Inequities and Biases in Global Healthcare

Social and economic factors play a very important role in how people live, both within and outside the health care system. These factors include demographic characteristics such as race, and socioeconomic factors such as education, income, housing, transportation, food insecurity etc.

In addition we see that there is an inherent bias within the health care system—about the uneven access, poor quality, and at times nonexistent care experienced by racial minorities.

We want to learn from you the key barriers restraining policy makers and health care providers to pay attention to social determinants of health and their effects on health outcomes.

Hi @Hlantum and @paulauerbach - Given your vast experience, you might have inputs to share on the inequities and biases in global healthcare.

Hi @ajchenx and @ymedan - We would love to hear your thoughts on the inequities and biases in global healthcare.

I don’t have a global perspective. I can only comment regarding the impact of technology on inequality.

As we move towards precision medicine, we increase inequality because both precise diagnostics (e.g. genomics-based) and treatments (e.g. CAR-T) are expensive and beyond the reach of most people.

The only sustainable approach would be to educate for health resilience and offer community-based resources and services to promote health.

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Hi @sahoo00 and @dhart - What is your take on the inequities and biases in global healthcare.

Hi @jkprivateoffice and @JameyEdwards,
As founders of health centers and your vast experience in the field, curious to know your thoughts on the inequities and biases in global healthcare.

Hi @mfree and @Agent_Aslan,
What are your thoughts on the inequities and biases in global healthcare.

Hi @mitjal and @KarenBett,
Given your experience in this field, you might have thoughts to share on the inequities and biases in global healthcare. We would love to have your opinion. Thanks.

Sorry, I do not know much about this.

Cheers, Mitja

It’s been great to see health equity take more of a front row seat recently in the US Healthcare. We are seeing the evolution of new roles in health systems centered around ensuring people have equitable access to inclusive care.


We need to take a more global look at “healthcare” and realize that it is more than the act of being treated in a healthcare facility or even telemedicine. A leading indicator of how healthy you are is actually your zip code. It gives you a perspective on the importance of geography, social status, education and other social determinants in terms of impact on health and wellness, many of which are out of control of the patient.

Globally similar bias exists.

When it comes to technology, recently there was research around skin pigmentation and how it affects pulse ox ( data.

We know that humans are biased and therefore our data is biased, so if we train an AI using that data and not adjusting for the bias, you end up with a biased AI.

During COVID, we saw the rapid adoption of telemedicine, but most platforms ignored the fact that they had patients whose first language wasn’t English or who were deaf and hard of hearing. The sites weren’t translated and then interpreters were not available on these calls. The result was LEP and deaf patients not using telemedicine as much as their english speaking counterparts. We led the way at Cloudbreak in terms of integrating our services onto those platforms to solve these types of issues.(

Resolving disparities and promoting health equity is a multi-pronged strategy taking a holistic view on what impacts these biases. From addressing food and medical deserts, to improving education, to increasing access to culturally competent care in multiple languages…to understanding that technology if not managed correctly can actually increase the digital divide instead of bridging it (need broadband and smartphone access for all).

Thinking about potential breakthrough solutions XPRIZE may be able to incentivize, I’d like to propose splitting this into two:

The first is your zip code predicting your health. That’s probably the most difficult one to solve, and would require policy more than technological changes? I imagine potential technological solutions would be in the areas of access and health education?

I’m more optimistic we could solve the second problem - biases in data and design - by 2040. Indeed, it sounds like you’re already working on that!

@EVSwanson, @sarahb, @adanvers, @areff2000, @stellunak, @AsaEkvall, @ymunoz70, and @BrendaMurphy, I remember you advised on our Gender Data Gap Prize Design. You may also have thought on this discussion. What are the problems we should be solving for, and how?