improve

scores on process measures. My research aims to extend the analysis to whether

improved scores on process measures indeed improve clinical outcomes within the

context of hospitals participating in pay-for-performance programs.

Pay-for-performance programs present incentives to providers to focus on the

measures that lead to increased reimbursement. There has been little research

conducted on whether unintended consequences o f P4P negatively impact patient

outcomes. My research on patient outcomes also aims to shed light on whether

focusing resources on the P4P measures results in decreased quality of care in non-

24


measured indicators which outweigh the positive benefits o f P4P programs or whether

P4P programs improve the overall quality of care through increased attention on

performance measures. If patient mortality increases through P4P, one should

investigate the former conclusion and if patient mortality decreases through P4P, one

should investigate the latter conclusion.

Hypothesis 2 a): Pay-for-performance programs will not improve

clinical outcomes.

Hypothesis 2 b): Pay-for-performance programs will improve clinical

outcomes.

C o n c e p t u a l M o d e l

Both aims of this study can be addressed through the same conceptual model.

The conceptual model is depicted in Attachment III. The conceptual model shows that

there are a number of factors that may affect outcomes for patients with acute

myocardial infarction. There are six main factors that may contribute to mortality rates

after an AMI: increased P4P scores on process measures; patient demographics;

hospital characteristics; patient’s medical condition; treatment; and patient behavior.

The belief is that all six factors can have a direct effect on patient outcomes.

There are many potential interactions between the six factors contributing to

outcomes. Patient demographics can affect patient behavior, treatment, and patient’s

medical condition. Hospital characteristics can affect treatment and pay-for-

performance process measures. A patient’s medical condition can affect treatment and

25


patient behavior. Patient behavior can affect treatment and a patient’s medical

condition.

The conceptual model elucidates the fact that there are many interrelated

components affecting outcomes, which is why it is essential to hold as many of these

factors possible constant in order to determine how increases/changes in P4P scores

alone affect patient outcomes.

D a t a

Main Analysis

Patients with acute myocardial infarctions seen at Scripps Memorial Hospital

Encinitas, Scripps Memorial Hospital La Jolla, Scripps Green Hospital, Scripps Mercy

Hospital San Diego, and Scripps Mercy Hospital Chula Vista from the time period

January 1, 2003 to December 31, 2005 are all included in this study. July 1, 2004 is

the date that the Scripps hospitals began participating in the CMS Hospital Quality

Alliance (HQ A) pay-for-reporting program, therefore, there are 18 months of data

from before the initiative began and 18 months o f data from after the initiative started.

The total patient population is 1,924 patients before participation in the Hospital

Quality Alliance and 2,030 patients after participation began, for a total of 3,954

patients included in the study. [Of note is the fact that data is not available from

Scripps Memorial Hospital Encinitas from January, 2003 to June, 2004. During that

timeframe Encinitas used the National Registry o f Myocardial Infarction (NRMI)

electronic application to track clinical information such as aspirin or beta blockers at

26


arrival instead of MIDAS+. The older NRMI data could not be located, therefore, it

was not included in this analysis.]

Scripps Health patient level data from its participation in the Hospital Quality

Alliance is being used. While the HQA is a pay-for-reporting (P4R) program, it is the

precursor to a P4P program beginning on October 1, 2008. While not a true P4P

program currently, since it is well known in the industry that it is moving towards a

P4P program, hospitals are trying to achieve high scores to well position themselves

for the future. Therefore, HQA data is used as an approximation for future P4P

keeping in mind that P4P results may be greater/different than that through P4R. For

example, a study by Lindenauer et al found that performance on P4P metrics improved

with both P4R and P4P programs, however, hospitals participating in P4P improved

their scores more than hospitals participating in P4R . 6 6 It is expected that directional

effects of P4R and P4P will be the same but that the magnitude o f the impact on

process measure scores and outcomes may be different.

Additional Analysis

An additional analysis was conducted using nation-wide hospital data. For the

additional analysis, data on compliance with seven AMI indicators was used. [The

indicator for PCI within 120 minutes was excluded due to inconsistency of data

collected, as one time period reported PTCA within 90 minutes instead o f PCI within

120 minutes.] An indicator was also added to denote an overall process measure score.

This indicator is called “Weighted Average Score” and was calculated by adding up

27


all the numerators for the seven individual measures and dividing that number by the

sum of all the individual measures’ denominators. Premier, Inc. hospitals’ data,

Scripps Health hospitals’ data, and other national Hospital Quality Alliance

participants’ data are included in this analysis. Observations were conducted over

various timeframes between October 1, 2002 and June 30, 2005.

Data from 10/02-9/03 (before the Premier Hospital Quality Incentive

Demonstration) was collected from the 54 Premier, Inc. hospitals participating in the

baseline data collection phase of the Demonstration. These 54 Premier, Inc. hospitals

represent 70,860 patients. Data from five Scripps Health facilities over the same time

period representing 4,511 patients was also used. (Scripps Memorial Hospital La Jolla

data was unable to be obtained from 1 0 /0 2 - 1 2 /0 2 , therefore, it was excluded from the

analysis.)

Another observation was conducted from 1/04-6/04 (after the Premier Hospital

Quality Incentive Demonstration and before the Hospital Quality Alliance). Data was

collected from 54 Premier, Inc. hospitals representing 37,422 patients for this time

frame. Data from five Scripps Health facilities over the same time period from 1/04-

6/04 representing 2,388 patients was also collected.

One more time frame of data was collected from 7/04-6/05 (after HQA). Data

from the same 54 Premier, Inc. hospitals representing 71,914 patients was collected as

well as data from the same five Scripps Health facilities representing 3,890 patients. In

addition, data from 4,180 other US hospitals (excluding the five Scripps Health and 54

Premier, Inc. facilities) participating in HQA representing 1,707,557 patients was

28


collected for the time period 7/04-6/05. [Note that additional Premier, Inc. hospitals

joined the Premier Hospital Quality Incentive Demonstration over time. Fifty-four

hospitals participated in the program from the time of baseline data collection and

those were the facility scores used for this analysis.]

D a t a S o u r c e s

The majority o f the data used for this research is gathered from the Scripps

Health electronic data systems, MIDAS+ and TRENDSTAR. See Table 1. Scripps

Health uses MIDAS+ for quality and outcomes reporting and maintenance.

Information compiled from chart reviews for the P4P measures used in this study is

stored in the MIDAS+ system. TRENDSTAR is another electronic database which is

used by Scripps Health for primarily financial purposes. TRENDSTAR houses both

clinical and financial data. The same patients are in both the MIDAS+ and

TRENDSTAR systems.

Table 1: Variable SourcesData Source Variables

MIDAS+ • Alive/dead status at discharge (and post-discharge for patients who have been readmitted)• Aspirin at Arrival• Aspirin at Discharge• Beta Blocker at Arrival• Beta Blocker at Discharge• ACEI or ARB for LVSD• Adult Smoking Cessation Advice/Counseling• PCI Received within 120 Minutes• Thrombolytic Agent Received within 30 Minutes o f Arrival

MIDAS+ or TRENDSTAR •A g e

29


• Race• Gender• Martial Status• Religion• Facility• Payor - Access to Care Proxy• Primary Care Physician - Access to Care Proxy• Coronary Artery Disease (CAD)• Prior Myocardial Infarction• Family History o f CAD• Dyslipidemia• Diabetes• Hypertension• Obesity• Depression• Smoking Status within last 12 months• Coronary Artery Bypass Graft (CABG) Surgery• Other Open Heart Surgery• Angioplasty / PCI Treatment• Thrombolysis Treatment• Other Primary Cardiac Procedures (Diagnostic or Treatment)• No Cardiac Treatment• Readmissions within 30 days

Social Security Death Index If patient is not classified as deceased in Scripps Health electronic records:• Date of Death

Interviews / Misc. Databases • Hospital Paid Full Time Employees (FTE) per Adjusted Occupied Bed• Hospital Rapid Response Team Available• Hospital Chest Pain Center Available• Hospital Cardiovascular Award (as determined by Solucient, U.S. News & World Report, etc.) during Year of Visit• Total Hospital Annual AMI Volume• Average Cardiologist Annual AMI Volume• Annual Hospital AMI Admissions per ICU Beds• Hospital Payor Mix• Teaching Hospital Status (as evidenced by a Graduate Medical Education program)• Surgical Back-up Available

30


The Social Security Death Index was used as one resource to determine post

discharge mortality of patients in this study. The Social Security Death Index is

populated by “the Death Master File (DMF) from the Social Security Administration

(SSA). The database currently contains over 79 million records. The latest update used

for this analysis reflects the most current information provided by the SSA for deaths

through September 30, 2006. The file is created from internal SSA records o f deceased

persons possessing social security numbers and whose deaths were reported to the

SSA. Often this was done in connection with filing for death benefits by a family

member, an attorney, a mortuary, etc. Each update of the DMF includes corrections to

old data as well as additional names. [NOTE: If someone is missing from the list, it

may be that the benefit was never requested, an error was made on the form requesting

the benefit, or an error was made when entering the information into the SSDI. ] ” 6 7 The

Social Security Death Index is also used by FlealthGrades, an independent healthcare

rating company, to determine 30-day post discharge and 180-day post discharge AMI

mortality rates . 6 8

D a t a E l e m e n t s

Table 2 lists all the variables included in the model under the Empirical

Variable column and also links those variables to the theoretical variables listed in the

conceptual model (Attachment III). Table 2 describes the predicted effect on outcomes

31


that each variable may have. The following section describes all variables in more

detail.

Table 2: Conceptual Domains, Theoretical Variables, Empirical Variables, and Prediction on Outcome

ConceptualDomain

Theoretical Variable Em pirical Variable Prediction

Patient Outcomes(DependentVariable)

Survival Post AMI Days Survival Post AMI

Not applicable, outcome o f interest

Alive/Dead at 30 days

Readmissions Readmissions within 30 days

Hospital Pay for Performance Scores

Aspirin at Arrival Aspirin at Arrival Administration o f these process measures will decrease mortality^0'56

Aspirin at Discharge Aspirin at DischargeBeta Blocker at Arrival

Beta Blocker at Arrival

Beta Blocker at Discharge


ACEI for LVSD ACEI for LVSDSmoking Cessation Advice

Smoking Cessation Advice

PCI Received within 120 Mins

PCI Received within 120 Mins

Thrombolytic Agent Received within 30 Mins

Thrombolytic Agent Received within 30 Mins

PatientDemographics

Age Age Older age is associated with increased risk o f mortality

Race Race Life expectancy rates are better for whites than for minorities so minorities have an increased risk o f mortality69

Gender Gender Unknown direction o f effect - being male is associated with increased risk o f mortality70, however, for risk o f mortality after an AMI, studies have shown similar mortality rates across gender70

A ccess to Care Insurance Status Having regular access to care is associated with decreased risk o f mortality71

Primary Care Physician (PCP)

32


Status

Education Data Unavailable Lower education andIncome lower income are both

associated with increased risk o f mortality.

Marital Status Martial Status Single and widowed individuals are associated with increased risk o f mortality compared to married people72

Religion Religion Religion may impact treatment decisions and patient behavior which may then impact outcomes yet direction o f its effect is unknown

HospitalCharacteristics

Facility Facility The aggregation o f the hospital characteristics in each facility may impact outcomes yet direction o f its effect is unknown

Nurse Staffing Ratio Minimum Nurse Staffing Ratio is set based upon bed type. Instead used Paid FTE per Adjusted Occupied Bed

Higher nurse/staff-to- patient staffing ratios may lead to greater compliance with performance metrics and better patient outcomes

Response Team to AMI in ED / Urgent Care

Rapid Response Team Having a Rapid Response Team or Chest Pain Center may lead to

Chest Pain Center quicker treatment and better compliance with performance metrics

Center o f Excellence / Award for Heart Care

Cardiovascular Care Award Received During Year o f Visit

Being designated as a Center o f Excellence for Heart Care may motivate compliance with performance metrics to continue to achieve recognition for high quality care

Total Hospital AMI Volume

Total Hospital AMI Admissions

Having higher volumes and more experience

Average Cardiologist AMI Volume

Average Cardiologist AMI Admissions

treating AMI patients may lead to better outcomes

Level o f Technology Available

No variable included as Scripps physicians felt that instead o f technology, bed level should be measured

Having more technology available may lead to quicker diagnosis and performance o f process measures

33


Ratio o f AMI Admissions to ICU Beds

Having more beds available may lead to quicker treatment and therefore better outcomes

Hospital Profitability / Payor Mix

Payor Mix Hospitals in better financial positioning may be better equipped to provide quick and appropriate care in accordance with performance metrics. Hospitals with better payor mix may have healthier patients who may have better outcomes

Teaching Hospital Status

Teaching Hospital Status

Internal data from Scripps shows that compliance on performance metrics is greater for teaching patients

Surgical Back-Up Surgical Back-Up Having surgical back-up may lead to better outcomes i f complications arise

Patient’s Medical Condition

CAD CAD Uncertain effect - previous diagnosis o f CAD could mean that disease is more severe (increased risk o f mortality) or it could mean that the patient has a regular source o f care which is why CAD was diagnosed before the heart attack (decreased risk o f mortality)

Prior MI Prior MI A prior MI is associated with increased risk o f mortality73

Family History o f CAD

Family History o f CAD

B elie f that family history o f CAD is associated with increased risk o f mortality b/c family history o f CAD is a risk factor for heart disease74

Dyslipidemia Dyslipidemia B elie f that dyslipidemia is associated with increased risk o f mortality b/c it is associated with progression o f cardiovascular disease75

34


Diabetes Diabetes Diabetes is associated with increased risk o f mortality after AMI76

Hypertension Hypertension B elief that hypertension is associated with increased risk o f mortality b/c it is a risk factor for AMI77

Obesity Obesity B elief that obesity is associated with increased risk o f mortality b/c it is a risk factor for heart disease74

Stress / Depression Depression Depression is associated with increased risk o f mortality after AMI78

Severity o f Illness/Comorbidities

Severity o f Illness; Risk o f Mortality excluded as data field not populated in 2003

The sicker the patient (the more comorbidities) the higher the risk o f mortality

Treatment Thrombolysis; Angioplasty; CABG Surgery; Other Open Heart Surgery; Other Cardiac Diagnostic or Treatment Procedure; No Treatment

Thrombolysis; Angioplasty; CABG Surgery; Other Open Heart Surgery; Other Cardiac Diagnostic or Treatment Procedure; N o Treatment

More invasive treatment (e.g. open heart surgery) may have high o f risk o f mortality, however, sicker patients may have no treatment or more invasive treatment than healthier pts

NA Months Since Admission to Censor Date

Months since admission to censor date may impact days survival as patients who were admitted later have less possible days o f survival until censor time than patients admitted earlier in the 3 year time period

Patient Behavior Diet N o variable included B elief that a diet lacking in fruits and vegetables is associated with increased risk o f mortality b/c it is a risk factor for AMI79

Alcohol Consumption N o variable included B elief that no alcohol consumption and excessive alcohol consumption are associated with increased risk o f mortality b/c they are risk factors for heart disease74

Exercise Level N o variable included B elief that having a low exercise level is

35


associated with increased risk o f mortality b/c it is a risk factor for heart disease74

Smoking Status Smoking Status within the past 12 months

B elie f that being a current smoker is associated with increased risk o f mortality b/c it is a risk factor for heart disease74

Dependent Variables

There are three dependent variables: days survival, alive/dead at 30 days, and

readmissions within 30 days. Mortality/survival time was chosen as a dependent

variable because it is the most crude outcome measure for patients undergoing an

acute myocardial infarction. The days survival time is coded as a continuous variable,

while alive/dead at 30 days is coded as a binary variable.

Readmission rate is another outcome indicator. This variable was chosen

because mortality rates can be inflexible and unlikely to change despite interventions.

Readmissions may be a more elastic variable and therefore, readmissions within 30

days were also tracked. The readmission variable is coded as a binary variable.

Predictor Variables

The predictor variables for this analysis were all chosen due to their

hypothesized relationship to patient outcomes (i.e. the dependent variables). (See

Table 2.) Most o f these variables were collected through the Scripps Health electronic

systems MIDAS+ and TRENDSTAR, although the hospital characteristic variables

36


were collected through interviews and other databases. For more detail about how

each variable is coded, please refer to Attachment IV.

Pay-for-Performance Scores

There are nine variables related to P4P scores which comprise the regressors of

interest. There are eight individual variables: 1) the percent o f patients without aspirin

contraindications who received aspirin within 24 hours before or after hospital arrival;

2 ) the percent of patients without aspirin contraindications who are prescribed aspirin

at hospital discharge; 3) the percent of patients without beta blocker contraindications

who received a beta blocker within 24 hours after hospital arrival; 4) the percent of

patients without beta blocker contraindications who are prescribed a beta blocker at

hospital discharge; 5) the percent o f patients with LVSD and without both ACEI and

ARB contraindications who are prescribed an ACEI or ARB at hospital discharge; 6 )

the percent of patients with history o f smoking cigarettes within the past 1 2 months

who are given smoking cessation advice or counseling during the hospital stay; the

percent o f patients receiving PCI during the hospital stay with time from hospital

arrival to PCI of 120 minutes or less; and 8 ) the percent of patients without

contraindications to thrombolysis receiving thrombolytic therapy during the hospital

stay and having a time from hospital arrival to thrombolysis of 30 minutes or less and

one aggregate P4P compliance variable.

Each of the eight individual variables is coded as a binary variable with the

option o f being non-applicable for a particular patient. Therefore, the aspirin at arrival

37


indicator is coded as 1 for yes, 0 for no, and 99 for not applicable. Not applicable

codes are used in instances such as a contraindication to aspirin at arrival (e.g. aspirin

allergy, active bleeding on arrival, Coumadin/warfarin as pre-arrival medication) or

where the measure does not apply such as no thrombolysis therapy was given or the

patient is not a smoker.

In addition to the eight individual regressors o f interest, another variable titled

“All Applicable P4P Measures” was created to reflect patients who received all

recommended AMI process measures where applicable. The all applicable P4P

measures variable is coded as 1 for yes and 0 for no with no option o f not applicable.

The process for determination o f the pay-for-performance scores is as follows:

all patients identified to have an acute myocardial infarction through a discharge

diagnosis coded as 410.00 through 410.92 are included in the MIDAS+ AMI Core

Measure Study. Abstractors review each patient record for those individuals included

in the AMI Core Measure Study and determine whether the patient had a

contraindication to any o f the eight measures. If the patient did have a

contraindication, he/she was excluded from the denominator for the compliance score

for that particular indicator. Therefore, the denominators for the eight individual

measures are different from each other. After each patient’s record is reviewed for

compliance with the performance metrics, a quality assurance check is completed by

another individual internally. After data is sent from MIDAS+ to CMS for

participation in the Hospital Quality Alliance, CMS’ designated Quality Improvement

Organization (QIO) Lumetra, does their own quality assurance check on each

38


hospital’s data. If the Lumetra quality assurance review does not indicate at least 80%

congruence with the hospital data then the hospital does not receive the full Medicare

payment associated with participation in HQA. There were no instances o f data

congruence between Scripps Health facilities and Lumetra less than 80% during this

study’s time frame.

Patient Demographics

There are certain patient attributes that are difficult, if near impossible, to

change. These variables categorized as Patient Demographics often have a significant

impact upon outcomes so it is important to control for as many of these variables as

possible. The patient demographic variables included in this model are: age; race;

gender; payor and primary care physician as proxies for access to care; martial status;

and religion. Ideally, patients with the same clinical condition irrespective o f these

variables should receive the same treatment, however, the variables can impact

treatment decisions and often do affect outcomes.

Hospital Characteristics

There are certain hospital characteristics that may give patients treated in one

hospital a better chance of survival than patients treated in another hospital with

different characteristics. The following hospital characteristics may advantage patients

and are therefore included in this model: high FTE per adjusted occupied bed rates;

having a Rapid Response Team; having a Chest Pain Center; being received an award

for Excellence for Cardiovascular Care; having high hospital and average physician

39


AMI volumes; having a low ratio of AMI patients to ICU beds; having better payor

mixes; being a teaching hospital; and having surgical back-up. These variables were

collected through interviews with physicians and cardiovascular administrators at

Scripps Health and through other databases used in the Strategic Planning, Human

Resources, and Finance departments. A variable for facility was also included in the

model.

Patient’s Medical Condition

A patient’s medical condition can have a large impact upon outcomes. It goes

without saying that sicker patients have an increased risk of mortality. Sicker patients

are determined to be those with multiple comorbidities and those with many or severe

complications. There are certain comorbidities that may increase a patient’s risk of

mortality due to the fact that these conditions are considered to be risk factors for heart

disease and/or AMI. The risk factors that are included as variables in this model are:

CAD; prior MI; family history of CAD; dyslipidemia; diabetes; hypertension; obesity;

and depression. Comorbidities were determined from additional chronic illnesses, self

medical history, and family medical history that were not only documented in the

medical record but also in either the MIDAS+ or TRENDSTAR electronic system.

Treatment

The type of treatment that a patient has may impact his/her survival. Surgeries

(CABG surgery and other open heart surgery) are invasive procedures that are

inherently more risky and may have higher rates of inpatient mortality than less

40


invasive procedures. Angioplasty and thrombolytic therapy are other common,

medically recognized treatment options for AMI patients. Patients who do not have

surgery, angioplasty, or thrombolytic therapy and who have another primary cardiac

diagnostic or treatment procedure or who have no cardiac treatment may be at higher

risk for short and long term mortality than those who have an intervention known to

improve outcomes for heart attack patients.

Months since admission to censor date is also included as an independent

variable. Potential days survival until the censor date of October 1, 2006 is longer for

patients admitted in 2003 than for patients admitted in 2004 or 2005. The months since

admission variable was added to help control for this difference.

Patient’s Behavior

While Patient Demographics captures patient attributes that are inherent or

hard to change, Patient’s Behavior is a category of variables that are more easily

subject to change. Perhaps the single most important patient behavior responsible for

increased morbidity and mortality is smoking status.

Smoking status is an item usually collected through Scripps Health’s paper

medical record documents rather than electronically. Unfortunately, not all providers

document smoking status in a consistent fashion in the paper chart. MIDAS+ does

collect whether a patient was a smoker within the past 12 months. For feasibility and

reliability purposes, I will use this data field and categorize smokers as those who have

41


smoked within the past 1 2 months and non-smokers as those who have not smoked

within the past 1 2 months.

S t u d y D e s ig n

This study has two aims: to determine the relationship between participation in

a pay-for-performance program and scores on process measures and to determine

whether pay-for-performance programs succeed in improving the overall quality of

patient care. Analyses to address both aims use the same study design: a quasi-

experimental time series design.

The study design for this analysis is depicted below.

Oi O2 O3 X O4 O5 06

Oi represents AMI performance metric scores from January to June, 2003, O2 from

July to December, 2003, O3 from January to June, 2004 O4 from July to December,

2004, O5 from January to June, 2005, and 06 from July to December, 2005. X

represents the implementation o f the Hospital Quality Alliance and Scripps Health

participation in this precursor to a pay-for-performance program. Based upon the

outcome patterns of patients treated in the Scripps Health system during the

observations one through six, one can infer whether pay-for-performance had an effect

on the performance metric scores.

According to Campbell and Stanley, the time series design controls many

internal threats to validity including maturation, testing, regression, selection,

80mortality, and interactions such as the interaction of selection and maturation.

42


Maturation could occur if providers were getting better at treating AMI patients over

time, however, it is unlikely maturation would occur over one time period and not the

others. Furthermore, the instrumentation for data collection did not change over this

time period, so instrumentation is not a likely threat to internal validity. However, the

time series design does not control for the effect o f history. If history is not controlled,

then one cannot rule out the rival hypothesis that a simultaneous event produced the

changed in performance metric scores rather than the pay-for-performance program.

An additional analysis was conducted to address the threat to internal validity

from history. This difference-in-differences analysis compares participants of pay-for-

performance programs. It is difficult to identify and measure performance o f control

group hospitals (i.e. hospitals that did not participate in the Hospital Quality Alliance)

as almost all hospitals nation-wide participate in the HQA. These non-participating

hospitals may be less comparable to the Scripps hospitals because they are unique

facilities such as the Naval Medical Center and the Veteran’s Administration.

However, there is a group of hospitals that began a true P4P program before the HQA

began. The Premier, Inc. hospital system began participating in a pay-for-performance

program as a demonstration project with CMS. The Premier Hospital Quality

Incentive Demonstration began in 2003 whereas the Hospital Quality Alliance began

in 2004.

For the Premier, Inc. hospitals, one would expect that if P4P led to better

compliance with performance metrics, then the Premier hospitals would experience a

higher rate o f compliance with the performance metrics once they began their P4P

43


program at the end of 2003 compared to before they began participating and compared

to other non-participants. When the rest of the nation began reporting the same

performance metrics, one would expect that Premier hospitals would be minimally if

at all affected.

