Getting Started: Building a Foundation to Address Disparities Through Data Collection

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The Disparities Solutions Center

Getting Started: Building a Foundation

to Address Disparities Through Data

Collection

October 16, 2006 1:00pm – 2:30pm EST

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A Toolkit for Collecting Race,

Ethnicity, and Primary Language

Information From Patients

Romana Hasnain-Wynia, PhD Vice President, Research

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Goals

¾ Assist hospitals, health systems, community health centers, health plans and other potential users in understanding the importance of:

• Accurate data collection

• Assessing organizational capacity to collect data

• Implementing a framework designed specifically for obtaining information from patients/enrollees about their race, ethnicity and primary language efficiently, effectively, and respectfully

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Goals

¾ Addressing disparities in healthcare within a quality of care framework

¾ Highlighting the importance of systematically collecting race, and ethnicity to improve quality of care

¾ Linking race and ethnicity data to quality of care measures to develop targeted interventions

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Why Collect Data On Patient

Race/Ethnicity and Language?

¾ Valid and reliable data are fundamental building blocks for identifying differences in care and developing

targeted interventions

¾ Being responsive to communities: Pressing community health problems such as disparities in care can be

addressed more effectively if health care organizations and health professionals build the trust of the

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Why Collect Data? (Continued)

¾ Link race and ethnicity information to quality measures to examine disparities and undertake targeted

interventions

¾ Ensure the adequacy of interpreter services, patient

information materials, and cultural competency training for staff

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Why Collect Data? (Continued)

External Factors

¾ Reporting to the Joint Commission on Accreditation of Healthcare Organizations

¾ National Committee Quality Assurance

¾ Reporting to Centers for Medicare & Medicaid Services (payer, purchaser regulator, insurer, works through

QIOs)

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What We Know About Data Collection

in Hospitals

Main Findings

¾ All the hospitals collected race but not ethnicity or primary language

¾ Categories varied across all the sites

¾ Staff mostly collected through observation

¾ Staff at some sites had been trained to “not ask”

¾ Most of the hospitals indicated that they did not use these data for quality improvement

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Barriers To Collecting Data

¾ Appropriate categories ¾ Patients’/enrollees

perceptions about why this information is being

collected

¾ Discomfort in explicitly

asking patients/enrollees to provide this information

¾ Validity and reliability of data ¾ Legal concerns ¾ System/organizational barriers ¾ Profiling ¾ Time-Consuming

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Nuts and Bolts of Data Collection

¾ First get organizational buy-in from leadership and

front-line staff

¾ Address discomfort ¾ Categories

¾ Staff training

¾ Start the dialogue with the community before implementing systematic data collection on race/ethnicity/language

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Recommendations For

Standardization

¾ Who provides the information—should always be patients or their caretakers; should never be done by observation alone

¾ When to collect—upon admission or patient

registration to ensure appropriate fields are completed when patient begins treatment (for plans, at enrollment) ¾ What racial and ethnic categories should be

used---start with the U.S. Census categories. Hospitals can provide more”fine-grained”categories if needed

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Recommendations (Continued)

¾ Where should data be stored --- in a standard format for easy linking to clinical data

¾ Patient concerns --- should be addressed up front and clearly prior to obtaining information

¾ Staff training --- need to provide on-going training and evaluation

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Are Categories a Problem?

¾ Patients asked to state race/ethnicity in terms of their choice.

¾ Asked standard 2-part Race/Ethnicity questions (OMB Categories)

• Latino/Hispanic?

• What is your race? (7 options read) ¾ Asked preference between two methods

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Race

Which category best describes your race? ¾ American Indian/Alaska Native

¾ Asian

¾ Black or African American

¾ Native Hawaiian/Other Pacific Islander ¾ White

¾ Multiracial ¾ Declined ¾ Unavailable

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If Using OMB Categories and Not

Wanting to Split Race/Ethnicity

¾ African American/Black ¾ Asian ¾ Caucasian/White ¾ Hispanic/Latino/White ¾ Hispanic/Latino/Black ¾ Hispanic/Latino/ Declined ¾ Native American ¾ Native Hawaiian/Pacific Islander ¾ Multiracial ¾ Declined ¾ Unavailable/Unknown

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Related Publications

¾ Hasnain-Wynia, R., Pierce, D. and Pittman, M. “Who, When and How:

The Current State of Race, Ethnicity, and Primary Language Data Collection in Hospitals.” May, 2004. The Commonwealth Fund.

¾ Baker DW, Cameron KA, Feinglass J, Georgas P, Foster S, Pierce D, Thompson J., Hasnain-Wynia R. “Patients’ Attitudes Toward Health Care Providers Collecting Information About Their Race And

Ethnicity.” J Gen Intern Med. Vol 20 (10). October 2005.

