Katelyn Banschbach ^1,2* Jade Singleton ³ Xing Wang ³ Sheetal S. Vora ⁴ Julia Harris ⁵ Ashley Lytch ⁶ Nancy Pan ^7,8 Julia Klauss ⁸ Danielle Fair ⁹ Erin Hammelev ⁹ Mileka Gilbert ¹⁰ Connor Kreese ¹¹ Ashley Machado ¹² Peter Tarczy-Hornoch ^13,14,15 Esi Morgan ^1,2

• ¹ Department of Pediatric Rheumatology, Seattle Children's Hospital, Seattle, Washington, United States
• ² Department of Pediatrics, School of Medicine, University of Washington, Seattle, Washington, United States
• ³ Biostatistics Epidemiology and Analytics in Research at Seattle Children's Hospital, Seattle, United States
• ⁴ Division of Pediatric Rheumatology, Atrium Health Levine Children’s Hospital and Wake Forest University School of Medicine, Charlotte, United States
• ⁵ Division of Pediatric Rheumatology, Children's Mercy Kansas City, Kansas City, Missouri, United States
• ⁶ Children's Research Institute, Children's Mercy Hospital, Kansas City, Kansas, United States
• ⁷ Department of Pediatrics, Weill Cornell Medicine, Cornell University, New York, New York, United States
• ⁸ Division of Pediatric Rheumatology, Hospital for Special Surgery, New York, New York, United States
• ⁹ Division of Pediatric Rheumatology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States
• ¹⁰ Division of Pediatric Rheumatology, Shawn Jenkins Children’s Hospital, Medical University of South Carolin, Charleston, United States
• ¹¹ Shawn Jenkins Children’s Hospital, Medical University of South Carolin, Charleston, Illinois, United States
• ¹² Division of Pediatric Rheumatology, Northwell Health, Cohen Children’s Medical Center, New York, United States
• ¹³ Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Seattle, Washington, United States
• ¹⁴ Division of Neonatology, Department of Pediatrics, School of Medicine, University of Washington, Seattle, Washington, United States
• ¹⁵ Paul G. Allen School of Computer Science & Engineering, College of Engineering, University of Washington, Seattle, Washington, United States

Introduction: Ensuring high quality race and ethnicity data within the electronic health record (EHR) and across linked systems, such as patient registries, is necessary to achieve a goal of inclusion of racial and ethnic minorities in scientific research and detect disparities associated with race and ethnicity. The project goal was to improve race and ethnicity data completion within the Pediatric Rheumatology Care Outcomes Improvement Network (PR-COIN) and assess impact of improved data completion on conclusions drawn from the registry.Methods: The projectThis is a mixed-methods quality improvement study which consisted of 5 parts:(1) Identifying baseline missing race and ethnicity data, (2) Survey of current collection and entry, (3) Data completion through audit and feedback cycles, (4) Assessment of impact on outcome measures, and (5) Participant interviews and thematic analysis.Results: Across 6 participating centers, 29% of patients were missing race and 31% were missing ethnicity, with. Of patients missing data, most patients were missing both race and ethnicity. Rates of missingness varied by data entry method (electronic vs manual). Recovered data had a higher percentage of patients with Other race or Hispanic/Latino ethnicity compared to patients with nonmissing race and ethnicity at baseline. Black patients had a significantly higher odds ratio of having a clinical juvenile arthritis disease activity score (cJADAS10) of ≥5 at first follow up compared to White patients. There was no significant change in odds of cJADAS10 ≥5 for race and ethnicity after data completion. Patients missing race and ethnicity were more likely to be missing cJADAS values which may affect the ability to detect changes in odds of cJADAS ≥5 after completion.Conclusions: About 1/3 of patients in a pediatric rheumatology registry were missing race and ethnicity data. After three audit and feedback cycles, centers decreased missing data by 94%, primarily via data recovery from the EHR. In this sample, completion of missing data did not change the findings related to differential outcomes by race. Recovered data was not uniformly distributed compared to those with non-missing race and ethnicity at baseline, suggesting that differences in outcomes after completing race and ethnicity data may be seen with larger sample sizes.