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Speaker 1: On this episode of News World. How is artificial intelligence

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Speaker 1: going to impact healthcare? Many experts have indicated AI may

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Speaker 1: change the way we diagnose disease, manage medical records, and

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Speaker 1: review medical data. High powered artificial intelligence and machine learning

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Speaker 1: are enabling scientists and doctors to create and analyze vast

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Speaker 1: amounts of data and to develop new molecularities to disrupt

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Speaker 1: targeted diseases. My guest today believes we are in the

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Speaker 1: cost of really significant medical breakthroughs. I'm really pleased to

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Speaker 1: welcome my guest, doctor Marshall Rangey. He is the former

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Speaker 1: executive Dean of the University of North Carolina School of Medicine,

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Speaker 1: is currently Executive vice president for Medical Affairs and Dean

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Speaker 1: of the Medical School for the University of Michigan, and

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Speaker 1: his most recent book is a techno medical thriller entitled

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Speaker 1: Coded to Kill. Marshall, thank you for joining me. I

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Speaker 1: think this is very cool.

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Speaker 2: It's a privilege to be on your show. Thank you

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Speaker 2: very much.

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Speaker 1: Was it a big shock moving from UNC to Michigan.

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Speaker 3: Well, I had to change sports. You'd see all basketball,

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Speaker 3: no football. Here is Maine, all football, no basketball, although

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Speaker 3: there's been some good teams.

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Speaker 1: Although Michigan at times has had very good men's basketball teams.

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Speaker 1: I was just curious. I thought that'd be almost a

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Speaker 1: culture shock. Now, in a piece you published in Real

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Speaker 1: Clear Health entitled We're on the Cusp of a Historic

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Speaker 1: Epoch of Discovery, you describe the medical breakthroughs that we're

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Speaker 1: experiencing right now. Will you discuss some of them?

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Speaker 2: Sure?

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Speaker 3: The AI movement's been termed a Promethean moment, meaning you

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Speaker 3: know everything's going to change about everything. I tend to

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Speaker 3: be more or less a believer of that. I'll just

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Speaker 3: give you a couple of examples. One has to do

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Speaker 3: you mentioned with drug discovery, and in the past, if

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Speaker 3: you had a great target that looked like it was

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Speaker 3: going to cure or significantly positively impacted the disease, you'd

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Speaker 3: get one hundred medicinal chemists working on creating a molecule,

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Speaker 3: and that process itself much less testing would take years. Now,

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Speaker 3: and I'm familiar with a drug discovery project which was

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Speaker 3: done using AI, taking a big database of drugs, loading

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Speaker 3: training the AI algorithm, and over about three days it

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Speaker 3: identified about five molecules to treat a disease that was

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Speaker 3: virtually untargetable today, and all five of these turned out

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Speaker 3: to be have potency and a couple of more compounds

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Speaker 3: unknown compounds. So it's almost like thinking back to natural products,

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Speaker 3: where you dig something up out of the soil, you

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Speaker 3: find a new antibiotic. So it finds things that I

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Speaker 3: find incomprehensible how it brings all that data together, but

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Speaker 3: it really does. And so if you think drug discovery,

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Speaker 3: I think that's awfully important because when we think about healthcare,

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Speaker 3: we have so many diseases that are difficult to treat.

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Speaker 3: Some are rare diseases, rare genetic diseases. I mean, one

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Speaker 3: of the biggest problems is type two diabetes, are Alzheimer's

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Speaker 3: and these are very difficult to treat diseases that I

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Speaker 3: think diabetes has gotten better, but I think we'll have

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Speaker 3: solutions for in the coming few years.

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Speaker 1: When you say a few years, are we talking a decade,

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Speaker 1: two decades and what are you thinking?

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Speaker 3: I'm thinking less than that, less than five years, maybe

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Speaker 3: as soon as two or three years. Because another approach

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Speaker 3: to drug discovery, which can be aided tremendously by AI

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Speaker 3: is using RNA therapies, inhibitory RNA therapies, and they also

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Speaker 3: can be developed very quickly in a very targeted way.

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Speaker 1: It's just kind of stress our regulatory system.

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Speaker 3: For sure, in a lot of different ways. So as you,

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Speaker 3: I'm sure know much more than I do about it.

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Speaker 3: Some of these new therapies are enormously expensive, so how

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Speaker 3: can we afford them? And that's where I'd like to

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Speaker 3: flip over to thinking about AI and healthcare. We have

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Speaker 3: so far to go in terms of reducing the cost

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Speaker 3: of healthcare, and we desperately need to do that. So

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Speaker 3: there are currently some small companies out there that enable

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Speaker 3: the use of an AI bought a one more place

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Speaker 3: a doctor or a nurse, but is available twenty four

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Speaker 3: to seven that can get far along in diagnosis. And

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Speaker 3: then if you take that along with some new technologies.

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Speaker 3: There was a I thought a fascinating paper in the

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Speaker 3: Washington Post about Alzheimer's disease where five different groups describe

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Speaker 3: five different ways of early diagnosis of Alzheimer's, recognizing patterns

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Speaker 3: that as people. As humans, the most brilliant humans can't

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Speaker 3: recognize these patterns because they bring to other so much data.