If data shows that the rates of improvement in P4P scores around the middle of

2004 were fairly small for Premier hospitals but fairly large for Scripps hospitals and

other hospitals nationwide, then one could assume that the Hospital Quality Alliance

lead to the increased scores on the P4P performance measures. Furthermore, if the

change in Premier, Inc. and Scripps Health scores from before to after participation in

the P4P/P4R program is similar, one can interpret that the intervention improved

performance on these quality indicators.

The study design for this additional analysis is represented as:

Oi X O2 X O3 Premier, Inc.O4 O5 X 06 Scripps Health

X 6 7 National Trends

Oi represents a composite performance weighted average score for seven AMI

measures from October, 2002 to September, 2003, O2 from January, 2004 to June,

2004, and O3 from July, 2004 to June, 2005 all for Premier, Inc. hospitals. O4

represents a weighted average compliance score for October 2002 to September, 2003,

O5 from January, 2004 to June, 2004, and 06 from July, 2004 to June, 2005 for

patients seen at Scripps Health facilities. O7 represents the compliance score from July,

2004 to June, 2005 for other national HQA participating hospitals. The first X in the

row labeled “Premier, Inc.” represents the implementation of Premier Hospital Quality

44


Incentive Demonstration program which began October 1, 2003, whereas the other

three X ’s represent implementation of the HQA.

This modified recurrent institutional cycle design does control for the threat of

history.

S t a t is t ic a l M e t h o d s

Study Aim 1

To test the relationship between participation in the HQA and process measure

scores, time series observations on process measure compliance were collected.

Logistic regression analyses were conducted to determine whether the slope and

intercept of the process measure compliance scores changed pre-HQA to post-HQA.

“Logistic regression is a mathematical modeling approach that can be used to describe

81the relationship of several x’s [covariates] to a dichotomous dependent variable.”

The equation for the logistic regression that was conducted is

y = po + Pifi + P2 O2 *x) + P3X

where y is compliance with the process measure, Po is the constant/intercept, p is the

coefficient, t is a variable for time and x is a variable to denote a post-HQA score.

The logistic regression analysis was run for each of the process measures

independently with the process measure as the dependent variable. The post-HQA

variable included in the analysis was coded as 0 for a pre-HQA score and 1 for a post-

HQA score. The other covariate included in the analysis was a time variable which

was coded as 1, 2 or 3 for the three observations before HQA and the three

45


observations after HQA. The last variable included in the analysis was an interaction

term between the post-HQA score and the time variable.

In order to test whether an internal threat to history was impacting the study

results, data from Scripps Health was compared to Premier, Inc. and other national

participants in the HQA in an additional data analysis. The absolute differences in

performance for different hospitals over the same time period and for the same

hospitals over different time periods were analyzed using t-tests to determine the

statistical significance of the change in scores.

A similar analysis to the logistic regression to test the change in process

measures scores before and after participation in the HQA was also conducted

between Scripps Health and Premier, Inc. performance. The equation for the logistic

regression is the same

y = Po+ Plft + P2 ( / 2 *x) + P3X

however, in the analysis between Scripps Health and Premier, Inc., y is the weighted

average process measure score, p 0 is the constant/intercept, p i - 3 are the coefficients, t

denotes pre/post intervention (either the P4P Premier Hospital Quality Incentive

Demonstration or the P4R HQA program) and x denotes a Premier, Inc. performance

score. The time variable is coded as 1 for before intervention and 2 for after

intervention. The site variable (x) is coded as 0 for Scripps Health and 1 for Premier,

Inc.

SPSS (a predictive analytic software application) was used to conduct all of the

statistical analyses for both study aims.

46


Study Aim 2

Survival analysis was used to determine pay-for-performance’s impact on

patient outcomes using days survival as the main outcome variable. Survival analysis

is the most appropriate statistical model to use because time is an important

component o f the outcome variable. “Survival analysis is a family o f techniques

dealing with the time it takes for something to happen: a cure, a failure, an employee

leaving, a relapse, a death, and so on.”

A Cox proportional hazard model was used to conduct the survival analysis.

By using a Cox proportional hazard model, no assumptions are made about the nature

or shape o f the hazard function. The model assumes that the underlying hazard rate

(rather than survival time) is a function of the independent variables . 8 3

The equation for the Cox proportional hazard model for individual i at time t is

hi(t) = X0 (Oexp{PiXii+...+pkxik}

The baseline hazard function (t) is the hazard for the individual when all

independent variables’ values are equal to zero, P is the coefficient, x is the covariate,

and h is the hazard o f death. “While no assumptions are made about the shape of the

underlying hazard function, the model equations.. .do imply two assumptions. First,

they specify a multiplicative relationship between the underlying hazard function and

the log-linear function of the covariates. This assumption is also called the

proportionality assumption. In practical terms, it is assumed that, given two

observations with different values for the independent variables, the ratio o f the hazard

functions for those two observations does not depend on time. The second

47


assumption... is that there is a log-linear relationship between the independent

variables and the underlying hazard function . ” 8 3 Significance o f interaction terms

between each covariate and a time variable were analyzed to determine whether the

proportionality assumption was valid.

The outcome variable, days survival, was calculated with a censor date o f

October 1, 2006, therefore, if a patient had not passed away before October 1, 2006,

then he/she was censored. “A survival time is described as censored when there is a

follow-up time but the event has not yet occurred or is not known to have occurred . ” 8 4

All the covariates listed in the Empirical Variable column of Table 2 were

included for block entry by forward stepwise selection in the survival analysis. A

probability o f 0.05 was required for stepwise entry and a probability of 0.10 was

required for stepwise removal. A designated maximum of 20 iterations was used in the

stepwise selection o f covariates. After covariates were added into the model, forced

entry of one pay-for-performance metric was added so that only one of the regressors

of interest was in the model at one time. Hazard ratios were obtained for each

regressor o f interest on the full population of 3,954 patients and for each six month

time period between January 1, 2003 and December 31, 2005.

In addition, an analysis with forced entry of all covariates with no regressors of

interest was conducted on the total patient population in order to see the effect on the

hazard function for each individual covariate including those that were consistently

excluded from the model when stepwise variable selection was employed.

48


While the primary data analysis was conducted using survival analysis,

additional outcome analyses were conducted using logistic regression.

The equation for a logistic model is:

z = a+PiXi+P2*2 +-• -+Pp^p

where z equals the odds o f mortality/readmissions, a is the intercept, P is the

coefficient, and x is the independent variable. All o f the covariates included in the

survival analysis (except months since admission to censor date) were included in the

logistic regression analyses, however, the outcome variable changed to alive/dead at

30 days and readmissions within 30 days. [The months since admission to censor date

variable was included in the survival analyses to control for the fact that patients

admitted earlier than others had greater potential days o f survival until censor time

than did patients admitted at later dates. With an outcome variable o f mortality at 30

days, this control variable is no longer needed as all patients were tracked for greater

than 30 days post-discharge regardless o f their admission or discharge date.] For the

logistic regressions, forward stepwise variable selection was employed with a

probability o f 0.05 for entry, 0.10 for removal and a maximum of 20 iterations. The

logistic regression analyses were conducted on the full model to determine whether

the process measures were significant predictors o f mortality and readmissions within

30 days. For measures that were statistically significant predictors of mortality and/or

readmissions, time series analyses looking at the hazard functions over all six

observational time periods were also conducted.

49


Further logistic regression analyses were conducted to see whether the slope

and intercept o f the 30-day, 90-day, and 180-day mortality rates changed from before

participation in the FIQA to after participation in the HQA.

The equation for this logistic regression is

y = (3o + Pih + $2 ^ 2 *x) + P3X

where y is the mortality rate, Po is the constant/intercept, p 1 .3 are the coefficients, 1 is a

variable for time and x is a variable to denote a post-HQA score.

The logistic regression analysis was run three times with 30-day, 90-day, and

180-day mortality each as the dependent variable. This analysis is similar to that

conducted on the process measure scores with the post-HQA variable coded as 0 for a

pre-HQA score and 1 for a post-HQA score, the time variable coded as 1, 2 or 3 for

the three observations before HQA and the three observations after HQA, and

inclusion of the interaction term between the post-HQA score and the time variable.

R e s u l t s

Patient Characteristics

Refer to Table 3 for a listing of variable frequencies for non-continuous

variables and to Table 4 for a listing of variable means for continuous variables. For

the total patient population of AMI patients discharged from Scripps Health facilities

from January 1, 2003 to December 31, 2005, 63% were men, 67% were Caucasian,

53% were married, and 69% stated a religious affiliation. The average age of patients

was 70 years. Thirty percent o f the patients were seen at Scripps Memorial Hospital

50


La Jolla, 29% at Scripps Mercy Hospital San Diego, 18% at Scripps Mercy Hospital

Chula Vista, 17% at Scripps Green Hospital, and six percent at Scripps Memorial

Hospital Encinitas. Thirty-one percent o f patients identified a primary care provider

and 96% identified a payor.

One hundred percent of patients had coronary artery disease, seven percent had

a previous myocardial infarction, two percent had a family history o f coronary artery

disease, 37% had dyslipidemia, 29% had diabetes, 62% had hypertension, seven

percent were obese, four percent were depressed and 18% were smokers within the

past 12 months. Again, it is important to note that the results are dependent upon

having comorbidities documented in the medical record as well as the electronic

system. The actual incidence o f comorbidities may be higher than these reported

statistics due to the fact that patients may not mention additional illnesses to their

physicians, physicians may not document all additional illnesses in the medical record,

and/or additional illnesses may be not be able to be documented through the electronic

systems as only a limited number of diagnoses can be tracked.

Out o f the various treatment options, eight percent of patients received CABG

surgery, one percent had other open heart surgery, 53% had a percutaneous coronary

intervention, four percent had thrombolytic therapy, 58% had another primary cardiac

diagnostic or treatment procedure (e.g., angiocardiogram, cardiac cath), and 23% had

no cardiac treatment. The percentages of patients with different treatment options add

up to more than 1 0 0 % because some patients had more than one o f the listed

interventions/treatments.

51


Hospital characteristics were also tracked. The mean number o f paid FTE per

adjusted occupied bed was 5.6, while the annual AMI admissions per ICU beds was

13.0. The mean value for annual hospital AMI admissions was 429 and the average

cardiologist AMI annual admissions was 18. Nine percent of patients were seen in a

hospital with a Rapid Response Team and 29% of patients were seen in a hospital with

a Chest Pain Center. Thirty-six percent o f patients were seen in a facility that received

a cardiovascular excellence award (e.g. Solucient, U.S. News & World Report) for the

year of service that the patient was seen. Fifty percent of patients were seen in

facilities with Graduate Medical Education programs and 76% of patients were seen in

facilities with cardiovascular surgery back-up capabilities. The mean payor mix for

Scripps Health hospitals was 37% Medicare, 13% Medicaid, 39% commercial, 8 %

other government payors/self-pay/no pay, and 3% other payors including Worker’s

Compensation.

Twenty-one percent o f total patients died before the censor date o f October 1,

2006. Ten percent o f patients died within 30 days of admission, 12% within 90 days of

admission, and 14% within 180 days o f admission. Five percent of patients were

readmitted within 30 days.

Table 3: Variable Frequencies for Non-Continuous Variables for Total Patient Population______________________________

Variable n %Sex

Male 2499 63%Female 1455 37%

EthnicityWhite 2662 67%

52


African/American 126 3%Native American 5 0%Asian/Pac Islander 434 11%Other 638 16%Unknown 89 2%

Marital StatusSingle 921 23%Widowed 687 17%Divorced/Separated 232 6%Married 2114 53%

ReligionStated Religion 2699 69%No Stated Religion 1219 31%

FacilityChula Vista 713 18%Encinitas 249 6%Green 663 17%La Jolla 1173 30%Mercy 1156 29%

Identified PCPNo/Unknown 2731 69%Yes 1223 31%

Identified PayorNo/Unknown 164 4%Yes 3790 96%

CensoredYes/Alive 3135 79%No/Dead 819 21%

ACEI or ARB for LVSDNo 125 23%Yes 430 77%Not Applicable 3399

Smoking Cessation AdviceNo 157 24%Yes 492 76%Not Applicable 3305

Aspirin at ArrivalNo 121 5%Yes 2449 95%Not Applicable 1384

Aspirin at Discharge No 185 6%Yes 2977 94%Not Applicable 792

Beta Blocker at ArrivalNo 205 9%

53


Yes 2010 91%Not Applicable 1739

Beta Blocker at DischargeNo 324 11%Yes 2742 89%Not Applicable 888

Thrombolysis w/in 30 minNo 113 75%Yes 38 25%Not Applicable 3803

PCI w/in 120 minNo 237 56%Yes 188 44%Not Applicable 3529

All Applicable P4P MeasuresNo 1055 27%Yes 2899 73%

CABG TreatmentNo 3650 92%Yes 304 8%

Other Open Heart SurgeryNo 3906 99%Yes 48 1%

PCI/Angioplasty TxNo 1849 47%Yes 2105 53%

Thrombolysis TreatmentNo 3787 96%Yes 167 4%

Oth Cardiac Proc (Dx or Tx)No 1670 42%Yes 2284 58%

No Cardiac TreatmentNo 3032 77%Yes 922 23%

CADNo 3 0%Yes 3951 100%

Prior MlNo 3661 93%Yes 293 7%

Family History of CADNo 3871 98%Yes 83 2%

DyslipidemiaNo 2478 63%

54


Yes 1476 37%Diabetes

No 2825 71%Yes 1129 29%

HypertensionNo 1503 38%Yes 2451 62%

ObesityNo 3683 93%Yes 271 7%

DepressionNo 3799 96%Yes 155 4%

SmokerNo or Unknown 3247 82%Yes 707 18%

Readmit w/in 30 daysNo 3748 95%Yes 206 5%

Rapid Response TeamNo 3616 91%Yes 338 9%

Chest Pain CenterNo 2798 71%Yes 1156 29%

Cardiovascular Award Yr of VisitNo 2514 64%Yes 1440 36%

Teaching Hospital StatusNo 1985 50%Yes 1969 50%

Surgical Back-UpNo 962 24%Yes 2992 76%

30-Day MortalityAlive 3554 90%Dead 400 10%




Table 4: Variable Means for Continuous Variables for Total Patient PopulationVariable MeanAge 69.67Months Since Admission 26.64Paid FTE/Adjusted Occupied Bed 5.60Hospital Annual AMI Admits 428.86Avg Annual Cardiologist AMI Admits 18.30Annual AMI Admits / ICU Beds 13.01Payor: % Medicare 36.59Payor: % MediCal 13.35Payor: % Commercial 38.65Payor: % Other Gvmt Payors / Self Pay 8.45Payor: % Other / Workers Comp 3.10

Some of the patient characteristics changed over the observational time

periods. See Attachment V for a full listing of variable frequencies at each

observational time period and Table 5 for means o f age and hospital characteristics at

each observational time period. The percent o f male patients ranged from 63% - 69%

in the time periods after participation in the HQA compared to 60% - 61% before. The

mean age o f patients ranged from 70 - 71 years during observations one through three

and ranged from 67 - 70 years during observations four through six. Observation time

period two and three (from July 1, 2003 to June 30, 2004) had a high percent of

Asian/Pacific Islander patients (18% - 22%) compared to the other time periods where

the percent of Asian/Pacific Islander patients ranged from five to nine. As previously

noted, AMI patients seen at Encinitas from January 1, 2003 to June 30, 2004 were not

included in the analysis, which impacted the percent o f patients seen at each Scripps

facility before compared to after participation in HQA. The total patient volume

remained similar over this time frame. Forty-five percent of patients from July 1, 2005

56


to December 31, 2005 identified a PCP compared to a range from 27% - 34% in the

other time periods.

[Because patients from Encinitas were not included before participation in the

HQA, analyses were conducted excluding Encinitas patients after participation in

HQA to see if the results would change. The general findings remained the same

whether post-HQA Encinitas patients were included or not.]

The percent o f patients treated with PCI generally trended upward over time.

The percent o f patients with thrombolytic therapy slightly decreased over time. There

was also an increase in other primary cardiac diagnostic and treatment procedures

from before to after participation in HQA. Conversely, the percent of patients

receiving no cardiac treatment decreased steadily from 30% during observation one to

17% in observation six.

There were small increases from before and after participation in HQA with

patients who had a family history of CAD (one percent before to three percent after)

and patients who had dyslipidemia (30% - 35% before to 40% - 47% after).

Hospital-wide paid FTE per adjusted occupied bed increased from 5.3 in

observation one to 5.8 in observation six. Rapid Response Teams began being

deployed in 2005 and were not present until then. The percent of patients seen in a

hospital who received a cardiovascular clinical excellence award during the year of the

patient’s visit was lower in 2003 (21% - 23%) than in 2004 and 2005 (33% - 48%).

Annual hospital AMI admissions decreased in 2005 (362 - 369) compared to 2003 and

57


2004 (444 - 464). The annual AMI admissions per ICU bed decreased in 2005 (mean

of 11) compared to a mean of 14 in 2003 and 2004

Changes in the regressors of interest over the time periods are detailed in

Figures 1-9. The majority of the pay-for-performance metrics increased in compliance

over this timeframe. Process measure scores are discussed in more detail in the

Process Measure Analysis section. [Note: The denominators (i.e., n) of the individual

regressors o f interest are different from each other during the same time periods

because of instances where the measure is not applicable to certain patients, as

explained in the Data Elements section.]

The percent o f patients who had died before the censor date was highest for the

earlier time periods (i.e., earlier discharge dates) and lowest for the later time periods,

which is as expected. Given that the follow-up time until censoring is longer for the

patients admitted earlier, there is greater opportunity for the patients in the earlier

observation periods to be determined to be deceased. The percent of patients who had

died within 30 days of discharge decreased from 13% in observation one to eight

percent in observation six. Similar results were seen in 90-day mortality which

decreased over the same time period from 15% to 1 0 %, and 180-day mortality which

decreased from 18% to 1 1 %.

Table 5: Means of Age and Hospital Characteristics at Each Observation Time Period1/1/03- 7/1/03- 1/1/04- 7/1/04- 1/1/05- 7/1/05-

Variables 6/30/03 12/31/03 6/30/04 12/31/04 6/30/05 12/31/05Age 71.03 71.75 70.09 68.78 67.43 69.52Paid FTE per Adj Occ Bed 5.33 5.52 5.48 5.71 5.73 5.84Hosp Annual AMI Admits 460.38 444.23 453.49 463.94 362.15 368.94

58


Avg Cardiologist AMI Vol 17.94 17.80 19.26 20.15 16.73 16.97AMI Admits/ICU Beds 14.21 13.95 13.65 13.90 10.89 10.99Payor: % Medicare 37.14 37.92 35.61 34.70 37.67 37.51Payor: % MediCal 14.00 13.93 13.84 12.97 12.75 12.63Payor: % Commercial 37.14 36.52 39.11 41.73 37.81 38.21Payor: % Oth Gvmt/SIf Py 8.90 8.87 8.25 7.58 8.76 8.69Payor: % Oth (Wrk Comp) 2.95 2.87 3.18 3.02 3.30 3.28

Pay-for-Performance Process Measure Analysis

Scripps Performance Results

Compliance with providing all the applicable AMI process measures increased

at Scripps Health over all six observations, going from 60% at observation one to 8 6 %

at observation six. See Figure 1. The logistic regression for patients receiving all

applicable P4P measures identified that time (beta, 0.28; p < 0.0001) and the post-

HQA/site (beta, 0.68; p = 0.0002) variables were statistically significant. See Table 6 .

Figure 1: All Applicable P4P Measure Compliance Over Time

All Applicable P4P Measures

100%90%80%70%60%50%40%30%20%10%0%

n=642

1/1/03-

n=548

7/1/03-

2Z2%_ * -75% '

n=734 n=836 n=668

1/1/04- 7/1/04-

86%

n=526

1/1/05- 7/1/05-6/30/03 12/31/03 6/30/04 12/31/04 6/30/05 12/31/05

59


Table 6: Logistic Regression Results of Scripps Process Measure Scores Before and After HQA________________________________________________________________Regressor of Interest B S.E. Wald df Sig. Exp(B)

95% Cl Exp(B) Lower Upper

All Applicable P4PTime 0.2784 0.0575 23.411 1 0.0000 1.3210 1.1801 1.4787Post 0.6754 0.1837 13.514 1 0.0002 1.9648 1.3707 2.8165Post by Time 0.0564 0.0921 0.3753 1 0.5401 1.0580 0.8833 1.2673Constant 0.1140 0.1240 0.8454 1 0.3579 1.1208

Aspirin at ArrivalTime 0.0877 0.1333 0.4329 1 0.5106 1.0917 0.8406 1.4178Post 0.0943 0.5025 0.0352 1 0.8511 1.0989 0.4104 2.9426Post by Time 0.4691 0.2807 2.7934 1 0.0947 1.5986 0.9222 2.7713Constant 2.5121 0.2794 80.837 1 0.0000 12.3313

Aspirin at DischargeTime 0.1729 0.1079 2.5697 1 0.1089 1.1888 0.9622 1.4687Post 0.7039 0.4060 3.0061 1 0.0829 2.0216 0.9123 4.4797Post by Time 0.2073 0.2168 0.9142 1 0.3390 1.2304 0.8044 1.8819Constant 2.0316 0.2276 79.657 1 0.0000 7.6264

Beta Blocker at ArrivalTime 0.1889 0.1048 3.2504 1 0.0714 1.2079 0.9837 1.4834Post 1.5529 0.4324 12.899 1 0.0003 4.7251 2.0248 11.027Post by Time -0.2458 0.2099 1.3705 1 0.2417 0.7821 0.5183 1.1802Constant 1.5353 0.2157 50.645 1 0.0000 4.6425

Beta Blocker at DischargeTime 0.1300 0.0892 2.1246 1 0.1450 1.1388 0.9562 1.3563Post 0.2992 0.3006 0.9909 1 0.3195 1.3488 0.7483 2.4313Post by Time 0.2590 0.1559 2.7594 1 0.0967 1.2956 0.9545 1.7587Constant 1.5555 0.1917 65.844 1 0.0000 4.7376

Smoking Cessation AdviceTime 0.3486 0.1433 5.9188 1 0.0150 1.4171 1.0701 1.8767Post 0.2455 0.4720 0.2706 1 0.6029 1.2783 0.5069 3.2238Post by Time 0.3639 0.2454 2.1987 1 0.1381 1.4390 0.8895 2.3280Constant 0.0492 0.3129 0.0248 1 0.8750 1.0505

ACEI or ARB for LVSDTime -0.0971 0.1608 0.3641 1 0.5462 0.9075 0.6621 1.2438Post -0.6955 0.5127 1.8407 1 0.1749 0.4988 0.1826 1.3624Post by Time 0.5573 0.2614 4.5455 1 0.0330 1.7460 1.0460 2.9143Constant 1.2881 0.3441 14.015 1 0.0002 3.6260

Thrombolysis w/in 30 minsTime 0.1698 0.3050 0.3099 1 0.5777 1.1851 0.6518 2.1548Post -0.8566 1.0094 0.7201 1 0.3961 0.4246 0.0587 3.0705Post by Time 0.1838 0.4901 0.1406 1 0.7077 1.2018 0.4598 3.1407Constant -1.1957 0.6010 3.9587 1 0.0466 0.3025

PCI w/in 120 minutesTime 0.8992 0.1952 21.220 1 0.0000 2.4575 1.6763 3.6029

60


Post 2.3538 0.5767 16.658 1 0.0000 10.5252 3.3989 32.593Post by Time -0.5450 0.2655 4.2141 1 0.0401 0.5799 0.3446 0.9756Constant -2.6526 0.4468 35.248 1 0.0000 0.0705

While the all applicable P4P measure improved consistently over the six

observation time periods, the change over time in the individual AMI process

measures was not as consistent. All o f the eight individual P4P measures increased

from observation one to observation six, however, most scores fluctuated over time.

Aspirin at arrival compliance ranged from 93% - 95% before the HQA to 96% - 99%

after the HQA. Beta blocker at arrival ranged from 85% - 89% before the HQA to

94% - 96% after the HQA. ACEI or ARB for LVSD ranged from 71% - 79% before

the HQA to 74% - 87% after the HQA. Smoking cessation advice ranged from 57% -

75% before the HQA to 71% - 89% after the HQA. Aspirin at discharge ranged from

90% - 94% before the HQA to 96% - 98% after the HQA. Beta blocker at discharge

ranged from 83% - 89% before the HQA to 90% - 95% after the HQA. Thrombolytic

agent within 30 minutes ranged from 24% - 37% before the HQA to 14% - 31% after

the HQA. PCI within 120 minutes ranged from 16% - 52% before the HQA to 53% -

71% after the HQA. See Figures 2-9.

