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Houston expert explains health care's inequity problem

"Better and personalized healthcare through AI is still a hugely challenging problem that will take an army of scientists and engineers." Photo via UH.edu

We are currently in the midst of what some have called the "wild west" of AI. Though healthcare is one of the most heavily regulated sectors, the regulation of AI in this space is still in its infancy. The rules are being written as we speak. We are playing catch-up by learning how to reap the benefits these technologies offer while minimizing any potential harms once they've already been deployed.

AI systems in healthcare exacerbate existing inequities. We've seen this play out into real-world consequences from racial bias in the American justice system and credit scoring, to gender bias in resume screening applications. Programs that are designed to bring machine "objectivity" and ease to our systems end up reproducing and upholding biases with no means of accountability.

The algorithm itself is seldom the problem. It is often the data used to program the technology that merits concern. But this is about far more than ethics and fairness. Building AI tools that take account of the whole picture of healthcare is fundamental to creating solutions that work.

The Algorithm is Only as Good as the Data

By nature of our own human systems, datasets are almost always partial and rarely ever fair. As Linda Nordling comments in a Nature article, A fairer way forward for AI in healthcare, "this revolution hinges on the data that are available for these tools to learn from, and those data mirror the unequal health system we see today."

Take, for example, the finding that Black people in US emergency rooms are 40 percent less likely to receive pain medication than are white people, and Hispanic patients are 25 percent less likely. Now, imagine the dataset these findings are based on is used to train an algorithm for an AI tool that would be used to help nurses determine if they should administer pain relief medication. These racial disparities would be reproduced and the implicit biases that uphold them would remain unquestioned, and worse, become automated.

We can attempt to improve these biases by removing the data we believe causes the bias in training, but there will still be hidden patterns that correlate with demographic data. An algorithm cannot take in the nuances of the full picture, it can only learn from patterns in the data it is presented with.

Bias Creep

Data bias creeps into healthcare in unexpected ways. Consider the fact that animal models used in laboratories across the world to discover and test new pain medications are almost entirely male. As a result, many medications, including pain medication, are not optimized for females. So, it makes sense that even common pain medications like ibuprofen and naproxen have been proven to be more effective in men than women and that women tend to experience worse side effects from pain medication than men do.

In reality, male rodents aren't perfect test subjects either. Studies have also shown that both female and male rodents' responses to pain levels differ depending on the sex of the human researcher present. The stress response elicited in rodents to the olfactory presence of a sole male researcher is enough to alter their responses to pain.

While this example may seem to be a departure from AI, it is in fact deeply connected — the current treatment choices we have access to were implicitly biased before the treatments ever made it to clinical trials. The challenge of AI equity is not a purely technical problem, but a very human one that begins with the choices that we make as scientists.

Unequal Data Leads to Unequal Benefits

In order for all of society to enjoy the many benefits that AI systems can bring to healthcare, all of society must be equally represented in the data used to train these systems. While this may sound straightforward, it's a tall order to fill.

Data from some populations don't always make it into training datasets. This can happen for a number of reasons. Some data may not be as accessible or it may not even be collected at all due to existing systemic challenges, such as a lack of access to digital technology or simply being deemed unimportant. Predictive models are created by categorizing data in a meaningful way. But because there's generally less of it, "minority" data tends to be an outlier in datasets and is often wiped out as spurious in order to create a cleaner model.

Data source matters because this detail unquestionably affects the outcome and interpretation of healthcare models. In sub-Saharan Africa, young women are diagnosed with breast cancer at a significantly higher rate. This reveals the need for AI tools and healthcare models tailored to this demographic group, as opposed to AI tools used to detect breast cancer that are only trained on mammograms from the Global North. Likewise, a growing body of work suggests that algorithms used to detect skin cancer tend to be less accurate for Black patients because they are trained mostly on images of light-skinned patients. The list goes on.

We are creating tools and systems that have the potential to revolutionize the healthcare sector, but the benefits of these developments will only reach those represented in the data.

So, what can be done?

Part of the challenge in getting bias out of data is that high volume, diverse and representative datasets are not easy to access. Training datasets that are publicly available tend to be extremely narrow, low-volume, and homogenous—they only capture a partial picture of society. At the same time, a wealth of diverse health data is captured every day in many healthcare settings, but data privacy laws make accessing these more voluminous and diverse datasets difficult.

Data protection is of course vital. Big Tech and governments do not have the best track record when it comes to the responsible use of data. However, if transparency, education, and consent for the sharing of medical data was more purposefully regulated, far more diverse and high-volume data sets could contribute to fairer representation across AI systems and result in better, more accurate results for AI-driven healthcare tools.

But data sharing and access is not a complete fix to healthcare's AI problem. Better and personalized healthcare through AI is still a hugely challenging problem that will take an army of scientists and engineers. At the end of the day, we want to teach our algorithms to make good choices but we are still figuring out what good choices should look like for ourselves.