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Speaker 3: So I think if you combine some of that, which

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Speaker 3: leads to earlier treatment for chronic diseases. The payback won't

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Speaker 3: be overnight, but the payback for the healthcare system of the

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Speaker 3: United States could be over a decade. We could really

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Speaker 3: see a dramatic reduction in what I'll call unnecessary testing.

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Speaker 3: Maybe it's necessary today, but it won't be necessary in

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Speaker 3: a couple of years.

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Speaker 1: Yeah. I had the experience. I went in to you

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Speaker 1: have my teeth cleaned last week, and the dentist now

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Speaker 1: has an artificial intelligence component. When they look at the

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Speaker 1: X rays, the artificial intelligence component scans the same data

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Speaker 1: and can identify at about ten times the accuracy what

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Speaker 1: the dentists can identify. It's a local dentist, not some

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Speaker 1: of the advanced research center. They said, it's just a

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Speaker 1: technology that's come online, and that it dramatically enhances his

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Speaker 1: ability to care, for example, very very early if you

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Speaker 1: have a cavity. You've got a couple of things you mentioned,

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Speaker 1: and one of them is this whole question of really

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Speaker 1: advanced imaging techniques that I think are going to revolutionize

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Speaker 1: our ability to have early intervention, which sometimes increases cost,

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Speaker 1: sometimes decreases, but it almost always extends.

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Speaker 2: Life, right. Absolutely.

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Speaker 3: Yeah, if you think of any chronic disease, the earlier

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Speaker 3: you're inter being, the better the outcome, So I couldn't

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Speaker 3: agree more. I'll tell you just this sort of a

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Speaker 3: weird research study I read some time ago recently. Really,

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Speaker 3: I had to do with looking at the concept that

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Speaker 3: the color of your tongue can predict disease, and this

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Speaker 3: is like ancient Chinese medicine. But the group that was

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Speaker 3: investigating it looked at using AI in that circumstance, and

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Speaker 3: AI was fantastic, and there was a small group. You know,

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Speaker 3: we're not ready to get AI o our tongues right now,

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Speaker 3: but it proved the point that AI can pick up

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Speaker 3: things we just don't see. But also we don't always

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Speaker 3: know what AI is looking at. So maybe it's not

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Speaker 3: looking at the color at all. Maybe it's looking at

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Speaker 3: the papules and the distribution of the papules of the time.

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Speaker 3: I don't really know, And that's one of the great

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Speaker 3: mysteries of AI. You don't know what all data brought in,

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Speaker 3: but it sure does turn out to be provocatively accurate.

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Speaker 1: How much research do you see long on both Uncn

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Speaker 1: and Michigan in the application of AI.

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Speaker 3: I don't follow things that you and see quite as

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Speaker 3: closely as I was, but I can tell you we

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Speaker 3: have an explosion of it. So we're looking at applications

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Speaker 3: of AI direct applications in healthcare. We're using AI to

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Speaker 3: be able to organize and scan and make much quicker

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Speaker 3: decisions about electronic medical records. I don't know if you've

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Speaker 3: ever requested your own electronic medical record, but you know

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Speaker 3: it's like page after page after page after page. And

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Speaker 3: AI does a fantastic job of just summarizing that and

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Speaker 3: summarizing maybe three hundred pages down to two or three pages.

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Speaker 3: And for your care provider, then the main thing I

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Speaker 3: think that helps is they don't miss stuff. I know

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Speaker 3: when I start looking through a medical record, after a while,

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Speaker 3: I'm just not en off. So it makes it more

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Speaker 3: accurate and much easier for the providers. The same thing

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Speaker 3: in terms of thinking about how we interact with what

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Speaker 3: we call the inbox, an epic where people ask a

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Speaker 3: lot of questions. We can't be sure that it's going

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Speaker 3: to answer every one of those, but if you get

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Speaker 3: like an excellent first draft and so it makes it

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Speaker 3: really speeds things up and getting back to patients in

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Speaker 3: real time.

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Speaker 2: So I think those are great.

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Speaker 3: But more importantly we're looking at applications of AI in

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Speaker 3: areas that do involve imaging, so like I exams ophthalmology

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Speaker 3: in interpreting mammograms and interpreting MRIs and cts. When a

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Speaker 3: radiologist looks at an MRI, you may have seen them

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Speaker 3: doing this in real time, but they're looking at thirty

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Speaker 3: or forty images. And one thing AI can certainly do

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Speaker 3: is say images one through thirty two, don't worry about

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Speaker 3: their normal image, thirty two, three, and thirty four you

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Speaker 3: better take a quick look at and then the radiologist

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Speaker 3: has much more time to ponder it and try to

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Speaker 3: put that together with the clinical scenario. But I think

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Speaker 3: the next step is going to be AI looking at

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Speaker 3: the medical record and AI looking into the images and

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Speaker 3: pulling it all together.

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Speaker 2: And I think that's just around the corner.