Analysis was conducted to determine whether compliance in these process

measures was statistically different after participation in the HQA compared to before

For four o f the AMI process measures: aspirin at arrival; aspirin at discharge; beta

blocker at discharge; and thrombolytic agent within 30 minutes, there were no

statistically significant terms of the equation. The post-HQA variable (beta, 1.55; p =

61


0.0003) was statistically significant in the beta blocker at arrival equation. Time was

the only variable with a statistically significant beta/coefficient for smoking cessation

advice (beta, 0.35; p = 0.015). ACEI or ARB for LVSD had statistically significant

coefficients for the interaction term between post-HQA and time (beta, 0.56; p =

0.033). The time variable (beta, 0.90; p < 0.0001), the post-HQA variable (beta, 2.35;

p < 0.0001), and the interaction term (beta, -0.55; p = 0.04) were all statistically

significant for PCI within 120 minutes. See Table 6 .

Figure 2: Aspirin at Arrival Compliance Over Time

Aspirin at Arrival

100%99%

96%

94%i3%92%

90%n=489 n=414 n=457 n=497 n=409 n=304

88%1/1/03- 7/1/03- 1/1/04- 7/1/04- 1/1/05- 7/1/05-6/30/03 12/31/03 6/30/04 12/31/04 6/30/05 12/31/05

62


Figure 3: Beta Blocker at Arrival Compliance Over Time

! Beta Blocker at Arrival

100%

I 96%95%

90% 89%

85%

80% r

n=420 n=380 n=400 n=344n=40575%

1/1/03-6/30/03

7/1/03-12/31/03

1/1/04-6/30/04

7/1/04-12/31/04

1/1/05-6/30/05

7/1/05-12/31/05

Figure 4: ACEI or ARB for LVSD Compliance Over Time

ACEI for LVSD

90%87%

85%80% 79%75%70%65%60%55%

*4%71%

n=89 n=94 n=69n=10650%1/1/03- 7/1/03- 1/1/04- 7/1/04- 1/1/05- 7/1/05-6/30/03 12/31/03 6/30/04 12/31/04 6/30/05 12/31/05


Figure 5: Smoking Cessation Advice/Counseling Compliance Over Time

Smoking Cessation Advice

95% 90% 85% 80% 75% 70% 65% 60%

; 55% ! 50%

89%

-♦ 73% 71%

57%n=101 n=130 n=129 n=93

1/1/03-6/30/03

7/1/03-12/31/03

7/1/05-12/31/05

1/1/04-6/30/04

7/1/04-12/31/04

1/1/05-6/30/05

Figure 6: Aspirin at Discharge Compliance Over Time

Aspirin at Discharge

100%98%98%

96%94%92%

156%

94%

90%88%

86% n=527 n=451 n=585 n=669 n=51684%

1/1/03- 7/1/03- 1/1/04- 7/1/04- 1/1/05- 7/1/05-6/30/03 12/31/03 6/30/04 12/31/04 6/30/05 12/31/05

64


Figure 7: Beta Blocker at Discharge Compliance Over Time


100%

95%

90% 90%89%

85%;3%

80%

n=491 n=439 n=649 n=515 n=41475%

1/1/03-6/30/03

7/1/03-12/31/03

1/1/04-6/30/04

7/1/04-12/31/04

1/1/05-6/30/05

7/1/05-12/31/05

Figure 8: Thrombolytic Agent Within 30 Minutes Compliance Over Time

Thrombosis w/in 30 min

100%90%80%70%60%50%40%30%20%10%0%

28%!4%4%

n=18 n=28 n=21 n=16n=38 n=30

1/1/03- 7/1/03- 1/1/04- 7/1/04- 1/1/05- 7/1/05-6/30/03 12/31/03 6/30/04 12/31/04 6/30/05 12/31/05

65


Figure 9: PCI Within 120 Minutes Compliance Over Time

PCI w/in 120 min

100%90%80%70%60%50%40%30%20%10%0%

!6%52%

28%

n=77 n=68n=72 n=67 n=58

1/1/03- 7/1/03- 1/1/04- 7/1/04- 1/1/05- 7/1/05-6/30/03 12/31/03 6/30/04 12/31/04 6/30/05 12/31/05

Premier, Inc. and Scripps Health Performance Results

The data collected in order to determine whether other events in history could

create another legitimate rival hypothesis to P4P as a causal factor for increased

performance metric scores showed that Premier, Inc. and Scripps Health scores

increased at each time interval analyzed. See Figure 10 and Table 7.

Scripps Health and Premier, Inc. hospitals started out at statistically

significantly different levels of performance with the AMI P4P indicators as measured

by the weighted average score (absolute difference, 7.7; 95 Cl, 6 .9-8.5; p < 0.0001).

See Table 8 . After Premier hospitals began participating in the Premier Hospital

Quality Incentive Demonstration, their performance increased (absolute difference,

1.3; 95 Cl, 0.9-1.6 ; p < 0.0001) as did Scripps (absolute difference, 2.7; 95 Cl, 1.0-4.3;

p = 0.002), although Scripps did not participate in any P4P or P4R program over this

66


time. The process measure scores of the two facilities were still significantly different

(absolute difference, 6.3; 95 Cl, 5.3-7.3; p < 0.0001) at the second observation from

1/04-6/04.

Between observation two (1/04-6/04) and observation three (7/04-6/05), both

health systems began participating in the HQA. From time two to time three, Premier,

Inc. improved its performance at a statistically significant level (absolute difference,

0.9; 95 Cl, 0.6-1.1; p < 0.0001) as did Scripps Health (absolute difference, 4.9; 95 Cl,

3.5-6.4; p < 0.0001), yet their performance was still statistically different from each

other (absolute difference, 2.2; 95 Cl, 1.5-2.9; p < 0.0001). [Of note for the time

period three is that the Scripps Health facilities’ performance was similar (i.e., not

statistically different) to that of other HQA participants nation-wide.]

Scripps Health and Premier, Inc. had different levels o f performance during the

first observation. The ceiling o f performance is 100%, so Premier, Inc. starting at 93%

compliance with the process measures had less opportunity to improve than did

Scripps Health at 85% compliance. Because the two systems started out at different

levels of performance, the slope and intercepts o f their levels o f performance were

tested against each other. The time variable (beta, 0.23; p = 0.002) and the Premier/site

variable (beta, 0.85; p < 0.0001) were both statistically significant in the comparison

of facility scores from observation one to observation two. See Table 9. From

observation two to observation three, the time variable (beta, 0.59; p < 0.0001), the

Premier variable (beta, 1.23 ; p < 0 .0001) and the time and site interaction variable

(beta, -0.41, p < 0.0001) were all statistically significant. See Table 10.

67


Figure 10: Premier, Inc. and Scripps Health Weighted Average AMI Process Measure Compliance Over Time

Weighted Average AMI Process Measure Score

100%

95%

90%

85%

80%

75%

10/02-9/03 1/04-6/04 7/04-6/05

■ Premier ■«— Scripps

Table 7: Premier, Inc., Scripps Health, and Other National HQA Participants’ Process Measure Scores

1 0 /0 2 -9 /0 3 1 /0 4 -6 /0 4 7/04-6/05Premier, Inc. % n % n % n

Aspirin on Arrival 96.4 (13,436) 95.9 (7,256) 96.1 (14,012)Aspirin at Discharge 96.8 (18,247) 96.9 (10,262) 96.8 (18,627)ACEI or ARB for LVSD 85.5 (4,115) 82.6 (2,384) 85.0 (2,394)Beta Blocker on Arrival 93.3 (11,495) 92.7 (6,136) 94.3 (11,272)Beta Blocker at Discharge 94.4 (17,538) 95.0 (10,016) 95.8 (19,155)Smoking Cessation Advice 78.3 (5,520) 90.4 (1,322) 93.6 (6,220)Thrombolysis w/in 30 mins 41.8 (509) 47.8 (46) 38.5 (234)Weighted Average Score 93.1 (70,860) 94.3 (37,422) 95.2 (71,914)

Scripps HealthAspirin on Arrival 93.6 (1,054) 94.4 (517) 97.0 (756)Aspirin at Discharge 92.0 (1,075) 93.0 (627) 96.5 (1,100)ACEI or ARB for LVSD 76.6 (197) 64.3 (84) 83.8 (74)Beta Blocker on Arrival 84.0 (907) 84.5 (465) 93.1 (625)Beta Blocker at Discharge 82.9 (1,006) 86.3 (608) 91.9 (1,073)Smoking Cessation Advice 54.1 (196) 75.0 (76) 78.6 (238)Thrombolysis w/in 30 mins 27.6 (76) 18.2 (11) 20.8 (24)Weighted Average Score 85.3 (4,511) 88.0 (2,388) 93.0 (3,890)

Oth Nat'l HQA Participants

68


Aspirin on Arrival 94.7 (376,314)Aspirin at Discharge 94.8 (406,184)ACEI or ARB for LVSD 82.3 (51,869)Beta Blocker on Arrival 90.6 (316,685)Beta Blocker at Discharge 93.4 (415,266)Smoking Cessation Advice 88.4 (129,657)Thrombolysis w/in 30 mins 38.0 (11,582)Weighted Average Score 92.4 (1,707,557)

Table 8: Differences in Performance Measure Scores Between Scripps Health, Premier, Inc., and Other National HQA Participants Before and After P4P and P4R

Wghtd Absolute DifferenceTimeframe Avg N Between Groups

Same Hosp; Diff Time date % # % 95% Cl p-valuePremier, Inc. Premier, Inc.

10/02-9/03 1/04 - 6/04

93.194.3

(70,860)(37,422)

1.3 0.9- 1.6 < 0.0001

Premier, Inc. Premier, Inc.

1/04 - 6/04 7/04 - 6/05

94.395.2

(37,422)(71,914)

0.9 0.6-1.1 < 0.0001

Scripps Health Scripps Health

10/02-9/03 1/04 - 6/04

85.388.0

(4,511)(2,388)

2.7 1.0-4.3 0.0022

Scripps Health Scripps Health

1/04 - 6/04 7/04 - 6/05

88.093.0

(2,388)(3,890)

4.9 3.5-6.4 < 0.0001

Same Time; Diff HospPremier, Inc. Scripps Health

10/02-9/0310/02-9/03

93.185.3

(70,860)(4,511)

7.7 6.9 - 8.5 < 0.0001

Premier, Inc. Scripps Health

1/04-6/041/04-6/04

94.388.0

(37,422)(2,388)

6.3 5.3-7.3 < 0.0001

Premier, Inc. Scripps Health

7/04 - 6/05 7/04 - 6/05

95.293.0

(71,914)(3,890) 2.2 1.5 -2.9 < 0.0001

Scripps Health 7/04 - 6/05 93.0 (3,890) 0.6 not statisticallyOth Nat'l HQA 7/04 - 6/05 92.4 (1,707,557) significantly different

Table 9: Scripps Health and Premier, Inc. Performance Before and After P4P95% Cl Exp(B)

Before P4P B S.E. Wald df Sig. Exp(B) Lower UpperTime 0.2326 0.0758 9.4159 1 0.0022 1.2618 1.0877 1.4639Premier 0.8546 0.1115 58.731 1 0.0000 2.3504 1.8890 2.9246Premier by Time -0.0204 0.0804 0.0641 1 0.8001 0.9798 0.8370 1.1470Constant 1.5295 0.1052 211.46 1 0.0000 4.6159

Note: Time coded as 1 for 10/02-9/03 and 2 for 1/04-6/04.

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Table 10: Scripps Health and Premier, Inc. Performance Before and After P4R

Before P4R B S.E. Wald df Sig. Exp(B)95% Cl Exp(B) Lower Upper

Time 0.5853 0.0889 43.379 1 0.0000 1.7956 1.5086 2.1373Premier 1.2277 0.1487 68.170 1 0.0000 3.4134 2.5504 4.5683Premier by Time -0.4138 0.0933 19.687 1 0.0000 0.6611 0.5507 0.7937Constant 1.4093 0.1408 100.23 1 0.0000 4.0931

Note: Time coded as 1 for 1/04-6/04 and 2 for 7/04-6/05.

Process-Outcomes Link Analysis

Survival Analysis Mortality Results

The results o f the survival analysis to test whether process measures affect

outcomes are presented in Table 12. This survival analysis was conducted on the total

population o f 3,954 patients for each regressor of interest. Three o f the regressors of

interest had a statistically significant impact on outcomes (i.e., better mortality) at a p-

value o f less than 0.05: aspirin at arrival (hazard ratio, 0.53; 95 Cl, 0.39-0.71 ; p <

0.0001); beta blocker at arrival (hazard ratio, 0,62; 95 Cl, 0.46-0.82; p = 0.0009); and

ACEI or ARB for LVSD (hazard ratio, 0.67; 95 Cl, 0.46-0.98; p = 0.04). Refer to

Attachment VI for the hazard functions o f covariates included in the full model

analysis o f each regressor of interest.

Table 11: Survival Analysis Results for Regressors of Interest in Total Population

Measure Name B S.E. Wald df Sig. Exp(B)95% Cl Exp(B)

Lower UpperAspirin at Arrival -0.6412 0.1536 17.435 1 0.0000 0.5267 0.3898 0.7116Aspirin at D/C -0.2995 0.1576 3.6134 1 0.0573 0.7412 0.5442 1.0094Beta Blocker at Arr -0.4832 0.1453 11.065 1 0.0009 0.6168 0.4640 0.8200Beta Blocker at D/C 0.0090 0.1576 0.0033 1 0.9544 1.0091 0.7409 1.3743Smk Cess Advice -0.0635 0.2922 0.0472 1 0.8281 0.9385 0.5294 1.6639ACEI for LVSD -0.4013 0.1934 4.3060 1 0.0380 0.6695 0.4583 0.9780Thromb 30 mins 0.8405 0.4584 3.3622 1 0.0667 2.3176 0.9437 5.6915PCI 120 mins 0.1076 0.2974 0.1308 1 0.7176 1.1136 0.6217 1.9945

70


All Applicable P4P 0.0145 0.0828 0.0306 1 0.8611 1.0146 0.8627 1.1933

While not statistically significant, it is interesting to note that the all applicable

P4P variable has a positive coefficient and a hazard ratio that is greater than 1.0,

denoting that receiving all applicable P4P process measures may be predictive of

worse outcomes (higher mortality). One possible reason for this finding is that

providing thrombolytic agents for AMI treatment is no longer the standard of care for

hospitals with catheterization laboratories (cath labs) and is now only common in rural

hospitals without high levels of technology available. All Scripps Health hospitals

have cath labs, therefore, if the variable changed to all applicable and recommended

P4P measures rather than all applicable P4P measures, the results may change.

Indeed, the results did change when an analysis was conducted using an all

applicable and recommended P4P measure that includes seven AMI process measures

and excludes the measure for thrombolytic agent received within 30 minutes. The all

applicable and recommended P4P variable now has a negative coefficient and a hazard

ratio less than one, denoting that it is predictive o f lower mortality. However, the

results are not statistically significant. See Table 13.

Table 12: All Applicable and Recommended P4P Variable Survival Analysis Results

Measure Name B S.E. Wald df Sig. Exp(B)95% Cl Exp(B) Lower Upper

All App & Rec P4P -0.1020 0.0858 1.4124 1 0.2347 0.9030 0.7632 1.0685

[Analyses were run using the all applicable and recommended P4P variable to

determine whether any of the main study findings changed using this variable instead

71


of the all applicable P4P variable. While the percent compliance during each

observation period was slightly higher for the all applicable and recommended P4P

variable than the all applicable P4P measure, the results of the analyses were similar in

that the rate of improvement before participation in the HQA was not statistically

different than the rate of improvement after participation in the HQA.]

Survival Analysis Times Series Mortality Results

A time series design was used to determine if there was any difference in the

regressors of interests’ impact on outcomes over time. One would expect the impact of

an indicator such as aspirin at arrival to be consistent over time. Some of the

regressors of interest showed no consistently statistically significant impact on

outcomes over time. For each time period, beta blocker at discharge, smoking

cessation advice/counseling, thrombolytic agent within 30 minutes, and PCI within

120 minutes did not affect outcomes at a statistically significant level. Aspirin at

arrival was found to be a significant prediction o f lower (i.e. better) mortality for two

o f the six observation time periods (hazard ratio, 0.51; 95 Cl, 0.27-0.98; p = 0.04 for

7/03- 12/03 and hazard ratio, 0.38; 95 Cl, 0.20-0.73; p = 0.004 for 7/04-12/04).

Aspirin at discharge was also a significant prediction of lower mortality for three

observation time periods (hazard ratio, 0.39; 95 Cl, 0.22-0.71; p = 0.002 for 1/03-6/03;

hazard ratio, 0.45; 95 Cl, 0.23-0.87; p = 0.02 for 7/04-12/04; and hazard ratio, 0.27; 95

Cl, 0.08-0.90; p = 0.03 for 1/05-6/05). Beta blocker at arrival was a predictor of lower

mortality for one time period (hazard ratio, 0.36; 95 Cl, 0.20-0.66; p = 0.001 for 1/04-

72


6/04). One observation time period (1/04-6/04) showed that ACEIs or ARBs for

LVSD improved outcomes (hazard ratio, 0.46; 95 Cl, 0.21-0.99; p = 0.05). Receiving

all recommended P4P measures was a predictor o f improved outcomes for observation

four from 7/04-12/04 (hazard ratio, 0.70; 95 Cl, 0.49 - 1.00; p = 0.05). The hazard

ratios differed over time, which is likely due to the small sample size within each six

month time period. However, the direction of the effect (e.g. negative coefficient and

hazard ratio less than 1 .0 ) was consistent for the time periods that were statistically

significant. See Table 13.

Table 13; Time Series Survival Analysis Results for Regressors o f Interest

B SE Wald df Sig. Exp(B)95% Cl Exp(B)

Lower UpperAspirin at ArrivalJan - June '03 -0.5961 0.3460 2.9681 1 0.0849 0.5510 0.2796 1.0855July - Dec '03 -0.6714 0.3334 4.0561 1 0.0440 0.5110 0.2658 0.9822Jan - June '04 -0.4739 0.3194 2.2021 1 0.1378 0.6225 0.3329 1.1642July - Dec '04 -0.9706 0.3375 8.2705 1 0.0040 0.3789 0.1955 0.7341Jan - June '05 -0.4411 0.4753 0.8613 1 0.3534 0.6433 0.2534 1.6331July - Dec '05 -0.7891 0.7480 1.1131 1 0.2914 0.5000 0.1049 1.9677Aspirin at D/CJan - June '03 -0.9377 0.3038 9.5270 1 0.0020 0.3915 0.2158 0.7102July - Dec '03 Coefficients did not converge for split file so no model fitted for this timeJan - June '04 -0.1060 0.3380 0.0983 1 0.7539 0.8995 0.4637 1.7446July - Dec '04 -0.7991 0.3375 5.6058 1 0.0179 0.4497 0.2321 0.8715Jan - June '05 -1.3268 0.6218 4.5534 1 0.0329 0.2653 0.0784 0.8975July - Dec '05 10.5639 433.66 0.0006 1 0.9806 38710.7 0.0000Beta Blocker at ArrivalJan - June '03 -0.2909 0.2717 1.1464 1 0.2843 0.7476 0.4389 1.2733July - Dec '03 -0.4630 0.3437 1.8148 1 0.1779 0.6294 0.3209 1.2344Jan - June '04 -1.0155 0.3084 10.841 1 0.0010 0.3622 0.1979 0.6630July - Dec '04 -0.7180 0.4015 3.1978 1 0.0737 0.4877 0.2220 1.0714Jan - June '05 -0.5114 0.3921 1.7012 1 0.1921 0.5996 0.2781 1.2932July - Dec '05 12.5175 396.34 0.0010 1 0.9748 273073 0.0000Beta Blckr at D/CJan - June '03 -0.2740 0.3479 0.6201 1 0.4310 0.7604 0.3845 1.5037July - Dec '03 Coefficients did not converge for split file so no model fitted for this timeJan - June '04 0.4048 0.4666 0.7526 1 0.3857 1.4990 0.6006 3.7411

73


July - Dec '04 -0.0740 0.3235 0.0523 1 0.8192 0.9287 0.4927 1.7507Jan - June '05 -0.7125 0.5462 1.7013 1 0.1921 0.4904 0.1681 1.4307July - Dec '05 -0.9228 0.6305 2.1421 1 0.1433 0.3974 0.1155 1.3675Smk Cess AdviceJan - June '03 -0.9137 0.6991 1.7080 1 0.1912 0.4011 0.1019 1.5786July - Dec '03 0.0733 1.1547 0.0040 1 0.9494 1.0760 0.1119 10.3451Jan - June '04 0.5500 0.5701 0.9305 1 0.3347 1.7332 0.5669 5.2984July - Dec '04 0.6463 0.6564 0.9696 1 0.3248 1.9085 0.5272 6.9083Jan - June '05 Coefficients did not converge for split file so no model fitted for this timeJuly - Dec '05 Coefficients did not converge for split file so no model fitted for this timeACEI/ARB for LVSDJan - June '03 -0.1198 0.4106 0.0851 1 0.7705 0.8871 0.3967 1.9837July - Dec '03 -0.4370 0.4163 1.1019 1 0.2938 0.6459 0.2856 1.4608Jan - June '04 -0.7807 0.3943 3.9205 1 0.0477 0.4581 0.2115 0.9921July - Dec '04 -0.7089 0.4504 2.4768 1 0.1155 0.4922 0.2036 1.1900Jan - June '05 0.3678 1.0585 0.1207 1 0.7283 1.4445 0.1814 11.5008July - Dec '05 -0.7841 0.8175 0.9200 1 0.3375 0.4565 0.0920 2.2664Thromb w/in 30Jan - June '03 -11.6878 462.55 0.0006 1 0.9798 0.0000 0July - Dec '03 Coefficients did not converge for split file so no model fitted for this timeJan - June '04 0.5166 1.2308 0.1762 1 0.6747 1.6764 0.1502 18.7084July - Dec '04 5.2525 6.6947 0.6156 1 0.4327 191.045 0.0004 9.5E+07Jan - June '05 9.0075 26.102 0.1191 1 0.7300 8163.87 0.0000 1.3E+26July - Dec '05 Coefficients did not converge for split file, so no model fitted for this timePCI w/in 120 minsJan - June '03 Coefficients did not converge for split file, so no model fitted for this timeJuly - Dec '03 Coefficients did not converge for split file, so no model fitted for this timeJan - June '04 0.0893 0.6056 0.0218 1 0.8827 1.0934 0.3337 3.5830July - Dec '04 Coefficients did not converge for split file, so no model fitted for this timeJan - June '05 -0.4601 0.7410 0.3856 1 0.5346 0.6312 0.1477 2.6970July - Dec '05 Coefficients did not converge for split file, so no model fitted for this timeAll Applicable P4PJan - June '03 0.2336 0.1804 1.6760 1 0.1955 1.2631 0.8869 1.7990July - Dec '03 -0.0105 0.1981 0.0028 1 0.9576 0.9895 0.6712 1.4589Jan - June '04 0.0323 0.1706 0.0358 1 0.8499 1.0328 0.7393 1.4429July - Dec '04 -0.3572 0.1814 3.8772 1 0.0489 0.6996 0.4903 0.9984Jan - June '05 -0.0395 0.2622 0.0227 1 0.8802 0.9612 0.5750 1.6069July - Dec '05 0.5410 0.4222 1.6416 1 0.2001 1.7177 0.7508 3.9298

Refer to Attachment VII for the hazard functions of covariates included in the

time series models for each regressor of interest.

74


Logistic Regression Mortality Results

In a logistic regression analysis with 30-day post-discharge mortality as the

outcome variable, aspirin at arrival (odds ratio, 0.39; 95 C l , 0.25-0.61; p < 0.0001),

aspirin at discharge (odds ratio, 0.41; 95 Cl, 0.22-0.75; p = 0.004), beta blocker at

arrival (odds ratio, 0.48; 95 Cl, 0.32-0.72; p < 0.0003), and ACEI or ARB for LVSD

(odds ratio, 0.35; 95 Cl, 0.17-0.72; p = 0.005) were all predictors o f lower patient

mortality. See Table 15.

Table 14: Alive/Dead at 30 Days Outcome Results for Regressors o f Interest in Total Population__________________________________________________________________

Measure Name B SE Wald df Sig. Exp(B)95% Cl Exp(B) Lower Upper

Aspirin at Arrival -0.9437 0.2287 17.020 1 0.0000 0.3892 0.2486 0.6093Aspirin at D/C -0.9013 0.3150 8.1889 1 0.0042 0.4061 0.2190 0.7528Beta Blocker at Arr -0.7301 0.2024 13.008 1 0.0003 0.4818 0.3240 0.7165Beta Blocker at D/C -0.4739 0.3037 2.4349 1 0.1187 0.6226 0.3433 1.1290Smk Cess Advice 0.6669 0.6756 0.9746 1 0.3235 1.9483 0.5183 7.3234ACEI for LVSD -1.0627 0.3755 8.0083 1 0.0047 0.3455 0.1655 0.7213Thromb 30 mins 2.4802 1.3502 3.3743 1 0.0662 11.944 0.8470 168.43PCI 120 mins -0.0729 0.4086 0.0318 1 0.8585 0.9297 0.4174 2.0709All Applicable P4P 0.1541 0.1293 1.4201 1 0.2334 1.1666 0.9054 1.5032

Logistic Regression Time Series Mortality Results

The hazard functions for the statistically significant regressors o f interest in the

full model were tracked over time using logistic regression. Aspirin at arrival was a

predictor o f better outcomes during observation two (odds ratio, 0.25; 95 Cl, 0.09-

0.70; p = 0.008). Aspirin at discharge was a predictor of better outcomes over time

period one (odds ratio, 0.22; 95 Cl, 0.08-0.62; p = 0.004), time period four (odds ratio,

0.18; 95 Cl, 0.05-0.64; p = 0.008), and time period five (odds ratio, 0.02; 95 Cl, 0.00-

75


0.37; p = 0.009). During observation three (odds ratio, 0.19, 95 Cl, 0.08-0.44; p =

0.0001) and observation five (odds ratio, 0.30, 95 Cl, 0.09-0.96; p = 0.04) beta blocker

at arrival was a significant predictor o f lower mortality. ACEI or ARB for LVSD had

a statistically significant positive impact on outcomes during time period three (odds

ratio, 0.10, 95 Cl, 0.02-0.60; p = 0.01). Similar to the survival analysis looking at the

process measures’ impact on mortality over time, the logistic regression over each six

month time period shows a mostly consistent pattern in the direction of the process

measures’ effect. See Table 16.

