your voice 4:
DESCRIPTION
“Collecting and Using Patient Demographic Data to Create Equitable Health Care Systems: Perspectives from a Community of Practice”. YOUR VOICE 4:. Kathryn Coltin, MPH Catherine West, MS, RN Cheri Wilson, MA, MHS, CPHQBoris Kalanj, LISW, Moderator. - PowerPoint PPT PresentationTRANSCRIPT
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