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  • Medicare Services Study showed variation in health care utilization based on race/ethnicity for: Mammography Amputations Influenza vaccination Summary point of study: “Providing health insurance is not enough to ensure that the program is used effectively and equitably by all beneficiaries.”
  • For next slide: Can you walk us through the specific changes you ended up making?
  • Transcript

    • 1. Collecting and Using Race, Ethnicity and Language Data: From Documentation to Action Romana Hasnain-Wynia, PhD Director, Center for Healthcare Equity Northwestern University, Feinberg School of Medicine April 23, 2008
    • 2. Institute of Medicine Definition of Health Care Disparity
      • Difference in treatment provided to members of different racial (or ethnic) groups that is not justified by the underlying health conditions or treatment preferences of patients.
    • 3. Health Care Should Be…
      • Safe
      • Effective
      • Patient-Centered
      • Timely
      • Efficient
      • Equitable
    • 4. Major Reports on Disparities
    • 5. Racial and ethnic disparities in health care
      • In patients with insurance, disparities exist for
          • Mammography (Gornick et al.)
          • Amputations (Gornick et al.)
          • Influenza vaccination (Gornick et al.)
          • Lung Ca Surgery ( Bach et al.)
          • Renal Transplantation (Ayanian et al.)
          • Cardiac catheterization & angioplasty (Harris et al, Ayanian et al.)
          • Coronary artery bypass graft (Peterson et al.)
          • Treatment of chest pain (Johnson et al.)
          • Referral to cardiology specialist care (Schulman et al.)
          • Pain management (Todd et al.)
    • 6. A National Problem
      • African Americans are:
        • Less likely to have a kidney transplant, surgery for lung cancer, bypass surgery
        • More likely to have a foot amputation
        • More likely to die prematurely
      • Latinos/Hispanics are:
        • Less likely to receive pain medications
      • What about other groups? Chinese? Vietnamese? Pakistanis? Nigerian? Somali? Haitian? etc….?
    • 7. Articulating Units of Accountability
      • We Do Not Know…
      • At which level the responsibility lies
        • National
        • State
        • Local
        • Organizational
      • Our patient populations
      • What works, what doesn’t
    • 8. Data Are Important!
      • IOM-Unequal Treatment: Confronting Racial and Ethnic Disparities in HealthCare
      • Physicians for Human Rights: Right to Equal Treatment
      • AHRQ: Healthcare Disparities Report
      • National Research Council: Eliminating Disparities-Measurement and Data Needs
    • 9. Questions
      • HOW to collect relevant data to assess:
      • WHY and HOW disparities occur
      • Which interventions are effective at reducing or eliminating disparities
      • What proportion of observed disparities are amenable to improvements in health care
    • 10. Data Collection
      • Why collect data?
        • Documentation of disparities across broad groups
        • Improve care for populations served
        • Reward good performance (e.g., Performance-based incentives such as P4P)
      • Broad categories for documentation
      • Granular data needed to improve quality of care within organizations (community health centers, hospitals, etc…)
    • 11. Why Collect Race/Ethnicity/Language Data?
      • Valid and reliable data are fundamental building blocks to improve quality of care
      • Link race and ethnicity information to quality measures
      • Ensure the adequacy of interpreter services, patient information materials, and cultural competency training for staff
      • Be responsive to your community
      • External Factors (broad)
      • Reporting to the JCAHO, CMS, NCQA
      • State mandates
      • Regional Comparisons
      • Performance based incentives (e.g., P4P)
      Internal Factors (granular)
    • 12. Perspectives on Data Collection
      • Hospitals
      • Health Plans
      • Medical Group Practices
      • Physician Practices (with <5 physicians)
    • 13. Hospitals State Mandate No Mandate Urban Rural Teaching Hosp . Non-Teaching Source: Who, When, and How: The Current State of Race, Ethnicity, and Primary Language Data Collection in Hospitals , Romana Hasnain-Wynia, Debra Pierce, and Mary A. Pittman, The Commonwealth Fund, May 2004 78% Report Collecting Race and Ethnicity 66% Report Collecting Primary Language Race/Ethnicity
    • 14. Health Plans
      • Health plans do not routinely capture information on race/ethnicity of their members and do not assess quality of care stratified by race and ethnicity (Nerenz, et al. 2002)
      • 54% collect race/ethnicity data
      • 56% collect primary language
      • 74% collect at enrollment
      • 5% collect after enrollment
      Source: AHIP, 2006
    • 15. Medical Group Practices
      • Less likely to collect race/ethnicity information than hospitals
      • 75% didn’t collect data because they thought it was unnecessary
      • Source: Nerenz, Currier, Paez, 2004
    • 16. Physician Practices with <5 Physicians
      • 45% collect
      • 78% that collected have EMR
      • Only 1 practice linked data to quality measures
      Hasnain-Wynia, R, et al. Commission to End Health Care Disparities Paper. 2007.
    • 17. Barriers To Collecting Data
      • Validity and reliability of data
      • Legal concerns
      • System/organizational barriers
      • Profiling
      • Time-Consuming
      • Patients’/enrollees perceptions about why this information is being collected
      • Discomfort in explicitly asking patients/enrollees to provide this information.
