Querying Patients About Race and Ethnicity


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Results from experiment with registrars on best way to ask patients to self-identify race & ethnicty. Experiment performed at Hennepin County Medical Center, a public safety net in Minneapolis MN. Presentation to MN Cancer Alliance, April 2006.

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Querying Patients About Race and Ethnicity

  1. 1. Minnesota Cancer Summit April 25, 2006 Querying Patients About Race and Ethnicity at a Public Safety Net Medical Center Yiscah Bracha,M.S. Research Director, CUH
  2. 2. Goal: <ul><li>Establish method to query patients about: </li></ul><ul><ul><li>Race </li></ul></ul><ul><ul><li>Ethnicity </li></ul></ul><ul><ul><li>Other personal demographic characteristics </li></ul></ul><ul><li>Qualities of method: </li></ul><ul><ul><li>Respectful towards patients </li></ul></ul><ul><ul><li>Quick and easy to administer </li></ul></ul><ul><ul><li>Captures clinical important differences </li></ul></ul><ul><ul><li>Enables reporting using OMB classification </li></ul></ul>
  3. 3. Setting: Hennepin County Medical Center <ul><li>Publicly-owned, urban, safety net in downtown Minneapolis, MN </li></ul><ul><li>Level one trauma center </li></ul><ul><li>Hospital: 19,000 patients per year </li></ul><ul><li>Clinics: 168,000 outpatients per year </li></ul><ul><ul><li>On-campus primary care (3 clinics) </li></ul></ul><ul><ul><li>Community-based primary care (3 clinics) </li></ul></ul><ul><ul><li>20+ on-campus specialty clinics </li></ul></ul>
  4. 4. HCMC Patient Population <ul><li>Multi-racial </li></ul><ul><ul><li>~30% American-born Caucasian </li></ul></ul><ul><ul><li>~20% African-American </li></ul></ul><ul><ul><li>~12% 1 st or 2 nd generation African immigrant </li></ul></ul><ul><ul><li>~21% Hispanic </li></ul></ul><ul><ul><li>~13% Asian, Native American, European immigrant </li></ul></ul><ul><li>Multi-ethnic </li></ul><ul><ul><li>African-American vs. African-born </li></ul></ul><ul><ul><li>European-American vs. European-born </li></ul></ul><ul><ul><li>Hmong vs. Vietnamese vs. Indian </li></ul></ul><ul><ul><li>Mexican vs. Ecuadoran vs. Columbian </li></ul></ul>
  5. 5. HCMC Patient Population (cont.) <ul><li>Multi-lingual </li></ul><ul><ul><li>Interpreters provided in > 60 languages </li></ul></ul><ul><ul><li>Many patients with limited English proficiency </li></ul></ul><ul><ul><li>Common non-English languages: </li></ul></ul><ul><ul><ul><li>Spanish </li></ul></ul></ul><ul><ul><ul><li>Somali </li></ul></ul></ul><ul><ul><ul><li>Hmong </li></ul></ul></ul><ul><ul><ul><li>Russian </li></ul></ul></ul>
  6. 6. HCMC – The Region’s Safety Net <ul><li>A major source of uncompensated care: </li></ul><ul><ul><li>88% for Hennepin County </li></ul></ul><ul><ul><li>20% for the entire state </li></ul></ul><ul><li>Payment sources for HCMC patients: </li></ul><ul><ul><li>Medicaid: 38.5% </li></ul></ul><ul><ul><li>Medicare: 12.1% </li></ul></ul><ul><ul><li>Uninsured: 23.6% </li></ul></ul><ul><ul><li>Private ins: 25.0% </li></ul></ul>