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Speaker 1: You know. One of the problems is political. We were

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Speaker 1: looking at a little company that had developed a computerized

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Speaker 1: eye exam. The normal model is you go see your

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Speaker 1: optometrist or ophthalmologists every two years in order to have

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Speaker 1: an exam so they can reorder whatever you might need,

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Speaker 1: and then you go back to the alternating year just

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Speaker 1: to have a quick checkup. Well, they have developed a

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Speaker 1: computerized scam that would perform the second year, and in

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Speaker 1: state after state, the ophthalmology and optometry lobbyists would get

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Speaker 1: it out lawed. I can't believe it, Yeah, because they

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Speaker 1: saw it as a direct threat to their income. One

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Speaker 1: of the great lessons for me when I stepped down speaker,

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Speaker 1: I took two areas, national security and healthcare, which were

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Speaker 1: both very complicated. Healthcare, I think is about ten times

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Speaker 1: more complicated than national security. It's unbelievably dense. But one

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Speaker 1: of the things I learned that was I think a

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Speaker 1: real surprise to me was the degree to which the

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Speaker 1: systems operate in an entirely human manner. That is, if

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Speaker 1: you're a hospital, your interest is optimizing the hospital, and

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Speaker 1: if you're the local pharmacy, your interest is optimizing the pharmacy.

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Speaker 1: And in the process, if that means that you're suboptimizing

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Speaker 1: the country, that's okay for you because you know your

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Speaker 1: pharmacy is doing well. So it made me realize how

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Speaker 1: you both had to figure out an improvement, but then

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Speaker 1: you had to figure out how to get through all

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Speaker 1: these mine fields to get the improvement both culturally adopted.

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Speaker 1: You know, would professionals be wing to do it and

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Speaker 1: to get it adopted by people who might feel that

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Speaker 1: their income, or their prestige or their status was threatened.

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Speaker 1: I don't know how much of that you experience in

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Speaker 1: a medical school, but it's been a fascinating challenge to me.

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Speaker 3: That's so interesting because it's absolutely true. And I'm currently

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Speaker 3: working on a book with Forbes. Forbes will ask CEOs

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Speaker 3: and different areas, right books and so ours is about

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Speaker 3: changes in healthcare, really disruptors in healthcare. And so all

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Speaker 3: these disruptors are companies like what you talked about, that

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Speaker 3: are coming in and saying, hey, there's a problem, there's

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Speaker 3: a pain point in medicine. We can fix it, and

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Speaker 3: they do. But then everyone's competing with everybody for that

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Speaker 3: same dollar.

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Speaker 2: And it's true.

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Speaker 3: You think about hospitals, to think about insurance companies, you

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Speaker 3: think about pharmacy benefit managers. They're all after that same

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Speaker 3: dollar and they're pretty successful, which I think does lead

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Speaker 3: to an awful lot of cost in healthcare. So I

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Speaker 3: can't tell you how happy I meant to hear that

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Speaker 3: somebody who has been is integally involved in decision making

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Speaker 3: at the national level understands this. It's a pleasant surprise

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Speaker 3: to me to.

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Speaker 1: Go back to artificial intelligence, to what extent is the

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Speaker 1: University of Michigan Hospital using this and trying to integrate

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Speaker 1: it into either accelerating speed or maximizing the ability to

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Speaker 1: deal with complexity.

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Speaker 3: We're currently I would say a year ago we were

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Speaker 3: using no AI. I'd say we're using AI and ten

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Speaker 3: percent of what we do now. I believe that in

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Speaker 3: the next year or two we'll be using it in

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Speaker 3: half of what we do. Because people are a little

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Speaker 3: anxious about to start with, but I think it's proven

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Speaker 3: that it has real value and is not just a spectacle.

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Speaker 2: It offers real value.

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Speaker 1: Is the early diagnostics or the ability to radically enhance

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Speaker 1: research or to create connectivity for the patients. I've always thought,

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Speaker 1: if you think about the number of times in your

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Speaker 1: life you'll go into a doctor or a dentist or

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Speaker 1: something and sit down and fill out paper. And I'm

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Speaker 1: now old enough when they say, you know, list all

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Speaker 1: of your drugs, Well, I carry all my drugs in

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Speaker 1: my phone, so I can pull up my phone and

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Speaker 1: do it. But I think back to my mother, who

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Speaker 1: did not have a phone at that time that was smart.

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Speaker 1: She had a ruggar phone and she's taking and out

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Speaker 1: of drugs. I am certain there are occasions where she

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Speaker 1: went to see a doctor or a hospital and only

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Speaker 1: gave them half the drugs she was using because she

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Speaker 1: just forgot. Shouldn't there be some way to almost automate

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Speaker 1: that whole process.

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Speaker 3: I think there is, and I think, particularly now that

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Speaker 3: we have electronic medical records that can connect to pharmacy records,

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Speaker 3: we do already look at pharmacy records when somebody says, yes,

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Speaker 3: I haven't missed by dose of lipator in three years,

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Speaker 3: you see they also haven't refilled their prescription in the

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Speaker 3: last two years. So something doesn't quite connect there. I

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Speaker 3: think as we get more and more connected now, I

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Speaker 3: do think that there's a danger of getting connections. I'll

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Speaker 3: come back to that in just a minute. But it's

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Speaker 3: long been a dogma that, wow, if we could just

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Speaker 3: pick medications or surgical procedures or whatever therapies that were

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Speaker 3: targeted toward the individual, rather than saying, well, we know

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Speaker 3: this approach works in sixty percent of people, and many

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Speaker 3: people take high pretension as an easy example. They may

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Speaker 3: try two or three or four drugs before they find

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Speaker 3: one that works. I think by combining when it's going

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Speaker 3: to be possible to combine genotype and other O mix

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Speaker 3: so to speak, the proteomics and others along with medical

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Speaker 3: history to be much more accurate in predicting the kind

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Speaker 3: of therapies that will.