Table 15: Alive/Dead at 30 Days Time Series Outcome Results for StatisticallySignificant Regressors of Interest in Total Population

B S.E. Wald df Sig. Exp(B)95% Cl Exp(B) Lower Upper

Aspirin at ArrivalJan - June '03 -0.8053 0.4378 3.3838 1 0.0658 0.4469 0.1895 1.0541July - Dec '03 -1.3747 0.5216 6.9469 1 0.0084 0.2529 0.0910 0.7030Jan - June '04 -0.8598 0.4913 3.0631 1 0.0801 0.4232 0.1616 1.1086July - Dec '04 -0.8426 0.6275 1.8034 1 0.1793 0.4306 0.1259 1.4728Jan - June '05 -1.1214 0.7527 2.2194 1 0.1363 0.3258 0.0745 1.4246July - Dec '05 19.9181 18881.9 0.0000 1 0.9992 4.5E+08 0.0000

Aspirin at DischargeJan - June '03 -1.5026 0.5193 8.3715 1 0.0038 0.2226 0.0804 0.6159July - Dec '03 -0.7341 0.8079 0.8258 1 0.3635 0.4799 0.0985 2.3378Jan - June '04 -0.2126 0.8459 0.0631 1 0.8016 0.8085 0.1540 4.2436July - Dec '04 -1.7081 0.6438 7.0386 1 0.0080 0.1812 0.0513 0.6400Jan - June '05 -3.9890 1.5295 6.8014 1 0.0091 0.0185 0,0009 0.3712July - Dec '05 17.3388 14093.9 0.0000 1 0.9990 3.4E+07 0.0000

Beta Blocker at ArrivalJan - June '03 -0.5804 0.4214 1.8974 1 0.1684 0.5597 0.2451 1.2782July - Dec '03 -0.6067 0.4654 1.6993 1 0.1924 0.5451 0.2189 1.3573Jan - June '04 -1.6853 0.4445 14.3767 1 0.0001 0.1854 0.0776 0.4430July - Dec '04 -0.9254 0.6881 1.8087 1 0.1787 0.3964 0.1029 1.5269Jan - June '05 -1.2079 0.5979 4.0806 1 0.0434 0.2988 0.0926 0.9647July - Dec '05 18.5513 11224.1 0.0000 1 0.9987 1.1E+08 0.0000

ACEI/ARB for LVSDJan - June '03 -1.1742 0.7463 2.4754 1 0.1156 0.3091 0.0716 1.3345July - Dec '03 -0.4290 0.8894 0.2326 1 0.6296 0.6512 0.1139 3.7221

76


Jan-June'04 -2.2266 0.8752 6.4717 1 0.0110 0.1079 0.0194 0.5998Ju ly-D ec '04 -0.3417 1.2451 0.0753 1 0.7837 0.7105 0.0619 8.1543Jan-June'05 -0.2003 8507.2 0.0000 1 1.0000 1.8141 0.0000 .Ju ly-D ec '05 -1.2879 1.2815 1.0099 1 0.3149 0.2759 0.0224 3.4002

Logistic Regression Morbidity Results

Besides mortality, another indicator o f patient outcomes is readmission rates.

Using readmissions within 30 days as the outcome variable, a logistic regression

analysis identified that aspirin at discharge (odds ratio, 3.1; 95 Cl, 1.23-7.79; p = 0.02)

was a predictor of more readmissions within 30 days of discharge while receiving all

applicable P4P measures was a predictor of less readmissions within 30 days (odds

ratio, 0.65; 95 Cl, 0.48-0.89; p = 0.007). See Table 17.

Table 16: Readmission in 30 Days Outcome Results for Regressors of Interest inTotal Population

Measure Name B SE Wald df Sig. Exp(B)95% Cl Exp(B) Lower Upper

Aspirin at Arrival 0.0327 0.4402 0.0055 1 0.9408 1.0332 0.4360 2.4483Aspirin at Discharge 1.1306 0.4705 5.7736 1 0.0163 3.0974 1.2317 7.7895Beta Blocker at Arr 0.5151 0.4342 1.4077 1 0.2354 1.6739 0.7147 3.9201Beta Blocker at D/C 0.2534 0.2692 0.8857 1 0.3467 1.2883 0.7601 2.1836Smk Cess Advice 0.0032 0.3770 0.0001 1 0.9933 1.0032 0.4792 2.1003ACEI for LVSD 0.0799 0.3718 0.0462 1 0.8299 1.0832 0.5227 2.2447Thromb 30 mins -0.2662 0.6521 0.1667 1 0.6831 0.7663 0.2135 2.7506PCI 120 mins -0.7717 0.5815 1.7613 1 0.1845 0.4622 0.1479 1.4448All Applicable P4P -0.4246 0.1585 7.1765 1 0.0074 0.6541 0.4794 0.8923

Logistic Regression Time Series Morbidity Results

Looking at the significant regressors o f interest’s impact on readmissions over

time shows that receiving all applicable P4P measures was a predictor o f fewer

readmissions within 30 days from 1/04-6/04 (odds ratio, 0.30; 95 Cl, 0.14-0.62; p =

77


0.001) and from 1/05-6/05 (odds ratio, 0.34; 95 Cl, 0.16-0.76; p = 0.008). However,

aspirin at discharge did not have a statistically significant impact on readmissions

within 30 days for any o f the six month time intervals. See Table 18.

Table 17: Readmission in 30 Days Outcome Results for Statistically Significant Regressors of Interest in Total Population__________________________________

B S.E. Wald df Sig. Exp(B)95% Cl Exp(B)

Lower UpperAll P4P Measures

Jan - June '03 0.4005 0.3954 1.0259 1 0.3111 1.4926 0.6876 3.2401July - Dec '03 0.2208 0.4592 0.2313 1 0.6306 1.2471 0.5071 3.0672Jan - June '04 -1.2137 0.3772 10.355 1 0.0013 0.2971 0.1418 0.6222July - Dec ’04 -0.4562 0.3353 1.8518 1 0.1736 0.6337 0.3284 1.2225Jan - June ’05 -1.0694 0.4056 6.9533 1 0.0084 0.3432 0.1550 0.7599July - Dec '05 0.1403 0.5704 0.0605 1 0.8058 1.1506 0.3762 3.5190

Aspirin at DischargeJan - June '03 1.0190 1.0412 0.9578 1 0.3278 2.7704 0.3600 21.3213July - Dec '03 1.8146 1.0516 2.9775 1 0.0844 6.1384 0.7815 48.2147Jan - June '04 0.4472 0.7761 0.3320 1 0.5645 1.5639 0.3417 7.1588July - Dec '04 1.1535 1.0760 1.1493 1 0.2837 3.1692 0.3847 26.1112Jan - June'05 18.0126 8903.7 0.0000 1 0.9984 6.6E+0.7 0July - Dec '05 18.4351 13508.2 0.0000 1 0.9989 1.01E+08 0

Covariates’ Impact on Outcomes Analysis

Test fo r Proportional Hazards Results

The Cox proportional hazards model assumes that the effect of each covariate

is the same at all points in time. If the effect of a covariate differs with time, then the

proportional hazard assumption is violated. One can identify when the proportionality

assumption has failed if the interaction term between the variable and time has a beta

coefficient that is at least twice as large as its standard error and if the interaction term

is statistically significant (p < 0.05). Forty-five of the 49 covariates have a non

significant interaction term between itself and time which indicates that the

78


proportional hazards assumption has not been violated. Four variables did fail the

proportionality assumption: Asian/Pacific Islander race (beta, -0.04; p = 0.008);

identified payor (beta, 0.14; p = 0.006); thrombolysis treatment (beta, -0.06; p =

0.005); and depression (beta, 0.05; p = 0.04). These four variables may be time

dependent or may be interacting with one or more covariates and time. See Table 11.

Table 18: Test for Proportional Hazards ResultsInteraction Variables B SE Wald df Sig. Exp(B)Chula Vista*time 0.6702 4 0.9549Encinitas*time 0.0280 0.0556 0.2533 1 0.6148 1.0284Green*time 0.0054 0.0317 0.0287 1 0.8654 1.0054La Jolla*time 0.0022 0.0310 0.0049 1 0.9443 1.0022Mercy*time -0.0106 0.0265 0.1603 1 0.6889 0.9894Age*time -0.0004 0.0003 1.3068 1 0.2530 0.9996Female*time -0.0120 0.0084 2.0464 1 0.1526 0.9881Single*time 1.1549 3 0.7638Widowed*time 0.0120 0.0122 0.9674 1 0.3253 1.0121Divorced/Sep*time 0.0122 0.0165 0.5441 1 0.4607 1.0122Married*time 0.0055 0.0104 0.2760 1 0.5994 1.0055White*time 11.5778 5 0.0411African American*time -0.0083 0.0236 0.1251 1 0.7235 0.9917Native American*time 1.7233 5.2730 0.1068 1 0.7438 5.6027Asian/Pac Isbtime -0.0377 0.0143 6.9601 1 0.0083 0.9630Other Race*time 0.0046 0.0127 0.1322 1 0.7162 1.0046Unk Race*time 0.0831 0.0468 3.1538 1 0.0758 1.0866Stated Religion*time -0.0042 0.0086 0.2410 1 0.6235 0.9958Identified PCP*time 0.0071 0.0083 0.7228 1 0.3952 1.0071Identified Payor*time 0.1353 0.0494 7.4856 1 0.0062 1.1448CABG Surgery*time 0.0106 0.0166 0.4093 1 0.5223 1.0107Oth Open Heart*time -0.0216 0.0337 0.4090 1 0.5225 0.9787PCI Tx*time 0.0083 0.0109 0.5860 1 0.4440 1.0084Thrombolysis Tx*time -0.0604 0.0213 8.0281 1 0.0046 0.9414Oth Card Dx Tx*time -0.0184 0.0161 1.3140 1 0.2517 0.9817No Cardiac Tx*time -0.0311 0.0185 2.8299 1 0.0925 0.9694CAD*time 0.0882 0.1661 0.2821 1 0.5953 1.0922Prior Ml*time 0.0026 0.0169 0.0246 1 0.8755 1.0026Family Hx CAD*time 0.0343 0.0660 0.2692 1 0.6039 1.0348Dyslipidemia*time 0.0157 0.0103 2.3093 1 0.1286 1.0158Diabetes*time -0.0007 0.0085 0.0070 1 0.9332 0.9993


Hypertension*time 0.0108 0.0078 1.8941 1 0.1687 1.0109Obesity*time -0.0024 0.0204 0.0139 1 0.9062 0.9976Depression*time 0.0504 0.0245 4.2307 1 0.0397 1.0516Smoker*time -0.0186 0.0125 2.2266 1 0.1357 0.9816FTE/Occ Bed*time 0.0161 0.0155 1.0713 1 0.3006 1.0162Rapid Resp Trrftime -0.0275 0.0477 0.3324 1 0.5643 0.9729Chest Pain Ctr*time -0.0052 0.0239 0.0475 1 0.8274 0.9948CV Award*time -0.0310 0.0255 1.4772 1 0.2242 0.9695Hosp AMI VoPtime 0.0000 0.0001 0.0616 1 0.8039 1.0000MD AMI VoPtime 0.0024 0.0028 0.7017 1 0.4022 1.0024Adm/ICU Beds*time -0.0038 0.0049 0.6010 1 0.4382 0.9962Medicare*time -0.0023 0.0069 0.1098 1 0.7403 0.9977MediCaPtime -0.0042 0.0103 0.1660 1 0.6837 0.9958CommerciaPtime -0.0030 0.0078 0.1469 1 0.7015 0.9970Oth Gvmt/SP*time 0 .Oth Pyr/WC*time 0 .Surg Backup*time -0.0018 0.0269 0.0047 1 0.9455 0.9982Teaching Hosp*time 0.0006 0.0252 0.0005 1 0.9823 1.0006

Covariate Survival Analysis Results

The following covariates were predictors o f lower mortality when in the full

model for survival analysis: widowed (hazard ratio, 0.80; 95 Cl, 0.64-0.99; p = 0.04);

identified a PCP (hazard ratio 0.76; 95 Cl, 0.65-0.99; p = 0.0005); CABG surgery

(hazard ratio, 0.58; 95 Cl, 0.40-0.83; p = 0.003); PCI treatment (hazard ratio, 0.48; 95

Cl, 0.39-0.59; p < 0.0001); dyslipidemia (hazard ratio, 0.42; 95 Cl, 0.34-0.50; p <

0.0001); hypertension (hazard ratio, 0.84; 95 Cl, 0.73-0.97; p - 0.02); and increased

rate o f AMI admission to ICU beds (hazard ratio, 0.64; 95 Cl, 0.42-0.98; p = 0.04).

Conversely, the following covariates were predictors o f higher (i.e., worse) mortality

when in the full model for survival analysis: Encinitas hospital (hazard ratio, 25.0; 95

Cl, 2.7-227.3; p = 0.004); Mercy hospital (hazard ratio, 13.4; 95 Cl, 2.0-89.6; p =

0.008); increased age (hazard ratio, 1.04; 95 Cl, 1.03-1.05; p < 0.0001); identified a

80


payor (hazard ratio, 3.0; 95 Cl, 1.46-5.98; p = 0.003); thrombolysis treatment (hazard

ratio, 1.55; 95 Cl, 1.00-2.38; p = 0.05); no cardiac treatment (hazard ratio 1.68; 95 Cl,

1.17-2.40; p = 0.005); diabetes (hazard ratio, 1.17; 95 Cl, 1.00-1.37; p = 0.05); and

increased annual hospital AMI admissions (hazard ratio, 1.02; 95 Cl, 1.00-1.03; p =

0.03). See Table 14.

Table 19: Covariate Hazard Functions

Covariates B SE Wald df Sig. Exp(B)95% Cl Exp(B)

Lower UpperChula Vista 13.793 4 0.0080Encinitas 3.2170 1.1272 8.1453 1 0.0043 24.954 2.7395 227.31Green 0.3869 2.6798 0.0208 1 0.8852 1.4724 0.0077 281.26La Jolla 4.8870 3.2021 2.3292 1 0.1270 132.56 0.2493 70482Mercy 2.5931 0.9706 7.1381 1 0.0075 13.371 1.9953 89.598Age 0.0377 0.0035 114.37 1 0.0000 1.0384 1.0313 1.0456Female -0.0589 0.0782 0.5665 1 0.4516 0.9428 0.8088 1.0990Single 8.7622 0.0326Widowed -0.2249 0.1098 4.1984 1 0.0405 0.7986 0.6440 0.9903Divorced/Separtd 0.1931 0.1504 1.6469 1 0.1994 1.2130 0.9032 1.6289Married -0.0560 0.0959 0.3413 1 0.5591 0.9455 0.7835 1.1410White 0.6209 0.9870African American 0.0208 0.1951 0.0114 1 0.9150 1.0210 0.6966 1.4965Native American 0.0747 1.0046 0.0055 1 0.9407 1.0775 0.1504 7.7188Asian/Pacific Isldr 0.0221 0.1227 0.0324 1 0.8572 1.0223 0.8038 1.3002Other -0.0492 0.1010 0.2376 1 0.6260 0.9520 0.7810 1.1603Unknown -0.1945 0.3590 0.2934 1 0.5881 0.8233 0.4073 1.6640Stated Religion 0.0552 0.0809 0.4658 1 0.4949 1.0568 0.9018 1.2383Mos Since Admit 0.0088 0.0114 0.6026 1 0.4376 1.0089 0.9866 1.0316Identified PCP -0.2747 0.0787 12.185 1 0.0005 0.7598 0.6512 0.8865Identified Payor 1.0844 0.3594 9.1063 1 0.0025 2.9578 1.4624 5.9822CABG Tx -0.5488 0.1867 8.6410 1 0.0033 0.5776 0.4006 0.8329Oth Open Heart 0.5468 0.3021 3.2771 1 0.0703 1.7278 0.9558 3.1234PCI Tx -0.7360 0.1096 45.071 1 0.0000 0.4790 0.3864 0.5938Thrombolysis Tx 0.4357 0.2206 3.9003 1 0.0483 1.5460 1.0033 2.3823Oth Cardiac Proc 0.2381 0.1567 2.3075 1 0.1288 1.2688 0.9332 1.7252No Cardiac Tx 0.5158 0.1831 7.9356 1 0.0048 1.6750 1.1699 2.3982CAD -0.8021 1.0143 0.6253 1 0.4291 0.4484 0.0614 3.2737Prior Ml -0.0971 0.1562 0.3863 1 0.5343 0.9075 0.6681 1.2326Family Hx CAD -0.5211 0.5849 0.7938 1 0.3730 0.5939 0.1887 1.8688Dyslipidemia -0.8797 0.1002 77.096 1 0.0000 0.4149 0.3409 0.5049Diabetes 0.1611 0.0802 4.0369 1 0.0445 1.1748 1.0040 1.3746


Hypertension -0.1721 0.0737 5.4537 1 0.0195 0.8419 0.7287 0.9727Obesity -0.2532 0.1918 1.7432 1 0.1867 0.7763 0.5331 1.1305Depression -0.3028 0.2061 2.1580 1 0.1418 0.7387 0.4932 1.1065Smkr Past 12 Mo -0.1255 0.1243 1.0206 1 0.3124 0.8820 0.6914 1.1253FTE/Adj Occ Bed 0.1742 0.1765 0.9736 1 0.3238 1.1903 0.8421 1.6823Rapid Resp Tm -0.7087 0.4351 2.6528 1 0.1034 0.4923 0.2098 1.1550Chest Pain Ctr 0CV Award Yr Visit -0.6358 0.3549 3.2096 1 0.0732 0.5295 0.2641 1.0616Hosp AMI Admits 0.0158 0.0071 4.9456 1 0.0262 1.0160 1.0019 1.0303Avg MD AMI Adm 0.0401 0.0512 0.6149 1 0.4329 1.0409 0.9416 1.1508AMI Adm/ICU Bd -0.4456 0.2150 4.2959 1 0.0382 0.6405 0.4202 0.9761Medicare -0.3454 0.3034 1.2965 1 0.2549 0.7079 0.3906 1.2830MediCal -0.4018 0.3715 1.1697 1 0.2795 0.6691 0.3231 1.3859Commercial -0.7990 0.4716 2.8700 1 0.0902 0.4498 0.1785 1.1336Oth Gvmt/Self Py -1.0648 0.6094 3.0529 1 0.0806 0.3448 0.1044 1.1384Oth Pyr Wrk Cmp 0Teaching Hosp -0.2640 0.4764 0.3070 1 0.5795 0.7680 0.3019 1.9536Surgical Backup 0a = Degree of freedom reduced because of constant or linearly dependent covariates b = Constant or Linearly Dependent Covariates Chest_Pain_Center = Facility_Code(4); Other_including_Workers_Comp = 180 + 4.979*Facility

Pay-for-Performance Outcomes Analysis

Mortality Pre and Post-Intervention Results

Thirty-day, 90-day, and 180-day mortality rates all improved from 2003 to

2005. Before participation in the HQA, 30-day mortality ranged from 11% - 13%,

while after participation in the HQA, the rate dropped between eight percent and nine

percent. Similarly, 90-day mortality went from 13% - 15% before the HQA to 10% -

11% after the HQA. Sixteen to 18% of patients died within 180 days before

participation in the HQA compared to 11% - 14% after. See Figures 11-13.

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Figure 11: 30-Day Mortality Rates Over Time

30-Day Mortality Rate

20%18%16%

10%8%6%4%2%0%

11%

1/1/03-6/30/03

7/1/03-12/31/03

1/1/04-6/30/04

7/1/04-12/31/04



1/1/05-6/30/05

7/1/05-12/31/05

20%18%16%14%12%10%

8%6%4%2%0%

T3%- 11%

10%

1/1/03-6/30/03

7/1/03-12/31/03

1/1/04-6/30/04

7/1/04-12/31/04

1/1/05-6/30/05

7/1/05-12/31/05

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20%18%16%14%12%10%

8%6%4%2%0%

.18%

16%.14%

12%- ♦ 11%

1/1/03-6/30/03

7/1/03-12/31/03

1/1/04-6/30/04

7/1/04-12/31/04

1/1/05-6/30/05

7/1/05-12/31/05

The results of the logistic regression to determine whether HQA impacted 30-

day, 90-day, or 180-day mortality showed that none of the variables (time, post-HQA,

and time/post-HQA interaction) were statistically significant in any o f the three

equations. See Tables 19-21.

Table 20: Logistic Regression Results of 30-Day Mortality Before and After HQA

30-Day Mortality B S.E. Wald df Sig. Exp(B)95% Cl Exp(B) Lower Upper

Time -0.0555 0.0830 0.4465 1 0.5040 0.9461 0.8040 1.1132Post -0.4796 0.2704 3.1441 1 0.0762 0.6191 0.3644 1.0518Post by Time 0.0336 0.1300 0.0667 1 0.7963 1.0341 0.8015 1.3342Constant -1.8791 0.1816 107.12 1 0 . 0 0 0 0 0.1527

Table 21: Logistic Regression Results o f 90-Day Mortality Before and After HQA


Time -0.0840 0.0777 1.1683 1 0.2798 0.9195 0.7896 1.0707Post -0.3131 0.2457 1.6232 1 0.2027 0.7312 0.4517 1.1836Post by Time -0.0164 0.1195 0.0189 1 0.8905 0.9837 0.7783 1.2433

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Constant -1.6424 0.1690 94.456 1 0.0000 0.1935

Table 22: Logistic Regression Results o f 180-Day Mortality Before and After HQA


Time -0.0803 0.0725 1.2255 1 0.2683 0.9228 0.8005 1.0638Post -0.2737 0.2290 1.4291 1 0.2319 0.7605 0.4855 1.1913Post by Time -0.0423 0.1115 0.1438 1 0.7046 0.9586 0.7705 1.1927Constant -1.4528 0.1582 84.375 1 0.0000 0.2339

Morbidity Pre and Post-Intervention Results

The rate o f readmissions within 30 days decreased from seven percent in the

beginning of 2003 to four percent at the end o f the 2005. Unlike the mortality rates,

the decline in morbidity (readmission) rates did not show a consistent improvement, as

scores fluctuated during this time. However, the results of the logistic regression

identified that neither the intercept of the post-HQA performance nor the rate of

readmissions post-participation in the HQA were statistically significant. This finding

was similar to the result of the logistic regression for mortality. See Figure 14 and

Table 22.

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Figure 14: 30-Day Readmission Rates Over Time

30-Day Readmission Rate

20%18%16%14%

1/1/03-6/30/03

7/1/03-12/31/03

1/1/04-6/30/04

7/1/04-12/31/04

1/1/05-6/30/05

7/1/05-12/31/05

Table 22: Logistic Regression Results o f Readmission Within 30-Days Before and After HQ A_______________________________________________________________

95% Cl Exp(B)30-Day Readmit B S.E. Wald df Sig. Exp(B) Lower UpperTime -0.1714 0.1201 2.0362 1 0.1536 0.8425 0.6657 1.0661Post -0.0712 0.3540 0.0405 1 0.8405 0.9312 0.4653 1.8636Post by Time -0.0108 0.1762 0.0037 1 0.9513 0.9893 0.7003 1.3975Constant -2.5307 0.2540 99.268 1 0.0000 0.0796

D is c u s s io n

Pay-for-Performance’s Impact on Process Measures

From 2003 to 2005, Scripps Health’s performance in AMI process measures

improved substantially. Compliance with all applicable P4P measures increased from

60% to 8 6 % from observation one to observation six. Over the same period of time,

aspirin at arrival improved from 93% to 99%. Beta blocker at arrival improved from

85% to 96%. ACEI or ARB for LVSD improved from 75% to 87%. Smoking

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cessation advice improved from 57% to 89%. Aspirin at discharge improved from

91% to 98%. Beta blocker at discharge improved from 86% to 95%. PCI within 120

minutes improved from 16% to 71%. Thrombolytic agent within 30 minutes was the

only indicator without any consistent improvement over the three years, perhaps

impacted by the infrequency o f patients receiving thrombolytic therapy. Thrombolytic

agent within 30 minute performance went from 24% in early 2003 to 31% in late

2005.

