YOUR VOICE 4:

October 20, 2010

DiversityRx: 7th National Conference on Quality Health Care for Culturally Diverse Populations, Baltimore, MD

“Collecting and Using Patient Demographic Data to Create Equitable Health Care Systems: Perspectives from a Community of Practice”

Kathryn Coltin, MPH Catherine West, MS, RNCheri Wilson, MA, MHS, CPHQ Boris Kalanj, LISW, Moderator

Community of Practice (CoP) #3: Participant Introductions Name Work Setting

Session Objectives

Provide audience members with meaningful, replicable information and best practices related to REAL data collection and use;

Outline barriers and best practices that are relevant to a variety of health care organizations (hospitals, clinics, health plans, etc.) at varying points on the continuum of implementation;

Discuss larger regulatory and HIT-related developments that impact this area of work;

Problem solve with audience members; and Highlight key benefits/outcomes of the CoP.

Goals of a CoP

To create an informative and supportive environment for people to learn more about the topic, share their expertise, get advice, and create a base of knowledge that will benefit others.

What is a CoP?

A small group (12-20 participants) of professional colleagues ‘Meet’ monthly on a specific topic Via teleconference or virtual learning platforms Purpose: to discuss common practice challenges and share

information about strategies and resources. Supported by a listserv for ongoing dialogue between meetings

and a wiki where the information base developed over the course of the project is documented for use by others.

Initial meeting period is 12 months—groups may continue to meet as interest and funding permit.

CoP expectations—attendance, participation, contribution

Why Focus on REAL Data? Minorities tend to receive a lower quality

of healthcare than non-minorities. For LEP patients: increased medical errors,

poorer follow-up and adherence to clinical instructions and poorer patient provider communication

Race, ethnicity, and language data collected is often inadequate and not available for quality improvement

Regulatory standards and HIT requirements

Regulatory Standards and Healthcare IT Title VI of the Civil Rights Act of 1964 CLAS Standards (2001) The Joint Commission Standards (effective

1/1/2011) NCQA Multicultural Health Standards (effective

7/1/2010) Meaningful Use of Electronic Health Records (EHRs)

(effective 1/1/2011) Healthcare Reform

American Recovery and Reinvestment Act (ARRA) (2009)

Patient Protection and Affordable Care Act (2010)

What Were Our Goals?

1. Consensus on standardized data collection methods

2. Best practices that ultimately improve the health of our communities (improved data collection and validity, strategies to address disparities)

3. Peer support and networking

4. Support in encouraging government entities to standardize (and support) data collection and use

5. Discussion of technical challenges of collecting granular data

6. Sharing outcomes of CoP with national/international audience

7. An analysis of the ROI of conducting this work

CoP Topics/Speakers

Erin Bowman, California Health Care Safety Net Institute and Its REAL Data Efforts

Dr. David Nerenz, Chair, IOM Subcommittee on Standardized Collection of Race/Ethnicity Data for Healthcare Quality Improvement

Nuts and Bolts of REAL Data Collection Disparities Solutions Center, (Massachusetts

General) Creating Equity Reports National Association of Public Hospitals and Health

Systems, Assuring Healthcare Equity HRET Toolkit

Topics Covered during the CoP Dr. Geniene Wilson, New Tools for Eliminating Health

Disparities: Collecting Demographic Data in an Electronic Health Record (Institute for Family Health)

Dr. Barrie Baker, Collecting Member Race/Ethnicity (Keystone Mercy Health System)

Kathryn Coltin, Harvard Pilgrim’s Equity Report: An Evolving Initiative

Cheri Wilson, REAL Data Quality Issues (Johns Hopkins Hospital)

Maria Moreno, Collecting REAL Data and EPIC Upgrade (Sutter Health Institute for Research and Education)

EPIC Vendor and Standardization

Community of Practice (CoP) #3 Participants Why applied to participate in the CoP? What we each brought to the CoP?

