Powerpoint Presentation

1,297 views

Published on

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,297
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
7
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • 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?
  • Powerpoint Presentation

    1. 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. 2. Institute of Medicine Definition of Health Care Disparity <ul><li>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. </li></ul>
    3. 3. Health Care Should Be… <ul><li>Safe </li></ul><ul><li>Effective </li></ul><ul><li>Patient-Centered </li></ul><ul><li>Timely </li></ul><ul><li>Efficient </li></ul><ul><li>Equitable </li></ul>
    4. 4. Major Reports on Disparities
    5. 5. Racial and ethnic disparities in health care <ul><li>In patients with insurance, disparities exist for </li></ul><ul><ul><ul><li>Mammography (Gornick et al.) </li></ul></ul></ul><ul><ul><ul><li>Amputations (Gornick et al.) </li></ul></ul></ul><ul><ul><ul><li>Influenza vaccination (Gornick et al.) </li></ul></ul></ul><ul><ul><ul><li>Lung Ca Surgery ( Bach et al.) </li></ul></ul></ul><ul><ul><ul><li>Renal Transplantation (Ayanian et al.) </li></ul></ul></ul><ul><ul><ul><li>Cardiac catheterization & angioplasty (Harris et al, Ayanian et al.) </li></ul></ul></ul><ul><ul><ul><li>Coronary artery bypass graft (Peterson et al.) </li></ul></ul></ul><ul><ul><ul><li>Treatment of chest pain (Johnson et al.) </li></ul></ul></ul><ul><ul><ul><li>Referral to cardiology specialist care (Schulman et al.) </li></ul></ul></ul><ul><ul><ul><li>Pain management (Todd et al.) </li></ul></ul></ul>
    6. 6. A National Problem <ul><li>African Americans are: </li></ul><ul><ul><li>Less likely to have a kidney transplant, surgery for lung cancer, bypass surgery </li></ul></ul><ul><ul><li>More likely to have a foot amputation </li></ul></ul><ul><ul><li>More likely to die prematurely </li></ul></ul><ul><li>Latinos/Hispanics are: </li></ul><ul><ul><li>Less likely to receive pain medications </li></ul></ul><ul><li>What about other groups? Chinese? Vietnamese? Pakistanis? Nigerian? Somali? Haitian? etc….? </li></ul>
    7. 7. Articulating Units of Accountability <ul><li>We Do Not Know… </li></ul><ul><li>At which level the responsibility lies </li></ul><ul><ul><li>National </li></ul></ul><ul><ul><li>State </li></ul></ul><ul><ul><li>Local </li></ul></ul><ul><ul><li>Organizational </li></ul></ul><ul><li>Our patient populations </li></ul><ul><li>What works, what doesn’t </li></ul>
    8. 8. Data Are Important! <ul><li>IOM-Unequal Treatment: Confronting Racial and Ethnic Disparities in HealthCare </li></ul><ul><li>Physicians for Human Rights: Right to Equal Treatment </li></ul><ul><li>AHRQ: Healthcare Disparities Report </li></ul><ul><li>National Research Council: Eliminating Disparities-Measurement and Data Needs </li></ul>
    9. 9. Questions <ul><li>HOW to collect relevant data to assess: </li></ul><ul><li>WHY and HOW disparities occur </li></ul><ul><li>Which interventions are effective at reducing or eliminating disparities </li></ul><ul><li>What proportion of observed disparities are amenable to improvements in health care </li></ul>
    10. 10. Data Collection <ul><li>Why collect data? </li></ul><ul><ul><li>Documentation of disparities across broad groups </li></ul></ul><ul><ul><li>Improve care for populations served </li></ul></ul><ul><ul><li>Reward good performance (e.g., Performance-based incentives such as P4P) </li></ul></ul><ul><li>Broad categories for documentation </li></ul><ul><li>Granular data needed to improve quality of care within organizations (community health centers, hospitals, etc…) </li></ul>
    11. 11. Why Collect Race/Ethnicity/Language Data? <ul><li>Valid and reliable data are fundamental building blocks to improve quality of care </li></ul><ul><li>Link race and ethnicity information to quality measures </li></ul><ul><li>Ensure the adequacy of interpreter services, patient information materials, and cultural competency training for staff </li></ul><ul><li>Be responsive to your community </li></ul><ul><li>External Factors (broad) </li></ul><ul><li>Reporting to the JCAHO, CMS, NCQA </li></ul><ul><li>State mandates </li></ul><ul><li>Regional Comparisons </li></ul><ul><li>Performance based incentives (e.g., P4P) </li></ul>Internal Factors (granular)
    12. 12. Perspectives on Data Collection <ul><li>Hospitals </li></ul><ul><li>Health Plans </li></ul><ul><li>Medical Group Practices </li></ul><ul><li>Physician Practices (with <5 physicians) </li></ul>
    13. 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. 14. Health Plans <ul><li>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) </li></ul><ul><li>54% collect race/ethnicity data </li></ul><ul><li>56% collect primary language </li></ul><ul><li>74% collect at enrollment </li></ul><ul><li>5% collect after enrollment </li></ul>Source: AHIP, 2006
    15. 15. Medical Group Practices <ul><li>Less likely to collect race/ethnicity information than hospitals </li></ul><ul><li>75% didn’t collect data because they thought it was unnecessary </li></ul><ul><li>Source: Nerenz, Currier, Paez, 2004 </li></ul>
