Transcript CoP4 Presentation at Diversity Rx in Baltimore2

YOUR VOICE 4:
“Collecting and Using Patient Demographic Data to Create Equitable Health Care
Systems: Perspectives from a Community of Practice”
Kathryn Coltin, MPH
Cheri Wilson, MA, MHS, CPHQ
Catherine West, MS, RN
Boris Kalanj, LISW, Moderator
DiversityRx: 7th National Conference on Quality Health Care for Culturally Diverse Populations,
Baltimore, MD
October 20, 2010
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.
2.
3.
4.
5.
6.
7.
Consensus on standardized data collection methods
Best practices that ultimately improve the health of our
communities (improved data collection and validity, strategies
to address disparities)
Peer support and networking
Support in encouraging government entities to standardize
(and support) data collection and use
Discussion of technical challenges of collecting granular data
Sharing outcomes of CoP with national/international audience
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, CPHQ
Faculty Research Associate
Program Director, Culture-QualityCollaborative (CQC)
Outline
About JHH
Project background
Data quality issues
Recommendations

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
Languages Identified in PSN Event
Reports
30
25
20
15
10
5
0
Languages Identified in Sunrise
40
28
35
35
22
30
25
6
5
4
3
2
20
1
1
1
1
1
15
10
9
7
5
1
0
ENG
N = 76
N = 67
KOR
SPA
None listed
N = 52
Race/Ethnicity
Race/Ethnicity in Sunrise
20
18
16
14
12
10
8
6
4
2
0
Race/Ethnicity in EPIC
19
25
16
21
19
20
7
7
15
3
12
10
10
5
3
2
0
O
W
Race/Ethnicity in EPR
N = 52
25
19
15
12
9
10
5
3
2
0
O
H
W
A
A
B
U
N = 67
22
20
N = 67
H
B
U
Datamart:
Inpatient Race and Ethnicity Data
FY2010
FY2010
Notes
RACE
%

(from
2009)
RANGE (19942010)
U.S. CENSUS
(BALTIMORE) 2000*
U.S. CENSUS (MD)
2008**
1- White
51.79%

(51.79%-55.70%)
31.6%
63.4%
2 - African American
3 - Asian or Pacific Islander
39.61%
2.13%
=

(38.95%-42.3%)
(.37%-2.13%)
64.3%
1.5%
29.4%
5.2%
4 - American Indian/Eskimo/Aleut
0.18%

(.06%-.18%)
0.3%
0.4%
5 - Other***
6 - Biracial**
9 - Unknown***
5.22%
0.75%
0.32%



(2.08%-5.22%)
(.04%-.75%)
(.06%-.41%)
--1.5%
---
--60.0%
---
ETHNICITY
%
RANGE (19942010)
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%)
---
---
* 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
FY2010
FY2010
Notes
RACE
%

(from
2009)
1- White
50.69%

(40.45%-51.07%)
31.6%
63.4%
2 - African American
3 - Asian or Pacific Islander
39.00%
2.47%


(39.00-54.08%)
(.60%-2.47%)
64.3%
1.5%
29.4%
5.2%
4 - American Indian/Eskimo/Aleut
0.19%

(.09%-.19%)
0.3%
0.4%
5 - Other***
6 - Biracial**
9 - Unknown***
5.34%
0.28%
2.04%



(2.99%-5.34%)
(.03%-.28%)
(.71%-2.04%)
--1.5%
---
--60.0%
---
U.S. CENSUS
(BALTIMORE) 2000*
1.7%
U.S. CENSUS (MD)
2008**
6.7%
RANGE (19982010)
U.S. CENSUS
(BALTIMORE) 2000*
U.S. CENSUS (MD)
2008**
1 - Spanish/Hispanic Origin
1.88%