The Johns Hopkins Hospital (JHH):

REAL Data Quality Issues

Cheri Wilson, MA, MHS, CPHQFaculty Research Associate

Program Director, Culture-Quality-Collaborative (CQC)

Outline

About JHHProject backgroundData quality issuesRecommendations

About JHH

JHH founded in 1889 1,085 licensed patient beds 46,775 inpatient admissions 421,933 outpatient encounters 1,714 full-time attending physicians 9,294 employees

Data Quality Issues:Primary Language

2822

6 5 4 3 2 1 1 1 1 105

1015202530

Languages Identified in PSN Event Reports

35

1

79

0

5

10

15

20

25

30

35

40

ENG KOR SPA None listed

Languages Identified in Sunrise

N = 76 N = 52

N = 67

Data Quality Issues:

Race/Ethnicity

1916

7 7

3

02468

101214161820

Race/Ethnicity in Sunrise

2119

1210

3 2

0

5

10

15

20

25

O H W A B U

Race/Ethnicity in EPIC

N = 52 N = 6722

19

12

9

3 2

0

5

10

15

20

25

O H W A B U

Race/Ethnicity in EPR

N = 67

Datamart:Inpatient Race and Ethnicity Data

RACE % (from 2009)

RANGE (1994-2010)

U.S. CENSUS (BALTIMORE) 2000*

U.S. CENSUS (MD) 2008**

FY2010 1- White 51.79% (51.79%-55.70%) 31.6% 63.4%

2 - African American 39.61% = (38.95%-42.3%) 64.3% 29.4%3 - Asian or Pacific Islander 2.13% (.37%-2.13%) 1.5% 5.2%

4 - American Indian/Eskimo/Aleut 0.18% (.06%-.18%) 0.3% 0.4%

5 - Other*** 5.22% (2.08%-5.22%) --- ---6 - Biracial** 0.75% (.04%-.75%) 1.5% 60.0%

9 - Unknown*** 0.32% (.06%-.41%) --- ---

FY2010 ETHNICITY %RANGE (1994-

2010)U.S. CENSUS

(BALTIMORE) 2000*U.S. CENSUS (MD)

2008**

1 - Spanish/Hispanic Origin 2.45% (.8%-2.45%) 1.7% 6.7%

2 - Not of Spanish/Hispanic Origin 97.13% (97.13%-99.66%) 98.3% 93.3%

9 - Unknown*** 0.42% (.03%-.42%) --- ---

Notes

* Separate categories in U.S. Census Date: Asian, Native Hawaiian and Other Pacific

Islander** Category added in 2006

*** Not a U.S. Census category

Datamart: Outpatient Race and Ethnicity Data

RACE % (from 2009)

RANGE (1998-2010)

U.S. CENSUS (BALTIMORE) 2000*

U.S. CENSUS (MD) 2008**

FY2010 1- White 50.69% (40.45%-51.07%) 31.6% 63.4%

2 - African American 39.00% (39.00-54.08%) 64.3% 29.4%3 - Asian or Pacific Islander 2.47% (.60%-2.47%) 1.5% 5.2%

4 - American Indian/Eskimo/Aleut 0.19% (.09%-.19%) 0.3% 0.4%

5 - Other*** 5.34% (2.99%-5.34%) --- ---6 - Biracial** 0.28% (.03%-.28%) 1.5% 60.0%

9 - Unknown*** 2.04% (.71%-2.04%) --- ---

FY2010 ETHNICITYRANGE (1998-

2010)U.S. CENSUS

(BALTIMORE) 2000*U.S. CENSUS (MD)

2008**1 - Spanish/Hispanic Origin 1.88% (.19%-1.88%) 1.7% 6.7%

2 - Not of Spanish/Hispanic Origin 96.09% (96.09%-99.54%) 98.3% 93.3%

9 - Unknown*** 2.04% (.37%-2.04%) --- ---

Notes

* Separate categories in U.S. Census Date: Asian, Native Hawaiian and Other Pacific

Islander** Category added in 2006

*** Not a U.S. Census category

Race: Data Elements

Race EPIC EPR Sunrise (POE)HSCRC (State

Reporting)

HRET Disparities Toolkit (based on OMB Federal

Reporting)A - Asian/Pacific Islander (Asian or Pacific Islander) X X X X American Indian/Alaska

Native XAsian X

B - African American (African American) X X X X

Biracial X Black/African American X

Caucasian/White X Declined X

H - Hispanic X X X I - American

Indian/Eskimo/Aleut (American

Indian/Eskimo/Aleut) X X X X

M - Multiracial (Multiracial) X X X XNative Hawaiian/Other

Pacific Islander XO - Other (Other) X X X X

U - Unknown (Unknown) X X X X Unavailable X

W - White (White) X X X X

Ethnicity: Data Elements

Ethnicity EPIC EPR Sunrise (POE)

HSCRC (State Reporting)

HRET Disparities Toolkit (based on OMB

Federal Reporting)Spanish/Hispanic

Origin X

Not of Spanish/Hispanic

Origin X

Unknown X

Hispanic or Latino * X

Not Hispanic or Latino * X

No separate category X X

Note

* Dropdown list, but

currently not populated

Recommendations Standardize the race, ethnicity, and primary language categories across information

systems EPIC

Ask all patients, not just new patients, about race, ethnicity, primary language, and interpreter needs.

