Literature DB >> 32075640

Structural racism in precision medicine: leaving no one behind.

Lester Darryl Geneviève1, Andrea Martani2, David Shaw2,3, Bernice Simone Elger2,4, Tenzin Wangmo2.   

Abstract

BACKGROUND: Precision medicine (PM) is an emerging approach to individualized care. It aims to help physicians better comprehend and predict the needs of their patients while effectively adopting in a timely manner the most suitable treatment by promoting the sharing of health data and the implementation of learning healthcare systems. Alongside its promises, PM also entails the risk of exacerbating healthcare inequalities, in particular between ethnoracial groups. One often-neglected underlying reason why this might happen is the impact of structural racism on PM initiatives. Raising awareness as to how structural racism can influence PM initiatives is paramount to avoid that PM ends up reproducing the pre-existing health inequalities between different ethnoracial groups and contributing to the loss of trust in healthcare by minority groups. MAIN BODY: We analyse three nodes of a process flow where structural racism can affect PM's implementation. These are: (i) the collection of biased health data during the initial encounter of minority groups with the healthcare system and researchers, (ii) the integration of biased health data for minority groups in PM initiatives and (iii) the influence of structural racism on the deliverables of PM initiatives for minority groups. We underscore that underappreciation of structural racism by stakeholders involved in the PM ecosystem can be at odds with the ambition of ensuring social and racial justice. Potential specific actions related to the analysed nodes are then formulated to help ensure that PM truly adheres to the goal of leaving no one behind, as endorsed by member states of the United Nations for the 2030 Agenda for Sustainable Development.
CONCLUSION: Structural racism has been entrenched in our societies for centuries and it would be naïve to believe that its impacts will not spill over in the era of PM. PM initiatives need to pay special attention to the discriminatory and harmful impacts that structural racism could have on minority groups involved in their respective projects. It is only by acknowledging and discussing the existence of implicit racial biases and trust issues in healthcare and research domains that proper interventions to remedy them can be implemented.

Entities:  

Keywords:  Ethics, research; Healthcare inequalities; Precision Medicine; Racial bias; Racial discrimination; Social justice

Year:  2020        PMID: 32075640     DOI: 10.1186/s12910-020-0457-8

Source DB:  PubMed          Journal:  BMC Med Ethics        ISSN: 1472-6939            Impact factor:   2.652


  12 in total

1.  Implicit bias in healthcare: clinical practice, research and decision making.

Authors:  Dipesh P Gopal; Ula Chetty; Patrick O'Donnell; Camille Gajria; Jodie Blackadder-Weinstein
Journal:  Future Healthc J       Date:  2021-03

2. 

Authors:  Susan P Phillips; Sheryl Spithoff; Amber Simpson
Journal:  Can Fam Physician       Date:  2022-08       Impact factor: 3.025

3.  Artificial intelligence and predictive algorithms in medicine: Promise and problems.

Authors:  Susan P Phillips; Sheryl Spithoff; Amber Simpson
Journal:  Can Fam Physician       Date:  2022-08       Impact factor: 3.025

4.  Parental Attitudes toward Artificial Intelligence-Driven Precision Medicine Technologies in Pediatric Healthcare.

Authors:  Bryan A Sisk; Alison L Antes; Sara Burrous; James M DuBois
Journal:  Children (Basel)       Date:  2020-09-20

5.  Why "sex as a biological variable" conflicts with precision medicine initiatives.

Authors:  Marina DiMarco; Helen Zhao; Marion Boulicault; Sarah S Richardson
Journal:  Cell Rep Med       Date:  2022-04-19

6.  Ensuring machine learning for healthcare works for all.

Authors:  Liam G McCoy; John D Banja; Marzyeh Ghassemi; Leo Anthony Celi
Journal:  BMJ Health Care Inform       Date:  2020-11

7.  The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations.

Authors:  Quynh Pham; Anissa Gamble; Jason Hearn; Joseph A Cafazzo
Journal:  J Med Internet Res       Date:  2021-02-10       Impact factor: 5.428

8.  Racial and Ethnic Inequities in Mortality During Hospitalization for Traumatic Brain Injury: A Call to Action.

Authors:  Emma A Richie; Joseph G Nugent; Ahmed M Raslan
Journal:  Front Surg       Date:  2021-06-02

Review 9.  Big Data and Digitalization in Dentistry: A Systematic Review of the Ethical Issues.

Authors:  Maddalena Favaretto; David Shaw; Eva De Clercq; Tim Joda; Bernice Simone Elger
Journal:  Int J Environ Res Public Health       Date:  2020-04-06       Impact factor: 3.390

Review 10.  Labels in a haystack: Approaches beyond supervised learning in biomedical applications.

Authors:  Artur Yakimovich; Anaël Beaugnon; Yi Huang; Elif Ozkirimli
Journal:  Patterns (N Y)       Date:  2021-12-10
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