Scripps Health has improved its performance in providing all applicable P4P

measures for AMI patients consistently during all six observation time periods from

2003 to 2005. The slope, measuring the rate o f improvement, before and after

participation in the HQA did not change, indicating that the rate o f improvement did

not change after participation in the HQA. The coefficients o f the post-HQA variable

and the interaction term suggest that initiation o f participation in the HQA may have

led or contributed to a lower than expected performance in providing all applicable

P4P measures in the forth observation given the performance prior to participation in

the HQA. The post-HQA intercept noting the different level o f performance in

providing all applicable P4P measures after initiation of the HQA was statistically

significant.

Aspirin at arrival, aspirin at discharge, beta blocker at discharge, and

thrombolytic agent received within 30 minutes did not have statistically significant

differences in performance from before participation in the HQA to after participation

in the HQA. The slope of performance with beta blocker at arrival did not change with

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participation in the HQA but its intercept for scores post-HQA did. Compliance with

providing beta blockers at arrival was at a higher level after participation in the HQA

began than would have been expected given the rate of improvement for this indicator

before participation in the HQA. Smoking cessation advice/counseling compliance did

not change in slope or intercept, however, the changes taking place over time (from

observation one through three to observation four through six) were statistically

significant, suggesting seasonal, cyclical, or time dependent change effects present.

The slope of performance with the ACEI or ARB for LVSD metric did change at a

statistically significant level, indicating that participation in the HQA may have

contributed to a change in improvement rate for this measure. Compliance with PCI

within 120 minutes changed dramatically from before to after participation in the

HQA. The intercept and the change in slope (i.e. the betas for the post-HQA variable

and the interaction term) indicate that participation in the HQA may have slowed the

level and rate of improvement in compliance with PCI within 120 minutes. However,

it is important to keep in mind that there is a ceiling for performance (i.e., 100%

compliance) and sustaining the high levels of improvement experienced before the

HQA may be unfeasible at later time periods.

In summary, the data analyses indicate that Scripps Health’s improvements in

P4P process measure compliance for AMI patients cannot be attributed to its

participation in the Hospital Quality Alliance. If the HQA had made an impact on

process measure compliance, one would have expected a significant change in slope

(i.e., increased slope indicates better performance) and/or a significant change in

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intercept of the post-HQA variable (i.e., intercept at higher value than expected due to

pre-HQA slope indicates better performance), which did not happen. In certain metrics

(e.g., all applicable P4P measures and PCI within 120 minutes), participation may

have negatively impacted the level o f performance and/or rate o f improvement in

performance, as the slope and/or intercept of post-HQA compliance was not as high as

expected based upon pre-HQA scores. Further analysis should be conducted with a

time fit (rather than a linear model) to take into account the asymptotic nature of

process measure performance. Should the results of the time fit model analysis be

similar to these results, then research on potential unintended consequences of

participation in the HQA is recommended.

Before drawing the conclusion that the HQA did not affect process measures, it

is important to note some limitations o f this analysis. The Scripps Health process

measure performance was improving before initiation of participation in the HQA.

Anticipation of the HQA and knowledge o f the process measures the HQA would

track may have motivated improvement in compliance with the measures during this

time. Conversely, there could have been other events in history, such as new journal

articles, that led to improvement before the HQA. The reason for improvement in

scores before participation in the HQA is unknown.

The HQA could have impacted performance in indirect ways. To prepare for

the HQA and reaching high levels o f process measure compliance, there may have

been changes in staffing, documentation, and care processes. These changes could all

impact process measure scores, however, these changes could take place gradually

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before and/or after the HQA. While motivated by the HQA, these changes may not be

captured between observation three and four when participation in the HQA began. If

changes did not occur during that time period, they would not be attributed to the

HQA given the study design that was used.

Perhaps beginning to collect and monitor data on the AMI process measures

motivated improvement in scores either irrespective of or in conjunction with

participation in the HQA. Williams et al suggest that collecting data on performance

? o

measures may be a sufficient catalyst for improvement. However, the results of this

study do not confirm other studies’ findings that P4P improves process measures.12,13

This research suggests that it may not be P4P (but perhaps collection and monitoring

o f data or publication of new evidence-based medicine) that drives improvement. On

the other hand, it is important to do further analysis to look for early and lagged effects

of participation in the HQA before concluding that the HQA had/has no impact on

process measure scores.

Had the results of the previous analysis shown that participation in the HQA

significantly impacted Scripps Health’s process measure scores, an analysis to test

whether the internal threat of history was present would have been conducted.

However, the null hypothesis for the first study aim held, which is that process

measures did not improve as a result of pay-for-performance. Nevertheless, the data

that would have been used to test the internal threat o f history was useful in comparing

Scripps Health performance with that of another hospital system, Premier, Inc., that

participated in an earlier P4P program as well as that o f other hospitals nation-wide

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participating in the HQA.

Scripps Health and Premier, Inc. started out with much different levels of

performance with the AMI process measures (absolute difference 7.7%), which

decreased over time, dropping to an absolute difference of 6.3% in time period two

and to 2.2% in time period three after Scripps began participating in the HQA. Scripps

Health’s performance from 7/04-6/05 was similar (not statistically different) to that of

other hospitals participating in the HQA nation-wide, which suggests that Premier,

Inc. had characteristics that encouraged or made it easier to comply with the AMI

process measures than other hospitals nationally. Premier, Inc. began using a

sophisticated reporting and analytics tool to monitor and track process measure

performance before Scripps Health did. According to Stephanie Alexander, Senior

Vice President for Premier Healthcare Informatics (personal communication, 2007),

the 54 hospitals that began participating in the Premier Hospital Quality Incentive

Demonstration we required to use the Premier decision support analytic tools to

collect, monitor, and analyze their performance on the 34 measures used in the

demonstration project before the program began. This is likely a key reason for the

variation in performance levels at the first observation, However, further research into

the differences between Premier, Inc. hospitals and others nation-wide (including

Scripps) is recommended.

The test to identify whether the P4P program that Premier, Inc. participated in

changed its performance compared to Scripps Health (the control) determined that the

Premier Hospital Quality Incentive Demonstration cannot be attributed to the

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improvement in scores. The slopes of the Premier, Inc. performance in comparison to

the Scripps Health performance over the same time period were not statistically

different. The time variable and the site variable were statistically significant,

indicating that the facilities started out at different levels of performance and that there

was significant increase in performance for both organizations from 10/02-9/03 to

1/04-6/04.

From 1/04-6/04 to 7/04-6/05, there was a statistically significant intervention

impacting Premier, Inc. and Scripps Health facilities’ performance on the AMI process

measures. The slopes for the two health system’s performance were statistically

different from each other. The statistical significance in slopes combined with the

variable coefficients indicates that participation in the HQA had a larger impact on

Scripps Health than it did for Premier, Inc. Given that Premier, Inc. was already

participating in a P4P program and Scripps Health began participating in a public

reporting initiative for the first time, it makes sense that Premier, Inc. would not have

experienced as great an impact as Scripps Health. In addition, the time and the site

variables were statistically significant suggesting that performance started out at

statistically different levels and that both systems experienced significant increases in

scores over this time frame.

The analysis comparing Scripps Health performance to itself suggests that

participation in the HQA did not change the rate o f improvement in Scripps Health’s

compliance with AMI process measures, yet the analysis comparing Scripps Health

performance to Premier, Inc. performance suggests otherwise. It is important to note

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that the time period for these analyses differs. The analysis comparing Scripps Health

to itself is a time series design looking at performance from 1/03-12/05 in six month

intervals. The analysis comparing Scripps Health to Premier, Inc. performance was

based upon scores from 1/04-6/04 compared to 7/04-6/05. In addition, the two

analyses used different metrics. The analysis comparing Scripps Health to itself used

an all applicable P4P metric as well as eight individual measures. The analysis

comparing Scripps Health to Premier, Inc. included a weighted average metric of the

numerators and denominators o f seven individual measures. Further analysis is

recommended to determine the impact of the HQA on process measure compliance. It

would be ideal to conduct an additional analysis comparing Scripps Health to a like

hospital system that did not participate in the HQA, yet it is unlikely that one exists.

The largest change in scores occurred for Premier, Inc. when it started

participating in its first intervention (the P4P program) and for Scripps Health when it

started participating in its only intervention (the P4R program). The larger change in

scores over the time period o f the first intervention for Premier, Inc. and the time

period of the only intervention for Scripps Health may indicate that improvement was

occurring from 10/02-6/05 but that the initiation of public interest in the AMI process

measure scores may have increased internal emphasis on performance. While it is

tempting to draw this conclusion, given the lack of statistical significance of the P4P

program and the conflicting results for the statistical significance o f the P4R program,

conclusions of this nature are unwarranted. Further analysis using a time fit or learning

model to account for the performance metric ceiling is recommended.

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Process-Outcomes Link

Although evidence-based literature suggests that compliance with the eight

AMI process measures used in this analysis should lead to improved patient outcomes

(i.e. increased probability o f survival), this study showed that not all o f the measures

were associated with a statistically significant reduction in mortality in the Scripps

Health system. A survival analysis on the full model of 3,954 patients using days

survival as the outcome variable indicated that aspirin at arrival, beta blocker at

arrival, and ACEI or ARB for LVSD were predictors of lower mortality at a p-value <

0.05. Aspirin at arrival was associated with a 47% lower risk of death (hazard ratio;

0.53), beta blocker at arrival was associated with a 26% lower risk o f death (hazard

ratio; 0.74), and ACEI or ARB for LVSD was associated with a 33% lower risk of

death (hazard ratio; 0.67).

Similarly, a logistic regression on the full model of patients using 30-day

mortality as the outcome variable indicated that all three of the above noted indicators

(aspirin at arrival, beta blocker at arrival, ACEI/ARB for LVSD) as well as aspirin at

discharge were all predictors of lower mortality at a p-value <0.01. Aspirin at arrival

was associated with a 61% lower odds o f death within 30 days (odds ratio; 0.39),

aspirin at discharge was associated with a 59% lower odds of death within 30 days

(odds ratio; 0.41), beta blocker at arrival was associated with a 52% lower odds of

death within 30 days (odds ratio; 0.48), and ACEI or ARB for LVSD was associated

with a 65% lower odds o f death within 30 days (odds ratio; 0.35).

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Further research on the P4P measures that were not associated with improved

outcomes at a statistically significant level (e.g., beta blocker at discharge, smoking

cessation advice, thrombolytic agent within 30 minutes, PCI within 120 minutes) is

recommended using a larger patient population. If analyses are conducted on a large

patient population and the results still show that these indicators are not predictors of

improved outcomes at a statistically significant level, then it may be important to

revisit the value o f including these measures in P4P programs. Although evidence-

based literature identifies certain process measures as predictive of better outcomes,

the literature also suggests that not all process measures are indeed good predictors of

outcomes.27’65 If there are other indicators that are statistically significant predictors of

lower mortality, then perhaps increased attention should be focused on those

indicators rather than these four indicators.

Outcomes data for Premier, Inc. patients was not able to be obtained, however,

analyses comparing Scripps Health and Premier, Inc. process measure compliance and

patient mortality could shed further light on the relationship between the AMI P4P

indicators and patient outcomes.

While mortality is the outcome o f most interest, as it is often the primary

concern of the patient and the healthcare provider, readmissions are a way to assess

potential morbidity. If a patient is readmitted to a hospital within 30-days, it may

indicate that the patient had a complication while recovering from earlier treatment. A

readmission may also indicate that appropriate care may not have been received at an

earlier visit, warranting a return with continued illness that is more severe and cannot

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be handled on an outpatient basis. A logistic regression on the total patient population

using readmissions within 30-days as the outcome variable identified that receiving

aspirin at discharge was associated with an increased probability of having a

readmission within 30-days, while receiving all recommended P4P measures was

associated with a decreased probability of having a readmission within 30-days. This

analysis shows that although receiving all applicable P4P measures is not a predictor

of lower mortality, it may be a predictor o f lower morbidity. Patients receiving all

applicable P4P measures had a 35% lower odds o f a readmission within 30 days (odds

ratio; 0.65).

Aspirin at discharge was found to be a significant predictor of higher morbidity

(readmissions within 30 days). This result may be confounded by the fact that the

patients who received aspirin at discharge had higher incidence o f comorbities that can

lead to disease complications. For example, there higher rates o f obesity (eight percent

o f patients with aspirin at discharge had obesity compared to three percent without)

and smokers (19% of patients with aspirin at discharge were current smokers

compared to 14% without aspirin at discharge). There was also a large difference in

patients receiving no cardiac treatment for AMI (17% of patients with aspirin at

discharge had no cardiac treatment compared to 40% without), which is a predictor of

higher mortality. Patients without cardiac treatment who die have no opportunity to be

readmitted, which may affect readmission rates. Further research is needed to

determine whether there are other characteristics o f patients receiving aspirin at

discharge that are associated with increased readmission rates. Confounding variables

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impacting these results may be a more likely hypothesis than concluding that

providing aspirin at discharge is indeed increasing readmission rates.

Covariates’ Impact on Outcomes

The test of the proportional hazard assumption was violated for four variables:

Asian/Pacific Islander race; identified payor; thrombolysis treatment; and depression.

“If we estimate a proportional hazard model when the assumption is violated for some

variable (thereby suppressing the interaction), then the coefficient we estimate for that

variable is a sort o f average effect over the range of times observed in the data.”85

Therefore, caution must be taken in making conclusions based upon these four

covariates. Asian/Pacific Islander race and depression did not have a statistically

significant impact on outcomes, while identified payor and thrombolysis treatment

were predictors of worse mortality. Thrombolytic administration is no longer the

recommended treatment for patients seen in hospitals with cath labs and was only

given to a small subset o f patients. All Scripps Health facilities have cath labs,

therefore, it is not surprising that thrombolysis treatment was associated with worse

outcomes. Patients with an identified payor had a mean age of 70 compared to a mean

age o f 54 for those patients without an identified payor. Age may be confounding the

negative effect on outcomes that identified payor is indicating. However, it is

important to note again that no conclusions should be made based upon the

thrombolytic agent within 30 minute or the identified payor variables.

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There were other covariates that were determined to have a statistically

significant impact on outcomes that did not violate the proportional hazards

assumption. Being widowed, having an identified PCP, having CABG surgery, having

PCI surgery, having dyslipidemia, having hypertension, and having a higher

admissions per 1CU bed ratio were all predictors of lower/better mortality. Being

treated at Scripps Memorial Hospital Encinitas or Scripps Mercy Hospital San Diego

was associated with worse outcomes than patients seen at Scripps Mercy Hospital

Chula Vista. Other predictors o f higher mortality rates were increased age, having no

cardiac treatment, having diabetes, and having higher hospital-wide annual AMI

admissions.

Being widowed was associated with a 20% lower risk of death (hazard ratio;

0.80) than being single. This finding is interesting considering that the literature

suggests that being single and being widowed are associated with higher rates of

mortality compared to married individuals.72 One difference in these populations that

could possibly account for some of the difference in risk of death was that only 9% of

widowers were current smokers compared to 28% of single people. Additional

analysis is recommended on possible differences in widowed and single patient

populations that may impact the risk of death.

Having an identified PCP was used as a proxy for access to care, the

assumption being that if a patient has an identified physician, then perhaps the patient

is being seen for regular preventive, well-check, and follow-up to treatment exams. As

noted in Table 2, having regular access to care is associated with decreased mortality,

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which was confirmed with this analysis. This study showed that having an identified

PCP was associated with a 24% lower risk of death (hazard ratio; 0.76) than patients

who did not have an identified PCP.

The belief before beginning the data analysis was that PCI procedures may be

associated with better outcomes as they are less invasive (and therefore less risky) and

require less recovery time than do open heart surgeries. It is interesting that CABG

surgery, as well as PCI treatment, was associated with improved outcomes. Patients

with CABG surgery had a 42% lower risk o f death (hazard ratio; 0.58) and patients

with PCI/angioplasty had a 52% lower risk of death (hazard ratio; 0.48) than patients

without these treatments. For more severe patients, CABG surgery may be preferable

to PCI treatment. For the patients in this study, both treatment options were shown to

be effective in reducing mortality.

The comorbidities dyslipidemia and hypertension were included in the analysis

because they are risk factors for heart disease. This analysis showed that patients who

had dyslipidemia had a 58% lower risk o f death (hazard ratio; 0.42) and patients who

had hypertension had a 16% lower risk o f death (hazard ratio; 0.84) than patients who

did not have these conditions. This finding was opposite of what was expected. One

explanation for this finding is that patients who have documented and coded illness are

likely being treated for their condition. Some individuals without documented and

coded illness may have other chronic conditions but may not be getting treatment or

having active management of their disease. Additionally, in some cases, patients

without the specified illness may be sicker than those who do have the specified

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illness. For instance, patients with hypotension may be too sick to treat and may have

a higher risk of death than hypertensive patients. Given that the direction o f the effect

on mortality was unexpected for patients with dyslipidemia and hypertension, further

research is recommended related to these comorbidities.

The ratio of AMI admissions per ICU beds was used as a proxy for technology.

The expectation before running the analysis was that the lower the ratio of admissions

per ICU bed, the higher the technology available for that facility. However, the results

turned out otherwise, with higher rates o f AMI admissions per ICU bed being a

predictor o f improved patient outcomes. As the ratio o f annual AMI admission per

ICU beds increases by one (e.g., from 13.0 to 14.0), there is a 36% lower risk o f death

(hazard ratio; 0.64). Perhaps for this study, a high ratio o f AMI volume to ICU beds is

an indicator of the fact that higher levels o f volume necessitate increased throughput

and efficiency which lead to faster time to treatment. According to one interventional

cardiologist at Scripps Green Hospital, the busier his day is, the more efficient and

responsive he is to staff. Again, additional research is needed to determine whether

this is a valid rival hypothesis.

Patients seen at Scripps Memorial Hospital Encinitas had a 25% higher risk of

death (hazard ratio; 25.0) and patients seen at Scripps Mercy Hospital San Diego had a

13% higher risk o f death (hazard function; 13.4) than patients seen at Scripps Mercy

Hospital Chula Vista. [Note that the interpretation of hazard functions is different for

categorical variables than for continuous and binary variables.] There may be

differences in the patient populations that confound and/or lead to these results. For

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instance, at Scripps Memorial Hospital Encinitas, 67% of patients received all

recommended P4P metrics compared to 73% at Scripps Mercy Hospital Chula Vista.

At Scripps Mercy Hospital San Diego only 30% o f patients had an identified PCP

(predictive o f lower mortality) than patients at Scripps Mercy Hospital Chula Vista

where 47% had an identified PCP. Follow-up analysis on differences between these

facilities is needed to better understand their impact on outcomes.

The data from this study confirmed what is well known in the general

population, which is that older patients have increased risk of death. In this study of

AMI patients, for each additional year older a patient is, his/her risk of dying is four

percent (hazard ratio; 1.04) greater that o f the previous year.

It is intuitive that patients receiving no cardiac treatment were shown to have

worse outcomes in this study as they may be too sick to treat. Standard interventions

for heart attack patients like PCI procedures and CABG surgeries are proven in

evidence-based literature to improve outcomes. However, patients may refuse

treatment, may have contraindications to treatment, or may not benefit from treatment

given the severity of their condition. Patients in this study who received no cardiac

treatment had 68% higher risk of death (hazard ratio; 1.68).

As noted in Table 2, other literature has shown that patients with diabetes who

have an AMI have a higher risk o f mortality. Similar to the literature on this subject,

this study confirmed that the comorbidity o f diabetes is a predictor of increased

mortality. Patients with diabetes had a 17% higher risk of death (hazard ratio; 1.17)

than those who did not.

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An interesting result of this analysis was that the higher the annual AMI

admissions, the worse the outcome. Each additional annual AMI admission is

predictive o f a two percent higher risk of death (hazard ratio; 1.02). Volume is often

used as an indicator o f high performing health care providers in recognition awards

such as the U.S. News & World Report or the Leapfrog Top 100 Hospitals list. It is

believed that the more often an organization or a clinician performs a certain service,

the better he/she/it is at it. The results o f this study indicate that higher hospital AMI

volume may be a predictor of worse outcomes (i.e., higher mortality). The non-

intuitive result illuminates the issue that annual AMI admissions is an indicator of

disease burden only. Volume does not denote whether optimal therapy was given.

Additional analysis using the number of CABG surgeries for patients who receive a

CABG surgery and number o f PCI procedures performed for patients who receive a

PCI, is recommended rather than the number of patients seen irrespective o f treatment.

Should results o f follow-up analysis support this study’s findings, a rival hypothesis

that if volumes increase past a certain threshold, resources may not be able to support

the increase in volume with the same high level of service should be investigated.

Perhaps the needed beds, cardiologists, or equipment is not available in a timely

manner if volumes are higher than ideal for a certain facility.

Pay-for-Performance’s Impact on Outcomes

The regression discontinuity analysis did not note any significant changes in

mortality due to participation in the HQA. Thirty-day, 90-day, and 180-day mortality

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rates did improve over the time period, however, the slope and intercept of the post-

HQA compared to pre-HQA time periods were not statistically significant. If the HQA

was responsible for a change in patient outcomes, one would expect the slope and/or

intercept of the performance after participation in the HQA to be different from the

performance before participation in the HQA. However, the slope and intercept did

not change at a statistically significant level, therefore, one cannot conclude that the

noted improvements in 30-day, 90-day, and 180-day mortality rates were caused by

pay-for-performance.

A trend line for readmission rates within 30 days shows a general downward

trend in readmission rates from 2003 to 2005, possibly indicating that patient

morbidity improved during this time. Again, if the HQA provided the impetus for

improvement, one would expect the slope and/or intercept o f the performance pre-

HQA to be different from post-HQA, which was not the case. Therefore, it is not

appropriate to conclude that the HQA improved Scripps Health’s readmission rate

within 30 days.

The randomized and non-randomized controlled trials on P4P suggest that the

majority o f studies/indicators analyzed achieved their desired outcome, however, some

studies also show that the desired outcomes were not achieved and that unintended

11 58effects o f P4P may exist. ’ This study’s results indicate that in a healthcare system

that is already improving performance on process measures liked to P4P/P4R

programs and already improving mortality rates, P4P/P4R participation does not break

that pattern. While participation in the HQA may have affected process and/or

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outcomes at Scripps Health, it cannot be confirmed by this study. The null hypothesis

for the second study aim held, which is that outcomes did not improve as a results of

P4P. However, this study is able to confirm that a health system can both participate in

a P4P/P4R program and continue to improve outcomes.

Some healthcare providers argue that the P4P metrics are not indicators that

should be of primary focus when providing patient care and that by participating in

public reporting and P4P programs, all one’s time is spent documenting information

and collecting new data rather than improving care. The results of this study challenge

the notion that participating in the HQA can be a hindrance to providing high quality

care, as Scripps was able to do both.

Summary

This is an exploratory analysis. The study results identified that Scripps Health

process measure compliance increased over the study time period, however, the

conclusion cannot be made that P4P caused the increase in performance levels over

this time. This study found that four of the eight individual AMI process measures

were predictors of improved outcomes at statistically significant levels. However, this

study also found that P4P was not the direct cause of improved patient outcomes.

At the onset of this research project, the intent was to find out whether P4P

achieved its intended effect of improving outcomes. The literature suggests that by

complying with certain process measures, outcomes should improve. The problem

with the current literature is that it assumes that if everything else is held constant and

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process measures improve, outcomes will improve. In the actual context o f hospital

operations, there are limited resources. In order to improve process measure scores,

another activity that may also lead to improved outcomes may be overlooked or

forgotten. There is the potential to decrease or not affect outcomes by participating in

P4P if limited resources are shifted to focus on activities that are not the most

important in ensuring the best possible outcomes. This research showed that for

Scripps Health, survival did improve from 2003 to 2005. Therefore, the potential

unintended effects of participation in the HQA did not have a significantly negative

impact on outcomes.

Further research to determine what factors led to the improved outcomes from

2003 to 2005 would be beneficial. For instance, the mean hospital paid FTE per

adjusted occupied beds variable increased over time. Further analysis could be done to

determine whether increases in staffing had a statistically significant impact on patient

mortality and if there were certain factors that led to increased staffing levels. In

addition, the percent o f patients with no cardiac treatment decreased during this time

period from 30% in early 2003 to 17% in late 2005, and having no cardiac treatment

was a statistically significant predictor o f higher mortality. Focusing on appropriate

acute care treatment options and treatment o f comorbid/chronic conditions is also

recommended.