  7. 7. Who should ask questions, and when? <ul><li>Registration/scheduling staff at 1 st encounter? </li></ul><ul><li>Clinical staff at time of visit? </li></ul><ul><ul><li>Consistent administration across system? </li></ul></ul><ul><ul><ul><li>Registrars: Yes Clinicians: No </li></ul></ul></ul><ul><ul><li>Uptake uniform across system? </li></ul></ul><ul><ul><ul><li>Registrars: Yes Clinicians: No </li></ul></ul></ul><ul><ul><li>Staff accustomed to eliciting sensitive information? </li></ul></ul><ul><ul><ul><li>Registrars: No Clinicians: Yes </li></ul></ul></ul><ul><ul><li>Patients assured of equal quality of care? </li></ul></ul><ul><ul><ul><li>Registrars: No Clinicians: Yes </li></ul></ul></ul>
  8. 8. Who asks Qs and when…? <ul><li>At HCMC, registration/scheduling staff will ask Qs: </li></ul><ul><ul><li>Over the telephone when patients call for appointment </li></ul></ul><ul><ul><li>In-person at a registration “zone” when patients initiate a walk-in visit </li></ul></ul><ul><ul><li>In-person in the emergency room </li></ul></ul>
  9. 9. When during interview should Qs be asked? <ul><li>Towards the beginning? </li></ul><ul><li>Towards the end? </li></ul><ul><ul><li>Registrar & patient established rapport? </li></ul></ul><ul><ul><ul><li>Beginning: No. End: Yes. </li></ul></ul></ul><ul><ul><li>Questions precede Qs about payment? </li></ul></ul><ul><ul><ul><li>Beginning: Yes. End: No. </li></ul></ul></ul><ul><ul><li>Patient still willing to answer Qs? </li></ul></ul><ul><ul><ul><li>Beginning: Yes. End: No. </li></ul></ul></ul>
  10. 10. When during interview are Qs asked? <ul><li>At HCMC, questions will be asked towards beginning of interview </li></ul><ul><ul><li>After identifying patient as new or existing </li></ul></ul><ul><ul><li>After obtaining address </li></ul></ul><ul><ul><li>Before asking Qs about payment source </li></ul></ul><ul><ul><li>Before scheduling appointment </li></ul></ul>
  11. 11. Which Qs and How Many Qs to Ask? <ul><li>Must balance competing needs: </li></ul><ul><ul><li>Interviewer/patient pair wants </li></ul></ul><ul><ul><ul><li>Qs that are easy to understand </li></ul></ul></ul><ul><ul><ul><li>Qs that are easy to answer </li></ul></ul></ul><ul><ul><ul><li>Ability for patient to use own words </li></ul></ul></ul><ul><ul><ul><li>No more than 3-4 minutes!!!! </li></ul></ul></ul><ul><ul><li>Downstream data users want: </li></ul></ul><ul><ul><ul><li>Forced response categories to assist analysis </li></ul></ul></ul><ul><ul><ul><li>Lots of information </li></ul></ul></ul><ul><ul><li>Limited available space on the screen </li></ul></ul>
  12. 12. Constraints: Computer vs. Paper <ul><li>Computer screen: </li></ul><ul><ul><li>Very limited screen space </li></ul></ul><ul><ul><ul><li>~15 characters available for Qs </li></ul></ul></ul><ul><ul><ul><li>No space for instructions to interviewers </li></ul></ul></ul><ul><ul><li>Drop-down response menu </li></ul></ul><ul><ul><ul><li>Offers unlimited number of response choices </li></ul></ul></ul><ul><ul><ul><li>Alphabetized </li></ul></ul></ul><ul><ul><ul><li>Response can be found by typing 1 st few letters </li></ul></ul></ul><ul><li>Constraints opposite when answers recorded on paper </li></ul>
  13. 13. Who are the downstream users? <ul><li>Clinicians </li></ul><ul><li>Interpreter services </li></ul><ul><li>Planning & Marketing </li></ul><ul><li>Registries & Databases </li></ul><ul><li>Performance Improvement Department </li></ul><ul><li>Public Health Departments </li></ul><ul><li>Clinical Researchers </li></ul>
