Abstract

Precision medicine 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. We analyse three nodes of a process flow where structural racism can affect PM’s implementation. These are: the collection of biased health data during the initial encounter of minority groups with the healthcare system and researchers, the integration of biased health data for minority groups in PM initiatives and 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. 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.