Rosenthal and Frank said that there is little empirical evidence to support the

effectiveness of paying for quality in healthcare.64 This study cannot support or refute

Rosenthal and Frank’s statement, however, it can pose additional questions and areas

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for research. For instance, other literature shows that public reporting increases

improvement beyond the collection of data and private reporting.86 This study does not

show a significant difference in improvement rates before and after public reporting,

so does collecting data result in improvements up to a certain point and then does

public reporting/P4P drive performance to a level which is not easily achieved without

a financial and/or reputation risk to the healthcare provider? Are there process

measures that may be more indicative of lower mortality than those currently used by

P4P and P4R programs? Can the early and lagged effects o f participation in P4R/P4P

programs be quantified so that the question o f whether it is effective to pay for quality

in healthcare be answered? The intent o f this research was to answer questions about

the value and impact of P4P. Instead of answering those questions, this analysis

identified additional areas of needed investigation.

L im it a t io n s

There are a number of limitations of this study. The limitations span from the

study design to the data that was able to be collected. Ideally, a true experimental

study design with patients seen from hospitals nation-wide randomized into pay-for-

performance and non-P4P payment methodologies would have been used. Ideally, all

the data identified in the conceptual model would have been able to be obtained and

the sample size for the patient-level data would have been similar to that which was

collected for the other nation-wide HQA participants (e.g., greater than one million) so

that one could have confidence that the sample size is large enough to elicit significant

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results. However, it is often unrealistic or unfeasible to conduct ideal studies. True

experimental study designs with randomized hospitals into P4P and non-P4P programs

have not yet been conducted and would require collaboration with payment

organizations such as insurance companies, employers, and/or the federal government.

It is also difficult to obtain large scale, national patient-level data with the patient

demographic, patient behavior, hospital characteristic, treatment, and outcome

information needed due to patient privacy rules. Given these constraints, a quasi-

experimental study was conducted. Inherent in this study are limitations.

One set o f limitations are those due to the data that was able to be collected.

Scripps Health patient level data was used rather than national data. Due to

relationships with Scripps Health and an understanding of the data collected and its

organizational characteristics, it was felt that a study using Scripps Health data could

be conducted in a timely fashion, whereas, contacting hospitals nationally and

establishing cooperation and Institutional Review Board approval could take years to

complete.

Scripps Health operates five hospital campuses in San Diego County. Each

hospital campus serves different populations o f patients as evidenced through the

different locations, payor mixes and religious affiliations. While each of the five

facilities is different from each other, there may be characteristics of Scripps Health

(such as the performance rate of process measures and outcomes before participation

in the HQA) that limit the ability to apply this study’s findings to other hospitals.

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External validity may be compromised due to the fact that patient data has been

collected from one health system in one geographical location.

The goal of this study is to determine how P4P affects patient outcomes,

however, this study uses scores from the Hospital Quality Alliance, which is a pay-for-

reporting program. Reimbursement for participation in the Hospital Quality Alliance is

the same for all participants regardless o f one’s process measure scores. The fact that

payment does not reward the high performers (and potentially disadvantage the low

performers) may limit the ability to extrapolate these findings to true P4P programs.

The HQA was chosen as the intervention because most hospitals have not participated

in a true P4P program and those that have (e.g., Premier, Inc.) do not make patient

level data available to the public. Since these findings are based upon a pay-for-

reporting system, they may underestimate the true effect of a P4P program. If high

performing hospitals were paid more than others, it may create a larger motivation to

improve one’s scores. Because Scripps Health has expected (and the expectation has

since been confirmed) that the Hospital Quality Alliance will eventually become a true

P4P program, this potential limitation may be mitigated but not eliminated.

Another potential limitation to this study is that the total patient population of

3,954 patients may be too small to yield statistically significant results on outcomes.

Results o f analyses on the full patient population and in the time series design that are

not currently statistically significant could become statistically significant if the

patient population was larger.

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As noted in Table 2, all o f the variables that should ideally be included in this

survival analysis (per the conceptual model) were not able to be included in this study.

The following data elements were not available for inclusion in the patient-level

analyses: patient’s education, income, severity of illness, diet, physical activity level,

and alcohol consumption. These variables did not exist on a standardized and

consistent basis in Scripps Health electronic systems from 2003 to 2005 and were

therefore not included in this analysis. Similarly, data from patients seen at Scripps

Memorial Hospital Encinitas from before participation in HQA were not able to be

included in this analysis as patient level data is not available from that time frame.

Even with the current data, there may be more than the ideal number of

variables included in the model. Given that this is not a definitive analysis, this

limitation may not be o f much concern, however, further analysis should weigh the

positive and negative effects o f including all variables in the conceptual model in the

study.

Post-discharge mortality was established using the Social Security Death

Index. The SSDI does not publish cause o f death, therefore mortality rates are based

on all potential causes of death rather than just deaths related to cardiac disease. This

methodology was applied consistently for all Scripps Health AMI patients over 2003

to 2005.

Another set of limitations exist related to the study design. As previously

mentioned, a true experimental study design which controls for all internal threats to

validity would be ideal. However, a quasi-experimental time series design was used

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for the main analyses with regression discontinuity analyses supporting the time series

results. In a time series design, the internal threat of history is not controlled. Had the

results of the time series analyses indicated that P4P was predictive o f improved

process measure compliance and/or lower mortality, the modified recurrent

institutional cycle design would have been used to determine whether the threat of

history was a likely threat to internal validity. While the additional analysis was not

needed, as the results of the time series design showed that P4P did not significantly

improve process measures or outcomes, it should be noted that other events in history

could be impacting the study results. For instance, introduction of new journal articles

and evidence-based care guidelines can impact compliance with process measures,

treatment, and subsequently patient outcomes. [Key evidence-based literature for each

of the AMI process measures was published before this study (with other articles

published during this study’s time frame), except for the cited evidence for smoking

cessation advice/counseling, which was first published in 2003.]

It is unlikely that maturation threats exist. It would be unusual for providers to

get better at treating AMI patients just between one observation time period and not

the others. Testing effects, if present, should also occur during each time period rather

than just one time period, so testing threats are likely controlled. Instrumentation is a

threat to internal validity if the intervention changes the way data is collected. There

were no significant changes in the data collection process using the Scripps Health

information systems during this time period. There may be slight variations due to

different abstractors used for process measure data collection. However, the required

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80% abstraction accuracy rate was always met, so while instrumentation could be a

threat to internal validity, it is unlikely to be a significant threat in this study.

Regression threats are controlled because regression towards the mean trends decline

over time and, therefore, would be an unlikely rival hypothesis for a change in scores

from after compared to before participation in the HQA. Selection is not a threat

because all patients selected by MIDAS+ for the AMI Focus Study based upon

diagnosis codes 410.00 - 410.92 are included in the analysis. Patients are not able to

drop out o f the study. Attrition is another unlikely threat as none of the patients or

hospitals dropped out of the study. Interactions such as those between selection and

maturation are also controlled with the time series design.

The time series study design, however, does not control for external threats to

validity such as interaction of testing and the intervention and the interaction between

selection and the intervention. Similar analyses to those conducted in this study using

different patient populations is recommended in order to substantiate the external

validity o f the results of this study.

The time series design compares data from after an intervention to before an

intervention, so the timing of the intervention is o f key importance. The analyses to

determine whether P4P affected process measures and/or outcomes used the date of

initiation o f HQA as the intervention date. Hospitals learned about the HQA before the

implementation date, however, from subjective discussions with California hospitals

in early 2004, many were not prepared for the HQA. Most hospitals mentioned a

desire and an intent to participate but some had to scramble to sign up before the

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deadline and the majority had not been actively focused on making improvements in

these measures. When participation in the HQA began on July 1, 2004, discharges

from the fourth quarter o f 2003 were submitted to the HQA. However, October 1,

2003 was not chosen as the intervention date because hospitals did not have the

opportunity to affect care processes for patients previously discharged. Therefore, the

implementation date for July 1, 2004 (the implementation date of the HQA) was

chosen as the intervention date. It is possible, however, for early or lagged effects to

have occurred. Efforts are being currently made at Scripps Health to improve AMI

process measure scores in anticipation o f CMS’ Values Based Purchasing plan. These

analyses did not specifically look for early or delayed effects of participation in the

HQA, although they could exist.

Most o f the analyses were conducted with patient-level data from Scripps

Health. However, for the modified recurrent institutional cycle design between

Premier, Inc. and Scripps Health, a major limitation exists. Scripps Health and

Premier, Inc. are different hospital systems and may be non-equivalent. Scripps Health

is located in San Diego, California, while the Premier, Inc. hospitals are located

nation-wide. Both hospital systems have different information technology systems

used to collect patient data and process measure compliance. Differences between

performance improvement activities and the systems’ Board/management

prioritization o f quality improvement is unknown. Both hospital systems are similar in

that they functioned from 2003 to 2005 in a largely decentralized fashion with

different (i.e. non-standardized) care processes utilized by the different facilities

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within the system depending on the unique environment, cultures of the patients

served, and employees. However, should the hospital systems be deemed to be non

equivalent, internal validity of the study findings from the additional analysis using

Scripps Health and Premier, Inc. data may be compromised.

Given the nature o f this research, it would be difficult to conduct a true

experimental study using a large sample size o f national patient-level data. Despite the

many limitations o f this research, the study results are interesting because they suggest

that P4P may not produce its desired results. CMS is moving towards focusing on P4P

(which it calls Values Based Purchasing) through an amalgamation of the HQA

program into one that reimburses providers more for higher levels o f compliance on

process measures. This study’s results suggests that if the goal o f CMS’s program

slated to begin in fiscal year 2009 is to improve the quality o f patient care (i.e.,

outcomes), that further research should be conducted in order to determine whether

that goal is a realistic outcome of P4P. Perhaps other areas of focus such as staffing

ratios and particular treatment modalities should be the focus o f a program whose

intent is to pay hospitals for better quality of patient care. Again, this research

identifies the need for additional analysis in order to determine where to focus

provider efforts in order to improve patient care.

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Attachment I: Premier Hospital Quality Incentive Demonstration87

According the CMS Fact Sheet, “The Premier Hospital Quality Incentive Demonstration will recognize and provide financial rewards to hospitals that demonstrate high quality performance in a number of areas o f acute care. The demonstration involves a CMS partnership with Premier, Inc., a nationwide organization of not-for-profit hospitals, and will reward participating top performing hospitals by increasing their payment for Medicare patients. Participating hospitals’ performance under the demonstration will be posted at www.cms.hhs.gov for health care professionals.

CMS is pursuing a vision to improve the quality o f health care by expanding the information available about quality o f care and through direct incentives to reward the delivery of superior quality care. Through the Premier Hospital Quality Incentive Demonstration, CMS aims to see a significant improvement in the quality of inpatient care by awarding bonus payments to hospitals for high quality in several clinical areas, and by reporting extensive quality data on the CMS web site. Premier was selected for the demonstration because, through its database o f hospitals in the Premier Perspective system, it has the ability to track and report quality data for 34 quality measures for each of its hospitals. This capability to immediately provide such a broad set of quality data makes the Premier database operationally unique and enables a rapid test of the concept of incentives for high performance in several areas o f quality.

Under the demonstration, top performing hospitals will receive bonuses based on their performance on evidence-based quality measures for inpatients with: heart attack, heart failure, pneumonia, coronary artery bypass graft, and hip and knee replacements. The quality measures proposed for the demonstration have an extensive record of validation through research, and are based on work by the Quality Improvement Organizations (QIOs), the Joint Commission on Accreditation of Healthcare Organizations (JCAHO), the Agency for Healthcare Research and Quality, the National Quality Forum (NQF), the Premier system and other CMS collaborators.

Hospitals will be scored on the quality measures related to each condition measured in the demonstration. Composite quality scores will be calculated annually for each demonstration hospital by ‘rolling-up’ individual measures into an overall quality score for each clinical condition. CMS will categorize the distribution o f hospital quality scores into deciles to identify top performers for each condition.

CMS will identify hospitals in the demonstration with the highest clinical quality performance for each of the five clinical areas. Hospitals in the top 20% of quality for those clinical areas will be given a financial payment as a reward for the quality of their care. Hospitals in the top decile of hospitals for a given diagnosis will be

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provided a 2% bonus o f their Medicare payments for the measured condition, while hospitals in the second decile will be paid a 1% bonus. The cost of the bonuses to Medicare will be about $7 million a year, or $21 million over three years.

In year three, hospitals that do not achieve performance improvements above demonstration baseline will have adjusted payments. The demonstration baseline will be clinical thresholds set at the year one cut-off scores for the lower 9th and 10th decile hospitals. Hospitals will receive 1% lower DRG payment for clinical conditions that score below the 9th decile baseline level and 2% less if they score below the 10th decile baseline level.

Hospitals participating in Premier Hospital Quality Incentive Demonstration reported previously collected quality data currently available in the Premier Perspective database to provide a historical reference on these quality indicators. The data was published at www.cms.hhs.gov in early 2004. The first year results will be reported in 2005 recognizing those hospitals with the highest quality and noting those hospitals that received bonus awards.

Participation in the demonstration is voluntary and open to hospitals in the Premier Perspective system as o f March 31, 2003. A total of 274 hospitals are participating in the demonstration. CMS will use the Premier demonstration as a pilot test o f this concept, and may develop a request for additional proposals for this concept once we obtain results from the evaluation of the Premier demonstration.”

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88Attachment II: Hospital Quality Initiative

According to the Hospital Quality Initiative Overview prepared by CMS, “Quality health care is a high priority for the Bush administration, the Department of Health and Human Services (HHS), and the Centers for Medicare & Medicaid Services (CMS). In November 2001, HHS announced the Quality Initiative to assure quality health care for all Americans through accountability and public disclosure. The Initiative is intended to (a) empower consumers with quality o f care information to make more informed decisions about their health care, and (b) encourage providers and clinicians to improve the quality o f health care.

The Quality Initiative was launched nationally in November 2002 for nursing homes, and was expanded in 2003 to the nation’s home health care agencies and hospitals. In 2004, the Quality Initiative was further expanded to officially include kidney dialysis facilities that provide services for patients with ESRD. This comprehensive approach to improving healthcare quality also includes the Doctor’s Office Quality-Information Technology (DOQ-IT) project.

The Hospital Quality Initiative uses a variety o f tools to help stimulate and support improvements in the quality o f care delivered by hospitals. The intent is to help improve hospitals’ quality of care by distributing objective, easy to understand data on hospital performance. This will encourage consumers and their physicians to discuss and make better informed decisions on how to get the best hospital care, create incentives for hospitals to improve care, and support public accountability.

CMS is working in conjunction with the Hospital Quality Alliance (HQA), a public- private collaboration on hospital measurement and reporting. This collaboration includes the American Hospital Association, the Federation o f American Hospitals, and the Association of American Medical Colleges, and is supported by Agency for Healthcare Research Quality (AHRQ), CMS and other organizations such as the National Quality Forum, the Joint Commission on Accreditation o f Healthcare Organizations, American Medical Association, Consumer-Purchaser Disclosure Project, AFL-CIO, AARP and the U.S. Chamber of Commerce. Through this initiative, a robust, prioritized and standardized set of hospital quality measures has been refined for use in voluntary public reporting. As the first step, Hospital Compare, a new website/webtool developed to publicly report valid, credible and user-friendly information about the quality of care delivered in the nation’s hospitals, debuted in April at www.hospitalcompare.hhs.gov and www.medicare.gov.

The Hospital Quality Initiative is complex and differs in several ways from the Nursing Home Quality Initiative and Home Health Quality Initiative. In the previous initiatives, CMS had well-studied and validated clinical data sets and standardized data transmission infrastructure from which to draw a number o f pertinent quality

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http://www.medicare.gov

measures for public reporting. In contrast with the earlier initiatives, there was no comprehensive data set on hospitals from which to develop the pertinent quality measures, nor are hospitals mandated to submit clinical performance data to CMS. However, section 501(b) of the Medicare Prescription Drug, Improvement, and Modernization Act of 2003 provided a strong incentive for eligible hospitals to submit quality data for ten quality measures known as the “starter set”. The law stipulates that a hospital that does not submit performance data for the ten quality measures will receive a 0.4 percentage points reduction in its annual payment update from CMS for FY 2005, 2006 and 2007.

The twenty measures currently reported on Hospital Compare include the ten starter measures plus additional measures that many hospitals also voluntarily report. The measures represent wide agreement from CMS, the hospital industry and public sector stakeholders such as the Joint Commission on Accreditation of Healthcare Organizations (JCAHO), the National Quality Forum (NQF), and the Agency for Healthcare Research and Quality (AHRQ).

The twenty hospital quality measures currently listed on Hospital Compare have gone through years o f extensive testing for validity and reliability by CMS and the QIOs, the Joint Commission on Accreditation o f Healthcare Organizations, the HQA and researchers. The hospital quality measures are also endorsed by the National Quality Forum, a national standards setting entity.

Hospital Quality MeasuresMeasure ConditionAspirin at arrival Acute Myocardial Infarction

(AMI)/Heart attackAspirin at dischargeBeta-Blocker at arrivalBeta-Blocker at dischargeACE Inhibitor or Angiotensin Receptor Blocker (ARB) for left ventricular systolic dysfunctionSmoking cessationThrombolytic agent received within 30 minutes of hospital arrivalPercutaneous Coronary Intervention (PCI) received within 120 minutes of hospital arrivalLeft ventricular function assessment Heart FailureACE Inhibitor or Angiotensin Receptor Blocker (ARB) for left ventricular systolic dysfunctionComprehensive discharge instructionsSmoking cessationInitial antibiotic received within 4 hours of Pneumonia

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hospital arrivalPneumococcal vaccination statusBlood culture performed before first antibiotic receivedSmoking cessationOxygenation assessmentAppropriate initial antibiotic selectionProphylactic antibiotic received within 1 hour prior to surgical incision

Surgical Infection Prevention

Prophylactic antibiotics discontinued within 24 hours after surgery end time

These measures were chosen because they are related to three serious medical conditions and prevention o f surgical infections and it is possible for hospitals to submit information on for public reporting today. Both JCAHO and CMS provide their own processes to submit data and use data edit procedures to check data for completeness and accuracy. In addition, the quality measures are well understood by providers and stakeholders and can be validated by CMS with existing resources through its QIO program. The ultimate goal of CMS and its collaborators in the HQA is for this set o f measures to be reported by all hospitals, and accepted by all purchasers, oversight and accrediting entities, payers and providers. In the future, additional quality measures will be added to Hospital Compare.

CMS, along with its sister agency AHRQ, is in the final stages developing a standardized survey of patient perspectives of their hospital care, known as Hospital CAHPS (HCAHPS). Information from this survey will be publicly reported on Hospital Compare in the future. The survey has been tested by hospitals in Arizona, Maryland and New York as part of a CMS pilot project. Additional testing occurred in Connecticut and select sites around the country. Public reporting of standardized measures on patients’ perspectives o f the quality of hospital care will encourage consumers and their physicians to discuss and make more informed decisions on how to get the best hospital care, as well as increase the public accountability o f hospitals.

The Quality Initiative employs a multi-pronged approach to support, provide incentives and drive systems and facilities - including the clinicians and professionals working in those settings - toward superior care through:• Ongoing regulation and enforcement conducted by State survey agencies and CMS• New consumer hospital quality information on our websites, www.hospitalcompare.hhs.gov and www.medicare.gov, and at 1-800-MEDICARE• The testing o f rewards for superior performance on certain measures of quality• Continual, community-based quality improvement resources through the QIOs• Collaboration and partnership to leverage knowledge and resources

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CMS will continue to conduct regulation and enforcement activities to ensure that Medicare hospitals comply with federal standards for patient health and safety and quality of care. The survey and certification program is a joint effort o f the federal and state governments to ensure safety and improve the quality of care in health care facilities. These activities provide an important view of the quality o f care in hospitals.

CMS and the HQA will conduct an integrated communications campaign to encourage consumers and their physicians to discuss and make informed decisions on how to get the best hospital care. They will encourage patients to access hospital quality information on www.hospitalcompare.hhs.gov and www.medicare.gov or by calling 1- 800-MEDICARE. CMS will also direct the QIOs to promote awareness, understanding and use o f quality measures by working with clinicians and intermediaries including primary care physicians, community organizations, and the media.

As part of the Hospital Quality Initiative, CMS is exploring pay-for-performance via the Premier Hospital Quality Incentive Demonstration. Under the demonstration, hospitals will receive bonuses based on their performance on quality measures selected for inpatients with specific clinical conditions: heart attack, heart failure, pneumonia, coronary artery bypass graft, and hip and knee replacements. Hospitals will be scored on the quality measures related to each condition measured. Composite scores will be calculated annually for each demonstration hospital. Separate scores will be calculated for each clinical condition by “rolling up” individual measures into an overall score.

CMS will categorize the distribution o f hospital scores into deciles to identify top performers for each condition. For each condition, all o f the hospitals in the top 50% will be reported as top performers. Those hospitals in the top 20% will be recognized and given a financial bonus. By the end of the demonstration, it is anticipated that participating hospitals will show improvement from performance in year one. In year three, hospitals will receive lower payments if they score below clinical baselines set in the first year for the bottom 20% of hospitals.

The QIOs will continue to work with hospitals to improve performance on the hospital-reported measures and to develop and implement continuous quality improvement programs. The QIOs have worked with physicians, hospitals, and other providers on improvement activities for the past 20 years and have seen providers achieve a 10-20% relative improvement in performance. For the past three years, the QIOs have been working with hospitals to improve performance on most of the starter set of 10 hospital quality measures. During this period, performance on these measures has improved across the country. As part o f this initiative, the QIOs are also working with community, health care and business organizations, and with the local media to

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provide quality information to the public and encourage hospitals to use the information to improve care.

To be effective, the Hospital Quality Initiative must truly be a collaborative effort with hospitals and their associations, physicians, other clinicians, federal and state agencies, QIOs, independent health care quality organizations, private purchasers, accrediting organizations, and consumer advocates. The initiative is designed to improve communication among all parties to positively impact quality o f care. By collaborating to expand knowledge and resources, all partners can achieve greater and immediate improvements in the quality o f hospital care. The HQA, mentioned earlier, is a prime example of a cooperative effort in the Hospital Quality Initiative.