AI presents the opportunity to bring greater personalization to healthcare, but it equally presents the risk of entrenching existing inequalities. We have the opportunity in front of us to take a considered approach to data collection, regulation, and use that will provide a fuller and fairer picture and enable the next steps for AI in healthcare.

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Angela Wilkins is the executive director of the Ken Kennedy Institute at Rice University.

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Building Houston

 
 

Fertitta and his family have gifted $50 million to UH's medical school. Photo courtesy

As Houston’s most high-profile billionaire and owner of the posh 5-star Post Oak Hotel and Houston Rockets, Tilman J. Fertitta has become synonymous with over-the-top opulence and big-time entertainment.

But the CEO of the massive Feritta Entertainment empire’s latest move has nothing to do with penthouses or point guards, but rather a legacy, game-changing appropriation meant to aid his home state’s health.

The longtime UH board member and former chairman and his family have just pledged $50 million to the University of Houston College of Medicine. In turn, the new medical school has been christened the Tilman J. Fertitta Family College of Medicine.

The projected school, upon completion. Rendering courtesy of University of Houston

This landmark gift aims to address the state’s critical primary care physician shortage, (especially in low-income and underserved communities), as well as attract innovation-focused scholars, UH notes.

Additionally, the grant is meant to further clinical and translational research, with an emphasis on population health, behavioral health, community engagement, and the social determinants of health, according to a press release.

Here is how the Fertitta family gift will be distributed:

  • $10 million funds five endowed chairs for faculty hires who are considered national stars in their fields with a focus on health care innovation. This portion of the gift will be matched one-to-one as part of the University’s “$100 Million Challenge” for chairs and professorships, doubling the endowed principal to $20 million.
  • $10 million establishes an endowed scholarship fund to support endowed graduate research stipends/fellowships for medical students.
  • $10 million will cover start-up costs for the Fertitta Family College of Medicine to enhance research activities including facilities, equipment, program costs and graduate research stipends/fellowships.
  • $20 million will create the Fertitta Dean’s Endowed Fund to support research-enhancing activities.

No stranger to writing big checks, Fertitta donated $20 million to UH Athletics — the largest individual donation ever — in 2016 to transform UH’s basketball arena into the now high-tech Fertitta Center.

CultureMap caught up with the CEO (who just sold his Golden Nugget gaming for $1.6 billion), best-selling author, and Billion Dollar Buyer to discuss his landmark gift.

CultureMap: Congratulations on this legacy grant, which has been a long time coming. What does this gift mean to you, now that it’s finally official?

Tilman Fertitta: This was a vision of our chancellors and, you know, I’m on my third, six-year term and not been the chairman for eight years — and we started working on this, seven, eight years ago.

To be able to be in the beginning and the nucleus, and the idea, and what we wanted, and to get the approval from Austin—to watch it come to fruition, how often does somebody get to do a naming gift at the same time they had a lot to do with the creation of the school? So, it was very special in my heart.

CM: Many know you as the CEO of a hospitality empire, author, and even TV personality. But not many know of your commitment to healthcare.


TF: I think there’s one thing in this world that we definitely should always be treated equally on, and that's that’s equal health care for all. This medical school will serve the whole community.

We’re trying to recruit students who want to be primary physicians who will take care of the community that we live in. It’s just something that was very important to me in my whole family.

CM: Academia, scholarship, and research aside, this could essentially be looked at as seed capital for a fledgling operation. Is that a fair assessment?

TF: I know where you’re going with this and yes, it’s no different than business.

I have the vision to know that being in nearly the third largest city in America and a top 100 university in the United States — as University of Houston is according to U.S. News & World Report — that I know what this is going to be in 50 years. It’s no different than looking at another business that you start and you can have the vision to see how successful it'll be in the years to come.

Being on the ground floor of the University of Houston Medical School and being a part of it from its inception, and to help the seed money that will attract other money, I know that in the years to come what a special nationwide medical school this is going to be — because it’s in one of the great cities of America.

So, to be a part of it today and still be a part of it when I’m not here 50 years from now, maybe even sooner than that [laughs], you know, it’s going to be something very special to always be attached to.

CM: Other Houston medical schools here have distinctions in pivotal research or groundbreaking procedures. Is there a specific direction you’d like UH Med to take, going forward?

TF: Honestly, you know, what I’ve been saying? There’s a significant shortage of primary care physicians, not only in the country, but in the state of Texas. We ranked number 47th in the nation.

What we need in the state of Texas, as well in Houston and everywhere, is primary care physicians to take care of your everyday people—and to see them to know if you need a specialist.

I hope that this medical school looks back and we see that they’re graduating more primary care physicians than any other university in the United States and that's our goal. We’re going to be a med school of the community.

CM: You have zero problem with issuing directives, Tilman. What’s your message to the first graduating class, the one that will initially benefit from this $50 million gold mine?

TF: Go out and take care of the people.

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This article originally ran on CultureMap.

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