Abstract B33: Who am I? Improving quality of data collection for race/ethnicity and language
Authors: Aristizabal Paula , Ani Foyinsola , Muro Erica Del , Cassidy Teresa , Roberts William , Stucky-Fisher Erin , Martinez Maria Elena 
Published: October 2015

Abstract
Introduction: Disparities in the quality of care provided to minorities has been documented in the literature. Reliable racial/ethnic reporting is critical, as initiatives to address healthcare disparities remain priorities on the national agenda. Hispanic children have been cited as having a higher incidence of leukemia/lymphoma but poorer survival rates. Accurate attribution of disease incidence and outcome to specific populations is central to ensuring appropriate access to care, family communication, resource distribution and funding for research. Analysis of 2000-2010 Hematology/Oncology data at Rady Children's Hospital San Diego (RCHSD) found a 13.02% discrepancy rate for race/ethnicity accuracy and 21% self-report rate. RCHSD is a pediatric medical center serving San Diego, Imperial, and southern Riverside counties in California, where Hispanic children comprise 42% of the population. While there is consensus regarding the importance of self-reporting of race/ethnicity, we identified both significant lack of self-reported race/ethnicity data and varied forms used to collect patient demographics at our site. Research has shown that most observers including administrative staff will accurately identify individuals as white or black, but Hispanic and multiracial individuals are often misidentified.
Purpose: The Global Aim of this study was to improve resource allocation, patient-provider engagement and access to race/ethnicity and language data for research through correct race/ethnicity/language attribution. Our SMART aim was to implement a uniform and accurate system for data collection on race/ethnicity and language for the hematology/oncology population at our hospital with a reduction of missing and discrepant data to &lt;2% within 6 months.
Design/Methods: We conducted a quality improvement pilot project to achieve our Global Aim. Plan-Do -Study-Act method was used. P: Key stakeholders used Fishbone analysis and flow charting and several barriers to processes and possible interventions were identified. A new single form (English and Spanish) was created to obtain self-reported race/ethnicity and information on preferred language of written medical information, and preferred spoken language. A decision map to aid parents in question answering and information sheet were also created. Staff was trained to assist parents and document in the Electronic Medical Record (EMR). D: Self-reported data was obtained from 200 patients during a 6-week period. S: Pre and Post rates of self-reported race/ethnicity and language data completion and accuracy rates were compared. Accuracy rates for race/ethnicity and language were calculated by comparing existing demographic information in the EMR system at RCHSD versus demographic information collected with the new form. A: Data was presented to Hospital Quality Council; plan to embed tools in EMR and pilot a second population.
Results: We found that race/ethnicity information was not collected in a uniform and consistent manner. Seven different demographic data collection forms were replaced by the new form. Discrepancy rate was reduced to 1.2%, a reduction of 90% (chi-square 19.073, p&lt;0.001) and self-report rate was increased to 97%, an increase of 76% (chi-square 191.318, p&lt;0.001). Forty-eight percent of individuals self-identified as Hispanic, 13% preferred Spanish as the language for spoken and written medical material, and in 21% patients, Spanish was the language spoken at home.
Conclusion: Identifying barriers, reducing variability with a single data collection tool, and adjunct tools improved race/ethnicity/language accuracy. Next steps include definitive implementation and expansion to entire hospital. Collecting accurate information on patients' race/ethnicity and language should be a universal practice, enabling to understand and address disparities in childhood cancer.
Citation Format: Paula Aristizabal, Foyinsola Ani, Erica Del Muro, Teresa Cassidy, William Roberts, Erin Stucky-Fisher, Maria Elena Martinez. Who am I? Improving quality of data collection for race/ethnicity and language. [abstract]. In: Proceedings of the Seventh AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 9-12, 2014; San Antonio, TX. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2015;24(10 Suppl):Abstract nr B33.