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Attachment III: Conceptual Model

Patient Behavior* Diet (Fruits/Veggies)

* Failure to Drink Any Alcohol* Exercise Level* Smoking Status

Treatm ent* Thrombolysis* Angioplasty

* CABG * Other Open Heart Surgery

* Other Cardiac Diagnostic and Treatment Services

* N o Treatment

OutcomeDays Survival;

Readmissions in 30 Days

Regressor of Interest Increased P4P scores

* Aspirin at Arrival * Aspirin at Discharge

* Beta Blocker at Arrival* Beta Blocker at Discharge* ACEI or ARB for LVSD

* Smoking Cessation Advice* PCI Received w/in 120 Mins

* Thrombolytic Agent Received w/in 30 Minutes o f Arrival

Patient Dem ographics* Age* Race

* Gender* Access to Care

* Education* Income

* Marital Status* Religion

Patient's M edical Condition Hospital Characteristics /* CAD W orking Environm ent

* Prior MI * Staffing Ratios* Family Hx o f CAD * Response Team to AMI in ED

* Dyslipidemia * Center o f Excellence for Heart Care* Diabetes * Hospital / MD AMI Volume

* Hypertension * Technology Available* Obesity * Hospital Wealth / Payor Mix

* Stress / Depression * Teaching Hospital Status* Severity o f Illness / Comorbidities * Surgical Back-up

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Attachment IV: Variable Coding

Outcome Survival Post AMI Days Survival Post AMI continuous variableAlive/Dead at 30 Days Dead = 1; Alive = 0

Readmissions Readmissions within 30 Days Readmission = 1; Not = 0Regressor of Interest

Aspirin at Arrival Aspirin at Arrival Yes = 1; No = 0; NA = 99Aspirin at Discharge Aspirin at Discharge Yes = 1; No = 0; NA = 99Beta Blocker at Arrival Beta Blocker at Arrival Yes = 1; No = 0; NA = 99Beta Blocker at Discharge Beta Blocker at Discharge Yes = 1; No = 0; NA = 99ACEI or ARB for LVSD ACEI for LVSD Yes = 1; No = 0; NA = 99Adult Smoking Cessation Advice Smoking Cessation Advice Yes = 1; No = 0; NA = 99PCI Received w/in 120 mins of Arrival

PCI Received within 120 Mins of Arrival

Yes = 1; No = 0; NA = 99

Thrombolytic Agent Received w/in 30 mins of Arrival

Thrombolytic Agent Received w/in 30 Mins of Arrival

Yes = 1; No = 0; NA = 99

All Applicable P4P measures All Applicable P4P measures Yes = 1; No = 0PatientDemographics

Age Age continuous variableRace Race categorical variable

White = 1 (excluded); African American = 2; 3 = Native American; 4 = Asian/Pacific Islander; 5 = Other; 6 = Unknown

Gender Gender Female = 1; Male = 0

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PatientDemographics(cont'd)

Access to Care Insurance Status (Payor) Insurance Listed = 1; No or Unknown = 0

PCP Status Insurance Listed = 1; No or Unknown = 0

Education Data Unavailable NAIncome Data Unavailable NAMartial Status Martial Status categorical variable

Single = 1 (excluded); Widowed = 2; Divorced/Separated = 3; Married = 4

Religion Religion Stated Religion = 1; Not = 0


Facility Facility categorical variable Chula Vista = 1 (excluded); Encinitas = 2; Green = 3; La Jolla = 4; Mercy San Diego = 5

Staffing Ratios Paid FTE per Adjusted Occupied Bed

continuous variable

Response Team to AMI in ED / Urgent Care

Rapid Response Team Yes = 1; No = 0Chest Pain Center Yes = 1; No = 0

Center of Excellence for Heart Care Center of Excellence for Cardiovascular Care

Yes = 1; No = 0

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AMI Volume Total Hospital AMI Annual Admissions

continuous variable

(cont'd) AMI Volume (cont'd) Average Cardiologist AMI Annual Admissions

continuous variable

Technology Available Ratio of AMI Admissions to ICU Beds

continuous variable

Hospital Wealth / Payor Mix Payor Mix: % Medicare; % Medical; % Commercial; % Other Governmental & Self Pay; % Other

all continuous variables

Teaching Hospital Status Teaching Hospital Status Yes = 1; No = 0Surgical Back-Up Surgical Back-Up Present Yes = 1; No = 0

Patient's CAD CAD Yes = 1; No = 0Medical Prior MI Prior MI Yes = 1; No = 0Condition Family History of CAD Family History of CAD Yes = 1; No = 0

High LDL / Low HDL Levels Dyslipidemia Yes = 1; No = 0Diabetes Diabetes Yes = 1; No = 0Hypertension Hypertension Yes = 1; No = 0Obesity Obesity Yes = 1; No = 0Stress / Depression Depression Yes = 1; No = 0Severity of Illness / Risk of Mortality

Inconsistent Data NA

Treatment Thrombolysis Thrombolysis Thrombolysis = 1; Not = 0Angioplasty Angioplasty Angioplasty = 1; Not = 0CABG CABG CABG= 1; Not = 0

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Treatment Other Open Heart Surgery Other Open Heart Surgery Other Surgery = 1; Not = 0(cont'd) Other Cardiac Diagnostic or Other Cardiac Diagnostic or Other Cardiac Dx or Tx = 1;

Treatment Procedure Treatment Procedure Not = 0No Treatment No Treatment No Treatment = 1; Not = 0Months Since Admission Months Since Admission continuous variable

Patient Diet (Fruits / Veggies) No variable included NABehavior Failure to Drink any Alcohol No variable included NA

Exercise Level No variable included NASmoking Status Smoking Status within the past 12

monthsYes = 1; No or Unknown = 0

toU i

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Attachment V: Covariate Frequencies Over Time

Observation 1 1/1/03-6/30/03

Observation 2 7/1/03-12/31/03

Observation 3 1/1/04-6/30/04

Observation 4 7/1/04-12/31/04

Observation 5 1/1/05-6/30/05

Observation 6 7/1/05-12/31/05

n % n % n % n % n % n %Total Patients 642 100% 548 100% 734 100% 836 l()U"„ 668 100% 526 100%

SexMale 391 61% 331 60% 441 60% 529 63% 442 66% 365 69%Female 251 39% 217 40% 293 40% 307 37% 226 34% 161 31%

Age< 5 0 56 9% 30 5% 70 10% 82 10% 75 11% 44 8%50-59 87 14% 79 14% 112 15% 139 17% 123 18% 91 17%60-69 121 19% 109 20% 144 20% 185 22% 151 22% 113 21%70-79 171 27% 156 28% 202 28% 221 26% 175 26% 116 22%80+ 203 32% 174 32% 206 28% 209 25% 148 22% 162 31%

i

Ethnicity i

White 480 75% 311 57% 434 59% 531 64% 495 74% 411 78%African American 8 1% 18 3% 36 5% 22 3% 25 4% 17 3%Native American 2 0% 1 0% 1 0% 1 0% 0 0% 0 0%Asian/Pac Islander 42 7% 120 22% 131 18% 73 9% 40 6% 28 5%Other 109 17% 97 18% 132 18% 199 24% 99 15% 2 0%Unknown 1 0% 1 0% 0 0% 10 1% 9 1% 68 13%

Marital StatusSingle 134 21% 111 20% 188 26% 199 24% 165 25% 124 24%Widowed 125 19% 121 22% 133 18% 132 16% 108 16% 68 13%Divorced/Separted 34 5% 36 7% 47 6% 44 5% 41 6% 30 6%Married 349 54% 280 51% 366 50% 461 55% 354 53% 304 58%

ReligionStated Religion 442 69% 377 69% 511 70% 606 72% 437 65% 326 67%

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Observation 1 1/1/03-6/30/03

Observation 2 7/1/03-1: 5 1 03

Observation 3 1/1/04-6/30/04

Observation 4 7/1/04-12/31/04

Observation 5 1/1/05-6/30/05

Observation 6 7/1/05-12/31/05

n % n O n 0//o n % n % n O./oNo Stated Religion 200 31% \ - \ 31% 223 30% 2 R) 28% 231 35% 164 33%

FacilityChula Vista 139 22% 124 23% 160 22% 138 17% 81 12% 71 13%Encinitas 0 0% 0 0% 0 0% 104 12% 81 12% 64 12%Green 127 20% 123 22% 122 17% 96 11% 112 17% 83 16%La Jolla 148 23% 113 21% 233 32% 315 38% 196 29% 168 32%Mercy 228 36% 188 34% 219 30% 183 22% 198 30% 140 27%

PCPNo/Unknown 469 73% 364 66% 515 70% 608 73% 486 73% 289 55%Yes 173 l_ 27% 184 34% 219 30% 228 27% 182 27% 237 45%

PayorNo/Unknown 23 4% 26 5% 23 3% 38 5% 28 4% 26 5%Yes 619 96% 522 95% 711 97% 798 95% 640 96% 500 95%

CensoredYes/Alive 451 70% 411 75% 551 75% 682 82% 576 86% 464 88%No/Dead 191 30% 137 25% 183 25% 154 18% 92 14% 62 12%

ACEI for LVSD :No 28 25% 19 21% 27 29% 28 26% 14 17% 9 13%Yes 85 75% 70 79% 67 71% 78 74% 70 83% 60 87%Not Applicable 529 459 640 730 584 457

Smoking Cessation AdviceNo 43 43% 18 25% 35 27% 37 29% 14 11% 10 11%Yes 58 57% 53 75% 95 73% 92 71% 111 89% 83 89%Not Applicable 541 477 604 707 543 433

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Observation 1 1/1/03-6/30/03

Observation 2 7/1/03-12/31/03

Observation 3 1/1/04-6/30/04

Observation 4 7/1/04-12/31/04

Observation 5 1/1/05-6/30/05

Observation 6 7/1/05-12/31/05

n 0//o n OA/Q n % n % n % n %Aspirin at Arrival i

No 36 7% 22 5% 29 6% 20 4% 10 2% 4 1%Yes 453 93% 392 95% 428 94% 477 96% 399 98% 300 99%Not Applicable 153 134 277 339 259 222

Aspirin at DischargeNo 48 9% 47 10% 38 6% 27 4% 18 3% 7 2%Yes 479 91% 404 90% 547 94% 642 96% 498 97% 407 98%Not Applicable 115 97 149 167 152 112

Beta Blocker at ArrivalNo h 64 15% 48 13% 44 11% 16 r 4% 22 6% 11 4%Yes 356 85% 332 87% 356 89% 389 96% 322 94% 255 96%Not Applicable 222 168 334 431 324 260

Beta Blocker at DischargeNo 71 14% 73 17% 64 11% 64 10% 31 6% 21 5%Yes 420 86% 366 83% 494 89% 585 90% 484 94% 393 95%Not Applicable 151 109 176 187 153 112

Thrombolytic w/in 30 minNo 29 76% 19 63% 13 72% 23 82% 18 86% 11 69%Yes 9 24% 11 37% 5 28% 5 18% 3 14% 5 31%Not Applicable 604 518 716 808 647 510

PCI w/in 120 minNo 70 84% 52 72% 32 48% 36 47% 30 44% 17 29%Yes 13 16% 20 28% 35 52% 41 53% 38 56% 41 71%Not Applicable 559 476 667 759 600 468

j;

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Observation 1 1/1/03-6/30/03

Observation 2 7/1/03-12/31/03

Observation 3 1/1/04-6/30/04

Observation 4 7/1/04-12/31/04

Observation 5 1/1/05-6/30/05

Observation 6 7/1/05-12/31/05

n /o n % n 0/1/o n % n % n %A ll Applicable P4P Measures p...............

No 259 40% 185 34% 205 28% 205 25% 126 19% 75 14%Yes 383 60% 363 66% 529 72% 631 75% 542 81% 451 86%

!

CABG SurgeryNo 591 92% 518 95% 689 94% 758 91% 614 92% 480 91%Yes 51 8% 30 5% 45 6% 78 9% 54 8% 46 9%

Other Open Heart SurgeryNo 637 99% 539 98% 728 99% 824 99% 658 99% 520 99%Yes 5 1% 9 2% 6 1% 12 1% 10 1% 6 1%

PCI/Angioplasty TxNo 370 58% 286 52% 361 49% 369 44% 256 38% 207 39%Yes 272 42% 262 48% 373 51% 467 56% 412 62% 319 61%

Thrombosis TreatmentNo 607 95% 513 94% 713 97% 806 96% 643 96% 505 96%Yes 35 5% 35 6% 21 3% 30 4% 25 4% 21 4%

Other Prim. Cardiac Proc. (dx or tx)No 322 50% 272 50% 358 49% 323 39% 215 32%

o00 34%Yes 320 50% 276 50% 376 51% 513 61% 453 68% 346 66%

N o Cardiac TxNo 450 70% 391 71% 522 71% 657 79% 575 86% 437 83%Yes 192 30% 157 29% 212 29% 179 21% 93 14% 89 17%

1CAD

No 1 0% 0 0% 0 0% 0 0% 0 0% 2 0%Yes 641 100% 548 100% 734 100% 836 100% 668 100% 524 100%

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Observation 1 1/1/03-6/30/03

Observation 2 7/1/03-12/31/03

Observation 3 1/1/04-6/30/04

Observation 4 7/1/04-12/31/04

Observation 5 1/1/05-6/30/05

Observation 6 7/1/05-12/31/05

11 Q //O a O a/O n % n % n % n %Prior MI

No 604 94% 507 93% 682 93% 767 92% 622 93% 479 91%Yes 38 6% 41 7% 52 7% 69 8% 46 7% 47 9%

1Family History o f CAD i ■

No i 635 99% 543 99% 724 99% 809 97% 650 97% 510 97%Yes 7 1% 5 1% 10 1% 27 3% 18 3% 16 3%

DyslipidemiaNo 452 70% 377 69% 480 65% 501 60% 391 59% 277 53%Yes 190 30% 171 31% 254 35% 335 40% 277 41% 249 47%

DiabetesNo 445 69% 399 73% 525 72% 605 72% 482 72% 369 70%Yes 197 31% 149 27% 209 28% 231 28% 186 28% 157 30%

Hypertension :No 252 39% 205 37% 282 38% 319 38% 260 39% 185 35%Yes 390 61% 343 63% 452 62% 517 62% r 408 61% 341 65%

Obesity---------- - - ........

No 599 93% 507 93% 687 94% 782 94% 616 92% 492 94%Yes 43 7% 41 7% 47 6% 54 6% 52 8% 34 6%

Depression 1

No 617 96% 522 95% 708 96% 802 96% 646 97% 504 96%Yes 25 4% 26 5% 26 4% 34 4% 22 3% 22 4%

SmokerNo or Unknown 533 83% 474 86% 592 81% 688 82% 535 80% 425 81%Yes 109 17% 74 14% 142 19% 148 18% 133 20% 101 19%

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Observation 1 Observation 2 Observation 3 Observation 4 Observation 5 Observation 61/1/03-6/30/03 7/1/03-12/31/03 1/1/04-6/30/04 7/1/04-12/31/04 1/1/05-6/30/05 7/1/05-12/31/05

n % n (1/0 u 0/' n % ii 0'I) n 0//o

Readmit w/in 30 daysNo 599 93% 524 96% 698 95% 788 94% 634 95% 505 96%Yes 43 7% 24 4% 36 5% 48 6% 34 5% 21 4%

Paid FTE per Adj Occ Bed4 .5 -4 .9 131 20% 64 12% 110 15% 27 3% 14 2% 12 2%5 .0 -5 .4 211 33% 196 36% 278 38% 223 27% 146 22% 53 10%5 .5 -5 .9 258 40% 182 33% 266 36% 372 44% 386 58% 350 67%6.0 - 6.4 42 7% 106 19% 80 11% 149 18% 70 10% 28 5%6.5+ 0 0% 0 0% 0 0% 65 8% 52 8% r “ 83 16%

Rapid Response TeamNo 642 100% 548 100% 734 100% 836 100% 470 70% 386 73%Yes 0 0% 0 0% 0 0% 0 0% 198 30% 140 27%

Chest Pain CenterNo 414 64% 360 66% 515 70% 653 78% 470 70% 386 73%Yes 228 36% 188 34% 219 30% 183 22% 198 30% 140 27%

CV Award During Yr o f VisitNo 494 77% 435 79% 393 54% 557 67% 360 54% 275 52%Yes 148 23% 113 21% 341 46% 279 33% 308 46% 251 48%

Annual AMI Admissions< 2 0 0 0 0% 0 0% 0 0% 0 0% 274 41% 218 41%200 - 399 266 41% 247 45% 282 42% 338 40% 198 30% 140 27%400 - 599 228 36% 188 34% 159 24% 183 22% 0 0% 0 0%600 - 800 0 0% 0 0% 233 35% 315 38% 196 29% 168 32%800+ 148 23% 113 21% 0 0% 0 0% 0 0% 0 0%

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Observation 1 1/1/03-6/30/03

Observation 2 7/1/03-12/31/03

Observation 3 1/1/04-6/30/04

Observation 4 7/1/04-12/31/04

Observation 5 1/1/05-6/30/05

Observation 6 7/1/05-12/31/05

n % n % n % n % n % n f). i)

A vg Cardiologist AMI Admits< 10 0 0% 0 0% o 0% 0 0% 81 12% 71 13%1 0 -2 0 494 77% 435 79% 501 68% 521 62% 391 59% 287 55%2 0 - 3 0 148 23% 113 21% 233 32% 315 38% 196 29% 168 32%

Annual AMI Admits/ICU Beds< 10 1 0% 0 0% 122 17% 96 11% 274 41% 218 41%10-14 493 77% 435 79% 379 52% 425 51% 198 30% 140 27%15-19 0 0% 0 0% 233 ^ 32% 315 38% 196 29% 168 32%20+ 148 23% 113 21% 0 0% 0 0% 0 0% 0 , 0%

Payor: % Medicare< 3 0 148 23% 113 21% 233 32% 315 38% 0 0% 0 0%3 0 - 3 9 367 57% 312 57% 379 52% 425 51% 556 83% 443 89%O

nIo

0 0% 0 0% 0 0% 0 0% 0 0% 0 0%50+ 127 20% 123 22% 122 17% 96 11% 112 17% 83 11%

Payor: % MediCal j0 127 20% 123 22% 122 17% 96 11% 112 17% 83 16%1-9 148 23% 113 H 21% 233 32% 315 38% 196 29% 168 32%10-19 0 i 0% 0 0% 0 0% 104 12% 0 0% 0 0%20+ 367 57% 312 57% 379 52% 321 38% 360 54% h 275 52%

Payor: % Commercial-------- --------- - ________ j. ...... . -------- 1 - --..—

< 2 0 139 22% 124 23% 160 j 22% 138 17% 81 12% 71 13%20-39 228 36% 188 r 3 4 % 219 30% 287 34% 279 42% 204 39%40-59 127 20% 123 22% 122 17% 96 11% 112 17% 83 16%60+ 148 23% 113 21% 233 32% 315 38% 196 29% 168 32%

!

Payor: % Oth Gvmt/Self Pay< 5 127 20% 123 22% 122 17% 96 11% 112 17% 83 16%

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U>

Observation 1 1/1/03-6/30/03

Observation 2 7/1/03-12/31/03

Observation 3 1/1/04-6/30/04

Observation 4 7/1/04-12/31/04

Observation 5 1/1/05-6/30/05

Observation 6 7/1/05-12/31/05

n °fl n « o n “h n % n % n %5-9 148 23% 1 11 21% 2''3 32% 419 50% 100 29% H>S 32%10-14 228 36% 188 34% 379 52% 321 38% 360 54% 275 52%15+ 139 22% 124 23% 0 0% 0 0% 0 0% 0 0%

Payor: % Oth Incl Wrk Comp< 5 642 100% 548 100% 734 100% 836 100% 668 100% 526 100%

Teaching Hospital StatusNo 287 45% 237 43% 393 54% 557 67% 279 42% 232 44%Yes , 355 55% 311 57% 341 46% 279 33% 389 58% 294 56%

iSurgical Back-up 1

No 139 22% 124 23% 160 J 22% 242 29% 162 24% 135 26%Yes 503 78% 424 77% 574 78% 594 71% r~506 76% 391 74%

30-Day MortalityAlive 561 87% 482 88% ^ 650 89% 766 92% 611 91% 484 92%Dead 81 13% 66 12% 84 11% ^ 70 8% 57 1 9% 42 8%

90-Day Mortality I

Alive 544 85% 473 86% 637 87% 741 89% 599 I 9o% n 476 90%Dead 98 15% 75 14% 97 13% 95 11% 69 10% 50 10%

1

180-Day Mortality |Alive 527 82% , 4 5 9 84% 619 84% 721 86% 589 88% 467 89%Dead 115 18% I- 89 16% 115 16% 115 14% 79 12% 59 11%

Attachment VI: Covariates Included in Stepwise Survival Analysis on Total Population

Results o f M odel w ith Aspirin at Arrival

Measure Name B SE Wald df Sig- L\p(IJ), 95.()‘, u Cl hxp(B)

Lower LpperChula Vista 33.8999 4 0.0000Encinitas -0.1210 0.1797 0.4537 1 0.5006 0.8860 0.6230 1.2600Green 0.0448 0.1706 0.0690 1 0.7927 1.0458 0.7486 1.4610La Jolla -0.6455 0.1442 20.0331 1 0.0000 0.5244 0.3953 0.6957Mercy 0.1140 0.1084 1.1075 1 0.2926 1.1208 0.9063 1.3860Age 0.0394 0.0038 106.6588 1 0.0000 1.0402 1.0324 1.0480Single 9.4984 0.0233Widowed -0.2831 0.1285 4.8578 1 0.0275 0.7534 0.5857 0.9691Divorced/Separated 0.1965 0.1725 1.2970 1 0.2548 1.2171 0.8679 1.7069Married -0.0301 0.1101 0.0746 1 0.7848 0.9704 0.7821 1.2040Identified PCP -0.3535 0.0933 14.3548 1 0.0002 0.7022 0.5848 0.8431Identified Payor 1.5366 0.5049 9.2615 1 0.0023 4.6486 1.7280 12.506PCI Tx -0.7567 0.1123 45.4293 1 0.0000 0.4692 0.3765 0.5847Dyslipidemia -0.9383 0.1238 57.4167 1 0.0000 0.3913 0.3070 0.4988Depression -0.5344 0.2717 3.8702 1 0.0492 0.5860 0.3441 0.9980Aspirin at Arrival -0.6412 0.1536 17.4353 1 0.0000 0.5267 0.3898 0.7116

Measure Name 11 SEif

Wald df sig. F.xp(B)95.0°.;, c i r.xpdD Lower tipper

Chula Vista 48.9382 4 0.0000Encinitas -0.0290 0.2276 0.0163 1 0.8984 0.9714 0.6218 1.5174Green -0.2023 0.2245 0.8124 1 0.3674 0.8168 0.5261 1.2682La Jolla -2.5615 0.5275 23.5792 1 0.0000 0.0772 0.0274 0.2170Mercy 0.2351 0.1464 2.5786 1 0.1083 1.2651 0.9495 1.6856Age 0.0399 0.0045 77.3883 1 0.0000 1.0407 1.0315 1.0499Female -0.2616 0.1011 6.6947 1 0.0097 0.7698 0.6314 0.9385Identified Payor 2.5948 1.0032 6.6895 1 0.0097 13.3937 1.8747 95.689PCI Tx -0.6575 0.1359 23.4197 1 0.0000 0.5181 0.3970 0.6762N o Cardiac Tx 0.4475 0.1382 10.4904 1 0.0012 1.5644 1.1933 2.0510Dyslipidemia -0.6225 0.1213 26.3342 1 0.0000 0.5366 0.4231 0.6806Diabetes 0.3941 0.1022 14.8755 1 0.0001 1.4830 1.2139 1.8118Rapid Resp Team -0.8133 0.2741 8.8022 1 0.0030 0.4434 0.2591 0.7588MD AMI Volume 0.1045 0.0338 9.5323 1 0.0020 1.1101 1.0389 1.1862Aspirin at Discharge -0.2995 0.1576 3.6134 1 0.0573 0.7412 0.5442 1.0094

Measure Name 11 SI.i

Wald d f Sig. 1 xptHJ! 95.ono ( i i:.\p (ii) I Lower Lpper

Chula Vista 37.1830 4 0.0000Encinitas -0.2397 0.2339 1.0501 1 0.3055 0.7869 0.4975 1.2445


Green 0.1535 0.1920 0.6391 1 0.4240 1.1659 0.8002 1.6988La Jolla -0.7041 0.1685 17.4664 1 0.0000 0.4945 0.3555 0.6880Mercy 0.2000 0.1200 2.7769 1 0.0956 1.2214 0.9654 1.5453Age 0.0366 0.0042 77.4819 1 0.0000 1.0373 1.0289 1.0458Single 8.9227 0.0303Widowed -0.2491 0.1441 2.9869 1 0.0839 0.7795 0.5877 1.0340Divorced/Separated 0.2986 0.1871 2.5478 1 0.1104 1.3479 0.9342 1.9448Married -0.0065 0.1233 0.0028 1 0.9576 0.9935 0.7802 1.2651Identified PCP -0.3104 0.1046 8.8048 1 0.0030 0.7332 0.5972 0.9000Identified Payor 1.7034 0.5823 8.5576 1 0.0034 5.4924 1.7544 17.195PCI Tx -0.9497 0.1270 55.9470 1 0.0000 0.3869 0.3016 0.4962Dyslipidemia -0.8586 0.1318 42.4619 1 0.0000 0.4238 0.3273 0.5486Depression -0.6319 0.3065 4.2489 1 0.0393 0.5316 0.2915 0.9694Beta Blocker at Arr -0.4832 0.1453 11.0646 1 0.0009 0.6168 0.4640 0.8200

Measure Name B SE Wald d f Sig, ExpiB)95.0% Cl Lxp(B) Lower Upper

Chula Vista 41.8478 4 0.0000Encinitas -0.1194 0.2466 0.2345 1 0.6282 0.8875 0.5474 1.4389Green -0.0740 0.2291 0.1043 1 0.7467 0.9287 0.5927 1.4551La Jolla -2.1889 0.5303 17.0356 1 0.0000 0.1120 0.0396 0.3168Mercy 0.2782 0.1499 3.4435 1 0.0635 1.3208 0.9845 1.7719Age 0.0382 0.0046 67.7096 1 0.0000 1.0389 1.0295 1.0484Female -0.2533 0.1044 5.8835 1 0.0153 0.7762 0.6325 0.9525Identified PCP -0.2637 0.1087 5.8852 1 0.0153 0.7682 0.6208 0.9506Identified Payor 2.6716 1.0032 7.0912 1 0.0077 14.4624 2.0243 103.32PCI Tx -0.7571 0.1392 29.5849 1 0.0000 0.4690 0.3571 0.6161N o Cardiac Tx 0.4565 0.1389 10.7993 1 0.0010 1.5786 1.2023 2.0725Dyslipidemia -0.6441 0.1232 27.3284 1 0.0000 0.5251 0.4125 0.6686Diabetes 0.4209 0.1050 16.0642 1 0.0001 1.5234 1.2400 1.8716Rapid Resp Team -0.8450 0.2740 9.5150 1 0.0020 0.4295 0.2511 0.7348MD AMI Volume 0.0773 0.0340 5.1842 1 0.0228 1.0804 1.0108 1.1547Beta Blocker at D/C 0.0090 0.1576 0.0033 1 0.9544 1.0091 0.7409 1.3743

Measure Name B SE Wald d f Sig. Exp(B)95.0% Cl E.xp(B) Lower Upper

Age 0.0522 0.0117 19.9919 1 0.0000 1.0535 1.1)297 1.0779Identified Payor 12.8558 302.21 0.0018 1 0.9661 382995 0.0000 7E+262N o Cardiac Tx 1.2843 0.3239 15.7179 1 0.0001 3.6120 1.9143 6.8151CV Award Yr Visit 0.8133 0.2774 8.5984 1 0.0034 2.2555 1.3096 3.8845Smk Cess Advice -0.0635 0.2922 0.0472 1 0.8281 0.9385 0.5294 1.6639

Measure Name B SE Wald df Sig.

