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