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Speaker 2: Be really useful.

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Speaker 3: So that's where I see us going, and that saves time,

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Speaker 3: it's more effective, and it saves money in the long run.

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Speaker 3: When I said I think there's a worry about all

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Speaker 3: this connectivity, that's actually one of the streams of my novel,

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Speaker 3: because once you're totally connected, you're totally connected. And all

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Speaker 3: it takes is one kind of rogue twenty year old

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Speaker 3: at Amazon to know that. G If I put together

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Speaker 3: Marshall's purchasing his medications, his EHR, what can I do

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Speaker 3: with that? And in my novel, it's what evil can

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Speaker 3: I do with it?

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Speaker 1: Given your prestige and your background and in health, what

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Speaker 1: led you to write a novel?

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Speaker 3: Well, a couple of things. So I started because I

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Speaker 3: had a patient. Mccardial just had a patient that came

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Speaker 3: in and he could have been a perfect character in

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Speaker 3: a Grisham novel. A attorney worked for the FBI, got

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Speaker 3: on drugs, lost his family and was kind of working

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Speaker 3: his way back.

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Speaker 2: And he had heart problems.

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Speaker 3: So one day he said, hey, you know John Grisham,

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Speaker 3: and I'm like, no, I don't think I do. And

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Speaker 3: he said he's an author, and I said, oh. He said,

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Speaker 3: I'll bring you a book. So I read a Grisham

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Speaker 3: book and I was forever hooked on Grisham.

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Speaker 2: I love the guy.

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Speaker 3: I loved these thrillers, and I thought, well, it'd be

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Speaker 3: fun to try to write one. I bet I can

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Speaker 3: do that. Of course, that was like fifteen years before

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Speaker 3: I finished my novel. But at the end of the day,

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Speaker 3: what I wanted to combine two things. One was to

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Speaker 3: make a thriller that people would like to read, and

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Speaker 3: the other was to hit an audience that isn't reading

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Speaker 3: editorials in the Washington Post or the New York Times

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Speaker 3: about some of the things they have to think about.

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Speaker 3: How do they protect their phi their protected health information.

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Speaker 3: How do they keep things out of the medical record

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Speaker 3: that don't want in the medical record. This is not

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Speaker 3: a real what do they call that? Not a teaser?

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Speaker 3: But there is a character in the novel that is

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Speaker 3: a Southern politician that somebody wants to kill. And they

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Speaker 3: figured out how they could kill them through the medical record.

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Speaker 3: I found this fascinating. I didn't know this, but in

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Speaker 3: big hospitals, when you see that ivy bag hanging, you think, well,

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Speaker 3: there's a farm tech or a pharmacist down there who

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Speaker 3: mix it up.

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Speaker 2: Well, the big hospitals, that's not the case.

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Speaker 3: They use robots, and the robots are incredibly accurate. But

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Speaker 3: you can imagine a scenario where somebody hacks in the

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Speaker 3: medical record, reprograms a robot and gives me something that

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Speaker 3: is lethal to me. So that's kind of one of

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Speaker 3: the themes that's swirling around in the novel.

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Speaker 1: Did you find it challenging to learn how to do

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Speaker 1: dialogue and because a novel is really different than writing

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Speaker 1: non fiction, Yeah.

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Speaker 2: I found it very challenging.

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Speaker 3: What helped me the most was for several years, I

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Speaker 3: get up early, so I do a little rite in

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Speaker 3: the morning, and inevitably somebody would aggravate me, so I'd

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Speaker 3: come home and write something really nasty about that person.

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Speaker 3: So after about five years, I was so proud of

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Speaker 3: my novel. I showed it to my wife and she said, well,

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Speaker 3: let's see. Now, they're about one hundred characters. There's no

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Speaker 3: dialogue and no plot. Maybe you ought to go off

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Speaker 3: to a writing course. So I did do that. I

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Speaker 3: don't know if you do creative writing or not. It's

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Speaker 3: fun to do because unlike everything that you and I

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Speaker 3: have to do in our lives, there's no background check.

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Speaker 2: It's just you make it up. So it's fiction.

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Speaker 1: Where was your writing course?

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Speaker 3: I went to a course that was put on by

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Speaker 3: company it's not run anymore, called ck Seak. It was

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Speaker 3: on Cape cod and it was hosted by two really

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Speaker 3: great doctor authors, Michael Palmer and Tess Garretts, and both

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Speaker 3: write thrillers. I went to it thinking, well, they're going

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Speaker 3: to make a brief appearance, but they were there for

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Speaker 3: three days. And I also went there thinking, wow, I'm

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Speaker 3: going to get into an issue. This is back in

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Speaker 3: two thousand and seven or eight, because how many doctor

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Speaker 3: writers can they be? And so I was lucky I

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Speaker 3: got in. They said, well, you're lucky you filled it up.

