Addressing Disparities Through Organizational Quality Improvement Efforts

David R. Nerenz, Ph.D.Center for Health Services

ResearchHenry Ford Health System

October 21, 2005

Overview

• Health Care Disparities• Reasons for Disparities• Hospitals, Health Plans, and Quality

Improvement• Three Challenges:

– Identifying Significant Disparities– Measuring Effects of QI Initiatives– Setting Priorities

“Disparities in the health care delivered to racial and ethnic minorities are real and are associated with worse outcomes in many cases, which is unacceptable.”

- Alan Nelson, retired physician, former president of the American Medical Association and chair of the committee that wrote the Institute of Medicine report, Unequal Treatment: Confronting Racial and Disparities in Health Care

IOM Report, 2002: Assessing the Quality of Minority Health Care

0.0

1.0

2.0

3.0

0-14 15-24 25-44 45-64 65+

Ratio of Minority-White Death Rates, 1994-1996

Ratios are based on deaths per 100,000 resident populationSOURCE: DHHS Health, Unites States, 1998

Age Groups

Death rates of minority Americans comparedto those of white Americans at various ages

African American

American Indian/Alaska Native

Latino

Asian/Pacific Islander

White,non-Latino

Screening: Percent with Early Stage Cancer* Among Women with Breast

Cancer, 1978-1987 (Detroit)

20%

24%

29%

15%16%

19%

0%

10%

20%

30%

1978-1981 1982-1984 1985-1987

WhiteBlack

*Tumors <2cm and no auxiliary lymph involvement at diagnosisSOURCE: Swanson, M et al., 1990

Indicators of Children’s Access to Care, 1987

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Adjusted Odds RatiosMinority Children vs. White Children

Ratios > 1.0 indicate minority

more likely than white

children

0.56

2.44

1.77

1.45

2.12

1.54

Has a usual Source of

Care

Does not see a

specific physician

No After-hours

Emergency Care

Travel time of 30 min.

+

Wait time of 60 min. +

Physician Not Seen for

Selected Symptoms

DATA: 1987 NMESSOURCE: Newacheck et at, 1996

Heart Procedure Rates for Blacks and Whites, 1980 vs.1993

0.0

1.0

1980 1993

Ratios <1.0

indicates Blacks

less likely than

Whites to undergo procedur

e1980 1993 1980 1993

Equal procedure rate

(Ratio of Black/White procedure rates) *

•Rates were age-adjusted•SOURCE: Gillum R.F., et al., 1997

Cardiac Catheterizati

on

Angioplasty Bypass Surgery

Coronary Artery Surgery Rates by Race and Disease Severity, 1984-1992

35%

45%

25%31%

0%

20%

40%

60%

80%

Source: Peterson, et al., 1997.

Perc

en

t R

eceiv

ing

Byp

ass

Su

rgery

Mild Disease Severe Disease

Whites

African Americans

Disparities in Cardiac Revascularization

5,000 Medicare beneficiaries in 5 states – 1991 and 1992

RAND appropriateness criteria

Some gender disparities noted as well

Epstein et al, Medical Care, 2003

0

20

40

60

80

100

CABG orPTCA

ClinicallyAppropriate

ClinicallyInappropriate

BlackWhite

Evidence of racial/ethnic differences in cardiac care

1984-2001

68 studies find a racial/ethnicdifference in care(84%)

11 studies find no racial/ethnic difference in care(14%)

2 studies find racial/ethnic minority group more likely than whites to receive appropriatecare (2%)

Total= 81 studiesSource: Kaiser Family Foundation

Minorities are Less Satisfied with The

Quality of Care They Receive

3136

29

3834

41*

24*

36*

0

20

40

60

Better Care With Different Health Plan Rate Care from Doctor as Excellent

Total White Black Hispanic

*Significantly different from whites at p<.05 or lessSource: The Commonwealth Fund Biennial Health Insurance Survey (2003)

Percent adults 19-64 privately insured

THE COMMONWEALTH

FUND

Isn’t It All About Poverty and Lack of Insurance?

