Illinois Mobile App Master Calendar

Abstract: To address longstanding racial and ethnic disparities in healthcare quality, healthcare organizations are increasingly engaging in collaborative equity-focused quality improvement (EF-QI) initiatives. High quality race and ethnicity data are critical for these efforts and are used to stratify quality measures and identify gaps in clinical care processes and outcomes. By aggregating data from organizations participating in collaborative EF-QI networks, clinical quality registries can support cross-organizational measurement and benchmarking of care quality, while also supporting sufficient sample sizes for meaningful analysis of data from minoritized populations that are small or hard to reach. However, the quality of race and ethnicity data in these databases is not guaranteed. Organizational variation in data practices underlying collection and standardization of these data can complicate efforts to share and integrate race and ethnicity data while preserving their meaning, thereby inhibiting interoperability and increasing the likelihood of information loss. 

This talk details findings from two studies examining race and ethnicity data in the clinical registry of the Michigan Emergency Department Improvement Collaborative (MEDIC), a statewide physician-led network of emergency departments with a shared goal of improving emergency care quality and lowering costs. Race and ethnicity data contributed to the registry by 32 MEDIC practice sites between 2020 and 2023 were extracted for this analysis, representing over 2 million patients. Racial and ethnic categories in the source data and registry data were compared to identify (a) the extent to which information was lost when these data were incorporated into the registry and (b) the impact of this information loss on quality measurement. While information loss occurred for all groups, racially and ethnically minoritized patients were more likely to be recategorized, misclassified, or omitted in the registry, which in turn decreased the reliability of stratified quality measures. These findings underscore how taken-for-granted organizational data practices can constrain well-intentioned, data-driven efforts to advance health equity.

Bio: Dr. Sarah El-Azab is an Assistant Professor of Health Administration in the Department of Health and Kinesiology at UIUC. As an interdisciplinary social scientist and health informaticist, her research (a) critically examines how the generation and use of data and data-driven technologies by healthcare organizations can contribute to discriminatory decision-making and (b) explores opportunities for advancing community-centered digital transformation of the U.S. healthcare system. Dr. El-Azab received her PhD in Health Services Organization and Policy from the University of Michigan and is an alumna of the Robert Wood Johnson Foundation Health Policy Research Scholars leadership program.