  14. 14. Clinical & Planning Staff Needs: <ul><li>Distinctions in data between: </li></ul><ul><ul><li>African-American vs. African-born </li></ul></ul><ul><ul><li>If African-born, which culture? </li></ul></ul><ul><ul><li>White Americans vs. new European immigrants </li></ul></ul><ul><li>Clinicians use distinctions to: </li></ul><ul><ul><li>Diagnose </li></ul></ul><ul><ul><li>Be aware of potential culturally-specific health factors (e.g. diet, smoking, pregnancy, family support, treatment preferences) </li></ul></ul><ul><li>Planning & marketing use distinctions to: </li></ul><ul><ul><li>Identify communities served by HCMC </li></ul></ul><ul><ul><li>Determine if HCMC is meeting community needs </li></ul></ul>
  15. 15. Reporting & Research Needs: <ul><li>Common reporting format set at higher level </li></ul><ul><ul><li>Departments maintaining registries: </li></ul></ul><ul><ul><ul><li>Certification, accreditation and funding </li></ul></ul></ul><ul><ul><ul><li>Core measure reporting </li></ul></ul></ul><ul><ul><li>Public health departments </li></ul></ul><ul><ul><ul><li>Epidemiology </li></ul></ul></ul><ul><ul><ul><li>Comparison with community health surveys </li></ul></ul></ul><ul><ul><li>Clinical Researchers </li></ul></ul><ul><ul><ul><li>Identify prospective participants for clinical trials </li></ul></ul></ul><ul><ul><ul><li>Examine aggregate data for trends </li></ul></ul></ul>
  16. 16. Office of Management & Budget (OMB) <ul><li>In U.S., OMB establishes reporting format </li></ul><ul><li>OMB requires 2 questions: </li></ul><ul><ul><li>Hispanic ethnicity? </li></ul></ul><ul><ul><li>Race? </li></ul></ul><ul><ul><ul><li>White </li></ul></ul></ul><ul><ul><ul><li>African-American or Black </li></ul></ul></ul><ul><ul><ul><li>Asian or Pacific Islander </li></ul></ul></ul><ul><ul><ul><li>Native American or Alaskan Native </li></ul></ul></ul><ul><ul><ul><li>Other </li></ul></ul></ul>
  17. 17. Who needs what? <ul><li>Registries * Clinical Researchers * Public Health Departments </li></ul><ul><li>Fixed response choices </li></ul><ul><li>OMB reporting format </li></ul><ul><li>Clinicians </li></ul><ul><li>Planning & Marketing </li></ul><ul><li>Fine distinctions </li></ul><ul><li>Interviewer/ </li></ul><ul><li>Patient Pair </li></ul><ul><li>Patient-perception </li></ul><ul><li>Simple </li></ul><ul><li>Short </li></ul>
  18. 18. Conflict area: Number of questions <ul><li>Downstream data users want extensive information </li></ul><ul><li>Interviewer/Patient pair wants speed </li></ul>
  19. 19. Conflict area: Use of OMB categories <ul><li>Desired by registries, public health departments, clinical researchers, to meet requirements set by NIH, CDC, etc. </li></ul><ul><li>Categories awkward for the interviewer/patient pair </li></ul><ul><li>Distinctions not fine enough for: </li></ul><ul><ul><li>Clinicians </li></ul></ul><ul><ul><li>Interpreter Services </li></ul></ul><ul><ul><li>Planning & Marketing </li></ul></ul>
  20. 20. Method of conflict resolution: <ul><li>If conflict is between downstream user and interviewer/patient pair, </li></ul><ul><ul><li>Resolve in favor of interviewer/patient pair </li></ul></ul><ul><li>Use of OMB classification scheme: </li></ul><ul><ul><li>CONDUCT EXPERIMENT!!! </li></ul></ul>
  21. 21. HCMC Patients Queried About: <ul><li>Birthplace (e.g. country) </li></ul><ul><li>Race </li></ul><ul><li>Ethnicity </li></ul><ul><li>Spoken language(s) </li></ul><ul><li>Religion </li></ul><ul><li>Marital status </li></ul>