Rates of Hospitalization for Coronary Artery Bypass Surgery Among Medicare

Beneficiaries, 1993

0

2

4

6

White

Black

Coronary Artery Bypass Surgery Procedures per 1000 beneficiaries per year*

<$13,001 $13,001- $16,300

$16,301- $20,500

*Rates were adjusted for age and sex to the total Medicare population.SOURCE: Gomick, ME et al., 1996

>$20,500

Annual Income

4.8

1.8

4.8

2.2

4.9

2.1

4.6

2.2

Infant Mortality Rates: Mothers 20+ Years by Education and

Race/Ethnicity, 1995

7.6

4.7

17.3

14.8

12

6.0

5.9

5.0

6.4

LatinoAfrican American

White

Infant deaths per 1,000 live births

DATA: CDC National Center for Health StatisticsSOURCE: DHHS. Health, United States, 1998

College +

High SchoolLess than High School

16

THE COMMONWEALTH

FUND

Across Income Groups, African Americans Are Most Likely to Go without Needed Care Because

of Cost

37

24

36

23

31 29

36*

52*

0

20

40

60

Inc ome below 200% FP L Inc ome 200% or above FP L

Total White Afric an Americ an H ispanic

^Did not fill prescription, did not get specialist care, or skipped recommended test because of cost.*Significantly different from whites at p<.05 or betterSource: The Commonwealth Fund Biennial Health Insurance Survey (2003)

Percent adults 19-64 privately insured going without needed care

Across Income Groups – Hispanics Are Most At Risk for Forgoing Preventive

Care

849087 9086

95

74*84*

0

50

100

Income below 200% FPL Income 200% or above FPL

Total White African American Hispanic

*Significantly different from whites at p<.05 or betterSource: The Commonwealth Fund Biennial Health Insurance Survey (2003)

Percent adults 19-64 privately insured with blood pressure check in past year

THE COMMONWEALTH

FUND

Evidence on Disparities

“Racial and ethnic minorities tend to receive a lower quality of health care than non-minorities, even when access-related factors, such as patient’s insurance status and income, are controlled. ” (my emphasis)

IOM Report, Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care, 2002

Some of these studies are pretty old – haven’t things

changed since people started studying this?

Reperfusion Therapy in Medicare Beneficiaries with

Acute MI

Group % Eligible

receiving reperfusion

White men 59%

White women 56%

Black men 50%

Black women 44%

Canto JG, Allison JJ, Kiefe CI, Fincher CI, Farmer R, Sekar P,Person S, Weissman NW.Relation of race and sex to the use of reperfusion therapy in Medicare beneficiaries with acute myocardial infarction. N Engl J Med 2000 Apr 13;342(15):1094-100

Disparities in Management of AMI – Changes Over Time (1994-2002)

• Data Source – NRMI

• 600,000 Patients• Significant

disparities in several measures; no change over time

• Some disparities became not significant in adjusted analyses

• Vaccarino et al, NEJM, August 18, 2005

Receipt of Major Surgical Procedures – Medicare Data

1992-2001• Focus on 9 surgical

procedures• Analysis by hospital

referral regions for three procedures

• No evidence of change in disparity over 10-year period

• Disparity reduction in 22/158 regions, but no elimination of disparity in any region

• Jha et al, NEJM, August 18, 2005

Disparities in Medicare Managed Care (HEDIS)

Measures Over Time• Standard, widely-

used quality measures

• Trends from 1997 or 1999 to present

• Improvements in quality overall, reduction in disparities in some HEDIS measures, but not all

• Trivedi et al, NEJM, August 18, 2005

Disparities in Medicare Managed Care (HEDIS)

Measures Over Time (Cont.)• Additional

HEDIS measures

• Change in disparity from first to most recent year

• Trivedi et al, NEJM, August 18, 2005

All of these studies involve large national samples – what about disparities within single

health care organizations?

Comprehensive Diabetes Care:

Foot Exam Performed

0

20

40

60

80

100

White African American Hispanic Asian Overall

White vs. African American (p<0.001), White vs. Hispanic (p<0.001) and White vs. Asian (p<0.001).

Rate

Asthma: Outpatient Follow-up

After Acute Episodes• Core concept:

Outpatient follow-up after either ER visit or admission

• Children 5-17 years old

• Standard based on national expert panel guidelines

0

10

20

30

40

50

60

70

Follow-up Rate

Caucasian

African-American

Comparison of non-Hispanic/Hispanic Breast Cancer Screening by Commercial, Medicare

Risk, and Medicaid Products in a Single Health Plan, 2000

0

20

40

60

80

100

Commercial Medicare Risk Medicaid

non-Hispanic Hispanic

Rate

P=.001 non-Hispanic population

Possible Explanations for Disparities

• Environmental Factors– Income/Poverty– Insurance coverage– Geographic access– Poor-quality facilities

and providers in minority neighborhoods

– Cultural competence of providers and systems

– Language barriers– “Institutional racism”

