Episode 115

Is A.I. the Future of Healthcare?

with John Glaser, Ph.D., Kevin B. Mahoney, & Sean Lane

June 1, 2023

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John Glaser, Ph.D., Kevin B. Mahoney, & Sean Lane

This episode features three leaders previously interviewed on The Gary Bisbee Show. John Glaser, Ph.D., is an Executive in Resident at Harvard Medical School and recently published “Advanced Introduction to Artificial Intelligence in Healthcare.” Kevin B. Mahoney is chief executive officer of the University of Pennsylvania Health System, a pillar of the Penn Medicine enterprise. Sean Lane is the CEO of Olive and CEO of Circulo Health. Previously, he was an Intelligence Officer for the National Security Agency.


“Everyone's talking about ChatGPT and AI? Well, this is something we've been talking about for a while: how do we use it—not as a gimmick, but how do we use it to advance science?” – Kevin B. Mahoney

John Glaser, Ph.D., Kevin B. Mahoney, & Sean Lane Tweet




The Gary Bisbee Show has always been intentional about delivering content related to the most recent developments in Healthcare technology and leadership. 

In recent months, this commitment has included a consistent consideration of the latest in Artificial Intelligence and some incisive conversation about what this emerging technology is likely to mean for health systems and the new health economy.

With the recent rollout of ChatGPT, A.I. appears poised to move out beyond diagnostic algorithms and Excel spreadsheets and into every aspect of our lives. Experts, including those involved in the development of these technologies, are recommending intentional discussion about the ethics of A.I. – some even recently recommend a halt on the development of new A.I. research.

Today, we will hear from John Glaser, Ph.D., Executive in Residence at Harvard Medical School; Kevin B. Mahoney, chief executive officer of the University of Pennsylvania Health System; and Sean Lane, CEO of Olive and CEO of Circulo Health.

To begin, let’s listen as John Glaser describes the relationship between A.I. and the health technologies that depend on it.


John Glaser: Nobody buys AI, they buy something else that performs better because of AI. So you say I want some software to help me with utilization management or appointment scheduling or diagnostic imaging. And you’re the vendor you say i Yes, I have a I say, Well, why does that help it do any better? What makes the performance gains worthwhile? By they did today, I’d ask the same questions that I’ve always asked. You have a supplier telling trying to sell me some software solution. How do you know that works? Show me that it does all the kinds of stuff here. So nobody, nobody buys AI . And related to that is an algorithm is not a skill. motion, which is not a company. So someone might say I got the greatest algorithm on the planet, I’m happy for it. On the other hand, that doesn’t mean that it fits into the workflow well, so it may not be a solution, because it’s really the only thing that’s got to happen. And even if it is a solution, it doesn’t mean you’ve got a company that’s got legs, you know, and all this kind of stuff. There are hundreds of AI algorithms related to radiology. The poor radiologist says, I’m overwhelmed here, you know, and so none of them will get real traction, because there’s just too many. So remember, you buy something else that is better, you buy a car that is safer, because of AI, you buy an MRI that’s more reliable because of AI. And the question is, you look at more, what am I buy? Why, and what does it do?


The important insight here is that A.I.’s value must be understood in terms of its productive outcome—what value or service does it actually provide? Next, let’s hear from Kevin B. Mahoney, as he emphasizes the need to separate the positive utility of A.I. from what might be an element of gimmick.


Kevin Mahoney: Everyone’s talking about chat, GPT and AI? Well, this is something we’ve been talking about for a while, again, how do we use it? Not as a gimmick, how do we use it to advance science? And we need to have that discussion with our trainees have that discussion with our leadership. So we use AI down in the MRI suite to reduce the number of scans a patient contrast that a patient needs, because too much radiation over your lifetime causes all kinds of other. If AI can help us predetermine how to reduce the number of scans with contrast. We’re going to be making an impact on people’s lives. So cyborg operations, AI, one of my favorite topics is reverse logistics. And again, we’re used to patients driving to us finding a parking spot, sitting in the waiting room. And, and how do we be more like Amazon where we have to get product and a person to somebody’s house? And how do we do that? How do we get the next generation of physician leaders comfortable with monitoring seven patients remotely for diabetes, as opposed to one in their, in their waiting room? And so I think we have a moral obligation? I think the hard part is, we all think our generation has it, right. We’re talking about the trainees at Penn, our younger than my children and their digital natives, you know, they’ve learned different they’ve had a different context and meaning meeting them where they are, but also trying to push them to think of a new healthcare system, not the one that I grew up in.


This is a profound insight, about the need for inter-generational communication and an intentional deliberation about what we want healthcare to look like in the future. Now, let’s hear from Sean Lane, whose company, Olive, is already deploying A.I. to streamline workflow and free up capacity:
Sean Lane: I’m a bit old school on the on AI in that, you know, I kind of take the Turing methodology of AI, which is an artificial intelligence is something that’s hard to distinguish from a human. In fact, the the quality of AI is often measured by a thing called the Turing test, which is your inability to tell it apart from a human. But there’s other pieces that are important machine intelligence is kind of the other half of AI, which involves things like machine learning and deep learning. Sometimes those tools are used when creating an artificial intelligence. And sometimes they’re not. At all we do both we do AI, we have these AI workers that are difficult to tell apart from a human. But we also use machine intelligence machine and deep learning to do a lot of the heavy lifting. In fact, all of our tools that that do basic automations are actually built on. Neural networks, called convolutional neural networks are CNNs, which allow us to kind of understand the software of healthcare. And it’s a constantly learning model…

We have to really think about how we use AI to free up human capacity. Because really, we have a human capacity problem in healthcare. There’s just not enough people. And we’re wasting too much of people’s time, on a lot of the things that we should be relying on artificial intelligence to do.
Each of these leaders understands that the A.I. revolution can improve the overall quality of healthcare if caregivers and leadership think intentionally about each new iteration, and make sure that A.I. is deployed in a way that keeps it aligned with the needs of consumers. The opportunity presented by the introduction of A.I. is vast—and will be positive, if leaders make wise choices about how it will be used. 

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