  22. 22. HCMC Experiment <ul><li>Conducted in January and February 2006 </li></ul><ul><li>Used four HCMC registrars/schedulers </li></ul><ul><ul><li>Three on telephone (two Spanish-speaking) </li></ul></ul><ul><ul><li>Two in person (one Spanish-speaking) </li></ul></ul><ul><ul><li>(One registrar both on phone and in person) </li></ul></ul><ul><li>Four methods tested </li></ul><ul><ul><li>Each tested by 2+ interviewers </li></ul></ul><ul><ul><li>Each tested on 2+ days </li></ul></ul><ul><ul><li>Each tested until > 30 interviews took place </li></ul></ul>
  23. 23. For each method tested: <ul><li>Same questions and order for birthplace, language, religion and marital status </li></ul><ul><li>Varied by questions about race & ethnicity </li></ul>
  24. 24. All Methods Marital status Race or ethnicity Question Religious preference Race or ethnicity Question Language(s) Birthplace
  25. 25. HCMC Experimental Methods <ul><li>Proposed data entry screen mimicked with Microsoft Access </li></ul><ul><li>Registrar switched to Access screen at appropriate time during live patient interview </li></ul><ul><li>Access recorded: </li></ul><ul><ul><li>Responses provided (including refusals) </li></ul></ul><ul><ul><li>Time to administer entire set of questions </li></ul></ul>
  26. 26. Outcomes of interest <ul><li>Registrar feedback on ease of administration </li></ul><ul><li>Percent questions refused </li></ul><ul><li>Percent incomplete interviews </li></ul><ul><li>Average administration time </li></ul>
  27. 27. Method One: <ul><li>What is your ethnicity? </li></ul><ul><ul><li>Over 60 possible choices suggested by </li></ul></ul><ul><ul><ul><li>Nationality </li></ul></ul></ul><ul><ul><ul><li>Religion </li></ul></ul></ul><ul><ul><ul><li>Race </li></ul></ul></ul><ul><ul><ul><li>Language </li></ul></ul></ul><ul><li>What is your race? </li></ul><ul><ul><li>White </li></ul></ul><ul><ul><li>Black or African American </li></ul></ul><ul><ul><li>Asian </li></ul></ul><ul><ul><li>Native American </li></ul></ul><ul><ul><li>Other </li></ul></ul>
  28. 28. Method One: Intent & Qualitative Results <ul><li>Intentions: </li></ul><ul><ul><li>Replicate OMB classification of race within ethnicity, BUT </li></ul></ul><ul><ul><li>Don’t limit ethnicity to Hispanic only </li></ul></ul><ul><li>General results: </li></ul><ul><ul><li>Ethnicity question coming first often confused patients; too many choices </li></ul></ul><ul><ul><li>Awkward to administer </li></ul></ul><ul><ul><li>Once patients provided birthplace & ethnicity, race question perceived as redundant </li></ul></ul>
  29. 29. Method Two: <ul><li>What is your race? </li></ul><ul><ul><li>White </li></ul></ul><ul><ul><li>Black or African American </li></ul></ul><ul><ul><li>Hispanic </li></ul></ul><ul><ul><li>Asian </li></ul></ul><ul><ul><li>Native American </li></ul></ul><ul><ul><li>Other </li></ul></ul><ul><li>What is your ethnicity? </li></ul><ul><ul><li>Over 60 possible choices suggested by </li></ul></ul><ul><ul><ul><li>Nationality </li></ul></ul></ul><ul><ul><ul><li>Religion </li></ul></ul></ul><ul><ul><ul><li>Race </li></ul></ul></ul><ul><ul><ul><li>Language </li></ul></ul></ul>
  30. 30. Method Two: Intent & Qualitative Results <ul><li>Intentions: </li></ul><ul><ul><li>Capture basic OMB race classification </li></ul></ul><ul><ul><li>Enable patients to convey identity in own words </li></ul></ul><ul><li>General results: </li></ul><ul><ul><li>Easiest of all methods to use </li></ul></ul><ul><ul><li>Patients willing and often eager to provide additional identifying information on top of basic race </li></ul></ul><ul><ul><li>Lose ability to report using OMB classification </li></ul></ul>