• Individual Factors– Cultural beliefs and

preferences– Trust in providers and

organizations (lack of)– Literacy– Biased clinical decision-

making– Some possible

biological differences

Conceptual Model of Health Care Disparities

Patient Factors

Health CareSystem Factors

Treatments Outcomes

Environmental Factors

Provider Factors

Conceptual Model of The Operation of a Car

Engine Factors

Chassis and Body Factors

Movement ofCar

Arrival atDestination

Environmental Factors

Driver Factors

Treatment Treatment or or

Service PtService PtReceivedReceived

ProviderDecision-making

(Diagnosis,Treatment)

ProviderInterpretation of

SymptomsProvider Provider cognition and affect cognition and affect

regarding patientregarding patient

Provider Provider CharacteristicsCharacteristics

Setting

Pt

Rac

e/et

hn

icit

yP

t R

ace/

eth

nic

ity

Cla

ss

Culture

Pt Cognitive & Affective Factors(e.g., acceptance of advice, attitude, self-efficacy, intention, feelings of

competence, attitude toward med care, trust)

Pt Behavior in Encounter

(e.g., question-asking self-disclosure, assertiveness)

Pt Satisfaction

Pt BehaviorPt Behavior (e.g. self management,

information-seeking, utilization)

Encounter characteristics

Provider Behavior in Encounter

(e.g., participatory style, warmth, content, information giving,

question-asking)

Understanding the Provider Contribution to Disparities

(Michelle VanRyn)

CHRONOLOGY BIRTH DIAGNOSIS TREATMENT

INN

AT

EE

XP

OS

UR

ES

/ A

CQ

UIR

ED

FA

CT

OR

SD

IAG

NO

ST

IC

IN

TE

NS

ITY

CA

NC

ER

-R

EL

AT

ED

Age

Sex / Gender

Race / ethnicity (B vs. W)

Socioeconomic status

Comorbidities (adverse)

Unstaged cancer

Stage (higher / advanced)

Symptoms (adverse)

No surgery, localized NSCLC

SU

RV

IVA

L

Marital status (spouseless)

Smoking status (current)

Illicit drug (use vs. not)

No chemotherapy, SCLC / advanced NSCLC

No Rx, any

Histology NOS

Conceptual Model for Contribution of Race/Ethnicity and SES to

Treatment and Outcome Disparities

SES

Race/Ethnicity

Education

Income

Job Class

Language

Culture

Biological Differences Bias/Discrimination in

Medical Settings

Dis

parit

ies

in T

reat

men

t

Historical Bias/Discrimination

Dis

parit

ies

in O

utco

mes

FinancialAccess

Benefits,ScheduleFlexibility

Tumor Histology,Comorbidities

Health Beliefs,Trust in Providers

Comprehension,Trust

Literacy

Disparities in Cardiac Surgery –

Steps in a Process• Notice and interpret symptoms• Get primary care doctor appointment• Get tests• Deal with insurance and payment

issues• Get referral to Cardiology• Get Cardiology appointment• Get additional tests• Discuss treatment options and ask

questions• Get referral to Cardiac Surgeon• Get appointment with surgeon• Get additional tests• Discuss treatment options and ask

questions• Get surgery scheduled• Arrange time off work and family

support• Deal with comorbid conditions• Get surgery

Disparities in Breast Cancer Treatments and Outcomes

Mammography

Histology

Stage at DiagnosisTreatment by

StageSurvival

Comorbidities,Obesity

Yood et al, OthersTammemagi et al, Griggs et al

Hershman et al,DignamStark et al

Grann et al

Bibb, Mandelblatt,Others

Bynum et alLipscombe et al

Chlebowski et al

Understanding Underlying Factors – Role of

Comorbidities

• Comorbidities as mediators of treatment choices, or of treatment effectiveness

• Comorbidities as predictors of survival or other health outcomes, independent of treatment for primary condition being studied

• Higher prevalence of comorbidities (e.g., hypertension, diabetes) among minority patients.

Survival Disparities in Breast Cancer – Role of Comorbidities

• Comorbidity and Survival Disparities Among Black and White Patients With Breast Cancer

• C. Martin Tammemagi, PhD; David Nerenz, PhD; Christine Neslund-Dudas, MA; Carolyn Feldkamp, PhD; David Nathanson, MD

JAMA. 2005;294:1765-1772.