Make interpreter needs more visible on the scheduling screens. Modify the question, “Do you currently have any special needs?” to include “need

an interpreter.” Currently includes such things as “need a wheelchair.” Sunrise

Determine who is responsible for identifying a patient’s race, ethnicity, and primary language as well as checking “Interpreter required” box.

Modify patient demographic form to state both race and ethnicity. Add a language field in the various information systems

Field to include not only foreign languages, but sign language and Braille as well. This will make it easier to identify and address the needs of these patient

populations. Review the Registration process to assure correct data and the need for an interpreter

is collected consistently

Collecting, Reporting and Using REaL Data To Reduce Health Care Disparities

Kathryn Coltin

Harvard Pilgrim Health Care

Diversity Rx Community of Practice 3

October 2010

Harvard Pilgrim Health Care Background and Context Harvard Pilgrim Health Care is a non-profit health plan serving over 1

million commercially-insured members in MA, ME, NH and RI. Of these, almost 70% reside in Massachusetts

In 2004 Harvard Pilgrim became one of ten founding members of the National Health Plan Collaborative to reduce racial & ethnic disparities.

This step fueled a steadily growing initiative to measure, report and reduce disparities in the care and service our members receive.

Harvard Pilgrim has been ranked the #1 health plan in the U.S. based on quality since 2005*.

Even so, disparities exist in the care some of our members receive.

The Commonwealth of Massachusetts mandated collection and reporting of patients’ race, ethnicity and language by acute care hospitals in January 2007 and extended this mandate to health plan collection of enrollees’ REaL data beginning July 2010.

$$$ Penalties are tied to non-compliance in achieving specified reporting thresholds.

*Based on NCQA’s U.S. News and World Report and Consumer Reports Best U.S. Health Plan Rankings

Harvard Pilgrim Health CareData Collection Channels—different strokes for different folks

Enrollment processPaper formsEDI transactions√ Online enrollments

Member Service initiatives Mailed correspondence√ Online services/Secure Member Web Portal√ Member surveys Telephonic services

Clinical Care initiatives√ Online services (Health Risk Assessment)√ Computerized telephonic services (IVR outreach

calls)√ Live telephonic care: Care/Case mgmt, Disease

mgmt Provider initiatives

√ Contracting requirements√ Enhancements to existing provider transactions Pay for reporting (based on EHR meaningful use

data)?

Language onlyMOST

LEAST

Acc

epta

bilit

y to

mem

bers

25

Harvard Pilgrim Health CareCollection of REaL Data

Secure web portal includes a Member Profile, which was modified to include Race, Ethnicity and Language preferences

Collecting REaL data from providers Harvard Pilgrim added self-reported REaL to medical record

documentation standards for physician offices in Dec. 2007

● December 2008 chart audit found average compliance rate <5%

Harvard Pilgrim began requesting REaL from MA hospitals and one large physician group in Fall 2008

● No standard file format or coding system has been adopted statewide to facilitate sharing data

● HPHC accepts hospital-specific file formats and codes, then maps fields and codes to HPHC standard data dictionary

● Negotiations with hospitals re sharing REaL data lengthy and not always productive; some have requested payment for data, while others have referred our request to the MA Hospital Association

● Administratively burdensome for hospitals to provide REaL data directly to each health plan; state agency should develop a mechanism to share the data hospitals currently report to the agency with all health plans in the state.26

Harvard Pilgrim Health CareCollection of REaL Data

Harvard Pilgrim Health CareUsing the data—first make it usable

Significant IT investments made since 2008 to enable collection, analysis and reporting of REaL data

Built electronic file feeds from each data channel to a staging area where automated standardization of file formats and coding occurs

Built tables in Enterprise Data Warehouse to house standardized REaL data that are uploaded from the staging area

Incorporated most recent RAND algorithms for indirect estimation of race/ethnicity using geo/surname coding

● Validated indirect estimates against self-reported race/ethnicity values

Built logic to reconcile conflicting REaL data values across self-reported data sources