¾ Baker DW, Cameron KA, Feinglass J, Georgas P, Foster S, Pierce D, Thompson J, Hasnain-Wynia R. “Development and Testing of a System to Rapidly and Accurately Collecting Patients’ Race And Ethnicity.” Am J

Public Health. Vol 96. no 3.2006

¾ Hasnain-Wynia, R and Baker D.W. “Obtaining Data on Patient Race, Ethnicity, and Primary Language in Health Care Organizations: Current Challenges and Proposed Solutions.”Health Services Research Vol 41

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HRET Websites

www.hretdisparities.org

(toolkit)

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Data as Building Blocks for Change

Carmella Bocchino, RN, MBA

Executive Vice President, Clinical Affairs and Strategic Planning

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Plans Collect Racial and Ethnic Data to Reduce Disparities and Improve Quality And Communications

• Support language and culturally appropriate communication to

enrollees

• Base quality improvement efforts to reduce disparities identified in

quality measures

• Identify enrollees with risk factors for certain conditions

• Assess variation in quality measures (such as HEDIS measures) by

racial and ethnic groups

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Individuals are Enrolled in Plans that Voluntarily Collect or Obtain Racial and Ethnic Data

50.9% 74.3% 78.2% 53.5% 0% 20% 40% 60% 80% 100%

Commercial (n=58) Medicare (n=33) Medicaid (n=46) All Plans (n=137)

2003-2004

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Methods Used by Plans to Voluntarily Collect or

Obtain Racial and Ethnic Information About Enrollees

Direct methods

• Self-identified during enrollment – 74.1%

• Through participation in special programs (e.g., Disease management, health education) – 35.4%

• Satisfaction surveys – 8.7%

• Collected by health plan after enrollment – 5.4%

Indirect methods

• Linked files w/ external sources (e.g., Federal and state agencies) – 40.1%

• Geocoding – 38.5%

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Racial and Ethnic Categories Included in Health Insurance Plan Data Collection Efforts

93.1% 92.9% 88.0% 22.3% 94.7% _93.4% _93.4% 0% 20% 40% 60% 80% 100%

Hispanic African American White Native American Asian American Pacific Islander Multiple Race/Ethnicity

2003-2004

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Concerns Affecting a Plan’s Decision Not to Collect Racial and Ethnic Data on Enrollees

• Enrollees’ reactions

• No good/reliable method for data collection

• Perceived federal laws/regulations inhibit collection of this data • Providers/employers are reluctant to supply this data

• Collection of this data is not common in health insurance plan’s market (s)

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Primary Language Data of Enrollees is Most

Commonly Collected or Obtained by Medicaid Plans

55.0% _52.1% 90.5% 56.4% 0% 20% 40% 60% 80% 100%

Commercial Medicare Medicaid All Plans

2003-2004

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Spanish and Asian Languages of Enrollees Are

Most Commonly Identified by Health Insurance Plans

2003-2004 96.7% 76.2% 72.8% 49.1% 43.1% 0% 20% 40% 60% 80% 100%

Spanish Chinese Korean Vietnamese Slavic Languages

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Enrollees Are Most Likely to Self-Identify Their

Primary Language Rather than Choose from a Defined List

71.5% 34.7% 38.5% 59.6% 72.5% 39.9% 68.5% 37.9% 0% 20% 40% 60% 80% 100%

Fill in "free text" field Choose from defined list of languages

Commercial Medicare Medicaid All Plans

2003-2004

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Tools to Address Disparities in Health Available on AHIP’s Website

• Highlights of AHIP/RWJF Quantitative and Qualitative Research • Data as Building Blocks for Change, a data collection toolkit

• Quality Interactions: A Patient-Based Approach to Cross-Cultural Care, a one hour CME course for physicians

• Communications Resources to Close the Gap, a compendium of tools and resources for health insurance plans, providers, and health care organizations

Available at:

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Conclusions and Challenges

• Health insurance plans use data on race, ethnicity, and primary language of their enrollees to:

– Enhance quality of care

– Design culturally and linguistically appropriate programs for diverse populations

– Improve language appropriate services and resources for individuals with limited English proficiency

• Results of the 2006 AHIP-RWJF follow-up survey highlights progress on collecting data on race, ethnicity, and primary language for addressing disparities and closing the gap in care.

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Conclusions and Challenges

• Ability and Intent for Data Collection Resides with: – Willingness and perception

– Standardization of categories

– Uniform methodology for collecting data • Cultural Competency

– Need to expand training of practicing physicians and as a part of a medical school curriculum

• Learning from Experiences/Best Practices – Adoption of workable solutions

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Collection of Race and Ethnicity Data

in a Complex Healthcare Organization

Rohit Bhalla, MD, MPH Montefiore Medical Center

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The Bronx, New York

U.S.A. Bronx 32% 52% 57% 32% 29% 30% 1.4 million 12% Black or African American race

15% Hispanic or Latino ethnicity

19% Speak a language other than English at home

(population > 5 years old)

12% Foreign born

13% Individuals below poverty level

25% Population below age 18

300 million Total population

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The Disparities Solutions Center Managed Care Infrastructure Emergency Dept. Hospital-Based Amb. Care Medical Group Ambulatory Specialty Care Ambulatory Care