• Approximately 900 Black and White Women with Breast Cancer, 10-Year Follow-up

Comorbidities and Breast Cancer (Cont.)

• African American women more likely than White women to have comorbidities

• Comorbidities associated with survival

• Comorbidities explained most of the disparity in all-cause survival, but not in cancer-specific survival.

DisparitiesDisparities in Lung Cancer Outcomes in Lung Cancer OutcomesHR (Black vs. White) = 1.21 (95% CI 1.05, 1.38; p = 0.008)HR (Black vs. White) = 1.21 (95% CI 1.05, 1.38; p = 0.008)Median survival Blacks = 8.5 months; Whites = 11.2 monthsMedian survival Blacks = 8.5 months; Whites = 11.2 months

Kaplan-Meier survival estimates, by race

Surv

ival

pro

porti

on

Follow-up in years0 1 2 3 4 5

0.00

0.25

0.50

0.75

1.00

Black

White

Kaplan Meier survival plots for 1154 LCA patients, Kaplan Meier survival plots for 1154 LCA patients, stratified bystratified by

.S

urvi

val p

ropo

rtion

Follow-up in years0 1 2 3 4 5

0.00

0.25

0.50

0.75

1.00

No comorbidity

Comorbidity.

Sur

viva

l pro

porti

on

Follow-up in years0 1 2 3 4 5

0.00

0.25

0.50

0.75

1.00

Absent

Present

Adverse comorbidity Adverse symptoms

Predictors of LCA survivalPredictors of LCA survival – hazard ratios & distributions (OR) by race/ethnicity – hazard ratios & distributions (OR) by race/ethnicity

Prognostic Factors Univariate HR Multivariate HR Black White Odds RatioB vs. W

Innate

Age (per 10 year increase) 1.16 (p <0.001) 1.24 (p <0.001) 67.5 yr 67.1 yr

Race/ethnicity (Black vs. White) 1.21 (p = 0.008) - -

Acquired

SES * (BGMHI $10,000) 0.92 (p < 0.001) $19,913 $38,822 0.37 (p < 0.001)

Marital status (spouseless vs. not) 1.27 (p = 0.001) 50.3% 37.1% 1.72 (p < 0.001)

Smoking status (current smk vs. not) 1.29 (p < 0.001) 1.32 (p < 0.001) 53.4% 44.2% 1.45 (p = 0.003)

Illicit drug use (user vs. not) 2.17 (p < 0.001) 1.99 (p = 0.004) 4.3% 0.3% 15.67 (p < 0.001)

Adverse comorbidity (≥1 vs. 0) 1.45 (p < 0.001) 1.42 (p < 0.001) 65.7% 59.0% 1.33 (p = 0.02)

Diagnostic intensity

Unstaged (vs. stage I) 4.61 (p < 0.001) 2.71 (p < 0.001) 7.8% 4.8% 1.69 (p = 0.03)

Histology (unspecified vs. SqCCA) 1.80 (p < 0.001) 1.26 (p = 0.03) 28.0% 20.9% 1.47 (p = 0.006)

Cancer-related factors

Adverse symptoms (≥1 vs. 0) 2.20 (p < 0.001) 1.65 (p < 0.001) 80.7% 70.5% 1.75 (p < 0.001)

Stage IStage IIStage IIIStage IV *

referent group2.16 (p < 0.001)3.49 (p < 0.001)7.09 (p < 0.001)

referent group2.11 (p = 0.001)3.37 (p < 0.001)6.90 (p < 0.001)

20.2%6.6%

33.2%40.0%

26.8%5.1%33.5%34.5%

OR (adv vs. local) =1.33 (p = 0.03)

Treatment

Surgery in localized NSCLC * 0.23 (p < 0.001) 55.2% 70.1% 0.53 (p = 0.01)

Chemotherapy (SCLC, III/IV NSCLC) 0.43 (p < 0.001) 45.9% 53.5% 0.74 (p = 0.03)

Any treatment (treated vs. not) 0.34 (p < 0.001) 70.6% 80.8% 0.57 (p < 0.001)

Surgery 0.51 (p < 0.001) 19.9% 30.4%

Chemotherapy 0.48 (p < 0.001) 41.3% 45.5%

Radiation therapy 0.89 (p = 0.15) 42.2% 43.1%

Why Think About Disparities in Terms of Quality of Care?

