Systematic Data Fitness Assessment Improves Validity and Replicability of Research Using Real-World Data [Poster]

Abstract

Secondary use of electronic health record (EHR) data gives investigators the ability to conduct research and derive insights at a large scale for a lesser cost. These data are complex, so multi-institutional research networks often implement broad data quality (DQ) programs to ensure the data is generally usable and meet network governance standards. However, study-specific needs are typically out of scope for these programs, leaving researchers with the responsibility of conducting ad hoc analyses to ensure the data is fit-for-use in the context of their study. Our team has developed a reusable, modularized tool to assess study-specific data quality (SSDQ), which is intended to reduce time spent developing ad hoc analyses and improve transparency and replicability in research. Using this tool as a foundation, we demonstrate the utility of undertaking rigorous DQ assessment by replicating a study examining the impact of hydroxyurea treatment in a cohort of pediatric patients with the SS/Sβ0 sickle-cell genotype. Poster designed and presented by Kaleigh Wieand on October 14, 2026 at the Science, Population Health, Analytics, Research, Collaboration, Community (SPARCC) data summit.