● Algorithm determines “best” REaL data for analysis and reporting

Self-reported REaL data trump indirectly estimated data for use in internal analyses to identify and monitor disparities in care

Harvard Pilgrim Health CareUsing the data—an evolving portfolio of measures

Annual since 2003 Preventive Screenings

Chlamydia screeningCancer screening

Breast CACervical CAColorectal CA

Chronic Disease CareAsthma meds

5-17 year olds18-56 year olds

Diabetes careHbA1c testingLDL-C testingRetinal screeningNephropathy monitoring

CAHPS measures of access & customer service

Added in 2006 Chronic Disease Care

Cardiovascular disease Persistent use of beta-

blocker after AMI LDL-C testing in CAD LDL-C control in CAD BP control in patients with

HTN Monitoring patients on

Persistent MedicationsDiabetes

HbA1c >9 (poor control) HbA1c <7 (good control) LDL-C <100 (good control)

Rheumatoid Arthritis (DMARDs) Acute Care

Inappropriate antibiotic use for adult bronchitis

Imaging for low back pain in adultsNote: Italics indicates outcome measures. Blue font indicates measures with observed

disparities, most of which have been reduced, though not yet eliminated

Added in 2007 Preventive Care/Access

Well VisitsInfants 0-15 mo.Children 3-6 yr.Adolescents 12-21yr.

Chronic Care Diabetes

BP control Acute Care

Strep Tx prior to antibiotic Rx for children w/ Pharyngitis

Appropriate antibiotic use for children w/URI

Added in 2010 Patients’ care

experiences Medical Home

Harvard Pilgrim Health CareUse of REaL Data for reporting—defining a disparity

Harvard Pilgrim defines an actionable disparity as a performance rate for a given population group that is >6 percentage points below that of the population group with the best rate (i.e., the benchmark group)

Why? This definition works across all types of disparities that we measure For racial/ethnic disparities, the white non-Hispanic population is

frequently not the benchmark population Comparison with the benchmark population is consistent with our

goal of assuring the highest quality care, not just equal care The margin of error on many measures is +/- 5% or higher Our overall population rates for most measures are above the

national 90th percentile rate Preventive care measures have very large denominators, so very

small differences (1-2%) are statistically significant, but not clinically significant

Acute illness and chronic disease measures have smaller denominators and large differences (>6 percentage points) are often not statistically significant, but can be clinically important

Harvard Pilgrim Health CareAnalyzing disparities—our Annual Equity Report

Measures for current year performance (or

two year performance for measures with small

Ns) are usually displayed using bars for each

reporting category within a measure.

Separate graphs are used to display

performance for each attribute (race,

ethnicity, gender, education, income, etc.).

HEDIS Rates for Comprehensive Diabetes Careby Indirectly Estimated Race/Ethnicity

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

HEDIS Measure

Pe

rfo

rma

nc

e R

ate

Black 91.0% 62.8% 92.3% 70.8%

Hispanic 88.7% 54.1% 88.4% 58.0%

Asian 91.6% 63.1% 92.0% 62.5%

White/other 88.5% 60.8% 90.9% 58.9%

HbA1c Rate Eye Exam Rate LDL Tx RateNephropathy

Monitoring Rate

Colorectal Cancer Screening Rates by Race/Ethnicity 2003-2009

55%

57%

59%

61%

63%

65%

67%

69%

71%

73%

75%

77%

79%

81%

2003 2004 2005 2006 2007 2008 2009

Performance Year

Perc

ent S

cree

ned

Black Hispanic Linear (Black) Linear (Hispanic)

Measures with data for

multiple years are trended on

separate line graphs showing

each group that had an

actionable disparity when

compared with the

benchmark group

Harvard Pilgrim Health CareInterventions to reduce disparities

Diabetic Eye Exams (2005-2009) ID physician practices with high concentration of Hispanic members

Solicit applications for funding of QI interventions (Quality Awards Program) Conduct community based interventions in communities with a high proportion

of Hispanic residents Offer onsite eye exams and patient education Pilot a member incentive to waive co-pay for eye exam

Remove referral requirement for dilated eye exam for diabetes

Asthma medications (2006-2009) Review and enhance all patient education materials

Update and improve existing materials Increase availability of materials in Spanish and other languages Lower the reading level and improve health literacy Promote through IVR outreach

Colorectal Cancer Screening (2005-2009) Enhance telephone-based outreach and bilingual educational mailings