• Relatively strong science base of published literature and evidence-based guidelines conceptual and moral clarity

• Build on existing staff, data collection infrastructure, and organizational relationships

• Build on existing QI concepts, models, and approaches

Disparities in Standard Hospital Measures of Quality

of Care

• JCAHO/CMS Standard Measure Set– CHF– AMI– Pneumonia

• Commonwealth Fund – HRET Project• RWJF Initiative – “Expecting Success”

CMS/JCAHO Measures for CHF and AMI

0

10

20

30

40

50

60

70

80

90

100

Disch Inst LVEF ACE @Disch

SmokingAdvice

Asprin -Arrival

Aspirin -Disch

ACEI forLVSD

SmokingAdvice

B-Block atDisch

B-Block @Arriv

Mortality

BlackWhite

*

*

Per

cent

Analysis of Disparities – Basic Requirements

• Well-defined, accepted measures of quality, access, satisfaction, clinical outcomes

• Data on race/ethnicity, SES, primary language

Methods of Data Collection – Direct from Members/Patients• Pros:

– Most Flexible– Generally Preferred

for Accuracy

• Cons:– Can be Expensive– Can Raise Concerns

about Health Plan’s Real Objectives

Methods of Data Collection – Geocoding

• Pros:– Relatively Easy,

Fast, and Inexpensive

– Requires only Information you Already Have

• Cons:– Won’t Work in

Residentially Integrated Areas

– Won’t Identify Small, Dispersed Groups

Methods of Data Collection – Surname Recognition

• Pros:– Relatively Easy, Fast,

and Inexpensive– Can be Combined

with Geocoding

• Cons:– Only Works for

Groups with Distinct Names

– May not Work in All Market Areas

Health Plans as Catalysts for Quality Improvement

As agents of purchasers, health plans:

• Organize Quality Improvement Projects and

Programs

• Define Important Quality Domains and Develop Measures

• Disseminate Practice Guidelines

• Identify High-Priority Target Populations

• Identify High-Priority Clinical Conditions

• Develop Incentive Systems

• Direct Communications to Members

Comparison of Caucasian and African American

HbA1c Testing in a Single Plan

0

20

40

60

80

100

1998 1999 2000

Caucasian African American

Rate

Multiple Disparities in HEDIS Measures

in Single Health Plan(Six-State Medicaid Project)

0

10

20

30

40

50

60

70

HbA1cTesting

GoodGlycemicControl

AppropriateAsthma

Meds

PrenatalCare

CaucasianAfrican American

Hispanic

Per

cent

Source: Single Health Plan analysis of HEDIS data – 2003, unpublished

Asthma Medication ManagementAsthma Medication ManagementReporting Year 2003Reporting Year 2003

African-AmericanAfrican-American CaucasianCaucasian

NumeratNumeratoror

DenominatDenominatoror

RateRate NumeratNumeratoror

DenominatDenominatoror

RateRate

All Co’sAll Co’s 411411 600600 69%69% 698698 921921 76%76%

AA 189189 272272 69%69% 174174 218218 80%80%

BB 153153 213213 72%72% 375375 499499 75%75%

CC 6969 115115 60%60% 149149 204204 73%73%

Breast Cancer Screening Breast Cancer Screening Reporting Year 2003Reporting Year 2003

African-AmericanAfrican-American CaucasianCaucasian

NumeratNumeratoror

DenominatDenominatoror

RateRate NumeratNumeratoror

DenominatDenominatoror

RateRate

All Co’sAll Co’s 11161116 14681468 76%76% 25812581 31683168 81%81%

AA 390390 519519 75%75% 536536 650650 82%82%

BB 435435 561561 78%78% 14151415 17191719 82%82%

CC 291291 388388 75%75% 630630 799799 79%79%

African-AmericanAfrican-American CaucasianCaucasian

NumeratNumeratoror

DenominatDenominatoror

RateRate NumeratNumeratoror

DenominatDenominatoror

RateRate

All Co’sAll Co’s 211211 304304 69%69% 689689 837837 82%82%

AA 9494 142142 66%66% 161161 204204 79%79%

BB 8585 116116 73%73% 349349 429429 81%81%

CC 3232 4646 70%70% 179179 204204 88%88%

Appropriate Testing for Children with Appropriate Testing for Children with Pharyngitis Pharyngitis Reporting Reporting Year 2003Year 2003