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Speaker 3: They're three hundred and fifty doctors coming, including two classmates

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Speaker 3: mine for medical school, so a lot of interest in

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Speaker 3: this field, and.

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Speaker 1: Most of them writing fiction or they just want to

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Speaker 1: learn how to write.

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Speaker 3: Most of them are writing fiction and it ranges from

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Speaker 3: some of the most bizarre stuff you can imagine, to

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Speaker 3: people trying to write things like I was writing.

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Speaker 1: I've written both fiction and nonfiction in there totally different challenges.

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Speaker 2: Tell me what you wrote about in fiction, because I

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Speaker 2: love reading fiction.

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Speaker 1: I started with a very good friend of min named

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Speaker 1: Bill Fortune, who's a history professor but also a professional

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Speaker 1: novelist and has written many, many novels. Our first book

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Speaker 1: was an alternative history in which Hitler is in a

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Speaker 1: car accident just before Pearl Harbor, and so Germany doesn't

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Speaker 1: attack us, and the result is we don't have an

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Speaker 1: excuse to declare war. So we turn to the Pacific,

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Speaker 1: and Hitler dominates in Europe and the novel is called

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Speaker 1: nineteen forty five. And so in this alternity of nineteen

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Speaker 1: forty five, the Germans are dominant in Europe, were dominant

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Speaker 1: in the Pacific, and Hitler has decided he has to

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Speaker 1: attack us, and he has been briefed that there's a

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Speaker 1: facility in Oakridge, Tennessee, developing a nuclear weapon. You've got

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Speaker 1: some fact in there, yeah, And so the whole thing

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Speaker 1: is an adventure story, you know, launching a specialized raid

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Speaker 1: to try to destroy this before the Americans can build it.

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Speaker 1: And I learned so much from Bell because, for example,

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Speaker 1: I would talk with you and say, the Germans had

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Speaker 1: arrived just off South Carolina, and the troops that were

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Speaker 1: going to go to Tennessee had to move from a

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Speaker 1: large ocean going ship to a small ship that was

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Speaker 1: going to take them up river. Well, by the time

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Speaker 1: Bill taught me how you write the last guy trying

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Speaker 1: to get off the ship, and the ship waving back

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Speaker 1: and forth, and the danger of being crushed, and in

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Speaker 1: the end he does get crushed. I mean he took

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Speaker 1: a couple of sentences and turned them into five pages

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Speaker 1: where you're sitting on the edge of your seat, thinking,

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Speaker 1: Oh my god, what's going to happen this. We went

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Speaker 1: on from there and wrote several books about the Revolutionary

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Speaker 1: War from Washington's perspective, and then we wrote a couple

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Speaker 1: of novels about the Civil War, and probably our best gambit,

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Speaker 1: we wrote an alternative history of Gettysburg. And we got

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Speaker 1: the Army War College in Carlisle, Pennsylvania. We got their

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Speaker 1: commanding general and the person who teaches their Gettysburgh class,

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Speaker 1: and we went out and we walked them through our

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Speaker 1: theory because we were both military historians, so we had

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Speaker 1: a theory of how this thing might have happened, and

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Speaker 1: we went out and literally walked the battlefield. And when

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Speaker 1: we got done, the guy who taught the cars said,

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Speaker 1: you know, that actually sounds more like Lee than what

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Speaker 1: he did that day. And so we wrote an alternative

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Speaker 1: history of Gettysburg, which then the two more alternative histories

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Speaker 1: of how the Civil War would have occurred in that setting.

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Speaker 2: What's that one called?

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Speaker 1: The first one's called Gettysburg.

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Speaker 2: Oh, I've seen that book. I've never read it.

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Speaker 1: Though we did it deliberately because Gettysburgh is the most

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Speaker 1: frequently written about event in American history. We were trying

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Speaker 1: to find some way to launch our fiction efforts. But

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Speaker 1: it was great fun. So when we describe things in

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Speaker 1: the book, that is how they were. You could go

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Speaker 1: and take our book and you could walk the whole

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Speaker 1: battlefield and get a sense for it. But it was

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Speaker 1: really fun, and it was fun to take personalities like

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Speaker 1: Lee or Mead or Longstreet and weave them in in

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Speaker 1: a way that worked well. No, I will tell you

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Speaker 1: I recently did a podcast with one of the great

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Speaker 1: American historians, and his father had written a book called

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Speaker 1: Killer Angels, which is the best book about Gettysburg ever written.

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Speaker 1: It's like poetry. I've written far more of nonfiction than fiction.

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Speaker 1: When you find somebody who has the rhythm and they

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Speaker 1: have the words and they suck you into their world,

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Speaker 1: I find it amazing.

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Speaker 2: I love it. I'll tell you what.

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Speaker 3: I didn't know this value, but I'm going to get

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Speaker 3: nineteen forty five in Gettysburg and read him.

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Speaker 1: When you're looking down the road the next four or

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Speaker 1: five years, do you think you're going to be able

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Speaker 1: to collaborate with the insurance companies and get them to

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Speaker 1: help accept and help implement the kind of changes that

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Speaker 1: are coming with AI.

