Introduction of a Health Care System Lens-of-Equity Measurement Strategy to Optimize Breast Cancer Screening

Abstract

Objectives

Health equity is greatly impacted by the systems and processes with which health systems deliver care. Given the minimal guidance on measurement and reporting of health inequities specific to key population health outcomes, a solution for measurement of health equity is proposed.

Methods

The concept of a lens of equity was adopted to disaggregate common measures such as breast cancer screening rates to expose inequities across neighborhoods and races in populations served. Two measures were introduced into the corporate measurement systems, race/ethnicity as measured in the electronic health record, and a surrogate measure of family income.

Results

An equity category was added to system scorecards and counted toward corporate goals along with data insights and discovery tools to support the efforts of the breast cancer screening improvement teams. Over a 1-year timeframe, Endeavor Health not only met but exceeded its breast cancer screening equity goal, increasing mammography adherence from 73 to 82.6% among residents in the lowest-income neighborhoods served.

Conclusion

The analytics and data systems that support complex health care measurement tools require diligent and thoughtful design to meet external reporting requirements and support the internal teams who aim to improve the care of populations served. The analytic approach presented may be readily extended to populations with other potentially impactful differences in social determinants and health status. A “lens-of-equity” tool may be established along similar lines, allowing policy and strategy initiatives to be appropriately targeted and successfully implemented.

Keywords

health equity - breast cancer screening - measurement systems - quality improvement - lens-of-equity - implementation - clinical informatics - professional training - education - socio-organizational issues - clinical research informatics

Ethical Approval

This data-based study was deemed to be a nonhuman subjects, quality improvement project, and therefore exempt.

Authors' Contributions

All authors and coauthors contributed significantly to this study. D.J. and C.K. contributed to the conceptualization and methodologic design of the work; D.J., C.K., and A.S. contributed to data curation, formal analysis, and data visualization; D.J., C.K., and A.S. shared in drafting, critical review, and final approval of the manuscript.

Publication History

Received: 09 October 2024

Accepted: 25 May 2025

Accepted Manuscript online:
02 June 2025

Article published online:
07 November 2025

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