1

: i \p (B )95.0% Cl I xp(B) Lower Upper

Age 0.0218 0.0079 7.5207 □ 0.0061 1.0220 1.0062 1.0380


Identified Payor 11.6020 158.40 0.0054 1 0.9416 109316 0.0000 7E+139N o Cardiac Tx 0.5439 0.1846 8.6767 1 0.0032 1.7227 1.1996 2.4738Dyslipidemia -0.8687 0.2414 12.9457 1 0.0003 0.4195 0.2613 0.6733ACEI for LVSD -0.4013 0.1934 4.3060 1 0.0380 0.6695 0.4583 0.9780

Measure Name B si-: Wald d f . Sig- l:\p (B )95.01 ii Cl l.\p (B ) Lower Upper

Age 0.0764 0.0185 1 7.02 ' 1 1 0.0000 1 1)794 1.0409 1.1193PCI Tx -2.3551 0.6772 12.0943 1 0.0005 0.0949 0.0252 0.3578MD AMI Volume 0.0969 0.0424 5.2141 1 0.0224 1.1018 1.0138 1.1973Thromb 30 mins 0.8405 0.4584 3.3622 1 0.0667 2.3176 0.9437 5.6915

Measure Name B SE Wald d f Sig. l'\p (B )95.0% Cl l \p(.B) Lower Upper

Age 0.0496 0.0123 16.2400 1 0.0001 1.0508 1.0258 1.0765Identified PCP -0.8192 0.4137 3.9203 1 0.0477 0.4408 0.1959 0.9918Oth Open Heart Surg 1.5377 0.7456 4.2537 1 0.0392 4.6538 1.0794 20.065Oth Cardiac Dx Tx 1.1058 0.3820 8.3787 1 0.0038 3.0218 1.4291 6.3892No Cardiac Tx 2.6608 0.7943 11.2211 1 0.0008 14.3070 3.0160 67.867Dyslipidemia -1.4195 0.4442 10.2107 1 0.0014 0.2418 0.1012 0.5776PCI 120 mins 0.1076 0.2974 0.1308 1 0.7176 1.1136 0.6217 1.9945

Measure Name B SE Wald d f Sig. h \p (B )95.0".,Cl INpfBj Lower Upper

Chula Vista 50.7085 4 0.0000Encinitas 0.1937 0.1837 1.1117 1 0.2917 1.2137 0.8467 1.7397Green -0.9376 0.5222 3.2234 1 0.0726 0.3916 0.1407 1.0898La Jolla 0.2066 0.3410 0.3673 1 0.5445 1.2295 0.6303 2.3986Mercy 0.8249 0.2668 9.5586 1 0.0020 2.2817 1.3525 3.8492Age 0.0387 0.0033 136.3460 1 0.0000 1.0394 1.0327 1.0462Single 9.2243 0.0265Widowed -0.2237 0.1073 4.3471 1 0.0371 0.7995 0.6479 0.9867Divorced/Separated 0.1878 0.1490 1.5895 1 0.2074 1.2066 0.9011 1.6158Married -0.0281 0.0928 0.0916 1 0.7622 0.9723 0.8106 1.1663Identified PCP -0.2923 0.0775 14.2395 1 0.0002 0.7465 0.6414 0.8689Identified Payor 1.1090 0.3585 9.5697 1 0.0020 3.0315 1.5014 6.1209CABG Tx -0.4582 0.1700 7.2617 1 0.0070 0.6324 0.4532 0.8825PCI Tx -0.7810 0.1045 55.8814 1 0.0000 0.4580 0.3732 0.5620No Cardiac Tx 0.2632 0.0981 7.1935 1 0.0073 1.3011 1.0734 1.5770Dyslipidemia -0.9066 0.0997 82.7575 1 0.0000 0.4039 0.3322 0.4910Diabetes 0.1668 0.0790 4.4570 1 0.0348 1.1815 1.0120 1.3793Hypertension -0.2001 0.0727 7.5856 1 0.0059 0.8186 0.7099 0.9439Rapid Resp Team -0.4787 0.1772 7.3004 1 0.0069 0.6196 0.4378 0.8768Payor: Medicare 0.0688 0.0330 4.3516 1 0.0370 1.0712 1.0042 1.1427All Applicable P4P 0.0145 0.0828 0.0306 1 0.8611 1.0146 0.8627 1.1933

136


Attachment VII: Covariates Included in Stepwise Survival Analysis with Times Serie Model

Measure Name B SE Wald df Siu.i

l.xpl B)95.0% Cl IAp<B) Lower Upper

Jan - June '03Age 0.0505 0.0080 39.8887 1 0.0000 1.0518 1.0355 1.0685Single 8.0661 3 0.0447Widowed -0.1343 0.2647 0.2575 1 0.6119 0.8743 0.5204 1.4689Divorced/Sep. 0.9478 0.3991 5.6408 1 0.0175 2.5800 1.1801 5.6402Married -0.0694 0.2357 0.0865 1 0.7686 0.9330 0.5878 1.4810PCI -0.5725 0.2332 6.0280 1 0.0141 0.5641 0.3572 0.8909Dyslipidemia -0.6977 0.2543 7.5297 1 0.0061 0.4977 0.3024 0.8193FTE/Occ Bed 0.5784 0.2509 5.3135 1 0.0212 1.7832 1.0905 2.9159AMI Vol/ICU Bed -0.0906 0.0313 8.3835 1 0.0038 0.9134 0.8591 0.9712

July - Dec '03Age 0.0318 0.0091 12.3088 1 0.0005 1.0323 1.0141 1.0508PCI -0.8266 0.2769 8.9131 1 0.0028 0.4375 0.2543 0.7528Dyslipidemia -1.0204 0.3156 10.4551 1 0.0012 0.3605 0.1942 0.6691MD AMI Vol -0.0829 0.0256 10.5325 1 0.0012 0.9204 0.8755 0.9677

Jan - June '04Age 0.0328 0.0075 18.9996 1 0.0000 1.0334 1.0182 1.0488Dyslipidemia -0.8441 0.2523 11.1895 1 0.0008 0.4300 0.2622 0.7050MD AMI Vol -0.0510 0.0162 9.9234 1 0.0016 0.9503 0.9206 0.9809Payor 11.7195 163.119 0.0052 1 0.9427 122945.2 0.0000 9E+143CABG 0.8636 0.3362 6.5977 1 0.0102 2.3717 1.2271 4.5840

July - Dec '04Age 0.0384 0.0082 22.0170 1 0.0000 1.0392 1.0226 1.0560PCI -1.1742 0.2564 20.9696 1 0.0000 0.3091 0.1870 0.5109Dyslipidemia -1.1427 0.3027 14.2534 1 0.0002 0.3190 0.1762 0.5773Diabetes 0.5383 0.2302 5.4678 1 0.0194 1.7130 1.0910 2.6897Chest Pain Ctr 0.5835 0.2149 7.3711 1 0.0066 1.7923 1.1762 2.7312

Jan - June '05Age 0.0464 0.0098 22.5312 1 0.0000 1.0474 1.0276 1.0677PCI -0.8223 0.2745 8.9734 1 0.0027 0.4394 0.2566 0.7526Dyslipidemia -1.0248 0.3476 8.6919 1 0.0032 0.3589 0.1816 0.7093CABG -5.4559 1.4727 13.7240 1 0.0002 0.0043 0.0002 0.0766PCP -0.7182 0.3023 5.6443 1 0.0175 0.4876 0.2696 0.8819Oth Open Heart 4.5779 1.0738 18.1760 1 0.0000 97.3080 11.8617 798.273Obesity -12.9575 333.47 0.0015 1 0.9690 0.0000 0.0000 2E+278

| July - D ec '05Dyslipidemia -1.4689 0.4867 9.1098 1 0.0025 0.2302 0.0887 0.5975Oth Open Heart 2.8566 1.0553 7.3274 1 0.0068 17.4014 2.1995 137.671Religious 1.2051 0.5319 5.1337 1 0.0235 3.3372 1.1766 9.4651No Cardiac Tx 1.3818 0.3471 15.8512 1 0.0001 3.9821 2.0169 7.8622

137


Significant C ovariates in Aspirin at Discharge M odel

Measure Name B SE Wald df Sin. I \p (B )95.0°v. Cl I \p (B ) l.uwcr I 'ppcr

Jan - June '03Age 0.0543 0.0087 39.4572 1 0.0000 1.05581 1.0381 1.0739PCI -0.4380 0.2220 3.8943 1 0.0484 0.6453 0.4177 0.9970Dyslipidemia -0.7086 0.2478 8.1765 1 0.0042 0.4924 0.3029 0.8002Diabetes 0.6023 0.1964 9.4020 1 0.0022 1.8262 1.2427 2.6838Chest Pain Ctr 0.6442 0.2211 8.4853 1 0.0036 1.9044 1.2346 2.9376Hosp AMI Admits -0.0040 0.0008 24.4023 1 0.0000 0.9961 0.9945, 0.9976

Jan - June '04Age 0.0452 0.0086 27.8069 1 0.0000 1.0462 1.0288 1.0639Dyslipidemia -0.5834 0.2363 6.0971 1 0.0135 0.5580 0.3512 0.8866CABG 0.8608 0.3428 6.3055 1 0.0120 2.3650 1.2079 4.6304MD AMI Vol -0.1141 0.0216 27.9946 1 0.0000 0.8921 0.8552 0.9307Mos Since Admit -0.1557 0.0597 6.7995 1 0.0091 0.8559 0.7614 0.9621

July - Dec '04Age 0.0339 0.0089 14.5106 1 0.0001 1.0345 1.0166 1.0527PCI -0.7643 0.2564 8.8848 1 0.0029 0.4656 0.2817 0.7697Dyslipidemia -0.5042 0.2474 4.1526 1 0.0416 0.6040 0.3719 0.9809MD AMI Vol -0.0615 0.0195 9.9108 1 0.0016 0.9403 0.9050 0.9771Single 5.3835 0.1458Widowed -0.4011 0.3147 1.6250 1 0.2024 0.6696 0.3614 1.2406Divorced/Separtd 0.4222 0.3928 1.1555 1 0.2824 1.5254 0.7064 3.2939Married -0.3166 0.2673 1.4034 1 0.2362 0.7286 0.4315 1.2303Oth Cardiac Dx/Tx -0.4196 0.2351 3.1865 1 0.0742 0.6573 0.4146 1.0420

Jan - June '05Age 0.0630 0.0184 11.7497 1 0.0006 1.0650 1.0273 1.1041PCI -1.1700 0.5024 5.4226 1 0.0199 0.3104 0.1159 0.8309Diabetes 0.9204 0.3894 5.5857 1 0.0181 2.5103 1.1701 5.3852N o Cardiac Tx 0.8925 0.4883 3.3406 1 0.0676 2.4413 0.9375 6.3577

July - Dec '05Age 0.0558 0.0204 7.4977 1 0.0062 1.0574 1.0160 1.1005PCI -2.1102 0.5679 13.8082 1 0.0002 0.1212 0.0398 0.3689White 10.6060 4 0.0314African American 0.7562 0.7693 0.9663 1 0.3256 2.1302 0.4716 9.6220Native American 0.9328 0.7608 1.5033 1 0.2202 2.5416 0.5722 11.2904Asian/Pacific Isl 2.9340 1.0701 7.5177 1 0.0061 18.8024 2.3087 153.131Other -0.9704 1.0392 0.8720 1 0.3504 0.3789 0.0494 2.9048

a = Degree o f freedom reduced because o f constant or linearly dependent covariates

Measure Name B SI. Wald d f Sig-1 '. INp(B)

| 95.0% Cl l-\p(U> j Lower 1 Ipper

Jan - June '03 |Age 0.0535 0.0087 38.1785 1 0.0000 1.0550 1.0372 1.0730PCI -0.6887 0.2727 6.3802 1 0.0115 0.5022 0.2943 0.8570Dyslipidemia -0.9782 0.3019 10.4962 1 0.0012 0.3760 0.2081 0.6795


Hosp AMI Admits -0.0018 0.0005 14.1942 1 0.0002 0.9982 0.9972 0.9991July - Dec. '03

> cro CD 0.0457 0.0106 18.6928 1 0.0000 1.0468 1.0253 1.0687PCI -1.0924 0.2987 13.3734 1 0.0003 0.3354 0.1868 0.6023Dyslipidemia -0.8866 0.3373 6.9094 1 0.0086 0.4120 0.2127 0.7981Single 16.5009 0.0009Widowed -0.8424 0.3901 4.6622 1 0.0308 0.4307 0.2005 0.9252Divorced/Separtd 0.6804 0.4396 2.3959 1 0.1217 1.9748 0.8343 4.6742Married 0.4372 0.3019 2.0973 1 0.1476 1.5483 0.8569 2.7978PCI -0.9102 0.2624 12.0356 1 0.0005 0.4024 0.2406 0.6730Prior M l -1.4522 0.7290 3.9683 1 0.0464 0.2341 0.0561 0.9769MD AMI Admits -0.0748 0.0264 8.0004 1 0.0047 0.9280 0.8811 0.9773

Jan - June '04Age 0.0208 0.0081 6.5930 1 0.0102 1.0210 1.0049 1.0373PCI -0.8080 0.2453 10.8525 1 0.0010 0.4457 0.2756 0.7209Dyslipidemia -0.6462 0.2613 6.1161 1 0.0134 0.5240 0.3140 0.8745Payor 11.8731 178.479 0.0044 1 0.9470 143361.2 0.0000 1E+157CABG 0.8169 0.3558 5.2720 1 0.0217 2.2636 1.1270 4.5463AMI Vol/ICU Bed -0.1674 0.0485 11.9217 1 0.0006 0.8459 0.7692 0.9302

July - Dec. '04Age 0.0339 0.0090 14.2293 1 0.0002 1.0344 1.0164 1.0528PCI -1.2278 0.2952 17.2981 1 0.0000 0.2929 0.1643 0.5225Dyslipidemia -0.6807 0.3052 4.9733 1 0.0257 0.5063 0.2783 0.9209Chest Pain Ctr 0.8264 0.2330 12.5856 1 0.0004 2.2852 1.4475 3.6075

Jan - June '05Age 0.0424 0.0111 14.6253 1 0.0001 1.0434 1.0209 1.0663PCI -0.9294 0.3180 8.5392 1 0.0035 0.3948 0.2117 0.7364Dyslipidemia -0.9419 0.3750 6.3094 1 0.0120 0.3899 0.1870 0.8131CABG -16.4216 257.908 0.0041 1 0.9492 0.0000 0.0000 3E+212Oth Open Heart 4.8938 1.1078 19.5168 1 0.0000 133.4662 15.2210 1170.3

July - Dec. '05Oth Open Heart 3.5584 1.0569 11.3359 1 0.0008 35.1061 4.4235 278.609No Cardiac Tx 1.7844 0.3606 24.4914 1 0.0000 5.9562 2.9380 12.0751

Measure Name

1

B ! SE

| '

Wald d f Sig.

[ ':

' I’.xpfB)95.0% Cl ExplB) Lower Upper

Jan - June '03Age 0.0500 0.0089 31.3390 1 0.0000 1.0513 1.0331 1.0699No Cardiac Tx 0.6518 0.2097 9.6655 1 0.0019 1.9190 1.2724 2.8942Dyslipidemia -1.0429 0.2641 15.5900 1 0.0001 0.3524 0.2100 0.5914Diabetes 0.6407 0.2122 9.1142 1 0.0025 1.8977 1.2520 2.8766CV Award Yr Visit -1.8926 0.4411 18.4049 1 0.0000 0.1507 0.0635 0.3577

Jan - June '04Age 0.0447 0.0088 25.6835 1 0.0000 1.0457 1.0278 1.0640CABG 0.9472 0.3397 7.7766 1 0.0053 2.5785 1.3251 5.0176MD AMI Admits -0.1130 0.0237 22.6504 1 0.0000 0.8931 0.8525 0.9357

July - Dec. '04


Age 0.0386 0.0089 18.6717 1 0.0000 1.0394 1.0213 1.0577MD AMI Admits -0.0758 0.0206 13.4932 1 0.0002 0.9270 0.8902 0.9652PCI -0.8372 0.2454 11.6383 1 0.0006 0.4329 0.2676 0.7003

Jan - June '05Age 0.0541 0.0163 11.0878 1 0.0009 1.0556 1.0225 1.0898No Cardiac Tx 0.8051 0.4509 3.1874 1 0.0742 2.2368 0.9243 5.4134PCI -1.0489 0.4544 5.3291 1 0.0210 0.3503 0.1438 0.8535

July - Dec. '05No Cardiac Tx 2.2814 0.4526 25.4048 1 0.0000 9.7900 4.0319 23.7716

Measure Name B Sh Wald df Siu. L.\p( B)95.0% Cl INplU) Lower Upper

Jan - June '03Age 0.0766 0.0260 8.6970 1 0.0032 1.0797 1.0260 1.13611

| July - Dec. '03 |Depression 2.4378 0.9145 7.1058 1 0.0077 11.4474 1.9067 1 68.72841

Jan - June ’04CABG 1.9190 0.8415 5.1999 1 0.0226 6.8140 1.3094 35.4586Oth Cardiac Dx/Tx -1.3918 0.5511 6.3796 1 0.0115 0.2486 0.0844 0.7321AMI Vol/ICU Bed -0.5172 0.1267 16.6729 1 0.0000 0.5962 0.4651 0.7642

July - Dec. '04Age 0.1327 0.0320 17.1989 1 0.0000 1.1420 1.0725 1.2159Chula Vista 12.3933 4 0.0147Encinitas -3.5539 1.1751 9.1473 1 0.0025 0.0286 0.0029 0.2863Green -2.8503 1.1405 6.2453 1 0.0125 0.0578 0.0062 0.5407La Jolla -3.3590 1.1058 9.2275 1 0.0024 0.0348 0.0040 0.3037Mercy -4.2626 1.2503 11.6238 1 0.0007 0.0141 0.0012 0.1633Oth Open Heart 3.6262 1.4727 6.0628 1 0.0138 37.5707 2.0954 673.634Hypertension 1.4512 0.6620 4.8062 1 0.0284 4.2683 1.1663 15.6215Mos Since Admit 0.7132 0.2077 11.7891 1 0.0006 2.0405 1.3581 3.0657

Measure Name B SE Wald d f ...Sig- .. E\p( B)95.0% Cl l .xp(B) Lower Upper

Jul\ - Dec '03Dyslipidemia -1.2148 0.5393 5.0738 □ 0.0243 0.2968 0.1031 0.8540

Jan - June '04Oth Cardiac Dx/Tx -1.4614 0.4071 12.8884 1 0.0003 0.2319 0.1044 0.5150Paid FTE/Occ Bed 0.9935 0.4541 4.7869 1 0.0287 2.7008 1.1091 6.5769

July - Dec '04Dyslipidemia -1.6722 0.7450 5.0376 1 0.0248 0.1878 0.0436 0.8090Oth Cardiac Dx/Tx -1.0289 0.4378 5.5221 1 0.0188 0.3574 0.1515 0.8430

Jan - June '05Age 0.0808 0.0342 5.5895 1 0.0181 1.0842 1.0139 1.1594No Cardiac Tx 1.2595 0.6344 3.9419 1 0.0471 3.5236 1.0163 12.2166

a = Constant or Linearly Dependent Covariates F a c ility C o d e(l) = 0 ; O th erO p en H eartS u rgery =


0 ; CAD - 1 ; Family Hx CAD = 0 ; Rapid_Response_Team = 0

Measure Name B SE ! Wald , df Sig. L \p(B )| 95.0% Cl l:\p (B )

Lower UpperJan - June '03

Age 0.0836 0.0490 2.9096 □ 0.0881 1.0872 0.9876 1.1969Jan - June '04

Age 0.1020 0.0407 6.2954 □ 0.0121 1.1074 1.0226 1.1993July - Dec '04

Age 0.3957 0.2354 2.8250 1 0.0928 1.4855 0.9364 2.3565Diabetes 9.9209 8.4119 1.3909 1 0.2382 20350.57 0.0014 2.9E+11

a = Constant or Linearly Dependent Covariates Facility Code( 1) = 0 ; Facility Code(2) = 0 ; R ace(l) = 0 ; Race(4) = 0 ; Other Open Heart Surgery = 0 ; No Cardiac Tx = 0 ; CAD = 1 ; Prior MI = 0 ; Rapid Response Team = 0b = Constant or Linearly Dependent Covariates Facility_Code(2) = 0 ; Race(4) = 0 ; CABG = 0 ; Other Open Heart Surgery = 0 ; N o Cardiac Tx = 0 ; CAD = 1 ; Family Hx CAD = 0

Measure Name mm SE , Wald , df Sig. . L \p(B )95.0°,, Cl L \p(B) Lower Upper

Jan - June '05Age 0.0758 0.0315 5.8122 1 0.0159 1.0788 1.0143 1.1474

Measure Namei

Bi

SE Wald d f Sin. INpiB)■ 95.0'.’,. Cl EAplB) | lam er Upper

Jan - June '03Age 0.0371 0.0066 31.9357 1 0.0000 1.0378 1.0245 1.0512Payor 11.5906 138.818 0.0070 1 0.9335 108080.5 0.0000 2E+123CABG -0.9255 0.3740 6.1230 1 0.0133 0.3963 0.1904 0.8250PCI -0.7964 0.1882 17.9007 1 0.0000 0.4509 0.3118 0.6521Dyslipidemia -0.8964 0.2114 17.9846 1 0.0000 0.4080 0.2696 0.6175Diabetes 0.3441 0.1597 4.6426 1 0.0312 1.4107 1.0316 1.9292FTE/Occ Bed 0.4571 0.2264 4.0755 1 0.0435 1.5795 1.0134 2.4617CV Award -0.6223 0.2620 5.6400 1 0.0176 0.5367 0.3211 0.8970

July - Dec '03Age 0.0337 0.0079 18.2644 1 0.0000 1.0343 1.0184 1.0504Payor 11.5852 161.289 0.0052 1 0.9427 107492.8 0.0000 2E+142PCI -1.0454 0.2176 23.0773 1 0.0000 0.3515 0.2295 0.5385Dyslipidemia -0.9363 0.2664 12.3549 1 0.0004 0.3921 0.2326 0.6608CV Award -1.0210 0.3075 11.0276 1 0.0009 0.3602 0.1972 0.6581PCP -0.6360 0.1926 10.9050 1 0.0010 0.5294 0.3629 0.7722

Jan - June '04Age 0.0365 0.0063 33.6451 1 0.0000 1.0372 1.0245 1.0501Payor 11.8038 164.896 0.0051 1 0.9429 133758.6 0.0000 3E+145PCI -0.4487 0.1822 6.0677 1 0.0138 0.6384 0.4468 0.9124Dyslipidemia -0.8713 0.2045 18.1546 1 0.0000 0.4184 0.2803 0.6247

141


MD AMI Vol -0.0456 0.0139 10.7542 1 0.0010 0.9554 0.9297 0.9818July - Dec '04

Age 0.0407 0.0068 36.0978 1 0.0000 1.0415 1.0278 1.0554PCI -1.0476 0.1977 28.0693 1 0.0000 0.3508 0.2381 0.5168Dyslipidemia -0.8596 0.2068 17.2810 1 0.0000 0.4233 0.2823 0.6349Diabetes 0.4099 0.1790 5.2455 1 0.0220 1.5066 1.0609 2.1395PCP -0.4957 0.1829 7.3493 1 0.0067 0.6091 0.4256 0.8717MD AMI Vol -0.0556 0.0154 13.0036 1 0.0003 0.9459 0.9177 0.9749Religious -0.3793 0.1731 4.7988 1 0.0285 0.6843 0.4874 0.9609

Jan - June '05Age 0.0499 0.0083 35.9481 1 0.0000 1.0512 1.0342 1.0685CABG -5.4805 1.2485 19.2685 1 0.0000 0.0042 0.0004 0.0481PCI -1.0113 0.2357 18.4052 1 0.0000 0.3637 0.2292 0.5774Dyslipidemia -1.1324 0.3074 13.5697 1 0.0002 0.3223 0.1764 0.5887PCP -0.6556 0.2704 5.8791 1 0.0153 0.5191 0.3056 0.8819Oth Open Hrt 4.7810 1.0373 21.2448 1 0.0000 119.2242 15.6112 910.53Hypertension -0.6189 0.2145 8.3263 1 0.0039 0.5385 0.3537 0.8199

July - Dec '05Age 0.0417 0.0109 14.6131 1 0.0001 1.0426 1.0205 1.0652PCI -1.2652 0.2949 18.4114 1 0.0000 0.2822 0.1583 0.5030Dyslipidemia -1.6908 0.3876 19.0319 1 0.0000 0.1844 0.0863 0.3941Thrombosis 1.7602 0.5663 9.6626 1 0.0019 5.8139 1.9162 17.6393


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Pay-For-performance's Impact on Overall Quality of Care for Acute Myocardial Infarction Patients