    16. 16. Physician Practices with <5 Physicians <ul><li>45% collect </li></ul><ul><li>78% that collected have EMR </li></ul><ul><li>Only 1 practice linked data to quality measures </li></ul>Hasnain-Wynia, R, et al. Commission to End Health Care Disparities Paper. 2007.
    17. 17. Barriers To Collecting Data <ul><li>Validity and reliability of data </li></ul><ul><li>Legal concerns </li></ul><ul><li>System/organizational barriers </li></ul><ul><li>Profiling </li></ul><ul><li>Time-Consuming </li></ul><ul><li>Patients’/enrollees perceptions about why this information is being collected </li></ul><ul><li>Discomfort in explicitly asking patients/enrollees to provide this information. </li></ul><ul><li>Appropriate categories </li></ul>
    18. 18. Tell People Why You are Asking <ul><li>“ 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.” </li></ul>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. 19. OMB Categories <ul><li>RACE QUESTION : </li></ul><ul><li>Which category best describes your race ? </li></ul><ul><li>American Indian/Alaska Native </li></ul><ul><li>Asian </li></ul><ul><li>Black or African American </li></ul><ul><li>Native Hawaiian/Other Pacific Islander </li></ul><ul><li>White </li></ul><ul><li>Multiracial </li></ul><ul><li>Declined </li></ul><ul><li>Unavailable/Unknown </li></ul><ul><li>ETHNICITY QUESTION : </li></ul><ul><li>Do you consider yourself Hispanic/Latino? </li></ul><ul><li>Yes </li></ul><ul><li>No </li></ul><ul><li>Declined </li></ul><ul><li>Unavailable/Unknown </li></ul>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. www.hretdisparities.org
    20. 20. If Using OMB Categories and Not Splitting Race/Ethnicity <ul><li>- African American/ Black </li></ul><ul><li>-Asian </li></ul><ul><li>-Caucasian/White </li></ul><ul><li>-Hispanic/Latino/White </li></ul><ul><li>-Hispanic/Latino/Black </li></ul><ul><li>-Hispanic/Latino/Declined </li></ul><ul><li>-Native American </li></ul><ul><li>-Native Hawaiian/Pacific Islander </li></ul><ul><li>-Multiracial </li></ul><ul><li>-Declined </li></ul><ul><li>-Unavailable/Unknown </li></ul>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. www.hretdisparities.org
    21. 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. 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. 23. A Project in Chicago ADVANCE Aligning Demographic Variables and National Clinical Evaluation
    24. 24. Goals: <ul><li>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). </li></ul><ul><li>Link patient demographic data with national clinical performance measures in an electronic health record system. </li></ul><ul><li>Show health care processes and outcomes for specific conditions stratified by key patient demographic information (to identify targeted opportunities for QI). </li></ul>
    25. 25. Collecting Patient Demographic Information: Practical Recommendations <ul><li>Household income </li></ul><ul><li>Payer source </li></ul><ul><li>Number of people in household </li></ul>Socioeconomic Status <ul><li>Years in school </li></ul><ul><li>SES </li></ul><ul><li>Race/ethnicity </li></ul><ul><li>Primary language </li></ul>Health Literacy Collect granular data that can be “rolled up” into broad OMB categories Race/Ethnicity How to Get useful information Demographic Data
    26. 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. 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. 28. Recommendations For Standardization <ul><li>Who provides the information </li></ul><ul><li>When to collect </li></ul><ul><li>Which racial and ethnic categories to use </li></ul><ul><li>Where and how data are stored </li></ul><ul><li>Address patients’ concerns </li></ul><ul><li>Provide staff training </li></ul>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. 29. <ul><li>Involve community leaders in all aspects of planning and design of </li></ul><ul><li>processes for data collection, analysis, and use of data for quality </li></ul><ul><li>improvement </li></ul><ul><li>Take all possible opportunities to communicate with community groups about the reasons for collection of data and use of data for quality improvement. </li></ul><ul><li>Collect the data in a meaningful context </li></ul><ul><li>Don’t break promises—if data on race/ethnicity are being used for </li></ul><ul><li>a specific purpose like expansion of interpreter services, then make sure those service expansions actually occur. </li></ul>Address Concerns of the Community
    30. 30. Systematic Implementation <ul><li>Conduct education and feedback sessions with leadership and staff </li></ul><ul><li>Define issues and concerns and identify how you will respond to them </li></ul><ul><li>Training and education components should include </li></ul><ul><ul><li>Policy context </li></ul></ul><ul><ul><li>Revised policies </li></ul></ul><ul><ul><li>New fields </li></ul></ul><ul><ul><li>Screens </li></ul></ul><ul><ul><li>Leadership-staff materials </li></ul></ul><ul><ul><li>Staff scripts </li></ul></ul><ul><ul><li>FAQs and potential answers </li></ul></ul><ul><ul><li>Specific scenarios </li></ul></ul><ul><ul><li>Staff questions </li></ul></ul><ul><ul><li>Monitoring </li></ul></ul>
    31. 31. Thank You! [email_address]

    ×