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The Journal of Applied Laboratory Medicine, 2020

DOI: 10.1093/jalm/jfaa143

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Recognizing Laboratory Medicine's Collaborative Role in Identifying and Eliminating Health Disparities

Journal article published in 2020 by Sarah E. Wheeler ORCID, Joanne H. Hasskamp, Octavia M. Peck Palmer
Distributing this paper is prohibited by the publisher
Distributing this paper is prohibited by the publisher

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Abstract

Abstract Background A health disparity is a health outcome that presents in a lesser or greater extent between populations. Health disparities in diseases are products of complex interactions between social, economic, and to a lesser extent, biological factors and can be mediated by structural racism and discriminatory policies. The objective of this review is to understand how both laboratorians and nonlaboratorians think about the relationship between laboratory medicine and health disparities and to highlight ways in which laboratory medicine can play a role in eliminating health disparities. Content We developed an electronic survey from which we selected the top responses reported by the 215 participants to frame a discussion around why laboratorians perceive health disparities exists, and how they can reduce health disparities Summary We found that both laboratorians and nonlaboratorians feel that laboratory medicine can and should play a role in reducing health disparities using many tools already in use in the clinical laboratory. The skills of laboratory workers in data generation, the establishment of reference ranges, control over the presentation of laboratory results, generation of test menus, and the development of novel diagnostics may impact health disparities. Laboratorians' responses in our survey indicated that they felt that they could reduce health disparities by using laboratory data to proactively track in cooperation with healthcare providers individuals with chronic conditions to prevent acute events, ensuring gender and ethnic diversity in new clinical trials, including appropriate curriculum in laboratory medicine training, using equations and reference intervals based on physiological differences and participating in unconscious bias training.