Quality Improvement Interventions

in Single Health Plans

• Patient Reminders• Provider Reminders• Culturally-Sensitive Member

Education Materials• Disease Management Programs• Partnerships with Community Groups

to Raise Awareness of Prevention

Improvements in Quality of Care for African American Health Plan

Members with Diabetes

HbA1c Testing

0

20

40

60

80

100

African American Members

Percent2003

2004

LDL-C Testing

0

20

40

60

80

100

African American Members

Percent 2003

2004

Another Approach to Evaluating

QI Program Success• Asthma severity

definition involving ER visits and admissions

• Focus on African-American members with asthma

• Used shift in distribution of severity categories as measure of program success

• Statistically significant using Chi-square test

0

10

20

30

40

50

60

70

80

90

100

Mild Moderate Severe

Pre-InterventionPost-InterventionP

erc

en

t

Childhood Immunization – Combo I – (HEDIS 1999 Definition)

0

5

10

15

20

25

2002 2003 Rolling2003-2004

First Q2004

HispanicTotal

Perc

ent

Example of Quality of Care Disparities

Found in HEDIS Data(Six-State Medicaid Project)

0

10

20

30

40

50

60

70

HbA1cTesting

GoodGlycemicControl

AppropriateAsthma

Meds

PrenatalCare

CaucasianAfrican American

Hispanic

Per

cent

Source: Single Health Plan analysis of HEDIS data – 2003, unpublished

Which Disparity to Work On?

• Largest absolute difference?• Statistical significance of difference?• Size of denominator population?• Likelihood of making a difference?• Cost-effectiveness of intervention(s)?• Something else?

– Purchaser incentives/preferences– Community preferences

Commonwealth Fund Simulation Modeling Project -

Basic Premise

• Limited budgets and other resources to invest in disparity reduction initiatives

• A reasonable evidence base exists for modeling effects of disparity reductions on several measures of health– Mortality– Quality of life– Attendance at work or school

HEDIS Effectiveness of Care Measures - 2004

• Childhood Immunization Status• Adolescent Immunization

Status• Appropriate Treatment for

Children With Upper Respiratory Infection

• Appropriate Testing for Children With Pharyngitis

• Colorectal Cancer Screening• Breast Cancer Screening• Cervical Cancer Screening• Chlamydia Screening in Women• Osteoporosis Management in

Women Who Had a Fracture• Controlling High Blood

Pressure• Beta-Blocker Treatment After a

Heart Attack

• Cholesterol Management After Acute Cardiovascular Event

• Comprehensive Diabetes Care• Use of Appropriate Medications

for People With Asthma• Follow-Up After Hospitalization

for Mental Illness• Antidepressant Medication

Management• Medical Assistance With

Smoking Cessation• Flu Shots for Adults Age 50–64• Flu Shots for Older Adults• Pneumonia Vaccination Status

for Older Adults• Medicare Health Outcomes

Survey• Management of Urinary

Incontinence in Older Adults

Simulation Models – Basic Features

• Generally use existing published data, although it’s possible to collect and use primary data.

• Define key clinical/biological events and assign probabilities (and utility values) to those events.

• Create model structure that matches essential features of key clinical or biological processes.

Example of State TransitionsWhen Cancer Found (Stage III)

Sensitivity Analysis - Diabetes

QALY Gains for Alternative Disparity Reduction Initiatives

0

10

20

30

40

50

Diabete

s - All

Diabete

s - H

bA1c

Beta

Blocke

r - "B

ase"

Beta

Blocke

r - "O

ptim

istic"

Asthma

Mam

mog

raphy

- "B

ase"

Mam

mog

raphy

- "O

ptim

istic"

QA

LY

Gain

Per

1,0

00

Simulation Models - Conclusions / Next Steps

• Under basic sets of assumptions, health benefits related to eliminating disparities in four existing HEDIS measures are relatively modest.

• There is a significant range – approximately a hundred-fold variation - of potential benefits across all measures examined.

• Health benefits associated with other quality of care disparities in these clinical populations may be more significant (e.g., other dimensions of diabetes care, breast cancer treatment vs. breast cancer screening)

• We would like to expand the modeling effort to include attention to these other quality of care disparities.

• As available data allow, these methods can be applied to other HEDIS measures and other specific groups.

Overall Conclusions

• Plenty of documentation of disparities – need to know more now about underlying reasons and potential solutions.

• Policy changes are important, but clinical change happens at the local, single organization level.

• Quality improvement concepts and structures are a useful way to address disparities.

• We’ve made some progress in handling data collection and measurement challenges, but we have much yet to do.