  31. 31. Method Three: <ul><li>What is your race? </li></ul><ul><ul><li>White </li></ul></ul><ul><ul><li>Black or African American </li></ul></ul><ul><ul><li>Hispanic – White </li></ul></ul><ul><ul><li>Hispanic – Black </li></ul></ul><ul><ul><li>Hispanic – Other </li></ul></ul><ul><ul><li>Asian </li></ul></ul><ul><ul><li>Native American </li></ul></ul><ul><ul><li>Other </li></ul></ul><ul><li>What is your ethnicity? </li></ul><ul><ul><li>Over 60 possible choices suggested by </li></ul></ul><ul><ul><ul><li>Nationality </li></ul></ul></ul><ul><ul><ul><li>Religion </li></ul></ul></ul><ul><ul><ul><li>Race </li></ul></ul></ul><ul><ul><ul><li>Language </li></ul></ul></ul>
  32. 32. Method Three: Intent & Qualitative Results <ul><li>Intentions: </li></ul><ul><ul><li>Enable reporting using OMB classification that crosses Hispanic ethnicity by race </li></ul></ul><ul><ul><li>Enable patients to convey identity in own words </li></ul></ul><ul><li>General results: </li></ul><ul><ul><li>Easy to use </li></ul></ul><ul><ul><li>Race within Hispanic did not encourage patients to select any particular race other than Hispanic </li></ul></ul><ul><ul><li>Latter result added time & complexity to Method Two for no additional value </li></ul></ul>
  33. 33. Method Four: <ul><li>Hispanic? </li></ul><ul><ul><li>Yes </li></ul></ul><ul><ul><li>No </li></ul></ul><ul><li>What is your race? </li></ul><ul><ul><li>White </li></ul></ul><ul><ul><li>Black or African American </li></ul></ul><ul><ul><li>Asian </li></ul></ul><ul><ul><li>Native American </li></ul></ul><ul><ul><li>Other </li></ul></ul><ul><li>What is your ethnicity? </li></ul><ul><ul><li>Over 60 possible choices suggested by </li></ul></ul><ul><ul><ul><li>Nationality </li></ul></ul></ul><ul><ul><ul><li>Religion </li></ul></ul></ul><ul><ul><ul><li>Race </li></ul></ul></ul><ul><ul><ul><li>Language </li></ul></ul></ul>
  34. 34. Method Four: Intent & Qualitative Results <ul><li>Intentions: </li></ul><ul><ul><li>Enable reporting using OMB classification that crosses Hispanic ethnicity by race </li></ul></ul><ul><ul><li>Enable patients to convey identity in own words </li></ul></ul><ul><li>General results: </li></ul><ul><ul><li>Confused most Hispanics because </li></ul></ul><ul><ul><ul><li>Did not understand race question after being asked about Hispanic ethnicity </li></ul></ul></ul><ul><ul><ul><li>Ethnicity question appeared redundant after being asked about birthplace (nationality) </li></ul></ul></ul>
  35. 35. Quantitative Results 2.4 1.9 2.9 2.3 Max Time (mins) 1.1 1.2 1.0 0.9 Avg Time (mins) 78.9 100.0 100.0 90.0 Race Q done (%) 86.8 97.4 100.0 93.3 Ethnicity Q done (%) 76 39 59 60 Interviews (n) Four Three Two One Method Outcomes of Interest
  36. 36. Other results <ul><li>Patients on telephone generally cooperative </li></ul><ul><li>Patients interviewed in person often not cooperative, for reasons unrelated to test: </li></ul><ul><ul><li>New registration system being implemented </li></ul></ul><ul><ul><li>In person interview unexpected by patients </li></ul></ul><ul><ul><li>Appointment delayed because of interview </li></ul></ul><ul><li>All registrars endorsed Method Two and criticized all other methods </li></ul>
  37. 37. General Conclusions <ul><li>Registrars accustomed to multi-ethnic population unfazed by asking Qs </li></ul><ul><li>Questions never took more than 3 mins to administer; average: 62 secs. </li></ul><ul><li>Ask general question about race first, follow up with ethnicity question to attain finer detail and specificity </li></ul>
