David W. Baker, M.D., and co-researchers at Northwestern University showed that "self-describing" data collection results in fewer missed results and unusable data than traditional methods of either providing standard descriptions or having a hospital registration clerk determine a patient's race or ethnicity.
Baker and colleagues asked 425 study participants to describe their race or ethnicity using any terms they wanted to use, and subsequently compared these responses to answers to several questions, including whether participants considered themselves Latino or Hispanic.
Participants were also asked which of the following categories best described their race: white; black or African-American; Asian, Native Hawaiian or Pacific Islander; American Indian or Native Alaskan; multiracial or another race. Participants were then asked which description method they preferred.
Overall, about a quarter of participants strongly preferred using their own words to describe themselves using a single term, for example, white, black or African American; Hispanic or Latino. Among the Latino/Hispanic and multiracial/multiethnic study participants, a majority preferred using their own terms to describe themselves.
Study data showed that rates of missing values and use of the term "other" for race were lower with the open-ended than with the closed questions. "The first step toward addressing disparities in health care for minority patients is to routinely collect data on patients' race, ethnicity and language and link these data to measures of quality, safety and utilization," the authors said. "With such information, provider organizations will be able to target quality improvement programs to address disparities at their own institutions," they said.
MEDICA.de; Source: Northwestern University