Adult M/S and Psych CHAM Moses Div 726 Beds Einstein Div. 396 beds Inpatient Care Certified HHA LTHHP Homecare Rehabilitation Unit Post-Acute & Long term Care

Montefiore Montefiore Medical Center An Integrated Delivery System

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BOSTON POST RD

MOSES DIVISION

WEILER DIVISION

MONTEFIORE MEDICAL PARK YONKERS DATA CENTER

DOBBS FERRY HOSPITAL BUHRE AVE

MONTENET

¾80 registration areas ¾650 registrars CROSS COUNTY HARRISON

FORDHAM FAMILY WEST FORDHAM_ROAD

CFCC CHCC 1982/1894 EASTCHESTER CMO MARBLE HILL FORDHAM HEALTH CENTER CASTLE HILL NORWOOD UNIVERSITY FAMILY HEALTH CENTER WILLIAMSBRIDGE WEST FARMS AGING IN AMERICA PROSPECT WHITE PLAINS JOHNSON AVE. MARBLE HILL KINGSBRIDGE HENRY HUDSON PKWY PARKCHESTER BURKE AVE 4514 BAINBRIDGE HARTSDALE LEGEND BULLARD AVE METHADONE BAINBRIDGE NURSING HOME JEROME AVE

FAMILY PRACTICE _SONET

HIGH SPEED SERVICE FRAME RELAY 56K SERVICE FRAME RELAY T1 SERVICE MMC FIBER EXTENSION ST. LAWRENCE MARAN PLACE CO-OP CITY LARCHMONT SOUTH BRONX CHILDRENS HEALTH 3550 JEROME ASTOR MAMARONECK 2005 JEROME WAKEFIELD BARNES AVE MEDICAL ARTS PAVILION FORDHAM PLAZA World Wide Web EDI to Outside Entities AIDS MENTAL HEALTH

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Factors We Considered

• Management

– Registration Quality Unit – Expertise

• Process: EHIT

• Content: HRET, RWJF • Data use

– Optimal categories

– Recoding old data to new – Interfaces – Monitoring • Workflow – Field order – Number of categories – Specific issues • “Other”

• Patient not present • Patient refusal

– Hard vs. Soft stops – Different care settings • Education

– Staff training

– Questions from patients and families

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Implementation

• Training and education components – Policy context – Revised policies – New fields – Screens – Leadership-staff materials – Staff scripts

– Patient FAQs and potential answers – Specific scenarios – Staff questions – Monitoring • Feedback sessions – Leadership – Staff – HRET • Helped define – Concerns to be responded to – Key deliverables • Presented back

– To leadership and staff – With multiple disciplines

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Fields and Categories

• Process

– Required fields – Ethnicity first • Ethnicity

– Hispanic or Latino – Not Hispanic or Latino – Declined – Patient unavailable • Preferred language – [Numerous] – Declined – Other – Patient unavailable • Race

– American Indian or Alaskan Native – Asian

– Black or African American

– Native Hawaiian or Other Pacific Islander – White

– Multiracial: Asian/Black-African American – Multiracial: Asian/White

– Multiracial: Black-African American/White – Multiracial: Other combination

– Declined

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Next Steps

• Monitoring data on category use – By field

– By care setting

• Review feedback from staff and patients • Refining workflow in specific settings • Additional materials

– Patients – Staff

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Addressing Racial And Ethnic Disparities

In Health Care: Aetna’s Data Collection

Experience

Maisha Cobb, Ph.D. Research Consultant

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The Aetna Foundation, 1982 - Present

Aetna Chairman Authorizes Data

Gathering

Gathering -- 20012001

IOM Report – Unequal Treatment, March 2002

Aetna Task Force

Assembled, September 2002

Data Collection Begins

October, 2002 October, 2002 Advisory Committee Convenes November, 2002

Initiative Formation

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Data Collection: Numbers Over Time

2,66 9,71 2 141, 008 486, 744 3,903 ,112 2,242 ,425 70,7 34 3,675 ,290 3,786 ,300 1,294 ,781 19,0 85 0 800,000 1,600,000 2,400,000 3,200,000 4,000,000

Dec '02Jun '03Dec '03Jun '04Dec '04Jun '05Dec '05Jun '06Jul ' 06

Aug '06

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Data Collection: Aetna Navigator Website

• Online benefits portal for members

– Provides multiple opportunities to reach member • Eliminate enrollment form challenges

– NJ mandates use of State enrollment forms

– NH won’t allow data collection through enrollment – Large clients complexities

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Data Collection: Numbers Over Time

2,66 9,71 2 Navigator 141, 008 486, 744 3,903 ,112 2,242 ,425 70,7 34 3,675 ,290 3,786 ,300 1,294 ,781 19,0 85 0 800,000 1,600,000 2,400,000 3,200,000 4,000,000

Dec '02Jun '03Dec '03Jun '04Dec '04Jun '05Dec '05Jun '06Jul ' 06

Aug '06

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