Abstract

While research and efforts to promote health equity abound, the persistence of disparities by race and ethnicity underscores the limitations of fragmented interventions and the need for systematic, multipronged approaches to health equity. The foundational step towards reducing health disparities is the establishment of the basic information needed to identify and measure those differences, i.e., the accurate capture of race and ethnicity information of all patients. To that end, we present a case study outlining a multifaceted approach for improving the capture of race and ethnicity data in an outpatient setting culminating in a 76% improvement in the completeness of this information. The effectiveness of this plan and its scalability within a large urban health system may benefit similar institutions seeking to improve the collection of race and ethnicity information and the reliability of their data. To this aim, we present an approach relying on the assessment and evaluation of system needs, modification of data infrastructure to align with goals, training, and education of relevant stakeholders, implementation and responsive action to results, and acknowledging limitations and lessons learned. We emphasize that cross-departmental collaboration, stakeholder engagement, institutional support, and culture of anti-racism were essential to the success of this initiative.

Highlights

  • The coronavirus disease 2019 (COVID-19) pandemic’s disproportionate burden on communities of color and the clamors for racial justice engulfing the nation make evident [1,2]

  • We present an approach relying on the assessment and evaluation of system needs, modification of data infrastructure to align with goals, training, and education of relevant stakeholders, implementation and responsive action to results, and acknowledging limitations and lessons learned

  • Through a series of systematic interventions coupled with patient and staff empowerment, Internal Medicine Associates (IMA) improved its capture of race and ethnicity data decisively

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Summary

Introduction

The coronavirus disease 2019 (COVID-19) pandemic’s disproportionate burden on communities of color and the clamors for racial justice engulfing the nation make evident [1,2]. According to the Agency for Health Care Research and Quality, an essential step towards the goal of equitable care is systematically identifying patient populations, addressing the needs of these populations, and monitoring improvements over time [5]. This process largely depends on the collection of granular demographic data like race, ethnicity, and language preference (REL) facilitating the stratification of quality measures at a level of detail that can identify variation in health and health care among vulnerable groups [6]. The challenge lies in identifying the best practices for the systematic and reliable identification of patient demographics

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