Taking action on inequities: a structural paradigm for quality and safety

As quality improvement and patient safety (QIPS) practitioners, we aspire to improve care for all patients, caregivers and families using improvement methods. While teams are trained to carefully implement the science of improvement, less is known of how to effectively incorporate equity into QIPS work. Should there be more projects focused specifically on equity, or should equity be embedded into all quality improvement? Inattention to the equity domain in improvement efforts ignores systemic biases and can worsen inequities in health outcomes. How to measure inequity, and growing calls to reframe health equity data measurement, presentation and analysis are central to this discourse. In this issue of BMJ Quality and Safety , Arrington and colleagues1 offer strategies to collect, share and interpret quality data using a racial equity lens. The authors first describe the problems with stratifying quality data by race and ethnicity, which can perpetuate the false notion that race or ethnicity is responsible for differences in health outcomes and inhibit teams from identifying embedded structural or systemic root causes of health inequities. They provide concrete examples of reimagining data collection and presentation that are actionable and feasible. These include considering root causes beyond describing differences among racial groups, choosing reference points equitably (eg, avoiding using outcomes of white patients as reference points), presenting the most specific level of aggregation (eg, identifying race as ‘Chinese’ rather than ‘Asian’), collecting data on strengths (eg, describing groups with positive outcomes) rather than deficits, measuring racism instead of race and collaborating with community partners. Using this framework, the narrative shifts away from race and ethnicity to a focus on unjust systems, structures and practices responsible for health inequities. As articulated by Arrington and colleagues,1 adopting a racial equity lens to the interpretation of stratified QIPS data is an essential skill that …