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Speaker 3: Well, not knowing who in the world might listen to this,

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Speaker 3: I'm gonna go ahead and tell you what I think, and

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Speaker 3: that is we have tried very hard to collaborate with

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Speaker 3: insurance companies. It's been difficult to collaborate because in so

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Speaker 3: many ways we're at odds financially, and so we haven't

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Speaker 3: tried around AI. I'll tell you one good experience. We

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Speaker 3: had a good experience with Blue Cross, Pushield and Michigan

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Speaker 3: around developing quality initiatives, and this was looking at the

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Speaker 3: quality of different kinds of operations across the state, if

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Speaker 3: you have your prostate taken out, or if you have

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Speaker 3: any kind of cancer surgery. And I think that actually

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Speaker 3: was very productive. We combined our data with their data,

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Speaker 3: their claims data, and it helped identify people that were

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Speaker 3: needed to improve their performance. And it was done in

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Speaker 3: a very protected environment. But I think that was good

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Speaker 3: for healthcare. Subsequently, we tried to collaborate in other areas unsuccessfully,

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Speaker 3: and so AI ought to be one that is un natural.

439
00:24:01,920 --> 00:24:03,360
Speaker 3: But I'll tell you a funny story I heard about

440
00:24:03,359 --> 00:24:06,520
Speaker 3: AI recently. So we have one consultant who's telling us

441
00:24:06,600 --> 00:24:10,960
Speaker 3: that we can come deliver to you AI to help

442
00:24:11,000 --> 00:24:15,840
Speaker 3: with pre authorization for procedures. So whatever you're going to have,

443
00:24:16,359 --> 00:24:20,399
Speaker 3: from an eye surgery to heart surgery, your insurance company

444
00:24:20,440 --> 00:24:22,720
Speaker 3: has to pre authorize that, and that can be a

445
00:24:22,720 --> 00:24:26,159
Speaker 3: difficult process, and we sometimes end up with somebody who's

446
00:24:26,800 --> 00:24:28,520
Speaker 3: on the gurney getting ready to have surgery and we

447
00:24:28,520 --> 00:24:30,959
Speaker 3: still don't have pre authorization, and if we don't get it,

448
00:24:31,280 --> 00:24:34,560
Speaker 3: we eat the entire procedure in terms of the cost.

449
00:24:35,240 --> 00:24:37,439
Speaker 3: So I thought, well, this could be really good, And

450
00:24:37,480 --> 00:24:40,480
Speaker 3: then I was at another meeting of colleagues. They said, man,

451
00:24:40,520 --> 00:24:43,120
Speaker 3: you got to be careful because the insurance companies now

452
00:24:43,160 --> 00:24:46,280
Speaker 3: are using AI to figure out how to best deny

453
00:24:46,359 --> 00:24:49,639
Speaker 3: pre authorization. So, you know, just a great example of

454
00:24:49,680 --> 00:24:51,720
Speaker 3: AI works in one way for us, another way for

455
00:24:51,720 --> 00:24:54,320
Speaker 3: the insurance companies. But I think if we can put

456
00:24:54,320 --> 00:24:56,960
Speaker 3: aside that and think about how we could collaborate in

457
00:24:57,040 --> 00:25:00,920
Speaker 3: terms of quality outcomes. And it's been talked about for decades,

458
00:25:01,000 --> 00:25:03,800
Speaker 3: have been the cost curve in healthcare. We've got to

459
00:25:03,840 --> 00:25:06,400
Speaker 3: do that. We're out of control right now. That's hard

460
00:25:06,440 --> 00:25:09,240
Speaker 3: for me to say because I've been in healthcare for

461
00:25:09,240 --> 00:25:12,080
Speaker 3: forty years now. I think AI, in the ways we

462
00:25:12,119 --> 00:25:15,119
Speaker 3: talked about it before, in terms of better more rapid,

463
00:25:15,200 --> 00:25:19,480
Speaker 3: earlier diagnosis, better selection of therapies, can make a big difference.

464
00:25:19,800 --> 00:25:23,040
Speaker 1: You know, as we think about this much more electronic world.

465
00:25:23,680 --> 00:25:26,760
Speaker 1: Your book Coded to Kill really centers around the hacking

466
00:25:27,160 --> 00:25:30,280
Speaker 1: of an electronic health record system. How much as we

467
00:25:30,320 --> 00:25:33,800
Speaker 1: put more and more things in an electronic format, how

468
00:25:33,880 --> 00:25:38,080
Speaker 1: much do you worry not just about the criminal hacker,

469
00:25:38,880 --> 00:25:41,679
Speaker 1: but also the North Koreans of the Chinese. I mean,

470
00:25:41,680 --> 00:25:45,560
Speaker 1: they are very sophisticated players. IS relatives have showed in Lebanon.

471
00:25:45,880 --> 00:25:48,520
Speaker 1: I mean, there are people who have capabilities that are

472
00:25:48,520 --> 00:25:49,240
Speaker 1: a little scary.

473
00:25:49,520 --> 00:25:51,800
Speaker 2: How on earth could they do that with the pagers?

