With each passing day, the pursuit of global health equity becomes more critical as humanitarian needs reach record-level highs. Last year, the World Health Organization found that half the world’s population is not fully covered by essential health services, and two billion people are facing economic hardship due to out-of-pocket spending.
Those gaps aren’t new, but they are growing—especially when monumental obstacles like pandemics and climate change make solving inequity an even bigger challenge. We’re told that advancing technology holds the promise of improving health outcomes and access, but how do we get there?
Biology and biotechnology Professor Karen Oates, who is also directing a newglobal health degree program at Worcester Polytechnic Institute, joined GBH’s All Things Considered host Arun Rath to discuss how to make sure new technology is used effectively and ethically. What follows is a lightly edited transcript of their conversation.
Arun Rath: To start off with a big-picture question, can you give us a sense of what global health equity looks like now and how it’s changed over the last decade?
Karen Oates: Sure. I think it’s important for us to understand the difference between public health and global health. Global health is really interested in the health issues that are transcendent against many different countries—big problems.
Although public health may be geographically located within a certain element, global health is really looking at those big, broad, interdisciplinary problems and getting an understanding of who is affected, why they are affected and what we can do to help alleviate some of these health inequities around the globe.
Rath: Hence, problems like pandemics and climate change, which are truly global problems?
Oates: Absolutely, and even some things we have to think about here in the United States—things like water or nutrition, things that affect the overall health of our planet.
Rath: As I mentioned, the Worcester Polytechnic Institute is soon going to have a master’s program that you’ll be leading that focuses on global public health. You said the program’s curriculum will be focused on how the future of global health lies at the intersection of technology, science and humanity. Can you talk about that?
Oates: I think that we’re really at an evolution in terms of health inequities and understanding how to alleviate them. We can do this primarily because artificial intelligence, machine learning, epidemiology and big databases are now almost at the fingertips of scientists worldwide.
What we plan to do is connect patterns, different sources of inequities and find those patterns—the connections between them. It’s almost like a triangulation of different types of problems that would be so difficult for us to identify the root cause of by individual researchers. It would take years, but with machine learning and artificial intelligence, the amount of time it’s taking us to really try to solve some of the world’s biggest problems, it’s going to take us a fraction of the time it used to.
Rath: You talk about triangulating like solving a problem by coming at it from multiple perspectives. How does AI and machine intelligence help you do that?
Oates: What we’re doing is we’re creating, say, a database that looks at a particular population that is affected by, say, heavy metal. Then, we can look at where different factories may be geographically. We can then look at water flow or even well water in that area. Is that the number of parameters that we can start to put together? What could be the cause? Where could the cause come from? And, ultimately, what can we do about it? This is all being empowered by AI. We’re putting together those large databases to solve that kind of problem.
Rath: When talking about underserved and resource-poor communities that are often disproportionately affected and often excluded in these conversations, I know it’s also a concern with artificial intelligence and data that there can be biases reflected in gaps in data. How do we implement this technology in a way that makes sure we are taking everything into account?
Oates: That’s an important question on several levels. One is: who’s data? Where’s the data coming from? What are the biases of the researcher who’s putting the data in? But I actually think what’s more important is that when we get that data, that data is transparent.
I think one of the wonderful things about this program is we’re paying a lot of attention to the fact that it’s going to be on-site, in-the-field work, and the people who are involved are the people who it affects the most. WPI is really lucky. We have had for over 40 years beautiful project centers around the world, and we’re going to take advantage of that by bringing in individuals from the region to be part of whatever studies we undertake.
Rath: It sounds like the data collection part of that is a pretty big human undertaking. How do you scale up for that?
Oates: We’d love to be able to go into a country and collect the data ourselves with the help of the individuals on-site and in the field. We want them to be part of solving the problem. We don’t really want to come in and say, “This is your problem.” We want them to be involved, and when we do that, we’ll have them be partners with us in everything we do.
Rath: It’s kind of amazing just hearing you talk about it—how there’s this combination of very basic-level human data gathering, along with really high-level machine intelligence.
Oates: That’s correct. The one thing that the evolution of global health is telling us is that AI and machine learning are all at a new level. We can now design things in the field, even something as simple as a water filtration system. We can design using the materials you find in a country with the talents of the individuals there.
Rath: How far away are we right now from being in a place where we are getting all of the data that you would like to see to be able to start putting these kinds of solutions into effect?
Oates: I think that’s very situational. I think when it comes to looking at, say, the use of a Geographic Information System for identifying water sources, we’re getting pretty close. We’re able to create pretty good databases on that when we’re looking at something like metal poisoning and its effects on children.
We’re still in the accumulating data stage, but this has evolved really quickly. The future of global health is going to really be about being able to identify good data sources, going out into the field, creating and verifying what you have in the databases, and then starting to connect.
When it comes to big data machine learning, we’re at the very beginning. This program at WPI uses AI to connect and find patterns, which would be very difficult for us to see without using big data and artificial intelligence. We’re using that data to help define and to help understand global problems.