      • Appropriate categories
    • 18. Tell People Why You are Asking
      • “ Now I would like you to tell me your Race and Ethnic Background. We use this to review the treatment patients receive and make sure everyone gets the highest quality of care.”
      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.
    • 19. OMB Categories
      • Which category best describes your race ?
      • American Indian/Alaska Native
      • Asian
      • Black or African American
      • Native Hawaiian/Other Pacific Islander
      • White
      • Multiracial
      • Declined
      • Unavailable/Unknown
      • Do you consider yourself Hispanic/Latino?
      • Yes
      • No
      • Declined
      • Unavailable/Unknown
      Hasnain-Wynia, R, Pierce, D, Reiter, J, Haque, A. and Greising, C. “Toolkit for Collecting Race, Ethnicity, and Primary Language Data from Patients. Version 2.0. September, 2007.
    • 20. If Using OMB Categories and Not Splitting Race/Ethnicity
      • - African American/ Black
      • -Asian
      • -Caucasian/White
      • -Hispanic/Latino/White
      • -Hispanic/Latino/Black
      • -Hispanic/Latino/Declined
      • -Native American
      • -Native Hawaiian/Pacific Islander
      • -Multiracial
      • -Declined
      • -Unavailable/Unknown
      Hasnain-Wynia, R, Pierce, D, Reiter, J, Haque, A. and Greising, C. “Toolkit for Collecting Race, Ethnicity, and Primary Language Data from Patients. Version 2.0. September, 2007.
    • 21. White Non-Hispanic 2.2 Black Non-Hispanic 17.5 Hispanic 9.8 Asian 32.4 Foreign-born 16.7 Source: Centers for Disease Control, National Center for HIV, STD, and TB Prevention 2000 Metropolitan Chicago Tuberculosis Rates per 100,000 population, by Race/Ethnicity and Foreign Born-Status
    • 22. CDC-Codes-Example Thai - - Sri Lankan - - Pakistani - - Okinawan - - Malaysian - - Laotian - - Korean - - Japanese - - Indonesian - - Taiwanese - - Chinese - - Cambodian - - Burmese 2032-1 R2.04 Bhutanese 2031-3 R2.03 Bangladeshi 2030-5 R2.02 Asian Indian 2029-7 R2.01 Unique Identifier Hierarchical Code OMB-Category—ASIAN 2028-9 R2
    • 23. A Project in Chicago ADVANCE Aligning Demographic Variables and National Clinical Evaluation
    • 24. Goals:
      • Standardize a process for collecting patient demographic data on patient race, ethnicity, language, health literacy (education), acculturation (years lived in the US), and socioeconomic status (family size, insurance, income).
      • Link patient demographic data with national clinical performance measures in an electronic health record system.
      • Show health care processes and outcomes for specific conditions stratified by key patient demographic information (to identify targeted opportunities for QI).
    • 25. Collecting Patient Demographic Information: Practical Recommendations
      • Household income
      • Payer source
      • Number of people in household
      Socioeconomic Status
      • Years in school
      • SES
      • Race/ethnicity
      • Primary language
      Health Literacy Collect granular data that can be “rolled up” into broad OMB categories Race/Ethnicity How to Get useful information Demographic Data
    • 26. Adult Diabetes Performance Measures-Current System Captures the following: Performance Measure Provider Number Birth Date Gender Hemoglobin A1c       Lipid profile       Fasting       Total Cholesterol       HDL-C       LDL-C       Triglycerides       Influenza Vacc       Foot Examination       Dilated Retinal Eye Exam       Smoking       Aspirin Use      
    • 27. Adult Diabetes Performance Measures-New System Would Capture the following: Performance Measure Provider number Birth Gender Race Ethnicity Lang Educ Years in US Fam Size Hemoglobin A1c                 Lipid profile                 Fasting                 Total Cholesterol                 HDL-C                 LDL-C                 Triglycerides                 Influenza Vacc                 Foot Examination                 Dilated Retinal Eye Exam                 Smoking                 Aspirin Use                
    • 28. Recommendations For Standardization
      • Who provides the information
      • When to collect
      • Which racial and ethnic categories to use
      • Where and how data are stored
      • Address patients’ concerns
      • Provide staff training
      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. August 2006.
    • 29.
      • Involve community leaders in all aspects of planning and design of
      • processes for data collection, analysis, and use of data for quality
      • improvement
      • Take all possible opportunities to communicate with community groups about the reasons for collection of data and use of data for quality improvement.
      • Collect the data in a meaningful context
      • Don’t break promises—if data on race/ethnicity are being used for
      • a specific purpose like expansion of interpreter services, then make sure those service expansions actually occur.
      Address Concerns of the Community
    • 30. Systematic Implementation
      • Conduct education and feedback sessions with leadership and staff
      • Define issues and concerns and identify how you will respond to them
      • Training and education components should include
        • Policy context
        • Revised policies
        • New fields
        • Screens
        • Leadership-staff materials
        • Staff scripts
        • FAQs and potential answers
        • Specific scenarios
        • Staff questions
        • Monitoring
    • 31. Thank You! [email_address]