474
00:25:51,840 --> 00:25:54,080
Speaker 1: You mean it was about a twelve year project?

475
00:25:54,280 --> 00:25:54,600
Speaker 2: Really?

476
00:25:54,880 --> 00:25:58,720
Speaker 1: Yeah, they ultimately got inside the supply chain and made

477
00:25:58,760 --> 00:26:03,440
Speaker 1: sure that only their got to Hesbela. It's a very elegant,

478
00:26:03,560 --> 00:26:06,879
Speaker 1: very difficult project, and it takes a country that's in

479
00:26:07,000 --> 00:26:10,280
Speaker 1: desperate efforts to survive. But if you think about it,

480
00:26:10,320 --> 00:26:12,440
Speaker 1: all these different kinds of things, how do you build

481
00:26:12,560 --> 00:26:15,760
Speaker 1: layers of defense so you can't be hacked into?

482
00:26:16,400 --> 00:26:16,600
Speaker 2: Right?

483
00:26:16,760 --> 00:26:20,040
Speaker 3: Well, I'm happy to both tell you and here you

484
00:26:20,119 --> 00:26:22,879
Speaker 3: critique it. Given your knowledge of national security, as you

485
00:26:22,960 --> 00:26:26,520
Speaker 3: know pretty much what we're thinking about. So when I

486
00:26:26,560 --> 00:26:29,000
Speaker 3: came here, one of the first questions I asked was

487
00:26:29,080 --> 00:26:33,160
Speaker 3: how often do we have people inappropriately accessing our medical records?

488
00:26:33,280 --> 00:26:35,560
Speaker 3: When I was at UNC, I got into this because

489
00:26:35,600 --> 00:26:38,399
Speaker 3: the dean at the time we had a bunch of

490
00:26:38,440 --> 00:26:42,560
Speaker 3: people mini staff and about forty faculty who had inappropriately

491
00:26:42,600 --> 00:26:45,800
Speaker 3: accessed the electronic medical record of one of the fabulous

492
00:26:45,800 --> 00:26:49,040
Speaker 3: basketball players who was out for a few games. And so,

493
00:26:49,480 --> 00:26:51,239
Speaker 3: lucky me, I got a sign to talk to all

494
00:26:51,280 --> 00:26:53,840
Speaker 3: those faculty about this and you know how it's a

495
00:26:53,880 --> 00:26:56,760
Speaker 3: federal crime, and try to scare the dickens out of him.

496
00:26:57,359 --> 00:26:58,600
Speaker 2: You know, I got the wildest stories.

497
00:26:58,640 --> 00:27:01,119
Speaker 3: One guy told me, well, sickle cell experts, so I

498
00:27:01,160 --> 00:27:02,840
Speaker 3: worried he might have sickle cell disease. So I was

499
00:27:02,840 --> 00:27:04,600
Speaker 3: looking to see if I could be helpful. I mean,

500
00:27:04,600 --> 00:27:06,960
Speaker 3: that wasn't it. He's a sports fan. I got here

501
00:27:06,960 --> 00:27:09,560
Speaker 3: and I said, does that ever happened at Michigan? They said, oh, no, no, no,

502
00:27:09,600 --> 00:27:12,080
Speaker 3: we know it doesn't. And I said, well, what automated

503
00:27:12,119 --> 00:27:14,159
Speaker 3: system are using to look at every medical record? There

504
00:27:14,200 --> 00:27:16,280
Speaker 3: are some out there, and they weren't using one. It

505
00:27:16,320 --> 00:27:18,200
Speaker 3: took two or three years to get them to use one,

506
00:27:18,440 --> 00:27:22,159
Speaker 3: and turned out we were having many, many people that

507
00:27:22,240 --> 00:27:25,240
Speaker 3: were inappropriately accessing medical records. Now that's not a hack

508
00:27:25,280 --> 00:27:27,920
Speaker 3: at all. They just have access. They find out you're

509
00:27:27,920 --> 00:27:30,040
Speaker 3: in the hospital, or the governors in the hospital, they say, huh,

510
00:27:30,119 --> 00:27:32,359
Speaker 3: let me see what's going on. And so we fired

511
00:27:32,359 --> 00:27:34,679
Speaker 3: a whole slew of people. It's taken about five years

512
00:27:35,160 --> 00:27:38,000
Speaker 3: to get it under control. Now we're down to about

513
00:27:38,040 --> 00:27:41,960
Speaker 3: twenty times a year that that happens. But that's trivial.

514
00:27:42,320 --> 00:27:45,200
Speaker 3: What I worry about is we get about between six

515
00:27:45,320 --> 00:27:49,639
Speaker 3: thousand and more attempted hacks a day externally, and most

516
00:27:49,640 --> 00:27:52,480
Speaker 3: of those I think of as like those robo calls

517
00:27:52,520 --> 00:27:56,000
Speaker 3: you get during dinner time, but they're about eighty a

518
00:27:56,080 --> 00:28:01,760
Speaker 3: month that are serious attempts from foreign actors and sometimes domestic.

