The pursuit of global health equity becomes more important by the day as humanitarian needs reach record levels. Last year, the World Health Organization found Half of the world’s population lacks access to essential health services, and 2 billion people face out-of-pocket financial hardship.
These disparities are not new, but they are widening, especially when major obstacles such as pandemics and climate change make solving them even more challenging. Advances in technology promise to improve health outcomes and increase access, but how can we get there?
Professor of Biology and Biotechnology Karen Oates is also the director of the new film.Global Health Degree Program Joined GBH after graduating from Worcester Polytechnic Institute Taking everything into account Moderated by Arun Rath, we discuss how to ensure new technology is used effectively and ethically. What follows is a lightly edited transcript of their conversation.
Arun Rath: Starting with a big-picture question, can you tell us about what global health equity currently looks like and how it has changed over the past decade?
Karen Oates: of course. I think it’s important to understand the difference between public health and global health. Global health is really concerned with health issues that go beyond many countries – big problems.
While public health may be geographically located within specific elements, global health actually looks at these big, broad, interdisciplinary questions and asks who is affected and why. It’s about understanding who is affected and what we can do to alleviate some of these health issues. Inequality around the world.
Russ: So are issues like pandemics and climate change truly global issues?
Oats: That’s definitely something we have to think about here in the United States, even things like water and nutrition that affect the health of the planet as a whole.
Russ: As I mentioned earlier, Worcester Polytechnic Institute will soon be opening a master’s program focused on global public health that you will lead. You mentioned that the program’s curriculum will focus on how the future of global health lies at the intersection of technology, science, and humanity. Can you talk about that?
Oats: I think we’re really evolving in terms of understanding health inequities and how to reduce them. We are able to do this primarily because artificial intelligence, machine learning, epidemiology, and large databases are at the fingertips of scientists around the world.
What we’re trying to do is connect patterns that are different causes of inequality and find the patterns, the connections between them. It’s like triangulating different kinds of problems that make it very difficult for individual researchers to identify the root cause. It will take years, but thanks to machine learning and artificial intelligence, actually trying to solve some of the world’s biggest problems will take a fraction of the time it used to.
Russ: You talk about triangulation in the same way as solving a problem by approaching it from multiple perspectives. How can AI and machine intelligence help make that happen?
Oats: What we’re doing is creating a database that looks at specific populations that are influenced by heavy metal, for example. You can then see where different factories are located geographically. That way, you can check the water flow and well water in the area. Is it a number of parameters that we can start assembling? What is the cause? Where does the cause come from? And ultimately, what can we do about it? All of this is powered by AI. We are compiling these large databases to solve such problems.
Russ: Gaps in data can also reflect bias when talking about underserved and under-resourced communities that are often disproportionately affected and excluded from conversations, and artificial intelligence I understand that this is a data concern. How can you implement this technology to ensure everything is considered?
Oats: That’s an important question on several levels. One is who owns the data? Where is the data coming from? What are the biases of the researchers inputting the data? But in reality, more importantly, when we get that data, is it transparent? I think that’s true.
I think one of the great things about this program is that we pay a lot of attention to the fact that it’s field work and that the people who are involved are the people who are impacting the program. Masu. number one. WPI is really lucky. We have been installing beautiful project centers all over the world for over 40 years. We will take advantage of this and invite local people to participate in any research we undertake.
Russ: The data collection part seems to be a fairly large-scale human task. How do we scale up to that?
Oats: We want to be able to go out there and collect data ourselves, in the field and with the help of people in the field. We want them to be part of solving problems. We don’t want to say, “This is your problem.” We want them to be involved, and in doing so, we are asking them to be partners in everything we do.
Russ: It’s amazing to hear how this combination of very basic human data collection and very sophisticated machine intelligence is done.
Oats: exactly. One thing the global health evolution is telling us is that AI and machine learning are all at a new level. Even something as simple as a water filtration system can now be designed on site. We can design using materials found in a country and the talents of its people.
Russ: How far are we now from having all the data you want to put this kind of solution into action?
Oats: I think it’s very circumstantial. For example, I think we’re getting pretty close to using geographic information systems to identify water sources. If you look at things like metal poisoning and its effects on children, you can build a pretty good database on that.
We are still at the stage of accumulating data, but this is evolving very quickly. The future of global health is actually going to be able to identify the right data sources, go out in the field, create and validate what’s in the database, and start making connections.
We are really in the early stages when it comes to big data machine learning. This program from WPI uses AI to connect and find patterns. This is extremely difficult without the use of big data and artificial intelligence. We use that data to help define and understand global issues.