摘要：Objectives . We assessed the feasibility of collecting race/ethnicity data from patients using their own preferred racial/ethnic terms. Methods . The 424 patients described their race/ethnicity using their own categories, and we compared their descriptions with their responses to the questions (1) “Do you consider yourself Latino or Hispanic?” and (2) “Which category best describes your race?” (7 response options in our computer interview). We also determined patients’ preferences between the 2 approaches. Results. seconds. Rates of missing values and categorization as “other” race were lower than with the closed questions. Agreement between racial/ethnic categorization with open-ended and closed responses was 93% (κ =0.88). Latino/Hispanic and multiracial/multiethnic individuals were more likely to prefer using their own categories to describe their race/ethnicity. Conclusions . Collecting race/ethnicity data using patients’ own racial/ethnic categories is feasible with the use of computerized systems to capture verbatim responses and results in lower rates of missing and unusable data than do standard questions. People of color and racial/ethnic minorities in the United States often receive lower-quality health care than Whites. 1 , 2 The first step toward addressing this problem is for health care providers to routinely collect data on patients’ race, ethnicity, and language and link these data to measures of quality, safety, and utilization. 1 – 5 With such information, provider organizations can target quality-improvement programs to reduce disparities at their own institutions. A recent survey found that only 78% of US hospitals systematically collected data on patients’ race or ethnicity. 6 Among those that did, more than half relied on registration clerks’ impressions of patients’ race and ethnicity rather than directly asking patients. When compared with patients’ self-report, staff impressions of patients’ race/ethnicity are reasonably accurate for Whites and Blacks but much less accurate for other groups. 7 – 11 In addition, relying on staff impressions results in high rates of patients having missing data or being classified as “unknown” or “other.” 4 , 7 Because of this, expert panels have recommended that patients’ race and ethnicity should be collected by self-report. 1 – 5 However, even if there is widespread adoption of the recommendation to collect patients’ race/ethnicity by self-report, current data collection methods are suboptimal. Providers typically ask questions that comply with the Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity from the Office of Management and Budget (OMB) and are used by the US Census Bureau: (1) “Do you consider yourself Latino or Hispanic?” and (2) “Which category best describes your race: White, Black or African American, Asian, Native Hawaiian or Pacific Islander, American Indian or Alaska Native, multiracial, or another race I did not mention?” 12 More-detailed information is needed to address health and health care disparities. 5 For example, several studies have emphasized the heterogeneity within the Hispanic/Latino population 13 – 18 ; categorizing individuals of Mexican, Cuban, and Puerto Rican ancestry as simply Hispanic/Latino may obscure disparities in access, utilization, or quality of care. 19 Important differences in risk factors for cardiovascular disease have been reported across Asian subgroups. 20 , 21 The increasing heterogeneity of the population of the United States makes the deficiencies of the traditional OMB race/ethnicity questions even more salient. How should immigrants from Poland, Iraq, and India classify themselves? Classifying them as White blurs important distinctions in their health beliefs, behaviors, and treatment by health care providers. Alternatively, classifying people from Iraq and India as Asian combines them with individuals of Hmong or Japanese ancestry, which is equally inappropriate. The OMB standards were expressly intended as a minimum data requirement, but few health care providers ask more detailed questions. In part these questions are not asked because collecting more-detailed information is problematic if a clerk must read a long list of options from which patients are asked to choose. An alternative is to allow patients to self-describe themselves and record their verbatim responses with computerized data collection tools. This should allow providers to analyze their data at different levels of complexity, ranging from small-subgroup analyses using unique categories (e.g., Korean or Puerto Rican) up to more coarse, aggregated categories (e.g., Asian or Hispanic/Latino). We undertook this project to develop an instrument that could rapidly and accurately collect patients’ self-described race/ethnicity. The first phase tested the feasibility of allowing patients to self-identify themselves with whatever terms they wanted. The second phase tested a computerized tool for capturing this information and sought to examine the terms used by patients to self-identify themselves, rates of missing values and refusals, and time required for data collection.