RANGE (19982010)
(.19%-1.88%)
2 - Not of Spanish/Hispanic Origin
96.09%

(96.09%-99.54%)
98.3%
93.3%
9 - Unknown***
2.04%

(.37%-2.04%)
---
---
ETHNICITY
* 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
A - Asian/Pacific Islander
(Asian or Pacific Islander)
American Indian/Alaska
Native
Asian
B - African American
(African American)
Biracial
Black/African American
Caucasian/White
Declined
H - Hispanic
I - American
Indian/Eskimo/Aleut
(American
Indian/Eskimo/Aleut)
EPIC
EPR
Sunrise (POE)
HSCRC (State
Reporting)
X
X
X
X
M - Multiracial (Multiracial)
Native Hawaiian/Other
Pacific Islander
O - Other (Other)
U - Unknown (Unknown)
Unavailable
W - White (White)
HRET Disparities Toolkit
(based on OMB Federal
Reporting)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Ethnicity: Data Elements
Ethnicity
EPIC
EPR
Sunrise
(POE)
Spanish/Hispanic
Origin
Not of
Spanish/Hispanic
Origin
Unknown
HRET Disparities
HSCRC (State
Toolkit (based on OMB
Reporting)
Federal Reporting)
X
X
X
Hispanic or Latino
*
X
Not Hispanic or Latino
*
X
No separate category
X
Note
* Dropdown
list, but
currently not
populated
X
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 Care
Data Collection Channels—different strokes for different folks
Acceptability to members
LEAST




MOST
Enrollment process
 Paper forms
 EDI 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 only
Harvard Pilgrim Health Care
Collection of REaL Data
Secure web portal includes a Member Profile, which was modified to include
Race, Ethnicity and Language preferences
25
Harvard Pilgrim Health Care
Collection of REaL Data

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
facilitate sharing data
●
●
to
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 Care
Using 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 Care
Using the data—an evolving portfolio of measures
 Added in 2006
 Annual since 2003
Preventive Screenings
Chlamydia screening
Cancer screening
Breast CA
Cervical CA
Colorectal CA
 Chronic Disease Care
Asthma meds
5-17 year olds
18-56 year olds
Diabetes care
HbA1c testing
LDL-C testing
Retinal screening
Nephropathy monitoring
 CAHPS measures of access &
customer service

 Added in 2007
 Preventive Care/Access
Chronic Disease Care
 Well Visits
Cardiovascular disease
Infants 0-15 mo.
 Persistent use of beta-blocker
Children 3-6 yr.
after AMI
Adolescents 12-21yr.
 LDL-C testing in CAD
 Chronic Care
 LDL-C control in CAD
 Diabetes
 BP control in patients with HTN
BP control
 Monitoring patients on Persistent
 Acute Care
Medications
Strep Tx prior to
Diabetes
antibiotic Rx for children
 HbA1c >9 (poor control)
w/ Pharyngitis
 HbA1c <7 (good control)
Appropriate antibiotic use
 LDL-C <100 (good control)
for children w/URI
Rheumatoid Arthritis (DMARDs)
 Added in 2010
 Acute Care

Patients’ care
Inappropriate antibiotic use for adult
experiences
bronchitis

Medical Home
Imaging for low back pain in adults

Note: Italics indicates outcome measures. Blue font indicates measures with observed
disparities, most of which have been reduced, though not yet eliminated
Harvard Pilgrim Health Care
Use 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 Care
Analyzing disparities—our Annual Equity Report
Measures for current year performance
100.0%
(or two year performance for measures with
90.0%
small Ns) are usually displayed using bars for
each reporting category within a measure.
Performance Rate

HEDIS Rates for Comprehensive Diabetes Care
by Indirectly Estimated Race/Ethnicity
80.0%
70.0%
60.0%
50.0%
40.0%
Separate graphs are used to display
performance for each attribute (race,
ethnicity, gender, education, income, etc.).
are trended on separate line graphs
showing each group that had an
actionable disparity when compared
Eye Exam Rate
LDL Tx Rate
Nephropathy
Monitoring Rate
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%
HEDIS Measure
Colorectal Cancer Screening Rates
by Race/Ethnicity 2003-2009
81%
Measures with data for multiple years
79%
77%
Percent Screened

