Querying Patients About Race and Ethnicity

www.CenterForUrbanHealth.org

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


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


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


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


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


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%


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


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


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.


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


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


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


Who are the downstream users?

• Clinicians• Interpreter services• Planning & Marketing• Registries & Databases • Performance Improvement

Department• Public Health Departments• Clinical Researchers


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


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


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


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


Conflict area: Number of questions

• Downstream data users want extensive information

• Interviewer/Patient pair wants speed


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


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!!!


HCMC Patients Queried About:

• Birthplace (e.g. country)• Race• Ethnicity• Spoken language(s)• Religion• Marital status


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


For each method tested:

• Same questions and order for birthplace, language, religion and marital status

• Varied by questions about race & ethnicity


All Methods

Birthplace Language(s)

Race or ethnicityQuestion

Religious preference

Race or ethnicity Question

Marital status


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


Outcomes of interest

• Registrar feedback on ease of administration

• Percent questions refused• Percent incomplete interviews• Average administration time


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


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


Method Two:


American Hispanic Asian Native American Other




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


Method Three:


American Hispanic – White Hispanic – Black Hispanic – Other Asian Native American Other




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


Method Four:

• Hispanic? Yes No


American Asian Native American Other




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)


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


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


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


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


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


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.


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


Thank You!


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


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


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


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


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

Health & Medicine

Querying Patients About Race and Ethnicity