  38. 38. Conclusions: Hispanic ethnicity <ul><li>Majority of HCMC Hispanic patients are Mexican </li></ul><ul><li>Mexicans think of ‘Hispanic’ as a distinct race, thus confused when asked for race after they have identified themselves </li></ul>
  39. 39. Conclusions: OMB Classification <ul><li>OMB classification system imposes an identity that differs from the way patients perceive themselves. From patients, generates: </li></ul><ul><ul><li>Confusion (at best) </li></ul></ul><ul><ul><li>Hostility (at worst) </li></ul></ul><ul><li>Service organization must be sensitive to those it serves. Cannot impose an identity. </li></ul><ul><li>OMB scheme pits needs of service providers against needs of researchers. Foments: </li></ul><ul><ul><li>Inconsistent reporting from service organizations </li></ul></ul><ul><ul><li>Wariness by service providers towards researchers </li></ul></ul>
  40. 40. Pragmatic Outcomes <ul><li>Method Two programmed into HCMC’s new electronic health record (EHR) </li></ul><ul><li>Method Two will be implemented during registration/scheduling process at HCMC </li></ul><ul><li>New method goes live in Summer 2006 </li></ul><ul><li>HCMC will develop a standard reporting algorithm to be used across campus to convert patient responses into reports for registries & researchers. </li></ul>
  41. 41. The future… <ul><li>Center for Urban Health at HCMC developing a data management and analytic infrastructure that will: </li></ul><ul><ul><li>Monitor completion of demographic fields </li></ul></ul><ul><ul><li>Create templates for generating reports showing population-based differences in patient outcomes and care </li></ul></ul><ul><ul><li>Templates will show all core measures by patient race and ethnicity </li></ul></ul>
  42. 42. Thank You!
  43. 43. Racial distribution of 236 respondents: <ul><li>Among Blacks, 19 (29.2%) are African-born </li></ul><ul><li>For no response to race: </li></ul><ul><ul><li>Arab: 1 </li></ul></ul><ul><ul><li>Indonesian 1 </li></ul></ul><ul><ul><li>Somali 5 </li></ul></ul><ul><li>Percent sum > 100 because of double counting </li></ul>3.8 9 No response given 6.4 15 Multi-Racial 1.3 3 Native American 2.1 5 Asian 28.0 66 Hispanic 27.5 65 Black or AA 34.7 82 White %* N RACE
  44. 44. Detail on Whites/Caucasian (n=82) % N ETHNICITY 9.8 8 Hispanic 11.0 9 No other detail given 1.2 1 Western European 1.2 1 Russian 1.2 1 Norwegian 2.4 2 German 3.7 3 Irish 69.5 57 American, American-born white, Caucasian, European-American
  45. 45. Detail on Blacks (n=65) 3.1 2 Hispanic 6.2 4 No other detail 1.5 1 Multi-racial 29.2 19 African, African-born 60.0 39 African-American, American-born Black % N ETHNICITY 42.1 8 Unknown 5.3 1 Togo 36.8 7 Somali 5.3 1 Liberian 10.5 2 Ethiopian % N African
  46. 46. Detail on Hispanic (n=69) 21.7 15 No other detail 4.5 3 Multi-racial (also listed under multi-racial) 12.1 8 White (also listed under White) 3.0 2 Black (also listed under Black) 51.5 34 Mexican 3.0 2 Guatamalen 7.6 5 Ecuadoran % N HISPANIC ORIGIN
  47. 47. Other detail ASIAN (n=5) 1 Unknown 1 Laotian 1 Indian 1 Chinese 1 Cambodian 1 Chippewa 1 Sioux 1 Unknown NATIVE AMERICAN 8 No other detail 3 Hispanic (also listed with Hispanic) 1 Chippewa (also listed with Native Am.) 1 Malodo 2 White MULTI-RACIAL (n=15)