IVR call offered in English or Spanish with culturally appropriate messaging Pilot for collection of self-reported race/ethnicity using IVR Supplemental educational materials available in Spanish and Portuguese Won 2007 NCQA Multicultural Innovation Award

Harvard Pilgrim Health CareTwo of our successes

Racial/Ethnic Performance Disparity by Year9.0%

7.5%7.1%

4.7% 4.9%

7.0%

3.8%

2.7%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

10.0%

HEDIS Measurement Year

Dif

fere

nce f

rom

Best

Perf

orm

ing

R

acia

l/E

thn

ic G

rou

p

Diabetes: Annual Eye Exam Adult Asthma: Appropriate Meds

Harvard Pilgrim Colorectal Cancer Screening Rates by Race/Ethnicity 2003-2009

55%

59%

63%

67%

71%

75%

79%

83%

2003 2004 2005 2006 2007 2008 2009

Performance Year

Pe

rce

nt

Sc

ree

ne

d

Black Hispanic Linear (Black) Linear (Hispanic)

Gap = 8.7 Gap = 3.8 Gap = 8.1

76.4%

68.3%

60.7%

69.4%

IVR IVR + Spanish

P4P

Is this a success???

34

Aligning Forces for Quality

Using Stratified Data for Quality Improvement: Examples from Speaking Together National Language Services Network

Catherine West, MS, RN

October 20, 2010

Diabetes Quality Indicatorsby Language and by Time

Low English Proficiency (n=276)

English (n=6,926)

Language not known (n=1,977)

6/30/2004 (n=6,098)

12/31/2007 (n=9,179)

Language Time

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

LEP (N=276) 93% 86% 54% 85% 76% 58% 83% 91% 84% 78% 81% 58% 22%

English (N=6,926) 95% 83% 52% 83% 73% 54% 79% 90% 81% 80% 77% 57% 23%

Not Know n (N=1,977) 93% 83% 50% 81% 72% 52% 79% 88% 91% 77% 74% 58% 20%

Total 6/30/2004 (N=6,098)* 92% 78% 42% 77% 66% 47% 59% 81% 71% 50% 61% 51% 14%

Total 12/31/2007 (N= 9,179) 94% 84% 51% 82% 73% 54% 79% 89% 82% 79% 76% 57% 22%

A1cTest

A1c<= 9%

A1c<= 7%

LDLCTest

LDLC < 130mg/d

L

LDLC < 100mg/d

L

On Statin

Monitor for

Nephrop-

Proteinuria

and on

FootExam

EyeExam

BP <135/80

Self Mgnt.Goal

0

20

40

60

80

100

2006Q4 2007Q1 2007Q2 2007Q3 2007Q4 2008Q1

Year-Quarter

<1010.3

39.6 41.746.6

65.8

Goal: 60%

0

20

40

60

80

100

2006Q4 2007Q1 2007Q2 2007Q3 2007Q4 2008Q1

Year-Quarter

<1010.3

39.6 41.746.6

65.8

Goal: 60%

Documentation of Self-Management Goal Setting with Diabetes Patients with Limited English Proficiency

Depression ScreeningClosing the Gap: Obtained 100% Depression Screening of

all Patients

0%

20%

40%

60%

80%

100%

Nov-06 Dec-06 Jan-07 Feb-07 Mar-07 Apr-07 May-07 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07

Month - Year

Spanish

Chinese

Total

Percent of families reporting child had to wait too long to see ED doctor

38%35%

62%

46%

0%

10%

20%

30%

40%

50%

60%

70%

English Speaking Spanish Speaking

2006

2007

Comparing Non LEP and LEP Patients Time to ED MD < = 30 minutes By APR-DRG Severity Levels

74.12%

54.49%

48.44%

33.26%

28.99%

46.67%

57.47%

50.00%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Severity Level 1 Severity Level 2 Severity Level 3 Severity Level 4

Perc

en

tag

e o

f E

nco

un

ters

% of Non-LEP Encounters < = 30minutes Time to MD % of LEP Encounters < = 30minutes Time to MD

Questions and Discussion

Small Group Discussion

Each group please assign a scribe to capture the themes discussed.

Discuss: What have been your experiences in collecting and

utilizing REaL data? What successes have you had? Any

strategies/resources you employed to get to these successes?

What have been the challenges? What would you like to achieve in your organizations

in the next 2 years?

Top 3 Issues from Small Groups