Collecting race data in medical settings improves healthcare delivery
Collecting and analyzing patient race data in medical settings is widely recognized across the literature as a foundational step for identifying systemic health disparities, guiding targeted interventions, and ultimately improving healthcare delivery for diverse populations.
The retrieved papers consistently demonstrate that tracking patient race and social determinants of health is a crucial diagnostic tool for uncovering disparities in care and treatment outcomes (such as in ophthalmology, neurology, cardiovascular care, and oncology). While collecting data alone does not automatically cure disparities, the literature overwhelmingly shows that it is a prerequisite for identifying inequities and designing equity-focused improvements, thus supporting the claim.
Elena M. Solli, Christina R. Prescott. Impact of Patient Race/Ethnicity on Premium Intraocular Lens Utilization. 2024. https://doi.org/10.1097/icl.0000000000001112
Retrospective chart review demonstrates that collecting and stratifying data by patient race/ethnicity successfully uncovers significant disparities in the utilization of premium intraocular lenses.
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Yarden S. Fraiman, Jeannette C. Myrick, Caroline Kagan, Dina Beauchamp, Peter Blades, Rachel Copertino, Elysia Larson, Cicely Fadel. Creating the Healthcare Racial Justice Assessment Tool (HC-RJAT): A Novel Tool to Identify Modifiable Loci of Structural Racism in the Healthcare Setting to Guide Equity-Focused Improvements. 2025. https://doi.org/10.1007/s40615-025-02516-4
Development of the Healthcare Racial Justice Assessment Tool highlights that identifying racial inequities in healthcare settings, which depends on race data collection, is a necessary precursor to equity-focused structural improvements.
Yuan S, Hou J, Yang X. Social Determinants of Health, Nursing Care Quality, and Patient Outcomes in Neurological Disorders: A Systematic Review.. 2026. https://doi.org/10.2147/rmhp.s597958
A systematic review on social determinants of health and neurological care quality emphasizes that systematic screening and disaggregated demographic data are essential to address racial and ethnic inequities in health outcomes.
Joshi U, Lanzas C. Exploring opportunities to improve health equity with machine learning and artificial intelligence in healthcare epidemiology.. 2026. https://doi.org/10.1017/ash.2026.10747
An overview of AI/ML in healthcare epidemiology notes that improving electronic health record data collection regarding race and ethnicity is crucial for identifying vulnerable populations and guiding equitable interventions.
Delisle H, Ingabire A, Søvold L, Vissandjee B. Interventions for health equity with a One Health focus: a review of reviews.. 2026. https://doi.org/10.3389/fpubh.2026.1736987
A scoping review on health equity actions underscores the recurrent need for disaggregated data to evaluate and address disparities in service delivery.
Tasdighi E, Jacob J, Patel K, Kulkarni A. Bridging the Gap: Multidisciplinary Decision Making to Address Systemic Barriers in Cardiovascular Care.. 2026. https://doi.org/10.1007/s11886-026-02375-3
A review on cardiovascular care notes that standardizing social risk and demographic data collection is an essential foundational step to reduce disparities in cardiovascular outcomes.
Sarah Sertich, Rina Yadav, John L. Villano. Patient-reported outcomes, race, and ethnicity in high-impact lung cancer clinical trials.. 2025. https://doi.org/10.1200/jco.2025.43.16_suppl.e23015
An analysis of lung cancer clinical trials shows that tracking and reporting participant race/ethnicity is vital for identifying under-enrollment of minority populations and aligning trials with patient demographics.
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