HbA1c Rate
75%
73%
71%
69%
67%
65%
63%
61%
59%
with the benchmark group
57%
55%
2003
2004
2005
2006
2007
2008
Performance Year
Black
Hispanic
Linear (Black)
Linear (Hispanic)
2009
Harvard Pilgrim Health Care
Interventions 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 Care
Two of our successes
10.0%
Racial/Ethnic Performance Disparity by Year
Difference from Best Performing
Racial/Ethnic Group
9.0%
9.0%
8.0%
7.5%
7.1%
7.0%
7.0%
6.0%
4.9%
4.7%
5.0%
3.8%
4.0%
2.7%
3.0%
2.0%
1.0%
0.0%
20
04
20
05
20
06
20
07
20
Diabetes: Annual Eye Exam
08
20
06
20
07
20
08
Adult Asthma: Appropriate Meds
HEDIS Measurement Year
Is this a success???
Harvard Pilgrim Colorectal Cancer Screening Rates
by Race/Ethnicity 2003-2009
83%
76.4%
Percent Screened
79%
75%
69.4%
68.3%
71%
67%
60.7%
63%
59%
Gap = 8.7
Gap = 3.8
55%
2003
2004
IVR 2005 IVR + 2006
Spanish
Gap = 8.1
2007
P4P
2008
2009
Performance Year
Black
Hispanic
Linear (Black)
Linear (Hispanic)
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
34
Diabetes Quality Indicators
by Language and by Time
100%
100%
90%
90%
80%
80%
70%
70%
60%
60%
50%
50%
40%
40%
30%
30%
20%
20%
10%
10%
0%
LDLC < LDLC <
Monitor Proteinu
LDLC
On
130mg/d 100mg/d
for
ria
Test
Statin
L
L
Nephrop- and on
Foot
Exam
Eye
Exam
BP <
135/80
Self
Mgnt.
Goal
A1c
Test
A1c
<= 9%
A1c
<= 7%
93%
86%
54%
85%
76%
58%
83%
95%
83%
52%
83%
73%
54%
79%
Low English
Proficiency
(n=276)
93%
83%
50%
81%
72%
English
92% (n=6,926)
78%
42%
77%
66%
Total 6/30/2004 (N=6,098)*
52%
Language
94% not
84% known
51%
Total 12/31/2007 (N= 9,179)
LEP (N=276)
English (N=6,926)
Language
Not Know n (N=1,977)
(n=1,977)
82%
73%
91%
84%
78%
81%
58%
22%
90%
81%
80%
77%
57%
23%
79%
6/30/2004
88%
91% (n=6,098)
77%
74%
58%
20%
47%
59%
81%
71% (n=9,179)
50%
61%
12/31/2007
51%
14%
54%
79%
57%
22%
Time
89%
82%
79%
76%
0%
Documentation of Self-Management Goal Setting with Diabetes
Patients with Limited English Proficiency
100
80
65.8
Goal: 60%
60
46.6
41.7
39.6
40
20
<10
10.3
0
2006Q4
2007Q1
2007Q2
2007Q3
Year-Quarter
2007Q4
2008Q1
Depression Screening
Closing the Gap: Obtained 100% Depression Screening of
all Patients
100%
80%
Spanish
Chinese
Total
60%
40%
20%
0%
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
Percent of families reporting child had to wait
too long to see ED doctor
70%
60%
62%
50%
40%
30%
46%
38%
2007
35%
20%
10%
0%
English Speaking
2006
Spanish Speaking
Comparing Non LEP and LEP Patients
Time to ED MD < = 30 minutes By APR-DRG Severity Levels
100%
90%
Percentage of Encounters
80%
74.12%
70%
57.47%
60%
48.44%
50%
54.49%
50.00%
46.67%
40%
33.26%
30%
28.99%
20%
10%
0%
Severity Level 1
Severity Level 2
% of Non-LEP Encounters < = 30minutes Time to MD
Severity Level 3
Severity Level 4
% of LEP Encounters < = 30minutes Time to
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