In 1973, an article entitled, “Does Race Interfere with the Doctor-Patient Relationship,” was published in the Journal of the American Medical Association. It pointed out systemic biases that impact the care for those in minority groups. Almost 50 years later, have we improved?
Though the article delineated a significant amount of attention that addresses health inequities and the underlying causes, we have made minimal progress when reviewing the data surrounding life expectancy, where there is still a significant gap that has not improved. Undoubtedly, the recent pandemic has magnified the situation of health disparities. Furthermore, what is more concerning is that as our country becomes more racially and ethnically diverse, these inequities are likely to worsen despite the shift in national demographics.
Therefore, to reverse this situation as a profession, we must create a structure that addresses and allows for change. This transformation will require leadership focus as well as adjustments regarding how we do our everyday jobs of delivering care. Prioritizing these issues in a structured format is essential to obtaining the optimal results. We must be aware and implement changes if we are truly differentiating our future from our past. We no longer have the luxury of just speaking to what we plan to do and instead we have to act.
Hence, as we focus on those, we ask to serve others and address health equity, it’s imperative to provide them with the relevant information needed to recognize the discrepancies and the self-development required that will drive change. We all have implicit and unintended biases. These are inherent to our nature, but we have to also remember, that our communities are trying to solve for their “dis-eases” of life, and therefore, our thinking must expand to a more holistic approach as we simultaneously attack health inequities.
Furthermore, our technologic processes must also adapt with a special focus on understanding health equities. For example, as we implement artificial intelligence and machine learning, are we asking the right questions? Will the implementation of such technologies worsen the situation since the current historical data has racial biases built into it? If we treat with inequities now, all future predictions based on present-day treatments will be even more problematic.
As we approach the 50th Anniversary of the publication, let us bolster our focus on our racial divide that impacts those we serve and our relationships with each other.