519
00:28:02,560 --> 00:28:05,880
Speaker 3: And what's said in healthcare, which I believe is sadly true,

520
00:28:05,960 --> 00:28:08,680
Speaker 3: is it's not if you will ever get hacked, it's

521
00:28:08,720 --> 00:28:10,840
Speaker 3: when you'll get hacked and what you do. And so

522
00:28:11,320 --> 00:28:13,160
Speaker 3: the layers we've tried to build, and we have lots

523
00:28:13,160 --> 00:28:15,679
Speaker 3: of security on top to try to permit the hacks.

524
00:28:16,320 --> 00:28:18,080
Speaker 3: But some of the best things we're doing, and is

525
00:28:18,119 --> 00:28:23,959
Speaker 3: AI enabled, actually is to detect inappropriate access into our

526
00:28:23,960 --> 00:28:27,280
Speaker 3: medical record and immediately coordinate off. So, you know, the

527
00:28:27,280 --> 00:28:31,400
Speaker 3: CrowdStrike thing that happened recently, we were back within hours,

528
00:28:31,560 --> 00:28:35,120
Speaker 3: a couple of hours, where somehow systems in Michigan were

529
00:28:35,119 --> 00:28:37,480
Speaker 3: down for two or three weeks. You know, we can't

530
00:28:37,480 --> 00:28:41,480
Speaker 3: do anything when your EHR is blocked. And our university

531
00:28:41,520 --> 00:28:44,959
Speaker 3: had a big hacked about a year ago during student

532
00:28:45,000 --> 00:28:47,560
Speaker 3: registration time that was in the medical school, and as

533
00:28:47,600 --> 00:28:50,640
Speaker 3: the university, it's like a pimple. How do we surround it?

534
00:28:50,680 --> 00:28:53,720
Speaker 3: So it doesn't spread. That's our strategy. I'd love to

535
00:28:53,760 --> 00:28:55,200
Speaker 3: hear what you think we ought to be doing.

536
00:28:55,520 --> 00:28:58,560
Speaker 1: The only advice I would have is to find one

537
00:28:58,600 --> 00:29:03,280
Speaker 1: of the most recent national security agency heads and bring

538
00:29:03,320 --> 00:29:06,240
Speaker 1: them in and ask them to review everything we have

539
00:29:06,360 --> 00:29:06,760
Speaker 1: not done.

540
00:29:06,760 --> 00:29:08,160
Speaker 2: That. That's a great idea.

541
00:29:08,680 --> 00:29:10,200
Speaker 1: I'm very close to one of the former in and

542
00:29:10,240 --> 00:29:14,479
Speaker 1: SAY heads. That's a world that is so sophisticated, and

543
00:29:14,520 --> 00:29:18,440
Speaker 1: it's still the leading capacity and an internet activity in

544
00:29:18,480 --> 00:29:21,200
Speaker 1: the world. Marshall, I want to thank you for joining me.

545
00:29:21,440 --> 00:29:24,120
Speaker 1: This has been a wide ranging and I think pretty

546
00:29:24,120 --> 00:29:28,160
Speaker 1: fascinating conversation. Your book Coded to Kill, I suspect is

547
00:29:28,200 --> 00:29:28,760
Speaker 1: the beginning of.

548
00:29:28,760 --> 00:29:30,560
Speaker 2: A whole new career, I hope.

549
00:29:30,560 --> 00:29:34,880
Speaker 1: So that's available now on Amazon and bookstores everywhere, and

550
00:29:34,960 --> 00:29:37,320
Speaker 1: I really appreciate you talking with us. I hope we

551
00:29:37,360 --> 00:29:39,280
Speaker 1: can do this again when your next book comes out.

552
00:29:39,560 --> 00:29:41,040
Speaker 2: Well, thank you again, it was awesome.

553
00:29:45,080 --> 00:29:48,080
Speaker 1: Thank you to my guest doctor Marshall Rangy. You can

554
00:29:48,120 --> 00:29:50,520
Speaker 1: get a link to buy his book Coded to Kill

555
00:29:50,800 --> 00:29:54,000
Speaker 1: on our show page at newtsworld dot com. Newtwell is

556
00:29:54,000 --> 00:29:58,160
Speaker 1: produced by Gager three sixty and iHeartMedia. Our executive producer

557
00:29:58,520 --> 00:30:02,800
Speaker 1: is Guernsey Sloan researcher is Rachel Peterson. The artwork for

558
00:30:02,840 --> 00:30:06,720
Speaker 1: the show was created by Steve Penley. Special thanks to

559
00:30:06,760 --> 00:30:09,800
Speaker 1: the team at gingrid Street sixty. If you've been enjoying Newtsworld,

560
00:30:10,160 --> 00:30:13,000
Speaker 1: I hope you'll go to Apple Podcasts and both rate

561
00:30:13,080 --> 00:30:15,920
Speaker 1: us with five stars and give us a review so

562
00:30:16,040 --> 00:30:19,320
Speaker 1: others can learn what it's all about. Right now listeners

563
00:30:19,320 --> 00:30:23,520
Speaker 1: of Newsworld consigner for my three freeweekly columns at gingridsweet

564
00:30:23,560 --> 00:30:28,200
Speaker 1: sixty dot com slash newsletter. I'm Newt Gingrich. This is Newsworld.